From 05626f9fc3b7084b34535bf0cd44f79d8fcee649 Mon Sep 17 00:00:00 2001
From: Jingtian Peng <pjt73651@gmail.com>
Date: Fri, 19 Aug 2016 22:54:58 +0800
Subject: [PATCH 001/223] Docs(Queue): Fix a PriorityQueue API missing bug

Add the description of  `class PriorityQueue()` API in /python/ops/data_flow_ops.py.
From line #714 to #759 you can check [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/data_flow_ops.py)
---
 tensorflow/g3doc/api_docs/python/io_ops.md | 51 ++++++++++++++++++++++
 1 file changed, 51 insertions(+)

diff --git a/tensorflow/g3doc/api_docs/python/io_ops.md b/tensorflow/g3doc/api_docs/python/io_ops.md
index c1433adbb5..39eb046845 100644
--- a/tensorflow/g3doc/api_docs/python/io_ops.md
+++ b/tensorflow/g3doc/api_docs/python/io_ops.md
@@ -2079,6 +2079,57 @@ queue has been closed.
 
 
 
+- - -
+
+### `class tf.PriorityQueue` {#PriorityQueue}
+
+A queue implementation that dequeues elements in prioritized order.
+
+See [`tf.QueueBase`](#QueueBase) for a description of the methods on
+this class.
+
+- - -
+
+
+#### `tf.PriorityQueue.__init__(capacity, types, shapes=None, names=None, shared_name=None, name='priority_queue')` {#PriorityQueue.__init__}
+
+Creates a queue that dequeues elements in a first-in first-out order.
+
+A `PriorityQueue` has bounded capacity; supports multiple concurrent
+producers and consumers; and provides exactly-once delivery.
+
+A `PriorityQueue` holds a list of up to `capacity` elements. Each
+element is a fixed-length tuple of tensors whose dtypes are
+described by `types`, and whose shapes are optionally described
+by the `shapes` argument.
+
+If the `shapes` argument is specified, each component of a queue
+element must have the respective fixed shape. If it is
+unspecified, different queue elements may have different shapes,
+but the use of `dequeue_many` is disallowed.
+
+Enqueues and Dequeues to the `PriorityQueue` must include an additional
+tuple entry at the beginning: the `priority`.  The priority must be
+an int64 scalar (for `enqueue`) or an int64 vector (for `enqueue_many`).
+
+##### Args:
+*  <b>`capacity`</b>: An integer. The upper bound on the number of elements
+    that may be stored in this queue.
+*  <b>`types`</b>:  A list of `DType` objects. The length of `types` must equal
+    the number of tensors in each queue element, except the first priority
+    element.  The first tensor in each element is the priority,
+    which must be type int64.
+*  <b>`shapes`</b>: (Optional.) A list of fully-defined `TensorShape` objects
+    with the same length as `dtypes`, or `None`.
+*  <b>`names`</b>: (Optional.) A list of string naming the components in the queue
+    with the same length as `dtypes`, or `None`.  If specified the dequeue
+    methods return a dictionary with the names as keys.
+*  <b>`shared_name`</b>: (Optional.) If non-empty, this queue will be shared under
+    the given name across multiple sessions.
+*  <b>`name`</b>: Optional name for the queue operation.
+
+
+
 
 ## Dealing with the filesystem
 
-- 
GitLab


From 3d3fbf4fb576f60c9f689cfe852d42c7ca84832f Mon Sep 17 00:00:00 2001
From: Pieter de Rijk <pieter@de-rijk.com>
Date: Sat, 20 Aug 2016 20:46:54 +0200
Subject: [PATCH 002/223] Modified tensorboard message with correct URL

---
 tensorflow/tensorboard/tensorboard.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/tensorflow/tensorboard/tensorboard.py b/tensorflow/tensorboard/tensorboard.py
index 7757956b38..6ca7dfc16b 100644
--- a/tensorflow/tensorboard/tensorboard.py
+++ b/tensorflow/tensorboard/tensorboard.py
@@ -138,7 +138,13 @@ def main(unused_argv=None):
 
   status_bar.SetupStatusBarInsideGoogle('TensorBoard %s' % tag, FLAGS.port)
   print('Starting TensorBoard %s on port %d' % (tag, FLAGS.port))
-  print('(You can navigate to http://%s:%d)' % (FLAGS.host, FLAGS.port))
+
+  hostname = socket.gethostname()
+  if FLAGS.host == "0.0.0.0":
+    print('(You can navigate to http://%s:%d)' % (socket.gethostname(), FLAGS.port))
+  else:
+    print('(You can navigate to http://%s:%d)' % (FLAGS.host, FLAGS.port))
+
   tb_server.serve_forever()
 
 
-- 
GitLab


From eec92db0eae46f3241be8a7472555854b8fa3b17 Mon Sep 17 00:00:00 2001
From: Pieter de Rijk <pieter@de-rijk.com>
Date: Sun, 21 Aug 2016 12:01:26 +0200
Subject: [PATCH 003/223] Modified the output message, so if FLAGS.host is set
 to 0.0.0.0 it will display the correct IP-address on the prompt

---
 tensorflow/tensorboard/tensorboard.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/tensorflow/tensorboard/tensorboard.py b/tensorflow/tensorboard/tensorboard.py
index 6ca7dfc16b..273065ea3f 100644
--- a/tensorflow/tensorboard/tensorboard.py
+++ b/tensorflow/tensorboard/tensorboard.py
@@ -139,9 +139,8 @@ def main(unused_argv=None):
   status_bar.SetupStatusBarInsideGoogle('TensorBoard %s' % tag, FLAGS.port)
   print('Starting TensorBoard %s on port %d' % (tag, FLAGS.port))
 
-  hostname = socket.gethostname()
   if FLAGS.host == "0.0.0.0":
-    print('(You can navigate to http://%s:%d)' % (socket.gethostname(), FLAGS.port))
+    print('(You can navigate to http://%s:%d)' % (socket.gethostbyname(socket.gethostname()), FLAGS.port))
   else:
     print('(You can navigate to http://%s:%d)' % (FLAGS.host, FLAGS.port))
 
-- 
GitLab


From 5b16538a6348da7191add43f0904047007125d94 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 22 Aug 2016 11:17:08 -0800
Subject: [PATCH 004/223] Add tests for _stochastic_dependencies_map Change:
 130967459

---
 .../kernel_tests/stochastic_graph_test.py     | 42 +++++++++++++++++++
 1 file changed, 42 insertions(+)

diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/stochastic_graph_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/stochastic_graph_test.py
index 172c474d13..1c21b786f7 100644
--- a/tensorflow/contrib/bayesflow/python/kernel_tests/stochastic_graph_test.py
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/stochastic_graph_test.py
@@ -354,5 +354,47 @@ class TestSurrogateLosses(tf.test.TestCase):
         self.assertAllClose(*sess.run([sl_dt2, sum([loss, dt2_term])]))
 
 
+class StochasticDependenciesMapTest(tf.test.TestCase):
+
+  def testBuildsMapOfUpstreamNodes(self):
+    dt1 = sg.DistributionTensor(distributions.Normal, mu=0., sigma=1.)
+    dt2 = sg.DistributionTensor(distributions.Normal, mu=0., sigma=1.)
+    out1 = dt1.value() + 1.
+    out2 = dt2.value() + 2.
+    x = out1 + out2
+    y = out2 * 3.
+    dep_map = sg._stochastic_dependencies_map([x, y])
+    self.assertEqual(dep_map[dt1], set([x]))
+    self.assertEqual(dep_map[dt2], set([x, y]))
+
+  def testHandlesStackedStochasticNodes(self):
+    dt1 = sg.DistributionTensor(distributions.Normal, mu=0., sigma=1.)
+    out1 = dt1.value() + 1.
+    dt2 = sg.DistributionTensor(distributions.Normal, mu=out1, sigma=1.)
+    x = dt2.value() + 2.
+    dt3 = sg.DistributionTensor(distributions.Normal, mu=0., sigma=1.)
+    y = dt3.value() * 3.
+    dep_map = sg._stochastic_dependencies_map([x, y])
+    self.assertEqual(dep_map[dt1], set([x]))
+    self.assertEqual(dep_map[dt2], set([x]))
+    self.assertEqual(dep_map[dt3], set([y]))
+
+  def testTraversesControlInputs(self):
+    dt1 = sg.DistributionTensor(distributions.Normal, mu=0., sigma=1.)
+    logits = dt1.value() * 3.
+    dt2 = sg.DistributionTensor(distributions.Bernoulli, logits=logits)
+    dt3 = sg.DistributionTensor(distributions.Normal, mu=0., sigma=1.)
+    x = dt3.value()
+    y = tf.ones((2, 2)) * 4.
+    z = tf.ones((2, 2)) * 3.
+    out = tf.cond(
+        tf.cast(dt2, tf.bool), lambda: tf.add(x, y), lambda: tf.square(z))
+    out += 5.
+    dep_map = sg._stochastic_dependencies_map([out])
+    self.assertEqual(dep_map[dt1], set([out]))
+    self.assertEqual(dep_map[dt2], set([out]))
+    self.assertEqual(dep_map[dt3], set([out]))
+
+
 if __name__ == "__main__":
   tf.test.main()
-- 
GitLab


From 6feae52e911bc5c4614073964f60cb96cfe9f128 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 22 Aug 2016 11:21:23 -0800
Subject: [PATCH 005/223] Update generated Python Op docs. Change: 130967884

---
 .../api_docs/python/contrib.framework.md      | 110 ++++++++++++++++++
 ...ontrib.framework.assign_from_checkpoint.md |  23 ++++
 ...tf.contrib.framework.assign_from_values.md |  24 ++++
 ...contrib.framework.assign_from_values_fn.md |  22 ++++
 ...rib.framework.assign_from_checkpoint_fn.md |  29 +++++
 tensorflow/g3doc/api_docs/python/index.md     |   4 +
 6 files changed, 212 insertions(+)
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.framework.assign_from_checkpoint.md
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.framework.assign_from_values.md
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.framework.assign_from_values_fn.md
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.framework.assign_from_checkpoint_fn.md

diff --git a/tensorflow/g3doc/api_docs/python/contrib.framework.md b/tensorflow/g3doc/api_docs/python/contrib.framework.md
index 0a6c811924..63f612dbfd 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.framework.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.framework.md
@@ -521,6 +521,116 @@ tensor (using `assert_global_step`). Otherwise find a global step tensor using
   was found.
 
 
+- - -
+
+### `tf.contrib.framework.assign_from_checkpoint(model_path, var_list)` {#assign_from_checkpoint}
+
+Creates an operation to assign specific variables from a checkpoint.
+
+##### Args:
+
+
+*  <b>`model_path`</b>: The full path to the model checkpoint. To get latest checkpoint
+      use `model_path = tf.train.latest_checkpoint(checkpoint_dir)`
+*  <b>`var_list`</b>: A list of `Variable` objects or a dictionary mapping names in the
+      checkpoint to the correspoing variables to initialize. If empty or None,
+      it would return  no_op(), None.
+
+##### Returns:
+
+  the restore_op and the feed_dict that need to be run to restore var_list.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the checkpoint specified at `model_path` is missing one of
+    the variables in `var_list`.
+
+
+- - -
+
+### `tf.contrib.framework.assign_from_checkpoint_fn(model_path, var_list, ignore_missing_vars=False, reshape_variables=False)` {#assign_from_checkpoint_fn}
+
+Returns a function that assigns specific variables from a checkpoint.
+
+##### Args:
+
+
+*  <b>`model_path`</b>: The full path to the model checkpoint. To get latest checkpoint
+      use `model_path = tf.train.latest_checkpoint(checkpoint_dir)`
+*  <b>`var_list`</b>: A list of `Variable` objects or a dictionary mapping names in the
+      checkpoint to the correspoing variables to initialize. If empty or None,
+      it would return  no_op(), None.
+*  <b>`ignore_missing_vars`</b>: Boolean, if True it would ignore variables missing in
+      the checkpoint with a warning instead of failing.
+*  <b>`reshape_variables`</b>: Boolean, if True it would automatically reshape variables
+      which are of different shape then the ones stored in the checkpoint but
+      which have the same number of elements.
+
+##### Returns:
+
+  A function that takes a single argument, a `tf.Session`, that applies the
+  assignment operation.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the checkpoint specified at `model_path` is missing one of
+    the variables in `var_list`.
+
+
+- - -
+
+### `tf.contrib.framework.assign_from_values(var_names_to_values)` {#assign_from_values}
+
+Creates an assignment operation from a given mapping.
+
+This function provides a mechanism for performing assignment of variables
+to values in a way that does not fill the graph with large assignment values.
+
+##### Args:
+
+
+*  <b>`var_names_to_values`</b>: A map from variable names to values.
+
+##### Returns:
+
+
+*  <b>`assign_op`</b>: An `Operation` that assigns each of the given variables to the
+    requested values.
+*  <b>`feed_dict`</b>: The feed dictionary to use when evaluating `assign_op`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if any of the given variable names were not found.
+
+
+- - -
+
+### `tf.contrib.framework.assign_from_values_fn(var_names_to_values)` {#assign_from_values_fn}
+
+Returns a function that assigns specific variables from the given values.
+
+This function provides a mechanism for performing assignment of variables
+to values in a way that does not fill the graph with large assignment values.
+
+##### Args:
+
+
+*  <b>`var_names_to_values`</b>: A map from variable names to values.
+
+##### Returns:
+
+  A function that takes a single argument, a `tf.Session`, that applies the
+  assignment operation.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if any of the given variable names were not found.
+
+
 - - -
 
 ### `tf.contrib.framework.create_global_step(graph=None)` {#create_global_step}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.framework.assign_from_checkpoint.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.framework.assign_from_checkpoint.md
new file mode 100644
index 0000000000..947e672688
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.framework.assign_from_checkpoint.md
@@ -0,0 +1,23 @@
+### `tf.contrib.framework.assign_from_checkpoint(model_path, var_list)` {#assign_from_checkpoint}
+
+Creates an operation to assign specific variables from a checkpoint.
+
+##### Args:
+
+
+*  <b>`model_path`</b>: The full path to the model checkpoint. To get latest checkpoint
+      use `model_path = tf.train.latest_checkpoint(checkpoint_dir)`
+*  <b>`var_list`</b>: A list of `Variable` objects or a dictionary mapping names in the
+      checkpoint to the correspoing variables to initialize. If empty or None,
+      it would return  no_op(), None.
+
+##### Returns:
+
+  the restore_op and the feed_dict that need to be run to restore var_list.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the checkpoint specified at `model_path` is missing one of
+    the variables in `var_list`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.framework.assign_from_values.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.framework.assign_from_values.md
new file mode 100644
index 0000000000..6560f08281
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.framework.assign_from_values.md
@@ -0,0 +1,24 @@
+### `tf.contrib.framework.assign_from_values(var_names_to_values)` {#assign_from_values}
+
+Creates an assignment operation from a given mapping.
+
+This function provides a mechanism for performing assignment of variables
+to values in a way that does not fill the graph with large assignment values.
+
+##### Args:
+
+
+*  <b>`var_names_to_values`</b>: A map from variable names to values.
+
+##### Returns:
+
+
+*  <b>`assign_op`</b>: An `Operation` that assigns each of the given variables to the
+    requested values.
+*  <b>`feed_dict`</b>: The feed dictionary to use when evaluating `assign_op`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if any of the given variable names were not found.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.framework.assign_from_values_fn.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.framework.assign_from_values_fn.md
new file mode 100644
index 0000000000..9a5a82c8c4
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.framework.assign_from_values_fn.md
@@ -0,0 +1,22 @@
+### `tf.contrib.framework.assign_from_values_fn(var_names_to_values)` {#assign_from_values_fn}
+
+Returns a function that assigns specific variables from the given values.
+
+This function provides a mechanism for performing assignment of variables
+to values in a way that does not fill the graph with large assignment values.
+
+##### Args:
+
+
+*  <b>`var_names_to_values`</b>: A map from variable names to values.
+
+##### Returns:
+
+  A function that takes a single argument, a `tf.Session`, that applies the
+  assignment operation.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if any of the given variable names were not found.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.framework.assign_from_checkpoint_fn.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.framework.assign_from_checkpoint_fn.md
new file mode 100644
index 0000000000..e4d183b990
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.framework.assign_from_checkpoint_fn.md
@@ -0,0 +1,29 @@
+### `tf.contrib.framework.assign_from_checkpoint_fn(model_path, var_list, ignore_missing_vars=False, reshape_variables=False)` {#assign_from_checkpoint_fn}
+
+Returns a function that assigns specific variables from a checkpoint.
+
+##### Args:
+
+
+*  <b>`model_path`</b>: The full path to the model checkpoint. To get latest checkpoint
+      use `model_path = tf.train.latest_checkpoint(checkpoint_dir)`
+*  <b>`var_list`</b>: A list of `Variable` objects or a dictionary mapping names in the
+      checkpoint to the correspoing variables to initialize. If empty or None,
+      it would return  no_op(), None.
+*  <b>`ignore_missing_vars`</b>: Boolean, if True it would ignore variables missing in
+      the checkpoint with a warning instead of failing.
+*  <b>`reshape_variables`</b>: Boolean, if True it would automatically reshape variables
+      which are of different shape then the ones stored in the checkpoint but
+      which have the same number of elements.
+
+##### Returns:
+
+  A function that takes a single argument, a `tf.Session`, that applies the
+  assignment operation.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If the checkpoint specified at `model_path` is missing one of
+    the variables in `var_list`.
+
diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md
index 9562ade43f..c93190c139 100644
--- a/tensorflow/g3doc/api_docs/python/index.md
+++ b/tensorflow/g3doc/api_docs/python/index.md
@@ -652,6 +652,10 @@
   * [`assert_or_get_global_step`](../../api_docs/python/contrib.framework.md#assert_or_get_global_step)
   * [`assert_same_float_dtype`](../../api_docs/python/contrib.framework.md#assert_same_float_dtype)
   * [`assert_scalar_int`](../../api_docs/python/contrib.framework.md#assert_scalar_int)
+  * [`assign_from_checkpoint`](../../api_docs/python/contrib.framework.md#assign_from_checkpoint)
+  * [`assign_from_checkpoint_fn`](../../api_docs/python/contrib.framework.md#assign_from_checkpoint_fn)
+  * [`assign_from_values`](../../api_docs/python/contrib.framework.md#assign_from_values)
+  * [`assign_from_values_fn`](../../api_docs/python/contrib.framework.md#assign_from_values_fn)
   * [`convert_to_tensor_or_sparse_tensor`](../../api_docs/python/contrib.framework.md#convert_to_tensor_or_sparse_tensor)
   * [`create_global_step`](../../api_docs/python/contrib.framework.md#create_global_step)
   * [`deprecated`](../../api_docs/python/contrib.framework.md#deprecated)
-- 
GitLab


From ee7332cec10ed8ee1f908f272e050d739fa5cf9e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 22 Aug 2016 11:44:02 -0800
Subject: [PATCH 006/223] Wrap all ops created by
 report_uninitialized_variables in a name_scope. Previously ops were created
 outside of a scope leading to the ugly occurrence of bare ops in TensorBoard.
 Change: 130970109

---
 tensorflow/python/ops/variables.py | 27 +++++++++++++--------------
 1 file changed, 13 insertions(+), 14 deletions(-)

diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py
index 4d1af8bcca..47ced90acd 100644
--- a/tensorflow/python/ops/variables.py
+++ b/tensorflow/python/ops/variables.py
@@ -1031,20 +1031,19 @@ def report_uninitialized_variables(var_list=None,
       for op in ops.get_default_graph().get_operations():
         if op.type in ["Variable", "AutoReloadVariable"]:
           var_list.append(op.outputs[0])
-  if not var_list:
-    # Return an empty tensor so we only need to check for returned tensor
-    # size being 0 as an indication of model ready.
-    return array_ops.constant([], dtype=dtypes.string, name=name)
-  else:
-    # Get a 1-D boolean tensor listing whether each variable is initialized.
-    variables_mask = math_ops.logical_not(
-        array_ops.pack([state_ops.is_variable_initialized(v) for v in var_list
-                       ]))
-    # Get a 1-D string tensor containing all the variable names.
-    variable_names_tensor = array_ops.constant([s.op.name for s in var_list])
-    # Return a 1-D tensor containing all the names of uninitialized variables.
-    return array_ops.boolean_mask(
-        variable_names_tensor, variables_mask, name=name)
+  with ops.name_scope(name):
+    if not var_list:
+      # Return an empty tensor so we only need to check for returned tensor
+      # size being 0 as an indication of model ready.
+      return array_ops.constant([], dtype=dtypes.string)
+    else:
+      # Get a 1-D boolean tensor listing whether each variable is initialized.
+      variables_mask = math_ops.logical_not(array_ops.pack(
+          [state_ops.is_variable_initialized(v) for v in var_list]))
+      # Get a 1-D string tensor containing all the variable names.
+      variable_names_tensor = array_ops.constant([s.op.name for s in var_list])
+      # Return a 1-D tensor containing all the names of uninitialized variables.
+      return array_ops.boolean_mask(variable_names_tensor, variables_mask)
 
 # pylint: disable=protected-access
 ops.register_tensor_conversion_function(Variable,
-- 
GitLab


From 317b6d0e3a66ea446e793eb65a397786e62f8d85 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Dan=20Man=C3=A9?= <danmane@google.com>
Date: Mon, 22 Aug 2016 11:46:23 -0800
Subject: [PATCH 007/223] Autogenerated Change: Release TensorBoard at TAG: 26
 Change: 130970371

---
 tensorflow/tensorboard/TAG                    |    2 +-
 .../tensorboard/dist/tf-tensorboard.html      | 6886 +++++++++--------
 2 files changed, 3558 insertions(+), 3330 deletions(-)

diff --git a/tensorflow/tensorboard/TAG b/tensorflow/tensorboard/TAG
index 6f4247a625..f64f5d8d85 100644
--- a/tensorflow/tensorboard/TAG
+++ b/tensorflow/tensorboard/TAG
@@ -1 +1 @@
-26
+27
diff --git a/tensorflow/tensorboard/dist/tf-tensorboard.html b/tensorflow/tensorboard/dist/tf-tensorboard.html
index 0c1a6111b2..dc39c8fa10 100644
--- a/tensorflow/tensorboard/dist/tf-tensorboard.html
+++ b/tensorflow/tensorboard/dist/tf-tensorboard.html
@@ -112,1506 +112,308 @@ var TF;
 </dom-module>
 
 
-<dom-module id="scrollbar-style" assetpath="../tf-dashboard-common/">
+<dom-module id="tf-smoothing-input" assetpath="../tf-event-dashboard/">
   <template>
+    <h3 class="title">Smoothing</h3>
+    <div class="smoothing-block">
+      <paper-slider id="slider" value="{{weight}}" immediate-value="{{_immediateWeightNumberForPaperSlider}}" type="number" step="[[step]]" min="[[min]]" max="[[max]]"></paper-slider>
+      <paper-input id="input" label="weight" no-label-float="" value="{{_inputWeightStringForPaperInput}}" type="number" step="[[step]]" min="[[min]]" max="[[max]]"></paper-input>
+    </div>
     <style>
-      .scrollbar::-webkit-scrollbar-track
-      {
-        visibility: hidden;
+      .title {
+        color: var(--paper-grey-800);
+        margin: 0;
+        font-weight: normal;
+        font-size: 14px;
+        margin-bottom: 5px;
       }
 
-      .scrollbar::-webkit-scrollbar
-      {
-        width: 10px;
+      .smoothing-block {
+        display: flex;
       }
 
-      .scrollbar::-webkit-scrollbar-thumb
-      {
-        border-radius: 10px;
-        -webkit-box-shadow: inset 0 0 2px rgba(0,0,0,.3);
-        background-color: var(--paper-grey-500);
-        color: var(--paper-grey-900);
+      paper-slider {
+        margin-left: 12px;
+        --paper-slider-knob-color: var(--tb-orange-strong);
+        --paper-slider-active-color: var(--tb-orange-strong);
+        flex-grow: 2;
       }
-      .scrollbar {
-        box-sizing: border-box;
+
+      paper-input {
+        --paper-input-container-focus-color: var(--tb-orange-strong);
+        --paper-input-container-input: {
+          font-size: 14px;
+        };
+        --paper-input-container-label: {
+          font-size: 14px;
+        };
+        width: 60px;
       }
     </style>
   </template>
+  <script>
+    Polymer({
+      is: "tf-smoothing-input",
+
+      properties: {
+        step: Number,
+        max: Number,
+        min: Number,
+
+        weight: {
+          type: Number,
+          value: 0.6,
+          notify: true
+        },
+
+        _immediateWeightNumberForPaperSlider: {
+          type: Number,
+          notify: true,
+          observer: '_immediateWeightNumberForPaperSliderChanged'
+        },
+
+        // Paper input treats values as strings even if you specify them as
+        // numbers.
+        _inputWeightStringForPaperInput: {
+          type: String,
+          notify: true,
+          observer: '_inputWeightStringForPaperInputChanged'
+        }
+      },
+
+      _updateWeight: _.debounce(function(val) {
+        this.weight = val;
+      }, 250),
+
+      _immediateWeightNumberForPaperSliderChanged: function() {
+        this._inputWeightStringForPaperInput =
+            this._immediateWeightNumberForPaperSlider.toString();
+        this._updateWeight.call(this, this._immediateWeightNumberForPaperSlider);
+      },
+
+      _inputWeightStringForPaperInputChanged: function() {
+        if (+this._inputWeightStringForPaperInput < 0) {
+          this._inputWeightStringForPaperInput = '0';
+        }
+        else if (+this._inputWeightStringForPaperInput > 1) {
+          this._inputWeightStringForPaperInput = '1';
+        }
+
+        var d = +this._inputWeightStringForPaperInput;
+        if (!isNaN(d)) {
+          this._updateWeight.call(this, d);
+        }
+      }
+    });
+  </script>
 </dom-module>
-<dom-module id="run-color-style" assetpath="../tf-dashboard-common/">
-  <template>
-    <style>
-    [color-class="light-blue"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-light-blue-500);
-      --paper-checkbox-checked-ink-color: var(--paper-light-blue-500);
-      --paper-checkbox-unchecked-color: var(--paper-light-blue-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-light-blue-900);
-    }
-    [color-class="red"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-red-500);
-      --paper-checkbox-checked-ink-color: var(--paper-red-500);
-      --paper-checkbox-unchecked-color: var(--paper-red-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-red-900);
-    }
-    [color-class="green"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-green-500);
-      --paper-checkbox-checked-ink-color: var(--paper-green-500);
-      --paper-checkbox-unchecked-color: var(--paper-green-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-green-900);
-    }
-    [color-class="purple"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-purple-500);
-      --paper-checkbox-checked-ink-color: var(--paper-purple-500);
-      --paper-checkbox-unchecked-color: var(--paper-purple-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-purple-900);
-    }
-    [color-class="teal"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-teal-500);
-      --paper-checkbox-checked-ink-color: var(--paper-teal-500);
-      --paper-checkbox-unchecked-color: var(--paper-teal-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-teal-900);
-    }
-    [color-class="pink"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-pink-500);
-      --paper-checkbox-checked-ink-color: var(--paper-pink-500);
-      --paper-checkbox-unchecked-color: var(--paper-pink-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-pink-900);
-    }
-    [color-class="orange"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-orange-500);
-      --paper-checkbox-checked-ink-color: var(--paper-orange-500);
-      --paper-checkbox-unchecked-color: var(--paper-orange-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-orange-900);
-    }
-    [color-class="brown"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-brown-500);
-      --paper-checkbox-checked-ink-color: var(--paper-brown-500);
-      --paper-checkbox-unchecked-color: var(--paper-brown-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-brown-900);
-    }
-    [color-class="indigo"] paper-checkbox {
-      --paper-checkbox-checked-color: var(--paper-indigo-500);
-      --paper-checkbox-checked-ink-color: var(--paper-indigo-500);
-      --paper-checkbox-unchecked-color: var(--paper-indigo-900);
-      --paper-checkbox-unchecked-ink-color: var(--paper-indigo-900);
-    }
-    </style>
-  </template>
-</dom-module>
-<dom-module id="tf-storage" assetpath="../tf-storage/">
- <script>/* Copyright 2015 Google Inc. All Rights Reserved.
+<script>/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 
-Licensed under the Apache License, Version 2.0 (the "License");
+Licensed under the Apache License, Version 2.0 (the 'License');
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
 
 http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
+distributed under the License is distributed on an 'AS IS' BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
-/* tslint:disable:no-namespace variable-name */
-/**
- * The Storage Module provides storage for URL parameters, and an API for
- * getting and setting TensorBoard's stateful URI.
- *
- * It generates URI components like: events&runPrefix=train*
- * which TensorBoard uses after like localhost:8000/#events&runPrefix=train*
- * to store state in the URI.
- */
+var __extends = (this && this.__extends) || function (d, b) {
+    for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p];
+    function __() { this.constructor = d; }
+    d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+};
 var TF;
 (function (TF) {
-    var URIStorage;
-    (function (URIStorage) {
-        /**
-         * A key that users cannot use, since TensorBoard uses this to store info
-         * about the active tab.
-         */
-        URIStorage.TAB = '__tab__';
+    var Backend;
+    (function (Backend) {
         /**
-         * The name of the property for users to set on a Polymer component
-         * in order for its stored properties to be stored in the URI unambiguously.
-         * (No need to set this if you want mutliple instances of the component to
-         * share URI state)
-         *
-         * Example:
-         * <my-component disambiguator="0"></my-component>
-         *
-         * The disambiguator should be set to any unique value so that multiple
-         * instances of the component can store properties in URI storage.
+         * Manages many fetch requests. Launches up to nSimultaneousRequests
+         * simultaneously, and maintains a LIFO queue of requests to process when
+         * more urls are requested than can be handled at once. The queue can be
+         * cleared.
          *
-         * Because it's hard to dereference this variable in HTML property bindings,
-         * it is NOT safe to change the disambiguator string without find+replace
-         * across the codebase.
-         */
-        URIStorage.DISAMBIGUATOR = 'disambiguator';
-        /**
-         * Return a boolean stored in the URI, given a corresponding key.
-         * Undefined if not found.
-         */
-        function getBoolean(key) {
-            var items = _componentToDict(_readComponent());
-            var item = items[key];
-            return item === 'true' ? true : item === 'false' ? false : undefined;
-        }
-        URIStorage.getBoolean = getBoolean;
-        /**
-         * Store a boolean in the URI, with a corresponding key.
-         */
-        function setBoolean(key, value) {
-            var items = _componentToDict(_readComponent());
-            items[key] = value.toString();
-            _writeComponent(_dictToComponent(items));
-        }
-        URIStorage.setBoolean = setBoolean;
-        /**
-         * Return a string stored in the URI, given a corresponding key.
-         * Undefined if not found.
-         */
-        function getString(key) {
-            var items = _componentToDict(_readComponent());
-            return items[key];
-        }
-        URIStorage.getString = getString;
-        /**
-         * Store a string in the URI, with a corresponding key.
-         */
-        function setString(key, value) {
-            var items = _componentToDict(_readComponent());
-            items[key] = value;
-            _writeComponent(_dictToComponent(items));
-        }
-        URIStorage.setString = setString;
-        /**
-         * Return a number stored in the URI, given a corresponding key.
-         * Undefined if not found.
-         */
-        function getNumber(key) {
-            var items = _componentToDict(_readComponent());
-            return items[key] === undefined ? undefined : +items[key];
-        }
-        URIStorage.getNumber = getNumber;
-        /**
-         * Store a number in the URI, with a corresponding key.
-         */
-        function setNumber(key, value) {
-            var items = _componentToDict(_readComponent());
-            items[key] = '' + value;
-            _writeComponent(_dictToComponent(items));
-        }
-        URIStorage.setNumber = setNumber;
-        /**
-         * Return an object stored in the URI, given a corresponding key.
-         * Undefined if not found.
-         */
-        function getObject(key) {
-            var items = _componentToDict(_readComponent());
-            return items[key] === undefined ? undefined : JSON.parse(atob(items[key]));
-        }
-        URIStorage.getObject = getObject;
-        /**
-         * Store an object in the URI, with a corresponding key.
+         * When a request is made, a Promise is returned which resolves with the
+         * parsed JSON result from the request.
          */
-        function setObject(key, value) {
-            var items = _componentToDict(_readComponent());
-            items[key] = btoa(JSON.stringify(value));
-            _writeComponent(_dictToComponent(items));
-        }
-        URIStorage.setObject = setObject;
-        /**
-         * Get a unique storage name for a (Polymer component, propertyName) tuple.
-         *
-         * DISAMBIGUATOR must be set on the component, if other components use the
-         * same propertyName.
-         */
-        function getURIStorageName(component, propertyName) {
-            var d = component[URIStorage.DISAMBIGUATOR];
-            var components = d == null ? [propertyName] : [d, propertyName];
-            return components.join('.');
-        }
-        URIStorage.getURIStorageName = getURIStorageName;
-        /**
-         * Return a function that:
-         * (1) Initializes a Polymer boolean property with a default value, if its
-         *     value is not already set
-         * (2) Sets up listener that updates Polymer property on hash change.
-         */
-        function getBooleanInitializer(propertyName, defaultVal) {
-            return _getInitializer(getBoolean, propertyName, defaultVal);
-        }
-        URIStorage.getBooleanInitializer = getBooleanInitializer;
-        /**
-         * Return a function that:
-         * (1) Initializes a Polymer string property with a default value, if its
-         *     value is not already set
-         * (2) Sets up listener that updates Polymer property on hash change.
-         */
-        function getStringInitializer(propertyName, defaultVal) {
-            return _getInitializer(getString, propertyName, defaultVal);
-        }
-        URIStorage.getStringInitializer = getStringInitializer;
-        /**
-         * Return a function that:
-         * (1) Initializes a Polymer number property with a default value, if its
-         *     value is not already set
-         * (2) Sets up listener that updates Polymer property on hash change.
-         */
-        function getNumberInitializer(propertyName, defaultVal) {
-            return _getInitializer(getNumber, propertyName, defaultVal);
-        }
-        URIStorage.getNumberInitializer = getNumberInitializer;
-        /**
-         * Return a function that:
-         * (1) Initializes a Polymer Object property with a default value, if its
-         *     value is not already set
-         * (2) Sets up listener that updates Polymer property on hash change.
-         *
-         * Generates a deep clone of the defaultVal to avoid mutation issues.
-         */
-        function getObjectInitializer(propertyName, defaultVal) {
-            var clone = _.cloneDeep(defaultVal);
-            return _getInitializer(getObject, propertyName, clone);
-        }
-        URIStorage.getObjectInitializer = getObjectInitializer;
-        /**
-         * Return a function that updates URIStorage when a string property changes.
-         */
-        function getBooleanObserver(propertyName, defaultVal) {
-            return _getObserver(getBoolean, setBoolean, propertyName, defaultVal);
-        }
-        URIStorage.getBooleanObserver = getBooleanObserver;
-        /**
-         * Return a function that updates URIStorage when a string property changes.
-         */
-        function getStringObserver(propertyName, defaultVal) {
-            return _getObserver(getString, setString, propertyName, defaultVal);
-        }
-        URIStorage.getStringObserver = getStringObserver;
-        /**
-         * Return a function that updates URIStorage when a number property changes.
-         */
-        function getNumberObserver(propertyName, defaultVal) {
-            return _getObserver(getNumber, setNumber, propertyName, defaultVal);
-        }
-        URIStorage.getNumberObserver = getNumberObserver;
-        /**
-         * Return a function that updates URIStorage when an object property changes.
-         * Generates a deep clone of the defaultVal to avoid mutation issues.
-         */
-        function getObjectObserver(propertyName, defaultVal) {
-            var clone = _.cloneDeep(defaultVal);
-            return _getObserver(getObject, setObject, propertyName, clone);
-        }
-        URIStorage.getObjectObserver = getObjectObserver;
-        /**
-         * Read component from URI (e.g. returns "events&runPrefix=train*").
-         */
-        function _readComponent() {
-            return TF.Globals.USE_HASH ? window.location.hash.slice(1) :
-                TF.Globals.FAKE_HASH;
-        }
-        /**
-         * Write component to URI.
-         */
-        function _writeComponent(component) {
-            if (TF.Globals.USE_HASH) {
-                window.location.hash = component;
+        var RequestCancellationError = (function (_super) {
+            __extends(RequestCancellationError, _super);
+            function RequestCancellationError() {
+                _super.apply(this, arguments);
+                this.name = 'RequestCancellationError';
             }
-            else {
-                TF.Globals.FAKE_HASH = component;
+            return RequestCancellationError;
+        }(Error));
+        Backend.RequestCancellationError = RequestCancellationError;
+        var RequestNetworkError = (function (_super) {
+            __extends(RequestNetworkError, _super);
+            function RequestNetworkError(req, url) {
+                _super.call(this);
+                this.message = "RequestNetworkError: " + req.status + " at " + url;
+                this.name = 'RequestNetworkError';
+                this.req = req;
+                this.url = url;
             }
-        }
-        /**
-         * Convert dictionary of strings into a URI Component.
-         * All key value entries get added as key value pairs in the component,
-         * with the exception of a key with the TAB value, which if present
-         * gets prepended to the URI Component string for backwards comptability
-         * reasons.
-         */
-        function _dictToComponent(items) {
-            var component = '';
-            // Add the tab name e.g. 'events', 'images', 'histograms' as a prefix
-            // for backwards compatbility.
-            if (items[URIStorage.TAB] !== undefined) {
-                component += items[URIStorage.TAB];
+            return RequestNetworkError;
+        }(Error));
+        Backend.RequestNetworkError = RequestNetworkError;
+        var RequestManager = (function () {
+            function RequestManager(nSimultaneousRequests, maxRetries) {
+                if (nSimultaneousRequests === void 0) { nSimultaneousRequests = 10; }
+                if (maxRetries === void 0) { maxRetries = 3; }
+                this._queue = [];
+                this._nActiveRequests = 0;
+                this._nSimultaneousRequests = nSimultaneousRequests;
+                this._maxRetries = maxRetries;
             }
-            // Join other strings with &key=value notation
-            var nonTab = _.pairs(items)
-                .filter(function (pair) { return pair[0] !== URIStorage.TAB; })
-                .map(function (pair) {
-                return encodeURIComponent(pair[0]) + '=' +
-                    encodeURIComponent(pair[1]);
-            })
-                .join('&');
-            return nonTab.length > 0 ? (component + '&' + nonTab) : component;
-        }
-        /**
-         * Convert a URI Component into a dictionary of strings.
-         * Component should consist of key-value pairs joined by a delimiter
-         * with the exception of the tabName.
-         * Returns dict consisting of all key-value pairs and
-         * dict[TAB] = tabName
-         */
-        function _componentToDict(component) {
-            var items = {};
-            var tokens = component.split('&');
-            tokens.forEach(function (token) {
-                var kv = token.split('=');
-                // Special backwards compatibility for URI components like #events
-                if (kv.length === 1 && _.contains(TF.Globals.TABS, kv[0])) {
-                    items[URIStorage.TAB] = kv[0];
-                }
-                else if (kv.length === 2) {
-                    items[decodeURIComponent(kv[0])] = decodeURIComponent(kv[1]);
-                }
-            });
-            return items;
-        }
-        /**
-         * Return a function that:
-         * (1) Initializes a Polymer property with a default value, if its
-         *     value is not already set
-         * (2) Sets up listener that updates Polymer property on hash change.
-         */
-        function _getInitializer(get, propertyName, defaultVal) {
-            return function () {
+            /* Gives a promise that loads assets from given url (respects queuing) */
+            RequestManager.prototype.request = function (url) {
                 var _this = this;
-                var URIStorageName = getURIStorageName(this, propertyName);
-                var setComponentValue = function () {
-                    var uriValue = get(URIStorageName);
-                    _this[propertyName] = uriValue !== undefined ? uriValue : defaultVal;
-                };
-                // Set the value on the property.
-                setComponentValue();
-                // Update it when the hashchanges.
-                window.addEventListener('hashchange', setComponentValue);
-            };
-        }
-        /**
-         * Return a function that updates URIStorage when a property changes.
-         */
-        function _getObserver(get, set, propertyName, defaultVal) {
-            return function () {
-                var URIStorageName = getURIStorageName(this, propertyName);
-                var newVal = this[propertyName];
-                if (!_.isEqual(newVal, get(URIStorageName))) {
-                    if (_.isEqual(newVal, defaultVal)) {
-                        _unset(URIStorageName);
-                    }
-                    else {
-                        set(URIStorageName, newVal);
+                var promise = new Promise(function (resolve, reject) {
+                    var resolver = { resolve: resolve, reject: reject };
+                    _this._queue.push(resolver);
+                    _this.launchRequests();
+                })
+                    .then(function () {
+                    return _this.promiseWithRetries(url, _this._maxRetries);
+                })
+                    .then(function (response) {
+                    // Success - Let's free space for another active
+                    // reqest, and launch it
+                    _this._nActiveRequests--;
+                    _this.launchRequests();
+                    return response;
+                }, function (rejection) {
+                    if (rejection.name === 'RequestNetworkError') {
+                        // If we failed due to network error, we should
+                        // decrement
+                        // _nActiveRequests because this request was
+                        // active
+                        _this._nActiveRequests--;
+                        _this.launchRequests();
                     }
+                    return Promise.reject(rejection);
+                });
+                return promise;
+            };
+            RequestManager.prototype.clearQueue = function () {
+                while (this._queue.length > 0) {
+                    this._queue.pop().reject(new RequestCancellationError('Request cancelled by clearQueue'));
                 }
             };
-        }
-        /**
-         * Delete a key from the URI.
-         */
-        function _unset(key) {
-            var items = _componentToDict(_readComponent());
-            delete items[key];
-            _writeComponent(_dictToComponent(items));
-        }
-    })(URIStorage = TF.URIStorage || (TF.URIStorage = {}));
-})(TF || (TF = {}));
-</script>
-</dom-module>
-
-
-<dom-module id="tf-multi-checkbox" assetpath="../tf-multi-checkbox/">
-  <style include="scrollbar-style"></style>
-  <style include="run-color-style"></style>
-
-  <template>
-      <paper-input id="runs-regex" no-label-float="" label="Write a regex to filter runs" value="{{regexInput}}"></paper-input>
-    <div id="outer-container" class="scrollbar">
-      <template is="dom-repeat" items="[[namesMatchingRegex]]">
-        <div class="run-row">
-          <div class="checkbox-container vertical-align-container">
-            <paper-checkbox class="checkbox vertical-align-center" name="[[item]]" checked$="[[_isChecked(item, runToIsCheckedMapping.*)]]" on-change="_checkboxChange"></paper-checkbox>
-          </div>
-          <div class="item-label-container">
-            <span>[[item]]</span>
-          </div>
-        </div>
-      </template>
-    </div>
-  <style>
-    paper-input {
-      --paper-input-container-focus-color: var(--tb-orange-strong);
-      --paper-input-container-input: {
-        font-size: 14px;
-      };
-      --paper-input-container-label: {
-        font-size: 14px;
-      };
-    }
-    :host {
-      display: flex;
-      flex-direction: column;
-      height: 100%;
-    }
-    #outer-container {
-      overflow-y: auto;
-      overflow-x: hidden;
-      width: 100%;
-      height: 0; /* Quirk to make firefox add scrolling instead of expand div */
-      flex-grow: 1;
-      flex-shrink: 1;
-      word-wrap: break-word;
-    }
-    .run-row {
-      padding-top: 5px;
-      padding-bottom: 5px;
-      display: flex;
-      flex-direction: row;
-      font-size: 13px;
-    }
-    .checkbox-container {
-      flex-grow: 0;
-      flex-shrink: 0;
-    }
-    .checkbox {
-      padding-left: 2px;
-      width: 32px;
-    }
-    .item-label-container {
-      flex-grow: 1;
-      flex-shrink: 1;
-      width: 0px; /* hack to get the flex-grow to work properly */
-    }
-    .tooltip-value-container {
-      display: flex;
-      justify-content: center;
-      flex-grow: 0;
-      flex-shrink: 0;
-      text-align:right;
-      padding-left: 2px;
-    }
-    .vertical-align-container {
-      display: flex;
-      justify-content: center;
-    }
-    .vertical-align-container .vertical-align-center {
-      align-self: center;
-    }
-    .vertical-align-container .vertical-align-top {
-      align-self: start;
-    }
-  </style>
-  </template>
-
-  <script>
-  Polymer({
-    is: "tf-multi-checkbox",
-    properties: {
-      names: Array, // All the runs in consideration
-
-      regexInput: {
-        type: String,
-        value: TF.URIStorage.getStringInitializer("regexInput", ""),
-        observer: "_regexInputObserver"
-      }, // Regex for filtering the runs
-      regex: {
-        type: Object,
-        computed: "makeRegex(regexInput)"
-      },
-      namesMatchingRegex: {
-        type: Array,
-        computed: "computeNamesMatchingRegex(names.*, regex)"
-      }, // Runs that match the regex
-      runToIsCheckedMapping: {
-        type: Object,
-        value: function() {return {};}
-      }, // run name -> Boolean (if its enabled)
-      // (Allows state to persist across regex filtering)
-      outSelected: {
-        type: Array,
-        notify: true,
-        computed: 'computeOutSelected(namesMatchingRegex.*, runToIsCheckedMapping.*)'
-      },
-      colorScale: {
-        type: Object,
-        observer: "synchronizeColors",
-      }, // map from run name to css class
-    },
-    listeners: {
-      'dom-change': 'synchronizeColors',
-    },
-    observers: [
-      "_initializeRunToIsCheckedMapping(names.*)",
-    ],
-    makeRegex: function(regex) {
-      try {
-        return new RegExp(regex)
-      } catch (e) {
-        return null;
-      }
-    },
-    _initializeRunToIsCheckedMapping: function(change) {
-      var runToIsCheckedMapping = _.clone(this.runToIsCheckedMapping);
-
-      this.names.forEach(function(n) {
-        if (runToIsCheckedMapping[n] == null) {
-          // runs default to on
-          runToIsCheckedMapping[n] = true;
-        }
-      });
-      this.runToIsCheckedMapping = runToIsCheckedMapping;
-    },
-    computeNamesMatchingRegex: function(__, ___) {
-      var regex = this.regex;
-      return this.names.filter(function(n) {
-        return regex == null || regex.test(n);
-      });
-    },
-    computeOutSelected: function(__, ___) {
-      var runToIsCheckedMapping = this.runToIsCheckedMapping;
-      return this.namesMatchingRegex.filter(function(n) {
-        return runToIsCheckedMapping[n];
-      });
-    },
-    synchronizeColors: function(e) {
-      var checkboxes = Array.prototype.slice.call(this.querySelectorAll("paper-checkbox"));
-      var scale = this.colorScale;
-      checkboxes.forEach(function(p) {
-        var color = scale.scale(p.name);
-        p.customStyle['--paper-checkbox-checked-color'] = color;
-        p.customStyle['--paper-checkbox-checked-ink-color'] = color;
-        p.customStyle['--paper-checkbox-unchecked-color'] = color;
-        p.customStyle['--paper-checkbox-unchecked-ink-color'] = color;
-      });
-      this.updateStyles();
-      // The updateStyles call fails silently if the browser doesn't have focus,
-      // e.g. if TensorBoard was opened into a new tab that isn't visible.
-      // As a workaround... we know requestAnimationFrame won't fire until the
-      // page has focus, so updateStyles again on requestAnimationFrame.
-      var _this = this;
-      window.requestAnimationFrame(function() {_this.updateStyles();});
-    },
-    _checkboxChange: function(e) {
-      var target = e.srcElement || e.target; // Firefox doesn't have srcElement.
-      var name = target.name;
-      var checked = target.checked;
-      this.runToIsCheckedMapping[name] = checked;
-      // n.b. notifyPath won't work because run names may have periods.
-      this.runToIsCheckedMapping = _.clone(this.runToIsCheckedMapping);
-    },
-    _isChecked: function(item, outSelectedChange) {
-      return this.runToIsCheckedMapping[item];
-    },
-    _initializeRuns: function(change) {
-      this.outSelected = change.base.slice();
-    },
-    _regexInputObserver: TF.URIStorage.getStringObserver("regexInput", ""),
-    toggleAll: function() {
-      var _this = this;
-      var allOn = this.namesMatchingRegex
-                    .filter(function(n) {return !_this.runToIsCheckedMapping[n]})
-                    .length === 0;
-      this.namesMatchingRegex.forEach(function(n) {_this.runToIsCheckedMapping[n] = !allOn});
-      this.runToIsCheckedMapping = _.clone(this.runToIsCheckedMapping);
-    },
-  });
-  </script>
-</dom-module>
-
-<dom-module id="tf-run-selector" assetpath="../tf-event-dashboard/">
-  <template>
-    <div id="top-text">
-      <h3 id="tooltip-help" class="tooltip-container">
-        Runs
-      </h3>
-    </div>
-    <tf-multi-checkbox id="multiCheckbox" names="[[runs]]" out-selected="{{outSelected}}" color-scale="[[colorScale]]"></tf-multi-checkbox>
-    <paper-button class="x-button" id="toggle-all" on-tap="_toggleAll">
-    Toggle All Runs
-    </paper-button>
-    <style>
-      :host {
-        display: flex;
-        flex-direction: column;
-        padding-bottom: 10px;
-        box-sizing: border-box;
-      }
-      #top-text {
-        width: 100%;
-        flex-grow: 0;
-        flex-shrink: 0;
-        padding-right: 16px;
-        padding-bottom: 6px;
-        box-sizing: border-box;
-        color: var(--paper-grey-800);
-      }
-      tf-multi-checkbox {
-        display: flex;
-        flex-grow: 1;
-        flex-shrink: 1;
-      }
-      .x-button {
-        font-size: 13px;
-        background-color: var(--tb-ui-light-accent);
-        margin-top: 5px;
-        color: var(--tb-ui-dark-accent);
-      }
-      #tooltip-help {
-        color: var(--paper-grey-800);
-        margin: 0;
-        font-weight: normal;
-        font-size: 14px;
-        margin-bottom: 5px;
-      }
-      paper-button {
-        margin-left: 0;
-      }
-    </style>
-  </template>
-  <script>
-  Polymer({
-    is: "tf-run-selector",
-    properties: {
-      outSelected: {type: Array, notify: true},
-      // runs: an array of strings, representing the run names that may be chosen
-      runs: Array,
-      colorScale: Object, // TF.ColorScale
-    },
-    _toggleAll: function() {
-      this.$.multiCheckbox.toggleAll();
-    },
-  });
-  </script>
-</dom-module>
-
-<dom-module id="tf-smoothing-input" assetpath="../tf-event-dashboard/">
-  <template>
-    <div class="smoothing-line">
-      <paper-checkbox checked="{{enabled}}">Exponential smoothing</paper-checkbox>
-      <div class="smoothing-block">
-        <paper-slider value="{{decay}}" immediate-value="{{_immediateDecayNumberForPaperSlider}}" type="number" step="[[step]]" min="[[min]]" max="[[max]]" disabled="[[!enabled]]"></paper-slider>
-        <paper-input id="input" label="decay" no-label-float="" value="{{_inputDecayStringForPaperInput}}" type="number" step="[[step]]" min="[[min]]" max="[[max]]" disabled="[[!enabled]]"></paper-input>
-      </div>
-    </div>
-    <style>
-      .smoothing-line {
-        margin-top: 15px;
-      }
-
-      .smoothing-block {
-        display: flex;
-      }
-
-      paper-checkbox {
-        --paper-checkbox-checked-color: var(--tb-ui-dark-accent);
-        --paper-checkbox-unchecked-color: var(--tb-ui-dark-accent);
-        font-size: 14px;
-      }
-
-      paper-slider {
-        margin-left: 12px;
-        --paper-slider-knob-color: var(--tb-orange-strong);
-        --paper-slider-active-color: var(--tb-orange-strong);
-        flex-grow: 2;
-      }
-
-      paper-input {
-        --paper-input-container-focus-color: var(--tb-orange-strong);
-        --paper-input-container-input: {
-          font-size: 14px;
-        };
-        --paper-input-container-label: {
-          font-size: 14px;
-        };
-        width: 60px;
-      }
-    </style>
-  </template>
-  <script>
-    Polymer({
-      is: "tf-smoothing-input",
-
-      properties: {
-        enabled: {
-          type: Boolean,
-          value: false,
-          notify: true,
-          observer: '_enabledChanged'
-        },
-
-        step: Number,
-        max: Number,
-        min: Number,
-
-        decay: {
-          type: Number,
-          value: 0.9,
-          notify: true
-        },
-
-        _immediateDecayNumberForPaperSlider: {
-          type: Number,
-          notify: true,
-          observer: '_immediateDecayNumberForPaperSliderChanged'
-        },
-
-        // Paper input treats values as strings even if you specify them as
-        // numbers.
-        _inputDecayStringForPaperInput: {
-          type: String,
-          notify: true,
-          observer: '_inputDecayStringForPaperInputChanged'
-        }
-      },
-
-      _updateDecay: _.debounce(function(val) {
-        this.decay = val;
-      }, 250),
-
-      _enabledChanged: function() {
-        // Workaround for paper-input bug on firefox :(
-        // Element crashes if we change value while it is disabled.
-        if (this.enabled) {
-          this.$.input.label = '';
-          this._immediateDecayNumberForPaperSliderChanged();
-        }
-      },
-
-      _immediateDecayNumberForPaperSliderChanged: function() {
-        if(!this.enabled) {
-          return;
-        }
-        this._inputDecayStringForPaperInput =
-            this._immediateDecayNumberForPaperSlider.toString();
-        this._updateDecay.call(this, this._immediateDecayNumberForPaperSlider);
-      },
-
-      _inputDecayStringForPaperInputChanged: function() {
-        if(!this.enabled) {
-           return;
-        }
-        if (+this._inputDecayStringForPaperInput < 0) {
-          this._inputDecayStringForPaperInput = '0';
-        }
-        else if (+this._inputDecayStringForPaperInput > 1) {
-          this._inputDecayStringForPaperInput = '1';
-        }
-
-        var d = +this._inputDecayStringForPaperInput;
-        if (!isNaN(d)) {
-          this._updateDecay.call(this, d);
-        }
-      }
-    });
-  </script>
-</dom-module>
-<style is="custom-style">
-
-  :root {
-    --tb-orange-weak: #ffa726;
-    --tb-orange-strong: #f57c00;
-    --tb-grey-darker: #e2e2e2;
-    --tb-grey-lighter: #f3f3f3;
-    --tb-ui-dark-accent: #757575;
-    --tb-ui-light-accent: #e0e0e0;
-    --tb-graph-faded: #e0d4b3;
-  }
-
-</style>
-
-<dom-module id="tf-option-selector" assetpath="../tf-option-selector/">
-  <template>
-    <div id="wrap">
-      <h3>[[name]]</h3>
-      <div class="content-wrapper"><content></content></div>
-    </div>
-    <style>
-      .content-wrapper ::content > * {
-        width: 30%;
-        font-size: 13px;
-        background: none;
-        margin-top: 10px;
-        color: var(--tb-ui-dark-accent);
-      }
-
-      .content-wrapper ::content :first-of-type {
-        margin-left: 0;
-      }
-
-      .content-wrapper ::content .selected {
-        background-color: var(--tb-ui-dark-accent);
-        color: white!important;
-      }
-
-      h3 {
-        color: var(--paper-grey-800);
-        margin: 0;
-        font-weight: normal;
-        font-size: 14px;
-        margin-bottom: 5px;
-        display: block;
-        pointer-events: none;
-      }
-    </style>
-  </template>
-  <script>
-    Polymer({
-      is: "tf-option-selector",
-      properties: {
-        name: String,
-        selectedId: {
-          type: String,
-          notify: true,
-          observer: '_selectedIdChanged'
-        }
-      },
-      attached: function() {
-        this.async(function() {
-          this.getEffectiveChildren().forEach(function(node) {
-            this.listen(node, 'tap', '_selectTarget');
-          }.bind(this));
-        });
-      },
-      _selectTarget: function(e) {
-        this.selectedId = e.currentTarget.id;
-      },
-      _selectedIdChanged: function() {
-        var selected = this.queryEffectiveChildren('#' + this.selectedId);
-        if (!selected) {
-          return;
-        }
-
-        this.getEffectiveChildren().forEach(function(node) {
-          node.classList.remove("selected");
-        });
-        selected.classList.add("selected");
-      }
-    });
-  </script>
-</dom-module>
-
-<dom-module id="tf-color-scale" assetpath="../tf-color-scale/">
-  <script>var TF;
-(function (TF) {
-    TF.palettes = {
-        googleStandard: [
-            '#db4437',
-            '#ff7043',
-            '#f4b400',
-            '#0f9d58',
-            '#00796b',
-            '#00acc1',
-            '#4285f4',
-            '#5c6bc0',
-            '#ab47bc' // purple 400
-        ],
-        googleCool: [
-            '#9e9d24',
-            '#0f9d58',
-            '#00796b',
-            '#00acc1',
-            '#4285f4',
-            '#5c6bc0',
-            '#607d8b' // blue gray 500
-        ],
-        googleWarm: [
-            '#795548',
-            '#ab47bc',
-            '#f06292',
-            '#c2185b',
-            '#db4437',
-            '#ff7043',
-            '#f4b400' // google yellow 700
-        ],
-        googleColorBlindAssist: [
-            '#ff7043',
-            '#00ACC1',
-            '#AB47BC',
-            '#2A56C6',
-            '#0b8043',
-            '#F7CB4D',
-            '#c0ca33',
-            '#5e35b1',
-            '#A52714',
-        ],
-        // These palettes try to be better for color differentiation.
-        // https://personal.sron.nl/~pault/
-        colorBlindAssist1: ['#4477aa', '#44aaaa', '#aaaa44', '#aa7744', '#aa4455', '#aa4488'],
-        colorBlindAssist2: [
-            '#88ccee', '#44aa99', '#117733', '#999933', '#ddcc77', '#cc6677',
-            '#882255', '#aa4499'
-        ],
-        colorBlindAssist3: [
-            '#332288', '#6699cc', '#88ccee', '#44aa99', '#117733', '#999933',
-            '#ddcc77', '#cc6677', '#aa4466', '#882255', '#661100', '#aa4499'
-        ],
-        // based on this palette: http://mkweb.bcgsc.ca/biovis2012/
-        colorBlindAssist4: [
-            '#FF6DB6', '#920000', '#924900', '#DBD100', '#24FF24', '#006DDB',
-            '#490092'
-        ],
-        mldash: [
-            '#E47EAD', '#F4640D', '#FAA300', '#F5E636', '#00A077', '#0077B8',
-            '#00B7ED'
-        ]
-    };
-})(TF || (TF = {}));
-</script>
-  <script>/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-// Example usage:
-// runs = ["train", "test", "test1", "test2"]
-// ccs = new TF.ColorScale();
-// ccs.domain(runs);
-// ccs.getColor("train");
-// ccs.getColor("test1");
-var TF;
-(function (TF) {
-    var ColorScale = (function () {
-        /**
-         * Creates a color scale with optional custom palette.
-         *  @param {string[]} [palette=TF.palettes.googleColorBlind] - The color
-         *                 palette you want as an Array of hex strings.
-         */
-        function ColorScale(palette) {
-            if (palette === void 0) { palette = TF.palettes.googleColorBlindAssist; }
-            this.identifiers = d3.map();
-            this.palette = palette;
-        }
-        /**
-         * Set the domain of strings.
-         * @param {string[]} strings - An array of possible strings to use as the
-         *                             domain for your scale.
-         */
-        ColorScale.prototype.domain = function (strings) {
-            var _this = this;
-            this.identifiers = d3.map();
-            strings.forEach(function (s, i) {
-                _this.identifiers.set(s, _this.palette[i % _this.palette.length]);
-            });
-            return this;
-        };
-        /**
-         * Use the color scale to transform an element in the domain into a color.
-         * @param {string} The input string to map to a color.
-         * @return {string} The color corresponding to that input string.
-         * @throws Will error if input string is not in the scale's domain.
-         */
-        ColorScale.prototype.scale = function (s) {
-            if (!this.identifiers.has(s)) {
-                throw new Error('String was not in the domain.');
-            }
-            return this.identifiers.get(s);
-        };
-        return ColorScale;
-    }());
-    TF.ColorScale = ColorScale;
-})(TF || (TF = {}));
-</script>
-  <script>
-    (function() {
-      Polymer({
-        is: "tf-color-scale",
-        properties: {
-          runs: {
-            type: Array,
-          },
-          outColorScale: {
-            type: Object,
-            readOnly: true,
-            notify: true,
-            value: function() {
-              return new TF.ColorScale();
-            },
-          },
-        },
-        observers: ['updateColorScale(runs.*)'],
-        updateColorScale: function(runsChange) {
-          this.outColorScale.domain(this.runs);
-        },
-      });
-    })();
-  </script>
-</dom-module>
-
-<dom-module id="tf-regex-group" assetpath="../tf-regex-group/">
-  <template>
-    <div class="regex-list">
-      <template is="dom-repeat" items="{{rawRegexes}}">
-        <div class="regex-line">
-          <paper-checkbox class="active-button" checked="{{item.active}}" disabled="[[!item.valid]]"></paper-checkbox>
-          <paper-input id="text-input" class="regex-input" label="Write a regex to create a tag group" no-label-float="" bind-value="{{item.regex}}" invalid="[[!item.valid]]" on-keyup="moveFocus"></paper-input>
-          <paper-icon-button icon="close" class="delete-button" aria-label="Delete Regex" tabindex="0" on-tap="deleteRegex"></paper-icon-button>
-        </div>
-        <style>
-          .regex-input {
-            width: 230px;
-            display: inline-block;
-            margin-left: -3px;
-          }
-
-          paper-checkbox {
-            --paper-checkbox-checked-color: var(--tb-ui-dark-accent);
-            --paper-checkbox-unchecked-color: var(--tb-ui-dark-accent);
-          }
-
-          .delete-button {
-            color: var(--paper-grey-700);
-            width: 40px;
-            height: 40px;
-            margin-right: -10px;
-          }
-
-          .regex-list {
-            margin-bottom: 10px;
-          }
-
-          paper-input {
-            --paper-input-container-focus-color: var(--tb-orange-strong);
-            --paper-input-container-input: {
-              font-size: 14px;
-            };
-            --paper-input-container-label: {
-              font-size: 14px;
-            };
-          }
-        </style>
-      </template>
-    </div>
-  </template>
-  <script>
-    Polymer({
-      is: "tf-regex-group",
-      properties: {
-        rawRegexes: {
-          type: Array,
-          value: function() {
-            return [{regex: "", active: true, valid: true}];
-          }
-        },
-        regexes: {type: Array, computed: "usableRegexes(rawRegexes.*)", notify: true},
-      },
-      observers: [
-        "addNewRegexIfNeeded(rawRegexes.*)",
-        "checkValidity(rawRegexes.*)",
-      ],
-      checkValidity: function(x) {
-        var match = x.path.match(/rawRegexes\.(\d+)\.regex/);
-        if (match) {
-          var idx = match[1];
-          this.set("rawRegexes." + idx + ".valid", this.isValid(x.value));
-        }
-      },
-      isValid: function(s) {
-        try {
-          new RegExp(s);
-          return true;
-        } catch (e) {
-          return false;
-        }
-      },
-      usableRegexes: function(regexes) {
-        var isValid = this.isValid;
-        return regexes.base.filter(function (r) {
-          // Checking validity here (rather than using the data property)
-          // is necessary because otherwise we might send invalid regexes due
-          // to the fact that this function can call before the observer does
-          return r.regex !== "" && r.active && isValid(r.regex);
-        }).map(function(r) {
-          return r.regex;
-        });
-      },
-      addNewRegexIfNeeded: function() {
-        var last = this.rawRegexes[this.rawRegexes.length - 1];
-        if (last.regex !== "") {
-          this.push("rawRegexes", {regex: "", active: true, valid: true});
-        }
-      },
-      deleteRegex: function(e) {
-        if (this.rawRegexes.length > 1) {
-          this.splice("rawRegexes", e.model.index, 1);
-        }
-      },
-      moveFocus: function(e) {
-        if (e.keyCode === 13) {
-          var idx = e.model.index;
-          var inputs = Polymer.dom(this.root).querySelectorAll(".regex-input");
-          if (idx < this.rawRegexes.length - 1) {
-            inputs[idx+1].$.input.focus();
-          } else {
-            document.activeElement.blur();
-          }
-        }
-      }
-    });
-  </script>
-</dom-module>
-
-
-<dom-module id="tf-categorizer" assetpath="../tf-categorizer/">
-  <template>
-    <div class="inputs">
-      <tf-regex-group id="regex-group" regexes="{{regexes}}"></tf-regex-group>
-    </div>
-    <div id="underscore-categorization">
-      <paper-checkbox checked="{{splitOnUnderscore}}">Split on underscores</paper-checkbox>
-    </div>
-    <style>
-      :host {
-        display: block;
-        padding-bottom: 15px;
-      }
-      paper-checkbox {
-        --paper-checkbox-checked-color: var(--paper-grey-600);
-        --paper-checkbox-unchecked-color: var(--paper-grey-600);
-        font-size: 14px;
-      }
-      #underscore-categorization {
-        color: var(--paper-grey-700);
-      }
-    </style>
-  </template>
-  <script>/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-var Categorizer;
-(function (Categorizer) {
-    /* Canonical TensorFlow ops are namespaced using forward slashes.
-     * This fallback categorizer categorizes by the top-level namespace.
-     */
-    Categorizer.topLevelNamespaceCategorizer = splitCategorizer(/\//);
-    // Try to produce good categorizations on legacy graphs, which often
-    // are namespaced like l1_foo/bar or l2_baz/bam.
-    // If there is no leading underscore before the first forward slash,
-    // then it behaves the same as topLevelNamespaceCategorizer
-    Categorizer.legacyUnderscoreCategorizer = splitCategorizer(/[\/_]/);
-    function fallbackCategorizer(s) {
-        switch (s) {
-            case 'TopLevelNamespaceCategorizer':
-                return Categorizer.topLevelNamespaceCategorizer;
-            case 'LegacyUnderscoreCategorizer':
-                return Categorizer.legacyUnderscoreCategorizer;
-            default:
-                throw new Error('Unrecognized categorization strategy: ' + s);
-        }
-    }
-    Categorizer.fallbackCategorizer = fallbackCategorizer;
-    /* An 'extractor' is a function that takes a tag name, and 'extracts' a
-     * category name.
-     * This function takes an extractor, and produces a categorizer.
-     * Currently, it is just used for the fallbackCategorizer, but we may want to
-     * refactor the general categorization logic to use the concept of extractors.
-     */
-    function extractorToCategorizer(extractor) {
-        return function (tags) {
-            if (tags.length === 0) {
-                return [];
-            }
-            var sortedTags = tags.slice().sort();
-            var categories = [];
-            var currentCategory = {
-                name: extractor(sortedTags[0]),
-                tags: [],
-            };
-            sortedTags.forEach(function (t) {
-                var topLevel = extractor(t);
-                if (currentCategory.name !== topLevel) {
-                    categories.push(currentCategory);
-                    currentCategory = {
-                        name: topLevel,
-                        tags: [],
-                    };
-                }
-                currentCategory.tags.push(t);
-            });
-            categories.push(currentCategory);
-            return categories;
-        };
-    }
-    function splitCategorizer(r) {
-        var extractor = function (t) {
-            return t.split(r)[0];
-        };
-        return extractorToCategorizer(extractor);
-    }
-    function defineCategory(ruledef) {
-        var r = new RegExp(ruledef);
-        var f = function (tag) {
-            return r.test(tag);
-        };
-        return { name: ruledef, matches: f };
-    }
-    Categorizer.defineCategory = defineCategory;
-    function _categorizer(rules, fallback) {
-        return function (tags) {
-            var remaining = d3.set(tags);
-            var userSpecified = rules.map(function (def) {
-                var tags = [];
-                remaining.forEach(function (t) {
-                    if (def.matches(t)) {
-                        tags.push(t);
-                    }
-                });
-                var cat = { name: def.name, tags: tags.sort() };
-                return cat;
-            });
-            var defaultCategories = fallback(remaining.values());
-            return userSpecified.concat(defaultCategories);
-        };
-    }
-    Categorizer._categorizer = _categorizer;
-    function categorizer(s) {
-        var rules = s.categoryDefinitions.map(defineCategory);
-        var fallback = fallbackCategorizer(s.fallbackCategorizer);
-        return _categorizer(rules, fallback);
-    }
-    Categorizer.categorizer = categorizer;
-    ;
-})(Categorizer || (Categorizer = {}));
-</script>
-  <script>
-    Polymer({
-      is: "tf-categorizer",
-      properties: {
-        regexes: {type: Array},
-        tags: {type: Array},
-        categoriesAreExclusive: {type: Boolean, value: true},
-        fallbackCategorizer: {
-          type: String,
-          computed: "chooseFallbackCategorizer(splitOnUnderscore)"
-        },
-        splitOnUnderscore: {
-          type: Boolean,
-          value: false,
-        },
-        categorizer: {
-          type: Object,
-          computed: "computeCategorization(regexes.*, categoriesAreExclusive, fallbackCategorizer)",
-        },
-        categories: {type: Array, value: function() {return [];}, notify: true, readOnly: true},
-      },
-      observers: ['recategorize(tags.*, categorizer)'],
-      computeCategorization: function(regexes, categoriesAreExclusive, fallbackCategorizer) {
-        var categorizationStrategy = {
-          categoryDefinitions: regexes.base,
-          categoriesAreExclusive: categoriesAreExclusive,
-          fallbackCategorizer: fallbackCategorizer,
-        };
-        return Categorizer.categorizer(categorizationStrategy);
-      },
-      recategorize: function() {
-        this.debounce("tf-categorizer-recategorize", function (){
-          var categories = this.categorizer(this.tags);
-          this._setCategories(categories);
-        })
-      },
-      chooseFallbackCategorizer: function(splitOnUnderscore) {
-        if (splitOnUnderscore) {
-          return "LegacyUnderscoreCategorizer";
-        } else {
-          return "TopLevelNamespaceCategorizer";
-        }
-      },
-    });
-  </script>
-</dom-module>
-
-<dom-module id="tf-chart-scaffold" assetpath="../tf-chart-scaffold/">
-  <template>
-    <content></content>
-    <style>
-      :host {
-        -webkit-user-select: none;
-        -moz-user-select: none;
-        display: flex;
-        flex-direction: column;
-        flex-grow: 1;
-        flex-shrink: 1;
-        position: relative;
-      }
-    </style>
-  </template>
-  <script>
-    Polymer({
-      is: "tf-chart-scaffold",
-      properties: {
-        tag: String,
-        dataProvider: Function,
-        visibleSeries: Array,
-        _attached: {
-          type: Boolean,
-          value: false
-        }
-      },
-      observers: [
-        "reload(tag, dataProvider)",
-        "_changeSeries(visibleSeries.*)"
-      ],
-      attached: function() {
-        this._attached = true;
-        this._changeSeries();
-      },
-      detached: function() {
-        this._attached = false;
-      },
-      reload: function() {
-        if (!this._attached) {
-          return;
-        }
-        else if (!this.dataProvider) {
-          throw new Error('tf-chart-scaffold requires a dataProvider.');
-        }
-        else if (!this.tag) {
-          throw new Error('tf-chart-scaffold requires a tag.');
-        }
+            /* Return number of currently pending requests */
+            RequestManager.prototype.activeRequests = function () {
+                return this._nActiveRequests;
+            };
+            /* Return total number of outstanding requests (includes queue) */
+            RequestManager.prototype.outstandingRequests = function () {
+                return this._nActiveRequests + this._queue.length;
+            };
+            RequestManager.prototype.launchRequests = function () {
+                while (this._nActiveRequests < this._nSimultaneousRequests &&
+                    this._queue.length > 0) {
+                    this._nActiveRequests++;
+                    this._queue.pop().resolve();
+                }
+            };
+            /**
+             * Try to request a given URL using overwritable _promiseFromUrl method.
+             * If the request fails for any reason, we will retry up to maxRetries
+             * times. In practice, this will help us paper over transient network issues
+             * like '502 Bad Gateway'.
+             * By default, Chrome displays network errors in console, so
+             * the user will be able to tell when the requests are failing. I think this
+             * is a feature, if the request failures and retries are causing any
+             * pain to users, they can see it and file issues.
+             */
+            RequestManager.prototype.promiseWithRetries = function (url, maxRetries) {
+                var _this = this;
+                var success = function (x) { return x; };
+                var failure = function (x) {
+                    if (maxRetries > 0) {
+                        return _this.promiseWithRetries(url, maxRetries - 1);
+                    }
+                    else {
+                        return Promise.reject(x);
+                    }
+                };
+                return this._promiseFromUrl(url).then(success, failure);
+            };
+            /* Actually get promise from url using XMLHttpRequest */
+            RequestManager.prototype._promiseFromUrl = function (url) {
+                return new Promise(function (resolve, reject) {
+                    var req = new XMLHttpRequest();
+                    req.open('GET', url);
+                    req.onload = function () {
+                        if (req.status === 200) {
+                            resolve(JSON.parse(req.responseText));
+                        }
+                        else {
+                            reject(new RequestNetworkError(req, url));
+                        }
+                    };
+                    req.onerror = function () {
+                        reject(new RequestNetworkError(req, url));
+                    };
+                    req.send();
+                });
+            };
+            return RequestManager;
+        }());
+        Backend.RequestManager = RequestManager;
+    })(Backend = TF.Backend || (TF.Backend = {}));
+})(TF || (TF = {}));
+</script><script>/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 
-        this.visibleSeries.forEach(function(name) {
-          this.dataProvider(this.tag, name).then(function(data) {
-            this.chart().setSeriesData(name, data);
-          }.bind(this));
-        }.bind(this));
-      },
-      _changeSeries: function() {
-        if (!this._attached) {
-           return;
-        }
-        else if (!this.visibleSeries) {
-          throw new Error('tf-chart-scaffold requires a visibleSeries.');
-        }
+Licensed under the Apache License, Version 2.0 (the 'License');
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
 
-        this.chart().setVisibleSeries(this.visibleSeries);
-        this.reload();
-      },
-      chart: function() {
-        var children = this.getEffectiveChildren();
-        if (!children.length) {
-          throw new Error('tf-chart-scaffold has no children');
-        }
+http://www.apache.org/licenses/LICENSE-2.0
 
-        var child = children[0];
-        if (!child.setVisibleSeries || !child.setSeriesData) {
-          throw new Error("tf-chart-scaffold's content doesn't implement the " +
-              "required interface");
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an 'AS IS' BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+var TF;
+(function (TF) {
+    var Backend;
+    (function (Backend) {
+        Backend.BAD_CHARACTERS = '#%&{}\\/<>*? $!\'":@+`|=() ';
+        /** Cleanup a url so that it can be loaded from a filesystem. */
+        function demoify(s) {
+            // for consistency with python's urllib.urlencode
+            s = s.replace(new RegExp('%20', 'g'), '+');
+            for (var i = 0; i < Backend.BAD_CHARACTERS.length; i++) {
+                var c = Backend.BAD_CHARACTERS[i];
+                s = s.replace(new RegExp('\\' + c, 'g'), '_');
+            }
+            return s;
         }
-        return child;
-      }
-    });
-  </script>
-</dom-module>
-
-<dom-module id="vz-line-chart" assetpath="../vz-line-chart/">
-  <template>
-    <div id="tooltip">
-      <table>
-        <thead>
-          <tr>
-            <th></th>
-            <th>Name</th>
-            <template is="dom-if" if="{{smoothingEnabled}}">
-              <th>Smoothed</th>
-            </template>
-            <th>Value</th>
-            <th>Step</th>
-            <th>Time</th>
-            <th>Relative</th>
-          </tr>
-        </thead>
-        <tbody>
-        </tbody>
-      </table>
-    </div>
-    <svg id="chartsvg"></svg>
-    <style>
-      :host {
-        -webkit-user-select: none;
-        -moz-user-select: none;
-        display: flex;
-        flex-direction: column;
-        flex-grow: 1;
-        flex-shrink: 1;
-        position: relative;
-      }
-      svg {
-        -webkit-user-select: none;
-        -moz-user-select: none;
-        flex-grow: 1;
-        flex-shrink: 1;
-      }
-      td {
-        padding-left: 5px;
-        padding-right: 5px;
-        font-size: 13px;
-        opacity: 1;
-      }
-      #tooltip {
-        pointer-events: none;
-        position: absolute;
-        opacity: 0;
-        box-shadow: 0 1px 4px rgba(0, 0, 0, 0.3);
-        font-size: 14px;
-        background: rgba(0, 0, 0, 0.8);
-        color: white;
-        border-radius: 4px;
-        line-height: 1.4em;
-        padding: 8px;
-        z-index: 5;
-        cursor: none;
-      }
-      .swatch {
-        border-radius: 50%;
-        width: 14px;
-        height: 14px;
-        display: block;
-        border: 2px solid rgba(0,0,0,0);
-      }
-      .closest .swatch {
-        border: 2px solid white;
-      }
-      th {
-        padding-left: 5px;
-        padding-right: 5px;
-        text-align: left;
-      }
-      .distant td {
-        opacity: 0.8;
-      }
-
-      .distant td.swatch {
-        opacity: 1;
-      }
-
-      .ghost {
-        opacity: 0.2;
-        stroke-width: 1px;
-      }
-
-    </style>
-  </template>
-  <script>/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+        Backend.demoify = demoify;
+        function queryEncoder(params) {
+            // It's important that the keys be sorted, so we always grab the right file
+            // if we are talking to the backend generated by serialze_tensorboard.py
+            if (params == null) {
+                return '';
+            }
+            var components = _.keys(params)
+                .sort()
+                .filter(function (k) { return params[k] !== undefined; })
+                .map(function (k) { return k + '=' + encodeURIComponent(params[k]); });
+            var result = components.length ? '?' + components.join('&') : '';
+            // Replace parens for consistency with urllib.urlencode
+            return result.replace(/\(/g, '%28').replace(/\)/g, '%29');
+        }
+        Backend.queryEncoder = queryEncoder;
+    })(Backend = TF.Backend || (TF.Backend = {}));
+})(TF || (TF = {}));
+</script><script>/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the 'License');
 you may not use this file except in compliance with the License.
@@ -1625,182 +427,635 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
-var __extends = (this && this.__extends) || function (d, b) {
-    for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p];
-    function __() { this.constructor = d; }
-    d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
-};
-var Plottable;
-(function (Plottable) {
-    var DragZoomLayer = (function (_super) {
-        __extends(DragZoomLayer, _super);
+var TF;
+(function (TF) {
+    var Backend;
+    (function (Backend) {
+        ;
+        /**
+         * The standard router for communicating with the TensorBoard backend
+         * @param dataDir {string} The base prefix for finding data on server.
+         * @param demoMode {boolean} Whether to modify urls for filesystem demo usage.
+         */
+        function router(dataDir, demoMode) {
+            if (dataDir === void 0) { dataDir = '/data'; }
+            if (demoMode === void 0) { demoMode = false; }
+            var clean = demoMode ? Backend.demoify : function (x) { return x; };
+            if (dataDir[dataDir.length - 1] === '/') {
+                dataDir = dataDir.slice(0, dataDir.length - 1);
+            }
+            function standardRoute(route, demoExtension) {
+                if (demoExtension === void 0) { demoExtension = '.json'; }
+                return function (tag, run) {
+                    var url = dataDir + '/' + route + clean(Backend.queryEncoder({ tag: tag, run: run }));
+                    if (demoMode) {
+                        url += demoExtension;
+                    }
+                    return url;
+                };
+            }
+            function individualImageUrl(query) {
+                var url = dataDir + '/' + clean('individualImage?' + query);
+                if (demoMode) {
+                    url += '.png';
+                }
+                return url;
+            }
+            function individualAudioUrl(query) {
+                var url = dataDir + '/' + clean('individualAudio?' + query);
+                if (demoMode) {
+                    url += '.wav';
+                }
+                return url;
+            }
+            function graphUrl(run, limit_attr_size, large_attrs_key) {
+                var query_params = [['run', clean(run)]];
+                if (limit_attr_size != null && !demoMode) {
+                    query_params.push(['limit_attr_size', String(limit_attr_size)]);
+                }
+                if (large_attrs_key != null && !demoMode) {
+                    query_params.push(['large_attrs_key', large_attrs_key]);
+                }
+                var query = query_params
+                    .map(function (param) {
+                    return param[0] + '=' + encodeURIComponent(param[1]);
+                })
+                    .join('&');
+                var url = dataDir + '/graph' + clean('?' + query);
+                if (demoMode) {
+                    url += '.pbtxt';
+                }
+                return url;
+            }
+            return {
+                runs: function () { return dataDir + '/runs' + (demoMode ? '.json' : ''); },
+                individualImage: individualImageUrl,
+                individualAudio: individualAudioUrl,
+                graph: graphUrl,
+                scalars: standardRoute('scalars'),
+                histograms: standardRoute('histograms'),
+                compressedHistograms: standardRoute('compressedHistograms'),
+                images: standardRoute('images'),
+                audio: standardRoute('audio'),
+                runMetadata: standardRoute('run_metadata', '.pbtxt'),
+            };
+        }
+        Backend.router = router;
+        ;
+    })(Backend = TF.Backend || (TF.Backend = {}));
+})(TF || (TF = {}));
+</script><script>/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+var TF;
+(function (TF) {
+    var Backend;
+    (function (Backend_1) {
+        Backend_1.TYPES = [
+            'scalar', 'histogram', 'compressedHistogram', 'graph', 'image', 'audio',
+            'runMetadata'
+        ];
+        /**
+         * The Backend class provides a convenient and typed interface to the backend.
+         *
+         * It provides methods corresponding to the different data sources on the
+         * TensorBoard backend. These methods return a promise containing the data
+         * from the backend. This class does some post-processing on the data; for
+         * example, converting data elements tuples into js objects so that they can
+         * be accessed in a more convenient and clearly-documented fashion.
+         */
+        var Backend = (function () {
+            /**
+             * Construct a Backend instance.
+             * @param router the Router with info on what urls to get data from
+             * @param requestManager The RequestManager, overwritable so you may
+             * manually clear request queue, etc. Defaults to a new RequestManager.
+             */
+            function Backend(r, requestManager) {
+                this.router = r;
+                this.requestManager = requestManager || new Backend_1.RequestManager();
+            }
+            /**
+             * Returns a listing of all the available data in the TensorBoard backend.
+             */
+            Backend.prototype.runs = function () {
+                return this.requestManager.request(this.router.runs());
+            };
+            /**
+             * Return a promise showing the Run-to-Tag mapping for scalar data.
+             */
+            Backend.prototype.scalarRuns = function () {
+                return this.runs().then(function (x) { return _.mapValues(x, 'scalars'); });
+            };
+            /**
+             * Return a promise showing the Run-to-Tag mapping for histogram data.
+             */
+            Backend.prototype.histogramRuns = function () {
+                return this.runs().then(function (x) { return _.mapValues(x, 'histograms'); });
+            };
+            /**
+             * Return a promise showing the Run-to-Tag mapping for image data.
+             */
+            Backend.prototype.imageRuns = function () {
+                return this.runs().then(function (x) { return _.mapValues(x, 'images'); });
+            };
+            /**
+             * Return a promise showing the Run-to-Tag mapping for audio data.
+             */
+            Backend.prototype.audioRuns = function () {
+                return this.runs().then(function (x) { return _.mapValues(x, 'audio'); });
+            };
+            /**
+             * Return a promise showing the Run-to-Tag mapping for compressedHistogram
+             * data.
+             */
+            Backend.prototype.compressedHistogramRuns = function () {
+                return this.runs().then(function (x) { return _.mapValues(x, 'compressedHistograms'); });
+            };
+            /**
+             * Return a promise showing list of runs that contain graphs.
+             */
+            Backend.prototype.graphRuns = function () {
+                return this.runs().then(function (x) { return _.keys(x).filter(function (k) { return x[k].graph; }); });
+            };
+            /**
+             * Return a promise showing the Run-to-Tag mapping for run_metadata objects.
+             */
+            Backend.prototype.runMetadataRuns = function () {
+                return this.runs().then(function (x) { return _.mapValues(x, 'run_metadata'); });
+            };
+            /**
+             * Return a promise of a graph string from the backend.
+             */
+            Backend.prototype.graph = function (tag, limit_attr_size, large_attrs_key) {
+                var url = this.router.graph(tag, limit_attr_size, large_attrs_key);
+                return this.requestManager.request(url);
+            };
+            /**
+             * Return a promise containing ScalarDatums for given run and tag.
+             */
+            Backend.prototype.scalar = function (tag, run) {
+                var p;
+                var url = this.router.scalars(tag, run);
+                p = this.requestManager.request(url);
+                return p.then(map(detupler(createScalar)));
+            };
+            /**
+             * Return a promise containing HistogramDatums for given run and tag.
+             */
+            Backend.prototype.histogram = function (tag, run) {
+                var p;
+                var url = this.router.histograms(tag, run);
+                p = this.requestManager.request(url);
+                return p.then(map(detupler(createHistogram))).then(function (histos) {
+                    // Get the minimum and maximum values across all histograms so that the
+                    // visualization is aligned for all timesteps.
+                    var min = d3.min(histos, function (d) { return d.min; });
+                    var max = d3.max(histos, function (d) { return d.max; });
+                    return histos.map(function (histo, i) {
+                        return {
+                            wall_time: histo.wall_time,
+                            step: histo.step,
+                            bins: convertBins(histo, min, max)
+                        };
+                    });
+                });
+            };
+            /**
+             * Return a promise containing ImageDatums for given run and tag.
+             */
+            Backend.prototype.image = function (tag, run) {
+                var url = this.router.images(tag, run);
+                var p;
+                p = this.requestManager.request(url);
+                return p.then(map(this.createImage.bind(this)));
+            };
+            /**
+             * Return a promise containing AudioDatums for given run and tag.
+             */
+            Backend.prototype.audio = function (tag, run) {
+                var url = this.router.audio(tag, run);
+                var p;
+                p = this.requestManager.request(url);
+                return p.then(map(this.createAudio.bind(this)));
+            };
+            /**
+             * Returns a promise to load the string RunMetadata for given run/tag.
+             */
+            Backend.prototype.runMetadata = function (tag, run) {
+                var url = this.router.runMetadata(tag, run);
+                return this.requestManager.request(url);
+            };
+            /**
+             * Get compressedHistogram data.
+             * Unlike other methods, don't bother reprocessing this data into a nicer
+             * format. This is because we will deprecate this route.
+             */
+            Backend.prototype.compressedHistogram = function (tag, run) {
+                var url = this.router.compressedHistograms(tag, run);
+                var p;
+                p = this.requestManager.request(url);
+                return p.then(map(detupler(function (x) { return x; })));
+            };
+            Backend.prototype.createImage = function (x) {
+                return {
+                    width: x.width,
+                    height: x.height,
+                    wall_time: timeToDate(x.wall_time),
+                    step: x.step,
+                    url: this.router.individualImage(x.query),
+                };
+            };
+            Backend.prototype.createAudio = function (x) {
+                return {
+                    content_type: x.content_type,
+                    wall_time: timeToDate(x.wall_time),
+                    step: x.step,
+                    url: this.router.individualAudio(x.query),
+                };
+            };
+            return Backend;
+        }());
+        Backend_1.Backend = Backend;
+        /** Given a RunToTag, return sorted array of all runs */
+        function getRuns(r) {
+            return _.keys(r).sort(compareTagNames);
+        }
+        Backend_1.getRuns = getRuns;
+        /** Given a RunToTag, return array of all tags (sorted + dedup'd) */
+        function getTags(r) {
+            return _.union.apply(null, _.values(r)).sort(compareTagNames);
+        }
+        Backend_1.getTags = getTags;
+        /** Compares tag names asciinumerically broken into components. */
+        function compareTagNames(a, b) {
+            var ai = 0;
+            var bi = 0;
+            while (true) {
+                if (ai === a.length)
+                    return bi === b.length ? 0 : -1;
+                if (bi === b.length)
+                    return 1;
+                if (isDigit(a[ai]) && isDigit(b[bi])) {
+                    var ais = ai;
+                    var bis = bi;
+                    ai = consumeNumber(a, ai + 1);
+                    bi = consumeNumber(b, bi + 1);
+                    var an = parseFloat(a.slice(ais, ai));
+                    var bn = parseFloat(b.slice(bis, bi));
+                    if (an < bn)
+                        return -1;
+                    if (an > bn)
+                        return 1;
+                    continue;
+                }
+                if (isBreak(a[ai])) {
+                    if (!isBreak(b[bi]))
+                        return -1;
+                }
+                else if (isBreak(b[bi])) {
+                    return 1;
+                }
+                else if (a[ai] < b[bi]) {
+                    return -1;
+                }
+                else if (a[ai] > b[bi]) {
+                    return 1;
+                }
+                ai++;
+                bi++;
+            }
+        }
+        Backend_1.compareTagNames = compareTagNames;
+        function consumeNumber(s, i) {
+            var decimal = false;
+            for (; i < s.length; i++) {
+                if (isDigit(s[i]))
+                    continue;
+                if (!decimal && s[i] === '.') {
+                    decimal = true;
+                    continue;
+                }
+                break;
+            }
+            return i;
+        }
+        function isDigit(c) { return '0' <= c && c <= '9'; }
+        function isBreak(c) {
+            // TODO(jart): Remove underscore when people stop using it like a slash.
+            return c === '/' || c === '_' || isDigit(c);
+        }
         /**
-         * Constructs a SelectionBoxLayer with an attached DragInteraction and
-         * ClickInteraction. On drag, it triggers an animated zoom into the box
-         * that was dragged. On double click, it zooms back out to the original
-         * view, before any zooming.
-         * The zoom animation uses an easing function (default
-         * d3.ease('cubic-in-out')) and is customizable.
-         * Usage: Construct the selection box layer and attach x and y scales,
-         * and then add the layer over the plot you are zooming on using a
-         * Component Group.
-         * TODO(danmane) - merge this into Plottable
+         * Given a RunToTag and an array of runs, return every tag that appears for
+         * at least one run.
+         * Sorted, deduplicated.
          */
-        function DragZoomLayer(xScale, yScale) {
-            _super.call(this);
-            this.isZoomed = false;
-            this.easeFn = d3.ease('cubic-in-out');
-            this._animationTime = 750;
-            this.xScale(xScale);
-            this.yScale(yScale);
-            this._dragInteraction = new Plottable.Interactions.Drag();
-            this._dragInteraction.attachTo(this);
-            this._doubleClickInteraction = new Plottable.Interactions.DoubleClick();
-            this._doubleClickInteraction.attachTo(this);
-            this.setupCallbacks();
+        function filterTags(r, runs) {
+            var result = [];
+            runs.forEach(function (x) { return result = result.concat(r[x]); });
+            return _.uniq(result).sort();
         }
+        Backend_1.filterTags = filterTags;
+        function timeToDate(x) { return new Date(x * 1000); }
+        ;
+        /**  Just a curryable map to make things cute and tidy. */
+        function map(f) {
+            return function (arr) { return arr.map(f); };
+        }
+        ;
         /**
-         * Register a method that calls when the DragZoom interaction starts.
+         * This is a higher order function that takes a function that transforms a
+         * T into a G, and returns a function that takes TupleData<T>s and converts
+         * them into the intersection of a G and a Datum.
          */
-        DragZoomLayer.prototype.interactionStart = function (cb) { this.onStart = cb; };
+        function detupler(xform) {
+            return function (x) {
+                // Create a G, assert it has type <G & Datum>
+                var obj = xform(x[2]);
+                // ... patch in the properties of datum
+                obj.wall_time = timeToDate(x[0]);
+                obj.step = x[1];
+                return obj;
+            };
+        }
+        ;
+        function createScalar(x) { return { scalar: x }; }
+        ;
+        function createHistogram(x) {
+            return {
+                min: x[0],
+                max: x[1],
+                nItems: x[2],
+                sum: x[3],
+                sumSquares: x[4],
+                bucketRightEdges: x[5],
+                bucketCounts: x[6],
+            };
+        }
+        ;
         /**
-         * Register a method that calls when the DragZoom interaction ends.
+         * Takes histogram data as stored by tensorboard backend and converts it to
+         * the standard d3 histogram data format to make it more compatible and easier
+         * to visualize. When visualizing histograms, having the left edge and width
+         * makes things quite a bit easier. The bins are also converted to have an
+         * uniform width, what makes the visualization easier to understand.
+         *
+         * @param histogram A histogram from tensorboard backend.
+         * @param min The leftmost edge. The binning will start on it.
+         * @param max The rightmost edge. The binning will end on it.
+         * @param numBins The number of bins of the converted data. The default of 30
+         * is a sensible default, using more starts to get artifacts because the event
+         * data is stored in buckets, and you start being able to see the aliased
+         * borders between each bucket.
+         * @return A histogram bin. Each bin has an x (left edge), a dx (width),
+         *     and a y (count).
+         *
+         * If given rightedges are inclusive, then these left edges (x) are exclusive.
          */
-        DragZoomLayer.prototype.interactionEnd = function (cb) { this.onEnd = cb; };
-        DragZoomLayer.prototype.setupCallbacks = function () {
-            var _this = this;
-            var dragging = false;
-            this._dragInteraction.onDragStart(function (startPoint) {
-                _this.bounds({
-                    topLeft: startPoint,
-                    bottomRight: startPoint,
-                });
-                _this.onStart();
-            });
-            this._dragInteraction.onDrag(function (startPoint, endPoint) {
-                _this.bounds({ topLeft: startPoint, bottomRight: endPoint });
-                _this.boxVisible(true);
-                dragging = true;
+        function convertBins(histogram, min, max, numBins) {
+            if (numBins === void 0) { numBins = 30; }
+            if (histogram.bucketRightEdges.length !== histogram.bucketCounts.length) {
+                throw (new Error('Edges and counts are of different lengths.'));
+            }
+            var binWidth = (max - min) / numBins;
+            var bucketLeft = min; // Use the min as the starting point for the bins.
+            var bucketPos = 0;
+            return d3.range(min, max, binWidth).map(function (binLeft) {
+                var binRight = binLeft + binWidth;
+                // Take the count of each existing bucket, multiply it by the proportion
+                // of overlap with the new bin, then sum and store as the count for the
+                // new bin. If no overlap, will add to zero, if 100% overlap, will include
+                // the full count into new bin.
+                var binY = 0;
+                while (bucketPos < histogram.bucketRightEdges.length) {
+                    // Clip the right edge because right-most edge can be infinite-sized.
+                    var bucketRight = Math.min(max, histogram.bucketRightEdges[bucketPos]);
+                    var intersect = Math.min(bucketRight, binRight) - Math.max(bucketLeft, binLeft);
+                    var count = (intersect / (bucketRight - bucketLeft)) *
+                        histogram.bucketCounts[bucketPos];
+                    binY += intersect > 0 ? count : 0;
+                    // If bucketRight is bigger than binRight, than this bin is finished and
+                    // there is data for the next bin, so don't increment bucketPos.
+                    if (bucketRight > binRight) {
+                        break;
+                    }
+                    bucketLeft = Math.max(min, bucketRight);
+                    bucketPos++;
+                }
+                ;
+                return { x: binLeft, dx: binWidth, y: binY };
             });
-            this._dragInteraction.onDragEnd(function (startPoint, endPoint) {
-                _this.boxVisible(false);
-                _this.bounds({ topLeft: startPoint, bottomRight: endPoint });
-                if (dragging) {
-                    _this.zoom();
+        }
+        Backend_1.convertBins = convertBins;
+    })(Backend = TF.Backend || (TF.Backend = {}));
+})(TF || (TF = {}));
+</script><script>var TF;
+(function (TF) {
+    var Backend;
+    (function (Backend) {
+        Backend.Behavior = {
+            properties: {
+                /** *** Required properties *** */
+                /** Data type. One of TF.Backend.TYPES */
+                dataType: {
+                    type: String,
+                    observer: '_throwErrorOnUnrecognizedType',
+                },
+                /** TF.Backend.Backend for data loading. */
+                backend: {
+                    type: Object,
+                },
+                /** Should it automatically load when configured ready? Default true. */
+                autoLoad: {
+                    type: Boolean,
+                    value: true,
+                },
+                /** *** Component-provided properties *** */
+                /** Every tag available for data type (sorted, dedpulicated) */
+                tags: {
+                    type: Array,
+                    readOnly: true,
+                    notify: true,
+                },
+                /** Every run available for data type (sorted) */
+                runs: {
+                    type: Array,
+                    readOnly: true,
+                    notify: true,
+                },
+                /** Mapping from runs to tags for the data type */
+                run2tag: {
+                    type: Object,
+                    readOnly: true,
+                    notify: true,
+                },
+                /** Promise provider for the data. Useful for passing to subcomponents */
+                dataProvider: { type: Function, computed: '_getDataProvider(dataType, backend)' },
+                /** Has the dashboard loaded yet? */
+                loadState: {
+                    type: String,
+                    value: 'noload',
+                    readOnly: true,
+                },
+                /**
+                 * True if dashboard has loaded, and no tags were found.
+                 * Persists through subsequent reloads (ie. still true while
+                 * next load is pending) so warning won't flash away every reload
+                 * when there is no data.
+                 */
+                dataNotFound: {
+                    type: Boolean,
+                    value: false,
+                    readOnly: true,
                 }
-                else {
-                    _this.onEnd();
+            },
+            observers: ['_do_autoLoad(dataType, backend, autoLoad)'],
+            /**
+             * Reloading works in two steps:
+             * Backend reload, which gets metadata on available runs, tags, etc from
+             *   the backend.
+             * Frontend reload, which loads new data for each chart or visual display.
+             * Backend reload logic is provided by this behaivor. The frontend reload
+             *   logic should be provided elsewhere, since it is component-specific.
+             * To keep things simple and consistent, we do the backend reload first,
+             *   and the frontend reload afterwards.
+             */
+            reload: function () {
+                var _this = this;
+                return this.backendReload().then(function (x) { return _this.frontendReload(); });
+            },
+            /**
+             * Load data from backend and then set run2tag, tags, runs, and loadState.
+             * Returns a promise that resolves/rejects when data is loaded.
+             */
+            backendReload: function () {
+                var _this = this;
+                if (this.dataType == null) {
+                    throw new Error('TF.Backend.Behavior: Need a dataType to reload.');
                 }
-                dragging = false;
-            });
-            this._doubleClickInteraction.onDoubleClick(this.unzoom.bind(this));
-        };
-        DragZoomLayer.prototype.animationTime = function (animationTime) {
-            if (animationTime == null) {
-                return this._animationTime;
-            }
-            if (animationTime < 0) {
-                throw new Error('animationTime cannot be negative');
-            }
-            this._animationTime = animationTime;
-            return this;
-        };
-        /**
-         * Set the easing function, which determines how the zoom interpolates
-         * over time.
-         */
-        DragZoomLayer.prototype.ease = function (fn) {
-            if (typeof (fn) !== 'function') {
-                throw new Error('ease function must be a function');
-            }
-            if (fn(0) !== 0 || fn(1) !== 1) {
-                Plottable.Utils.Window.warn('Easing function does not maintain invariant ' +
-                    'f(0)==0 && f(1)==1. Bad behavior may result.');
-            }
-            this.easeFn = fn;
-            return this;
-        };
-        // Zoom into extent of the selection box bounds
-        DragZoomLayer.prototype.zoom = function () {
-            var x0 = this.xExtent()[0].valueOf();
-            var x1 = this.xExtent()[1].valueOf();
-            var y0 = this.yExtent()[1].valueOf();
-            var y1 = this.yExtent()[0].valueOf();
-            if (x0 === x1 || y0 === y1) {
-                return;
-            }
-            if (!this.isZoomed) {
-                this.isZoomed = true;
-            }
-            this.interpolateZoom(x0, x1, y0, y1);
-        };
-        // Restore the scales to their state before any zoom
-        DragZoomLayer.prototype.unzoom = function () {
-            if (!this.isZoomed) {
-                return;
-            }
-            this.isZoomed = false;
-            // Some Plottable magic follows which ensures that when we un-zoom, we
-            // un-zoom to the current extent of the data; i.e. if new data was loaded
-            // since we zoomed, we should un-zoom to the extent of the new data.
-            // this basically replicates the autoDomain logic in Plottable.
-            // it uses the internal methods to get the same boundaries that autoDomain
-            // would, but allows us to interpolate the zoom with a nice animation.
-            var xScale = this.xScale();
-            var yScale = this.yScale();
-            xScale._domainMin = null;
-            xScale._domainMax = null;
-            yScale._domainMin = null;
-            yScale._domainMax = null;
-            var xDomain = xScale._getExtent();
-            var yDomain = yScale._getExtent();
-            this.interpolateZoom(xDomain[0], xDomain[1], yDomain[0], yDomain[1]);
-        };
-        // If we are zooming, disable interactions, to avoid contention
-        DragZoomLayer.prototype.isZooming = function (isZooming) {
-            this._dragInteraction.enabled(!isZooming);
-            this._doubleClickInteraction.enabled(!isZooming);
-        };
-        DragZoomLayer.prototype.interpolateZoom = function (x0f, x1f, y0f, y1f) {
-            var _this = this;
-            var x0s = this.xScale().domain()[0].valueOf();
-            var x1s = this.xScale().domain()[1].valueOf();
-            var y0s = this.yScale().domain()[0].valueOf();
-            var y1s = this.yScale().domain()[1].valueOf();
-            // Copy a ref to the ease fn, so that changing ease wont affect zooms in
-            // progress.
-            var ease = this.easeFn;
-            var interpolator = function (a, b, p) {
-                return d3.interpolateNumber(a, b)(ease(p));
-            };
-            this.isZooming(true);
-            var start = Date.now();
-            var draw = function () {
-                var now = Date.now();
-                var passed = now - start;
-                var p = _this._animationTime === 0 ?
-                    1 :
-                    Math.min(1, passed / _this._animationTime);
-                var x0 = interpolator(x0s, x0f, p);
-                var x1 = interpolator(x1s, x1f, p);
-                var y0 = interpolator(y0s, y0f, p);
-                var y1 = interpolator(y1s, y1f, p);
-                _this.xScale().domain([x0, x1]);
-                _this.yScale().domain([y0, y1]);
-                if (p < 1) {
-                    Plottable.Utils.DOM.requestAnimationFramePolyfill(draw);
+                if (this.backend == null) {
+                    throw new Error('TF.Backend.Behavior: Need a backend to reload.');
                 }
-                else {
-                    _this.onEnd();
-                    _this.isZooming(false);
+                var runsRoute = this.backend[this.dataType + 'Runs'].bind(this.backend);
+                this._setLoadState('pending');
+                return runsRoute().then(function (x) {
+                    _this._setLoadState('loaded');
+                    if (_.isEqual(x, _this.run2tag)) {
+                        // If x and run2tag are equal, let's avoid updating everything
+                        // since that can needlessly trigger run changes, reloads, etc
+                        return x;
+                    }
+                    _this._setRun2tag(x);
+                    var tags = TF.Backend.getTags(x);
+                    _this._setDataNotFound(tags.length === 0);
+                    _this._setTags(tags);
+                    _this._setRuns(TF.Backend.getRuns(x));
+                    return x;
+                }, function (fail) {
+                    _this._setLoadState('failure');
+                    return fail;
+                });
+            },
+            _do_autoLoad: function (type, backend, autoLoad) {
+                if (autoLoad) {
+                    this.reload();
                 }
-            };
-            draw();
+                ;
+            },
+            _getDataProvider: function (dataType, backend) {
+                return this.backend[this.dataType].bind(this.backend);
+            },
+            _throwErrorOnUnrecognizedType: function (dataType) {
+                if (TF.Backend.TYPES.indexOf(dataType) === -1) {
+                    throw new Error('TF.Backend.Behavior: Unknown dataType ' + dataType);
+                }
+            },
         };
-        return DragZoomLayer;
-    }(Plottable.Components.SelectionBoxLayer));
-    Plottable.DragZoomLayer = DragZoomLayer;
-})(Plottable || (Plottable = {}));
+    })(Backend = TF.Backend || (TF.Backend = {}));
+})(TF || (TF = {}));
+</script>
+
+
+
+
+
+<dom-module id="tf-color-scale" assetpath="../tf-color-scale/">
+  <script>var TF;
+(function (TF) {
+    TF.palettes = {
+        googleStandard: [
+            '#db4437',
+            '#ff7043',
+            '#f4b400',
+            '#0f9d58',
+            '#00796b',
+            '#00acc1',
+            '#4285f4',
+            '#5c6bc0',
+            '#ab47bc' // purple 400
+        ],
+        googleCool: [
+            '#9e9d24',
+            '#0f9d58',
+            '#00796b',
+            '#00acc1',
+            '#4285f4',
+            '#5c6bc0',
+            '#607d8b' // blue gray 500
+        ],
+        googleWarm: [
+            '#795548',
+            '#ab47bc',
+            '#f06292',
+            '#c2185b',
+            '#db4437',
+            '#ff7043',
+            '#f4b400' // google yellow 700
+        ],
+        googleColorBlindAssist: [
+            '#ff7043',
+            '#00ACC1',
+            '#AB47BC',
+            '#2A56C6',
+            '#0b8043',
+            '#F7CB4D',
+            '#c0ca33',
+            '#5e35b1',
+            '#A52714',
+        ],
+        // These palettes try to be better for color differentiation.
+        // https://personal.sron.nl/~pault/
+        colorBlindAssist1: ['#4477aa', '#44aaaa', '#aaaa44', '#aa7744', '#aa4455', '#aa4488'],
+        colorBlindAssist2: [
+            '#88ccee', '#44aa99', '#117733', '#999933', '#ddcc77', '#cc6677',
+            '#882255', '#aa4499'
+        ],
+        colorBlindAssist3: [
+            '#332288', '#6699cc', '#88ccee', '#44aa99', '#117733', '#999933',
+            '#ddcc77', '#cc6677', '#aa4466', '#882255', '#661100', '#aa4499'
+        ],
+        // based on this palette: http://mkweb.bcgsc.ca/biovis2012/
+        colorBlindAssist4: [
+            '#FF6DB6', '#920000', '#924900', '#DBD100', '#24FF24', '#006DDB',
+            '#490092'
+        ],
+        mldash: [
+            '#E47EAD', '#F4640D', '#FAA300', '#F5E636', '#00A077', '#0077B8',
+            '#00B7ED'
+        ]
+    };
+})(TF || (TF = {}));
 </script>
   <script>/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 
@@ -1808,7 +1063,7 @@ Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
 
-    http://www.apache.org/licenses/LICENSE-2.0
+http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
@@ -1816,677 +1071,796 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
-/* tslint:disable:no-namespace variable-name */
-var VZ;
-(function (VZ) {
-    var LineChart = (function () {
-        function LineChart(xType, colorScale, tooltip) {
-            this.seriesNames = [];
-            this.name2datasets = {};
-            this.colorScale = colorScale;
-            this.tooltip = tooltip;
-            this.datasets = [];
-            // lastPointDataset is a dataset that contains just the last point of
-            // every dataset we're currently drawing.
-            this.lastPointsDataset = new Plottable.Dataset();
-            this.nanDataset = new Plottable.Dataset();
-            // need to do a single bind, so we can deregister the callback from
-            // old Plottable.Datasets. (Deregistration is done by identity checks.)
-            this.onDatasetChanged = this._onDatasetChanged.bind(this);
-            this.buildChart(xType);
-        }
-        LineChart.prototype.buildChart = function (xType) {
-            if (this.outer) {
-                this.outer.destroy();
-            }
-            var xComponents = VZ.ChartHelpers.getXComponents(xType);
-            this.xAccessor = xComponents.accessor;
-            this.xScale = xComponents.scale;
-            this.xAxis = xComponents.axis;
-            this.xAxis.margin(0).tickLabelPadding(3);
-            this.yScale = new Plottable.Scales.Linear();
-            this.yAxis = new Plottable.Axes.Numeric(this.yScale, 'left');
-            var yFormatter = VZ.ChartHelpers.multiscaleFormatter(VZ.ChartHelpers.Y_AXIS_FORMATTER_PRECISION);
-            this.yAxis.margin(0).tickLabelPadding(5).formatter(yFormatter);
-            this.yAxis.usesTextWidthApproximation(true);
-            this.dzl = new Plottable.DragZoomLayer(this.xScale, this.yScale);
-            var center = this.buildPlot(this.xAccessor, this.xScale, this.yScale);
-            this.gridlines =
-                new Plottable.Components.Gridlines(this.xScale, this.yScale);
-            this.center =
-                new Plottable.Components.Group([this.gridlines, center, this.dzl]);
-            this.outer = new Plottable.Components.Table([
-                [this.yAxis, this.center],
-                [null, this.xAxis]
-            ]);
-        };
-        LineChart.prototype.buildPlot = function (xAccessor, xScale, yScale) {
-            var _this = this;
-            this.scalarAccessor = function (d) { return d.scalar; };
-            this.smoothedAccessor = function (d) { return d.smoothed; };
-            var linePlot = new Plottable.Plots.Line();
-            linePlot.x(xAccessor, xScale);
-            linePlot.y(this.scalarAccessor, yScale);
-            linePlot.attr('stroke', function (d, i, dataset) {
-                return _this.colorScale.scale(dataset.metadata().name);
-            });
-            this.linePlot = linePlot;
-            var group = this.setupTooltips(linePlot);
-            var smoothLinePlot = new Plottable.Plots.Line();
-            smoothLinePlot.x(xAccessor, xScale);
-            smoothLinePlot.y(this.smoothedAccessor, yScale);
-            smoothLinePlot.attr('stroke', function (d, i, dataset) {
-                return _this.colorScale.scale(dataset.metadata().name);
-            });
-            this.smoothLinePlot = smoothLinePlot;
-            // The scatterPlot will display the last point for each dataset.
-            // This way, if there is only one datum for the series, it is still
-            // visible. We hide it when tooltips are active to keep things clean.
-            var scatterPlot = new Plottable.Plots.Scatter();
-            scatterPlot.x(xAccessor, xScale);
-            scatterPlot.y(this.scalarAccessor, yScale);
-            scatterPlot.attr('fill', function (d) { return _this.colorScale.scale(d.name); });
-            scatterPlot.attr('opacity', 1);
-            scatterPlot.size(VZ.ChartHelpers.TOOLTIP_CIRCLE_SIZE * 2);
-            scatterPlot.datasets([this.lastPointsDataset]);
-            this.scatterPlot = scatterPlot;
-            var nanDisplay = new Plottable.Plots.Scatter();
-            nanDisplay.x(xAccessor, xScale);
-            nanDisplay.y(function (x) { return x.displayY; }, yScale);
-            nanDisplay.attr('fill', function (d) { return _this.colorScale.scale(d.name); });
-            nanDisplay.attr('opacity', 1);
-            nanDisplay.size(VZ.ChartHelpers.NAN_SYMBOL_SIZE * 2);
-            nanDisplay.datasets([this.nanDataset]);
-            nanDisplay.symbol(Plottable.SymbolFactories.triangleUp);
-            this.nanDisplay = nanDisplay;
-            return new Plottable.Components.Group([nanDisplay, scatterPlot, smoothLinePlot, group]);
-        };
-        /** Updates the chart when a dataset changes. Called every time the data of
-         * a dataset changes to update the charts.
+// Example usage:
+// runs = ["train", "test", "test1", "test2"]
+// ccs = new TF.ColorScale();
+// ccs.domain(runs);
+// ccs.getColor("train");
+// ccs.getColor("test1");
+var TF;
+(function (TF) {
+    var ColorScale = (function () {
+        /**
+         * Creates a color scale with optional custom palette.
+         *  @param {string[]} [palette=TF.palettes.googleColorBlind] - The color
+         *                 palette you want as an Array of hex strings.
          */
-        LineChart.prototype._onDatasetChanged = function (dataset) {
-            if (this.smoothingEnabled) {
-                this.resmoothDataset(dataset);
-                this.updateSpecialDatasets(this.smoothedAccessor);
-            }
-            else {
-                this.updateSpecialDatasets(this.scalarAccessor);
-            }
-        };
-        /** Constructs special datasets. Each special dataset contains exceptional
-         * values from all of the regular datasets, e.g. last points in series, or
-         * NaN values. Those points will have a `name` and `relative` property added
-         * (since usually those are context in the surrounding dataset).
-         * The accessor will point to the correct data to access.
+        function ColorScale(palette) {
+            if (palette === void 0) { palette = TF.palettes.googleColorBlindAssist; }
+            this.identifiers = d3.map();
+            this.palette = palette;
+        }
+        /**
+         * Set the domain of strings.
+         * @param {string[]} strings - An array of possible strings to use as the
+         *                             domain for your scale.
          */
-        LineChart.prototype.updateSpecialDatasets = function (accessor) {
-            var lastPointsData = this.datasets
-                .map(function (d) {
-                var datum = null;
-                // filter out NaNs to ensure last point is a clean one
-                var nonNanData = d.data().filter(function (x) { return !isNaN(accessor(x, -1, d)); });
-                if (nonNanData.length > 0) {
-                    var idx = nonNanData.length - 1;
-                    datum = nonNanData[idx];
-                    datum.name = d.metadata().name;
-                    datum.relative =
-                        VZ.ChartHelpers.relativeAccessor(datum, -1, d);
-                }
-                return datum;
-            })
-                .filter(function (x) { return x != null; });
-            this.lastPointsDataset.data(lastPointsData);
-            // Take a dataset, return an array of NaN data points
-            // the NaN points will have a "displayY" property which is the
-            // y-value of a nearby point that was not NaN (0 if all points are NaN)
-            var datasetToNaNData = function (d) {
-                var displayY = null;
-                var data = d.data();
-                var i = 0;
-                while (i < data.length && displayY == null) {
-                    if (!isNaN(accessor(data[i], -1, d))) {
-                        displayY = accessor(data[i], -1, d);
-                    }
-                    i++;
-                }
-                if (displayY == null) {
-                    displayY = 0;
-                }
-                var nanData = [];
-                for (i = 0; i < data.length; i++) {
-                    if (!isNaN(accessor(data[i], -1, d))) {
-                        displayY = accessor(data[i], -1, d);
-                    }
-                    else {
-                        data[i].name = d.metadata().name;
-                        data[i].displayY = displayY;
-                        data[i].relative = VZ.ChartHelpers.relativeAccessor(data[i], -1, d);
-                        nanData.push(data[i]);
-                    }
-                }
-                return nanData;
-            };
-            var nanData = _.flatten(this.datasets.map(datasetToNaNData));
-            this.nanDataset.data(nanData);
-        };
-        LineChart.prototype.setupTooltips = function (plot) {
-            var _this = this;
-            var pi = new Plottable.Interactions.Pointer();
-            pi.attachTo(plot);
-            // PointsComponent is a Plottable Component that will hold the little
-            // circles we draw over the closest data points
-            var pointsComponent = new Plottable.Component();
-            var group = new Plottable.Components.Group([plot, pointsComponent]);
-            var hideTooltips = function () {
-                _this.tooltip.style('opacity', 0);
-                _this.scatterPlot.attr('opacity', 1);
-                pointsComponent.content().selectAll('.point').remove();
-            };
-            var enabled = true;
-            var disableTooltips = function () {
-                enabled = false;
-                hideTooltips();
-            };
-            var enableTooltips = function () { enabled = true; };
-            this.dzl.interactionStart(disableTooltips);
-            this.dzl.interactionEnd(enableTooltips);
-            pi.onPointerMove(function (p) {
-                if (!enabled) {
-                    return;
-                }
-                var target = {
-                    x: p.x,
-                    y: p.y,
-                    datum: null,
-                    dataset: null,
-                };
-                var centerBBox = _this.gridlines.content().node().getBBox();
-                var points = plot.datasets().map(function (dataset) { return _this.findClosestPoint(target, dataset); });
-                var pointsToCircle = points.filter(function (p) { return p != null &&
-                    Plottable.Utils.DOM.intersectsBBox(p.x, p.y, centerBBox); });
-                var pts = pointsComponent.content().selectAll('.point').data(pointsToCircle, function (p) { return p.dataset.metadata().name; });
-                if (points.length !== 0) {
-                    pts.enter().append('circle').classed('point', true);
-                    pts.attr('r', VZ.ChartHelpers.TOOLTIP_CIRCLE_SIZE)
-                        .attr('cx', function (p) { return p.x; })
-                        .attr('cy', function (p) { return p.y; })
-                        .style('stroke', 'none')
-                        .attr('fill', function (p) { return _this.colorScale.scale(p.dataset.metadata().name); });
-                    pts.exit().remove();
-                    _this.drawTooltips(points, target);
-                }
-                else {
-                    hideTooltips();
-                }
-            });
-            pi.onPointerExit(hideTooltips);
-            return group;
-        };
-        LineChart.prototype.drawTooltips = function (points, target) {
+        ColorScale.prototype.domain = function (strings) {
             var _this = this;
-            // Formatters for value, step, and wall_time
-            this.scatterPlot.attr('opacity', 0);
-            var valueFormatter = VZ.ChartHelpers.multiscaleFormatter(VZ.ChartHelpers.Y_TOOLTIP_FORMATTER_PRECISION);
-            var dist = function (p) {
-                return Math.pow(p.x - target.x, 2) + Math.pow(p.y - target.y, 2);
-            };
-            var closestDist = _.min(points.map(dist));
-            points = _.sortBy(points, function (d) { return d.dataset.metadata().name; });
-            var rows = this.tooltip.select('tbody')
-                .html('')
-                .selectAll('tr')
-                .data(points)
-                .enter()
-                .append('tr');
-            // Grey out the point if any of the following are true:
-            // - The cursor is outside of the x-extent of the dataset
-            // - The point is rendered above or below the screen
-            // - The point's y value is NaN
-            rows.classed('distant', function (d) {
-                var firstPoint = d.dataset.data()[0];
-                var lastPoint = _.last(d.dataset.data());
-                var firstX = _this.xScale.scale(_this.xAccessor(firstPoint, 0, d.dataset));
-                var lastX = _this.xScale.scale(_this.xAccessor(lastPoint, 0, d.dataset));
-                var s = _this.smoothingEnabled ? d.datum.smoothed : d.datum.scalar;
-                var yD = _this.yScale.domain();
-                return target.x < firstX || target.x > lastX || s < yD[0] ||
-                    s > yD[1] || isNaN(s);
-            });
-            rows.classed('closest', function (p) { return dist(p) === closestDist; });
-            // It is a bit hacky that we are manually applying the width to the swatch
-            // and the nowrap property to the text here. The reason is as follows:
-            // the style gets updated asynchronously by Polymer scopeSubtree observer.
-            // Which means we would get incorrect sizing information since the text
-            // would wrap by default. However, we need correct measurements so that
-            // we can stop the text from falling off the edge of the screen.
-            // therefore, we apply the size-critical styles directly.
-            rows.style('white-space', 'nowrap');
-            rows.append('td')
-                .append('span')
-                .classed('swatch', true)
-                .style('background-color', function (d) { return _this.colorScale.scale(d.dataset.metadata().name); });
-            rows.append('td').text(function (d) { return d.dataset.metadata().name; });
-            if (this.smoothingEnabled) {
-                rows.append('td').text(function (d) { return isNaN(d.datum.smoothed) ? 'NaN' :
-                    valueFormatter(d.datum.smoothed); });
-            }
-            rows.append('td').text(function (d) {
-                return isNaN(d.datum.scalar) ? 'NaN' : valueFormatter(d.datum.scalar);
+            this.identifiers = d3.map();
+            strings.forEach(function (s, i) {
+                _this.identifiers.set(s, _this.palette[i % _this.palette.length]);
             });
-            rows.append('td').text(function (d) { return VZ.ChartHelpers.stepFormatter(d.datum.step); });
-            rows.append('td').text(function (d) { return VZ.ChartHelpers.timeFormatter(d.datum.wall_time); });
-            rows.append('td').text(function (d) { return VZ.ChartHelpers.relativeFormatter(VZ.ChartHelpers.relativeAccessor(d.datum, -1, d.dataset)); });
-            // compute left position
-            var documentWidth = document.body.clientWidth;
-            var node = this.tooltip.node();
-            var parentRect = node.parentElement.getBoundingClientRect();
-            var nodeRect = node.getBoundingClientRect();
-            // prevent it from falling off the right side of the screen
-            var left = Math.min(0, documentWidth - parentRect.left - nodeRect.width - 60);
-            var top = parentRect.height + VZ.ChartHelpers.TOOLTIP_Y_PIXEL_OFFSET;
-            this.tooltip.style('transform', 'translate(' + left + 'px,' + top + 'px)');
-            this.tooltip.style('opacity', 1);
+            return this;
         };
-        LineChart.prototype.findClosestPoint = function (target, dataset) {
-            var _this = this;
-            var points = dataset.data().map(function (d, i) {
-                var x = _this.xAccessor(d, i, dataset);
-                var y = _this.smoothingEnabled ? _this.smoothedAccessor(d, i, dataset) :
-                    _this.scalarAccessor(d, i, dataset);
-                return {
-                    x: _this.xScale.scale(x),
-                    y: _this.yScale.scale(y),
-                    datum: d,
-                    dataset: dataset,
-                };
-            });
-            var idx = _.sortedIndex(points, target, function (p) { return p.x; });
-            if (idx === points.length) {
-                return points[points.length - 1];
+        /**
+         * Use the color scale to transform an element in the domain into a color.
+         * @param {string} The input string to map to a color.
+         * @return {string} The color corresponding to that input string.
+         * @throws Will error if input string is not in the scale's domain.
+         */
+        ColorScale.prototype.scale = function (s) {
+            if (!this.identifiers.has(s)) {
+                throw new Error('String was not in the domain.');
             }
-            else if (idx === 0) {
-                return points[0];
+            return this.identifiers.get(s);
+        };
+        return ColorScale;
+    }());
+    TF.ColorScale = ColorScale;
+})(TF || (TF = {}));
+</script>
+  <script>
+    (function() {
+      Polymer({
+        is: "tf-color-scale",
+        properties: {
+          runs: {
+            type: Array,
+          },
+          outColorScale: {
+            type: Object,
+            readOnly: true,
+            notify: true,
+            value: function() {
+              return new TF.ColorScale();
+            },
+          },
+        },
+        observers: ['updateColorScale(runs.*)'],
+        updateColorScale: function(runsChange) {
+          this.outColorScale.domain(this.runs);
+        },
+      });
+    })();
+  </script>
+</dom-module>
+<dom-module id="tf-storage" assetpath="../tf-storage/">
+ <script>/* Copyright 2015 Google Inc. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+/* tslint:disable:no-namespace variable-name */
+/**
+ * The Storage Module provides storage for URL parameters, and an API for
+ * getting and setting TensorBoard's stateful URI.
+ *
+ * It generates URI components like: events&runPrefix=train*
+ * which TensorBoard uses after like localhost:8000/#events&runPrefix=train*
+ * to store state in the URI.
+ */
+var TF;
+(function (TF) {
+    var URIStorage;
+    (function (URIStorage) {
+        /**
+         * A key that users cannot use, since TensorBoard uses this to store info
+         * about the active tab.
+         */
+        URIStorage.TAB = '__tab__';
+        /**
+         * The name of the property for users to set on a Polymer component
+         * in order for its stored properties to be stored in the URI unambiguously.
+         * (No need to set this if you want mutliple instances of the component to
+         * share URI state)
+         *
+         * Example:
+         * <my-component disambiguator="0"></my-component>
+         *
+         * The disambiguator should be set to any unique value so that multiple
+         * instances of the component can store properties in URI storage.
+         *
+         * Because it's hard to dereference this variable in HTML property bindings,
+         * it is NOT safe to change the disambiguator string without find+replace
+         * across the codebase.
+         */
+        URIStorage.DISAMBIGUATOR = 'disambiguator';
+        /**
+         * Return a boolean stored in the URI, given a corresponding key.
+         * Undefined if not found.
+         */
+        function getBoolean(key) {
+            var items = _componentToDict(_readComponent());
+            var item = items[key];
+            return item === 'true' ? true : item === 'false' ? false : undefined;
+        }
+        URIStorage.getBoolean = getBoolean;
+        /**
+         * Store a boolean in the URI, with a corresponding key.
+         */
+        function setBoolean(key, value) {
+            var items = _componentToDict(_readComponent());
+            items[key] = value.toString();
+            _writeComponent(_dictToComponent(items));
+        }
+        URIStorage.setBoolean = setBoolean;
+        /**
+         * Return a string stored in the URI, given a corresponding key.
+         * Undefined if not found.
+         */
+        function getString(key) {
+            var items = _componentToDict(_readComponent());
+            return items[key];
+        }
+        URIStorage.getString = getString;
+        /**
+         * Store a string in the URI, with a corresponding key.
+         */
+        function setString(key, value) {
+            var items = _componentToDict(_readComponent());
+            items[key] = value;
+            _writeComponent(_dictToComponent(items));
+        }
+        URIStorage.setString = setString;
+        /**
+         * Return a number stored in the URI, given a corresponding key.
+         * Undefined if not found.
+         */
+        function getNumber(key) {
+            var items = _componentToDict(_readComponent());
+            return items[key] === undefined ? undefined : +items[key];
+        }
+        URIStorage.getNumber = getNumber;
+        /**
+         * Store a number in the URI, with a corresponding key.
+         */
+        function setNumber(key, value) {
+            var items = _componentToDict(_readComponent());
+            items[key] = '' + value;
+            _writeComponent(_dictToComponent(items));
+        }
+        URIStorage.setNumber = setNumber;
+        /**
+         * Return an object stored in the URI, given a corresponding key.
+         * Undefined if not found.
+         */
+        function getObject(key) {
+            var items = _componentToDict(_readComponent());
+            return items[key] === undefined ? undefined : JSON.parse(atob(items[key]));
+        }
+        URIStorage.getObject = getObject;
+        /**
+         * Store an object in the URI, with a corresponding key.
+         */
+        function setObject(key, value) {
+            var items = _componentToDict(_readComponent());
+            items[key] = btoa(JSON.stringify(value));
+            _writeComponent(_dictToComponent(items));
+        }
+        URIStorage.setObject = setObject;
+        /**
+         * Get a unique storage name for a (Polymer component, propertyName) tuple.
+         *
+         * DISAMBIGUATOR must be set on the component, if other components use the
+         * same propertyName.
+         */
+        function getURIStorageName(component, propertyName) {
+            var d = component[URIStorage.DISAMBIGUATOR];
+            var components = d == null ? [propertyName] : [d, propertyName];
+            return components.join('.');
+        }
+        URIStorage.getURIStorageName = getURIStorageName;
+        /**
+         * Return a function that:
+         * (1) Initializes a Polymer boolean property with a default value, if its
+         *     value is not already set
+         * (2) Sets up listener that updates Polymer property on hash change.
+         */
+        function getBooleanInitializer(propertyName, defaultVal) {
+            return _getInitializer(getBoolean, propertyName, defaultVal);
+        }
+        URIStorage.getBooleanInitializer = getBooleanInitializer;
+        /**
+         * Return a function that:
+         * (1) Initializes a Polymer string property with a default value, if its
+         *     value is not already set
+         * (2) Sets up listener that updates Polymer property on hash change.
+         */
+        function getStringInitializer(propertyName, defaultVal) {
+            return _getInitializer(getString, propertyName, defaultVal);
+        }
+        URIStorage.getStringInitializer = getStringInitializer;
+        /**
+         * Return a function that:
+         * (1) Initializes a Polymer number property with a default value, if its
+         *     value is not already set
+         * (2) Sets up listener that updates Polymer property on hash change.
+         */
+        function getNumberInitializer(propertyName, defaultVal) {
+            return _getInitializer(getNumber, propertyName, defaultVal);
+        }
+        URIStorage.getNumberInitializer = getNumberInitializer;
+        /**
+         * Return a function that:
+         * (1) Initializes a Polymer Object property with a default value, if its
+         *     value is not already set
+         * (2) Sets up listener that updates Polymer property on hash change.
+         *
+         * Generates a deep clone of the defaultVal to avoid mutation issues.
+         */
+        function getObjectInitializer(propertyName, defaultVal) {
+            var clone = _.cloneDeep(defaultVal);
+            return _getInitializer(getObject, propertyName, clone);
+        }
+        URIStorage.getObjectInitializer = getObjectInitializer;
+        /**
+         * Return a function that updates URIStorage when a string property changes.
+         */
+        function getBooleanObserver(propertyName, defaultVal) {
+            return _getObserver(getBoolean, setBoolean, propertyName, defaultVal);
+        }
+        URIStorage.getBooleanObserver = getBooleanObserver;
+        /**
+         * Return a function that updates URIStorage when a string property changes.
+         */
+        function getStringObserver(propertyName, defaultVal) {
+            return _getObserver(getString, setString, propertyName, defaultVal);
+        }
+        URIStorage.getStringObserver = getStringObserver;
+        /**
+         * Return a function that updates URIStorage when a number property changes.
+         */
+        function getNumberObserver(propertyName, defaultVal) {
+            return _getObserver(getNumber, setNumber, propertyName, defaultVal);
+        }
+        URIStorage.getNumberObserver = getNumberObserver;
+        /**
+         * Return a function that updates URIStorage when an object property changes.
+         * Generates a deep clone of the defaultVal to avoid mutation issues.
+         */
+        function getObjectObserver(propertyName, defaultVal) {
+            var clone = _.cloneDeep(defaultVal);
+            return _getObserver(getObject, setObject, propertyName, clone);
+        }
+        URIStorage.getObjectObserver = getObjectObserver;
+        /**
+         * Read component from URI (e.g. returns "events&runPrefix=train*").
+         */
+        function _readComponent() {
+            return TF.Globals.USE_HASH ? window.location.hash.slice(1) :
+                TF.Globals.FAKE_HASH;
+        }
+        /**
+         * Write component to URI.
+         */
+        function _writeComponent(component) {
+            if (TF.Globals.USE_HASH) {
+                window.location.hash = component;
             }
             else {
-                var prev = points[idx - 1];
-                var next = points[idx];
-                var prevDist = Math.abs(prev.x - target.x);
-                var nextDist = Math.abs(next.x - target.x);
-                return prevDist < nextDist ? prev : next;
+                TF.Globals.FAKE_HASH = component;
             }
-        };
-        LineChart.prototype.resmoothDataset = function (dataset) {
-            var _this = this;
-            var data = dataset.data();
-            // EMA with first step initialized to first element.
-            data.forEach(function (d, i) {
-                if (i === 0) {
-                    d.smoothed = d.scalar;
+        }
+        /**
+         * Convert dictionary of strings into a URI Component.
+         * All key value entries get added as key value pairs in the component,
+         * with the exception of a key with the TAB value, which if present
+         * gets prepended to the URI Component string for backwards comptability
+         * reasons.
+         */
+        function _dictToComponent(items) {
+            var component = '';
+            // Add the tab name e.g. 'events', 'images', 'histograms' as a prefix
+            // for backwards compatbility.
+            if (items[URIStorage.TAB] !== undefined) {
+                component += items[URIStorage.TAB];
+            }
+            // Join other strings with &key=value notation
+            var nonTab = _.pairs(items)
+                .filter(function (pair) { return pair[0] !== URIStorage.TAB; })
+                .map(function (pair) {
+                return encodeURIComponent(pair[0]) + '=' +
+                    encodeURIComponent(pair[1]);
+            })
+                .join('&');
+            return nonTab.length > 0 ? (component + '&' + nonTab) : component;
+        }
+        /**
+         * Convert a URI Component into a dictionary of strings.
+         * Component should consist of key-value pairs joined by a delimiter
+         * with the exception of the tabName.
+         * Returns dict consisting of all key-value pairs and
+         * dict[TAB] = tabName
+         */
+        function _componentToDict(component) {
+            var items = {};
+            var tokens = component.split('&');
+            tokens.forEach(function (token) {
+                var kv = token.split('=');
+                // Special backwards compatibility for URI components like #events
+                if (kv.length === 1 && _.contains(TF.Globals.TABS, kv[0])) {
+                    items[URIStorage.TAB] = kv[0];
                 }
-                else {
-                    d.smoothed = (1.0 - _this.smoothingDecay) * d.scalar +
-                        _this.smoothingDecay * data[i - 1].smoothed;
+                else if (kv.length === 2) {
+                    items[decodeURIComponent(kv[0])] = decodeURIComponent(kv[1]);
                 }
             });
-        };
-        LineChart.prototype.getDataset = function (name) {
-            if (this.name2datasets[name] === undefined) {
-                this.name2datasets[name] = new Plottable.Dataset([], { name: name });
-            }
-            return this.name2datasets[name];
-        };
+            return items;
+        }
         /**
-         * Update the selected series on the chart.
+         * Return a function that:
+         * (1) Initializes a Polymer property with a default value, if its
+         *     value is not already set
+         * (2) Sets up listener that updates Polymer property on hash change.
          */
-        LineChart.prototype.setVisibleSeries = function (names) {
-            var _this = this;
-            this.seriesNames = names;
-            names.reverse(); // draw first series on top
-            this.datasets.forEach(function (d) { return d.offUpdate(_this.onDatasetChanged); });
-            this.datasets = names.map(function (r) { return _this.getDataset(r); });
-            this.datasets.forEach(function (d) { return d.onUpdate(_this.onDatasetChanged); });
-            this.linePlot.datasets(this.datasets);
-            if (this.smoothingEnabled) {
-                this.smoothLinePlot.datasets(this.datasets);
-            }
-        };
+        function _getInitializer(get, propertyName, defaultVal) {
+            return function () {
+                var _this = this;
+                var URIStorageName = getURIStorageName(this, propertyName);
+                var setComponentValue = function () {
+                    var uriValue = get(URIStorageName);
+                    _this[propertyName] = uriValue !== undefined ? uriValue : defaultVal;
+                };
+                // Set the value on the property.
+                setComponentValue();
+                // Update it when the hashchanges.
+                window.addEventListener('hashchange', setComponentValue);
+            };
+        }
         /**
-         * Set the data of a series on the chart.
+         * Return a function that updates URIStorage when a property changes.
          */
-        LineChart.prototype.setSeriesData = function (name, data) {
-            this.getDataset(name).data(data);
-        };
-        LineChart.prototype.smoothingUpdate = function (decay) {
-            var _this = this;
-            this.smoothingDecay = decay;
-            this.datasets.forEach(function (d) { return _this.resmoothDataset(d); });
-            if (!this.smoothingEnabled) {
-                this.linePlot.addClass('ghost');
-                this.scatterPlot.y(this.smoothedAccessor, this.yScale);
-                this.smoothingEnabled = true;
-                this.smoothLinePlot.datasets(this.datasets);
-            }
-            this.updateSpecialDatasets(this.smoothedAccessor);
-        };
-        LineChart.prototype.smoothingDisable = function () {
-            if (this.smoothingEnabled) {
-                this.linePlot.removeClass('ghost');
-                this.scatterPlot.y(this.scalarAccessor, this.yScale);
-                this.smoothLinePlot.datasets([]);
-                this.smoothingEnabled = false;
-                this.updateSpecialDatasets(this.scalarAccessor);
-            }
-        };
-        LineChart.prototype.renderTo = function (target) { this.outer.renderTo(target); };
-        LineChart.prototype.redraw = function () { this.outer.redraw(); };
-        LineChart.prototype.destroy = function () { this.outer.destroy(); };
-        return LineChart;
-    }());
-    VZ.LineChart = LineChart;
-})(VZ || (VZ = {}));
+        function _getObserver(get, set, propertyName, defaultVal) {
+            return function () {
+                var URIStorageName = getURIStorageName(this, propertyName);
+                var newVal = this[propertyName];
+                if (!_.isEqual(newVal, get(URIStorageName))) {
+                    if (_.isEqual(newVal, defaultVal)) {
+                        _unset(URIStorageName);
+                    }
+                    else {
+                        set(URIStorageName, newVal);
+                    }
+                }
+            };
+        }
+        /**
+         * Delete a key from the URI.
+         */
+        function _unset(key) {
+            var items = _componentToDict(_readComponent());
+            delete items[key];
+            _writeComponent(_dictToComponent(items));
+        }
+    })(URIStorage = TF.URIStorage || (TF.URIStorage = {}));
+})(TF || (TF = {}));
 </script>
+</dom-module>
+
+<dom-module id="tf-regex-group" assetpath="../tf-dashboard-common/">
+  <template>
+    <div class="regex-list">
+      <template is="dom-repeat" items="{{rawRegexes}}">
+        <div class="regex-line">
+          <paper-checkbox class="active-button" checked="{{item.active}}" disabled="[[!item.valid]]"></paper-checkbox>
+          <paper-input id="text-input" class="regex-input" label="Write a regex to create a tag group" no-label-float="" bind-value="{{item.regex}}" invalid="[[!item.valid]]" on-keyup="moveFocus"></paper-input>
+          <paper-icon-button icon="close" class="delete-button" aria-label="Delete Regex" tabindex="0" on-tap="deleteRegex"></paper-icon-button>
+        </div>
+        <style>
+          .regex-input {
+            width: 230px;
+            display: inline-block;
+            margin-left: -3px;
+          }
+
+          paper-checkbox {
+            --paper-checkbox-checked-color: var(--tb-ui-dark-accent);
+            --paper-checkbox-unchecked-color: var(--tb-ui-dark-accent);
+          }
+
+          .delete-button {
+            color: var(--paper-grey-700);
+            width: 40px;
+            height: 40px;
+            margin-right: -10px;
+          }
+
+          .regex-list {
+            margin-bottom: 10px;
+          }
+
+          paper-input {
+            --paper-input-container-focus-color: var(--tb-orange-strong);
+            --paper-input-container-input: {
+              font-size: 14px;
+            };
+            --paper-input-container-label: {
+              font-size: 14px;
+            };
+          }
+        </style>
+      </template>
+    </div>
+  </template>
+  <script>
+    Polymer({
+      is: "tf-regex-group",
+      properties: {
+        rawRegexes: {
+          type: Array,
+          value: function() {
+            return [{regex: "", active: true, valid: true}];
+          }
+        },
+        regexes: {type: Array, computed: "usableRegexes(rawRegexes.*)", notify: true},
+      },
+      observers: [
+        "addNewRegexIfNeeded(rawRegexes.*)",
+        "checkValidity(rawRegexes.*)",
+      ],
+      checkValidity: function(x) {
+        var match = x.path.match(/rawRegexes\.(\d+)\.regex/);
+        if (match) {
+          var idx = match[1];
+          this.set("rawRegexes." + idx + ".valid", this.isValid(x.value));
+        }
+      },
+      isValid: function(s) {
+        try {
+          new RegExp(s);
+          return true;
+        } catch (e) {
+          return false;
+        }
+      },
+      usableRegexes: function(regexes) {
+        var isValid = this.isValid;
+        return regexes.base.filter(function (r) {
+          // Checking validity here (rather than using the data property)
+          // is necessary because otherwise we might send invalid regexes due
+          // to the fact that this function can call before the observer does
+          return r.regex !== "" && r.active && isValid(r.regex);
+        }).map(function(r) {
+          return r.regex;
+        });
+      },
+      addNewRegexIfNeeded: function() {
+        var last = this.rawRegexes[this.rawRegexes.length - 1];
+        if (last.regex !== "") {
+          this.push("rawRegexes", {regex: "", active: true, valid: true});
+        }
+      },
+      deleteRegex: function(e) {
+        if (this.rawRegexes.length > 1) {
+          this.splice("rawRegexes", e.model.index, 1);
+        }
+      },
+      moveFocus: function(e) {
+        if (e.keyCode === 13) {
+          var idx = e.model.index;
+          var inputs = Polymer.dom(this.root).querySelectorAll(".regex-input");
+          if (idx < this.rawRegexes.length - 1) {
+            inputs[idx+1].$.input.focus();
+          } else {
+            document.activeElement.blur();
+          }
+        }
+      }
+    });
+  </script>
+</dom-module>
+<style is="custom-style">
+
+  :root {
+    --tb-orange-weak: #ffa726;
+    --tb-orange-strong: #f57c00;
+    --tb-grey-darker: #e2e2e2;
+    --tb-grey-lighter: #f3f3f3;
+    --tb-ui-dark-accent: #757575;
+    --tb-ui-light-accent: #e0e0e0;
+    --tb-graph-faded: #e0d4b3;
+  }
+
+</style>
+
+<dom-module id="tf-categorizer" assetpath="../tf-dashboard-common/">
+  <template>
+    <div class="inputs">
+      <tf-regex-group id="regex-group" regexes="{{regexes}}"></tf-regex-group>
+    </div>
+    <div id="underscore-categorization">
+      <paper-checkbox checked="{{splitOnUnderscore}}">Split on underscores</paper-checkbox>
+    </div>
+    <style>
+      :host {
+        display: block;
+        padding-bottom: 15px;
+      }
+      paper-checkbox {
+        --paper-checkbox-checked-color: var(--paper-grey-600);
+        --paper-checkbox-unchecked-color: var(--paper-grey-600);
+        font-size: 14px;
+      }
+      #underscore-categorization {
+        color: var(--paper-grey-700);
+      }
+    </style>
+  </template>
   <script>/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 
-Licensed under the Apache License, Version 2.0 (the "License");
+Licensed under the Apache License, Version 2.0 (the 'License');
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
 
     http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
+distributed under the License is distributed on an 'AS IS' BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
-/* tslint:disable:no-namespace variable-name */
-var VZ;
-(function (VZ) {
-    var ChartHelpers;
-    (function (ChartHelpers) {
-        ChartHelpers.Y_TOOLTIP_FORMATTER_PRECISION = 4;
-        ChartHelpers.STEP_FORMATTER_PRECISION = 4;
-        ChartHelpers.Y_AXIS_FORMATTER_PRECISION = 3;
-        ChartHelpers.TOOLTIP_Y_PIXEL_OFFSET = 20;
-        ChartHelpers.TOOLTIP_CIRCLE_SIZE = 4;
-        ChartHelpers.NAN_SYMBOL_SIZE = 6;
-        /* Create a formatter function that will switch between exponential and
-         * regular display depending on the scale of the number being formatted,
-         * and show `digits` significant digits.
-         */
-        function multiscaleFormatter(digits) {
-            return function (v) {
-                var absv = Math.abs(v);
-                if (absv < 1E-15) {
-                    // Sometimes zero-like values get an annoying representation
-                    absv = 0;
-                }
-                var f;
-                if (absv >= 1E4) {
-                    f = d3.format('.' + digits + 'e');
-                }
-                else if (absv > 0 && absv < 0.01) {
-                    f = d3.format('.' + digits + 'e');
-                }
-                else {
-                    f = d3.format('.' + digits + 'g');
-                }
-                return f(v);
-            };
-        }
-        ChartHelpers.multiscaleFormatter = multiscaleFormatter;
-        function accessorize(key) {
-            return function (d, index, dataset) { return d[key]; };
-        }
-        ChartHelpers.accessorize = accessorize;
-        ChartHelpers.stepFormatter = Plottable.Formatters.siSuffix(ChartHelpers.STEP_FORMATTER_PRECISION);
-        function stepX() {
-            var scale = new Plottable.Scales.Linear();
-            var axis = new Plottable.Axes.Numeric(scale, 'bottom');
-            axis.formatter(ChartHelpers.stepFormatter);
-            return {
-                scale: scale,
-                axis: axis,
-                accessor: function (d) { return d.step; },
-            };
-        }
-        ChartHelpers.stepX = stepX;
-        ChartHelpers.timeFormatter = Plottable.Formatters.time('%a %b %e, %H:%M:%S');
-        function wallX() {
-            var scale = new Plottable.Scales.Time();
-            return {
-                scale: scale,
-                axis: new Plottable.Axes.Time(scale, 'bottom'),
-                accessor: function (d) { return d.wall_time; },
-            };
-        }
-        ChartHelpers.wallX = wallX;
-        ChartHelpers.relativeAccessor = function (d, index, dataset) {
-            // We may be rendering the final-point datum for scatterplot.
-            // If so, we will have already provided the 'relative' property
-            if (d.relative != null) {
-                return d.relative;
-            }
-            var data = dataset.data();
-            // I can't imagine how this function would be called when the data is
-            // empty (after all, it iterates over the data), but lets guard just
-            // to be safe.
-            var first = data.length > 0 ? +data[0].wall_time : 0;
-            return (+d.wall_time - first) / (60 * 60 * 1000); // ms to hours
-        };
-        ChartHelpers.relativeFormatter = function (n) {
-            // we will always show 2 units of precision, e.g days and hours, or
-            // minutes and seconds, but not hours and minutes and seconds
-            var ret = '';
-            var days = Math.floor(n / 24);
-            n -= (days * 24);
-            if (days) {
-                ret += days + 'd ';
-            }
-            var hours = Math.floor(n);
-            n -= hours;
-            n *= 60;
-            if (hours || days) {
-                ret += hours + 'h ';
-            }
-            var minutes = Math.floor(n);
-            n -= minutes;
-            n *= 60;
-            if (minutes || hours || days) {
-                ret += minutes + 'm ';
-            }
-            var seconds = Math.floor(n);
-            return ret + seconds + 's';
-        };
-        function relativeX() {
-            var scale = new Plottable.Scales.Linear();
-            return {
-                scale: scale,
-                axis: new Plottable.Axes.Numeric(scale, 'bottom'),
-                accessor: ChartHelpers.relativeAccessor,
-            };
+var Categorizer;
+(function (Categorizer) {
+    /* Canonical TensorFlow ops are namespaced using forward slashes.
+     * This fallback categorizer categorizes by the top-level namespace.
+     */
+    Categorizer.topLevelNamespaceCategorizer = splitCategorizer(/\//);
+    // Try to produce good categorizations on legacy graphs, which often
+    // are namespaced like l1_foo/bar or l2_baz/bam.
+    // If there is no leading underscore before the first forward slash,
+    // then it behaves the same as topLevelNamespaceCategorizer
+    Categorizer.legacyUnderscoreCategorizer = splitCategorizer(/[\/_]/);
+    function fallbackCategorizer(s) {
+        switch (s) {
+            case 'TopLevelNamespaceCategorizer':
+                return Categorizer.topLevelNamespaceCategorizer;
+            case 'LegacyUnderscoreCategorizer':
+                return Categorizer.legacyUnderscoreCategorizer;
+            default:
+                throw new Error('Unrecognized categorization strategy: ' + s);
         }
-        ChartHelpers.relativeX = relativeX;
-        // a very literal definition of NaN: true for NaN for a non-number type
-        // or null, etc. False for Infinity or -Infinity
-        ChartHelpers.isNaN = function (x) { return +x !== x; };
-        function getXComponents(xType) {
-            switch (xType) {
-                case 'step':
-                    return stepX();
-                case 'wall_time':
-                    return wallX();
-                case 'relative':
-                    return relativeX();
-                default:
-                    throw new Error('invalid xType: ' + xType);
+    }
+    Categorizer.fallbackCategorizer = fallbackCategorizer;
+    /* An 'extractor' is a function that takes a tag name, and 'extracts' a
+     * category name.
+     * This function takes an extractor, and produces a categorizer.
+     * Currently, it is just used for the fallbackCategorizer, but we may want to
+     * refactor the general categorization logic to use the concept of extractors.
+     */
+    function extractorToCategorizer(extractor) {
+        return function (tags) {
+            if (tags.length === 0) {
+                return [];
             }
-        }
-        ChartHelpers.getXComponents = getXComponents;
-    })(ChartHelpers = VZ.ChartHelpers || (VZ.ChartHelpers = {}));
-})(VZ || (VZ = {}));
+            var sortedTags = tags.slice().sort();
+            var categories = [];
+            var currentCategory = {
+                name: extractor(sortedTags[0]),
+                tags: [],
+            };
+            sortedTags.forEach(function (t) {
+                var topLevel = extractor(t);
+                if (currentCategory.name !== topLevel) {
+                    categories.push(currentCategory);
+                    currentCategory = {
+                        name: topLevel,
+                        tags: [],
+                    };
+                }
+                currentCategory.tags.push(t);
+            });
+            categories.push(currentCategory);
+            return categories;
+        };
+    }
+    function splitCategorizer(r) {
+        var extractor = function (t) {
+            return t.split(r)[0];
+        };
+        return extractorToCategorizer(extractor);
+    }
+    function defineCategory(ruledef) {
+        var r = new RegExp(ruledef);
+        var f = function (tag) {
+            return r.test(tag);
+        };
+        return { name: ruledef, matches: f };
+    }
+    Categorizer.defineCategory = defineCategory;
+    function _categorizer(rules, fallback) {
+        return function (tags) {
+            var remaining = d3.set(tags);
+            var userSpecified = rules.map(function (def) {
+                var tags = [];
+                remaining.forEach(function (t) {
+                    if (def.matches(t)) {
+                        tags.push(t);
+                    }
+                });
+                var cat = { name: def.name, tags: tags.sort() };
+                return cat;
+            });
+            var defaultCategories = fallback(remaining.values());
+            return userSpecified.concat(defaultCategories);
+        };
+    }
+    Categorizer._categorizer = _categorizer;
+    function categorizer(s) {
+        var rules = s.categoryDefinitions.map(defineCategory);
+        var fallback = fallbackCategorizer(s.fallbackCategorizer);
+        return _categorizer(rules, fallback);
+    }
+    Categorizer.categorizer = categorizer;
+    ;
+})(Categorizer || (Categorizer = {}));
 </script>
   <script>
     Polymer({
-      is: "vz-line-chart",
+      is: "tf-categorizer",
       properties: {
-        /**
-         * Scale that maps series names to colors. The default colors are from
-         * d3.scale.category10() scale. Use this property to replace the default
-         * line colors with colors of your own choice.
-         * @type {Plottable.Scales.Color}
-         * @required
-         */
-        colorScale: {
-          type: Object,
-          value: function() {
-            return new Plottable.Scales.Color()
-                .range(d3.scale.category10().range());
-          }
-        },
-
-        /**
-         * Decay (between 0.0 and 1.0) of the exponential smoothing. A higher
-         * decay results in more smoothing.
-         */
-        smoothingDecay: {
-          type: Number,
-          value: 0.9
-        },
-
-        /**
-         * Whether exponential smoothing is enabled or not. If true, smoothed
-         * lines will be plotted in the chart while the unsmoothed lines will be
-         * ghosted in the background.
-         */
-        smoothingEnabled: {
-          type: Boolean,
-          value: false
-        },
-
-        /**
-         * The way to display the X values. Allows:
-         * - "step" - Linear scale using the  "step" property of the datum.
-         * - "wall_time" - Temporal scale using the "wall_time" property of the
-         * datum.
-         * - "relative" - Temporal scale using the "relative" property of the
-         * datum if it is present or calculating from "wall_time" if it isn't.
-         */
-        xType: {
+        regexes: {type: Array},
+        tags: {type: Array},
+        categoriesAreExclusive: {type: Boolean, value: true},
+        fallbackCategorizer: {
           type: String,
-          value: 'step'
+          computed: "chooseFallbackCategorizer(splitOnUnderscore)"
         },
-
-        _attached: Boolean,
-        _chart: Object,
-        _visibleSeriesCache: {
-          type: Array,
-          value: function() { return [] }
+        splitOnUnderscore: {
+          type: Boolean,
+          notify: true,
+          value: TF.URIStorage.getBooleanInitializer('splitOnUnderscore',
+              false),
+          observer: '_splitOnUnderscoreObserver'
         },
-        _seriesDataCache: {
+        categorizer: {
           type: Object,
-          value: function() { return {} }
+          computed: "computeCategorization(regexes.*, categoriesAreExclusive, fallbackCategorizer)",
         },
-        _makeChartAsyncCallbackId: {
-          type: Number,
-          value: null
-        }
+        categories: {type: Array, value: function() {return [];}, notify: true, readOnly: true},
       },
-      observers: [
-        "_makeChart(xType, colorScale, _attached)",
-        "_reloadFromCache(_chart)",
-        "_smoothingChanged(smoothingDecay, smoothingEnabled, _chart)"
-      ],
-
-      /**
-       * Sets the series that the chart displays. Series with other names will
-       * not be displayed.
-       *
-       * @param {String[]} names Array with the names of the series to
-       * display.
-       */
-      setVisibleSeries: function(names) {
-        this._visibleSeriesCache = names;
-        if (this._chart) {
-          this._chart.setVisibleSeries(names);
-          this.redraw();
-        }
+      observers: ['recategorize(tags.*, categorizer)'],
+      computeCategorization: function(regexes, categoriesAreExclusive, fallbackCategorizer) {
+        var categorizationStrategy = {
+          categoryDefinitions: regexes.base,
+          categoriesAreExclusive: categoriesAreExclusive,
+          fallbackCategorizer: fallbackCategorizer,
+        };
+        return Categorizer.categorizer(categorizationStrategy);
       },
-
-      /**
-       * Sets the data of one of the series. Note that to display this series
-       * its name must be in the setVisibleSeries() array.
-       *
-       * @param {String} name Name of the series.
-       * @param {VZ.ChartHelpers.ScalarDatum[]} data Data of the series. This is
-       * an array of objects with at least the following properties:
-       * - step: (Number) - index of the datum.
-       * - wall_time: (Date) - Date object with the datum's time.
-       * - scalar: (Number) - Value of the datum.
-       */
-      setSeriesData: function(name, data) {
-        this._seriesDataCache[name] = data;
-        if (this._chart) {
-          this._chart.setSeriesData(name, data);
+      recategorize: function() {
+        this.debounce("tf-categorizer-recategorize", function (){
+          var categories = this.categorizer(this.tags);
+          this._setCategories(categories);
+        })
+      },
+      chooseFallbackCategorizer: function(splitOnUnderscore) {
+        if (splitOnUnderscore) {
+          return "LegacyUnderscoreCategorizer";
+        } else {
+          return "TopLevelNamespaceCategorizer";
         }
       },
+      _splitOnUnderscoreObserver: TF.URIStorage.getBooleanObserver(
+          'splitOnUnderscore', false)
+    });
+  </script>
+</dom-module>
 
-      /**
-       * Re-renders the chart. Useful if e.g. the container size changed.
-       */
-      redraw: function() {
-        this._chart.redraw();
+<dom-module id="tf-chart-scaffold" assetpath="../tf-dashboard-common/">
+  <template>
+    <content></content>
+    <style>
+      :host {
+        -webkit-user-select: none;
+        -moz-user-select: none;
+        display: flex;
+        flex-direction: column;
+        flex-grow: 1;
+        flex-shrink: 1;
+        position: relative;
+      }
+    </style>
+  </template>
+  <script>
+    Polymer({
+      is: "tf-chart-scaffold",
+      properties: {
+        tag: String,
+        dataProvider: Function,
+        visibleSeries: Array,
+        _attached: {
+          type: Boolean,
+          value: false
+        }
       },
+      observers: [
+        "reload(tag, dataProvider)",
+        "_changeSeries(visibleSeries.*)"
+      ],
       attached: function() {
         this._attached = true;
+        this._changeSeries();
       },
       detached: function() {
         this._attached = false;
       },
-      ready: function() {
-        this.scopeSubtree(this.$.tooltip, true);
-        this.scopeSubtree(this.$.chartsvg, true);
-      },
-      _makeChart: function(xType, colorScale, _attached) {
-        if (this._makeChartAsyncCallbackId === null) {
-          this.cancelAsync(this._makeChartAsyncCallbackId);
+      reload: function() {
+        if (!this._attached) {
+          return;
+        }
+        else if (!this.dataProvider) {
+          throw new Error('tf-chart-scaffold requires a dataProvider.');
+        }
+        else if (!this.tag) {
+          throw new Error('tf-chart-scaffold requires a tag.');
         }
 
-        this._makeChartAsyncCallbackId = this.async(function() {
-          this._makeChartAsyncCallbackId = null;
-          if (!this._attached) return;
-          if (this._chart) this._chart.destroy();
-          var tooltip = d3.select(this.$.tooltip);
-          var chart = new VZ.LineChart(xType, colorScale, tooltip);
-          var svg = d3.select(this.$.chartsvg);
-          chart.renderTo(svg);
-          this._chart = chart;
-        }, 350);
-      },
-      _reloadFromCache: function() {
-        if(this._chart) {
-          this._chart.setVisibleSeries(this._visibleSeriesCache);
-          this._visibleSeriesCache.forEach(function(name) {
-            this._chart.setSeriesData(name, this._seriesDataCache[name] || []);
+        this.visibleSeries.forEach(function(name) {
+          this.dataProvider(this.tag, name).then(function(data) {
+            this.chart().setSeriesData(name, data);
           }.bind(this));
-        }
+        }.bind(this));
       },
-      _smoothingChanged: function() {
-        if(!this._chart) {
-          return;
+      _changeSeries: function() {
+        if (!this._attached) {
+           return;
         }
-        if(this.smoothingEnabled) {
-          this._chart.smoothingUpdate(this.smoothingDecay);
+        else if (!this.visibleSeries) {
+          throw new Error('tf-chart-scaffold requires a visibleSeries.');
         }
-        else {
-          this._chart.smoothingDisable();
+
+        this.chart().setVisibleSeries(this.visibleSeries);
+        this.reload();
+      },
+      chart: function() {
+        var children = this.getEffectiveChildren();
+        if (!children.length) {
+          throw new Error('tf-chart-scaffold has no children');
         }
+
+        var child = children[0];
+        if (!child.setVisibleSeries || !child.setSeriesData) {
+          throw new Error("tf-chart-scaffold's content doesn't implement the " +
+              "required interface");
+        }
+        return child;
       }
     });
   </script>
 </dom-module>
 
-<dom-module id="tf-collapsable-pane" assetpath="../tf-collapsable-pane/">
+<dom-module id="tf-collapsable-pane" assetpath="../tf-dashboard-common/">
   <template>
     <button class="heading" on-tap="togglePane" open-button$="[[opened]]">
     <span class="name">[[name]]</span>
@@ -2543,585 +1917,907 @@ var VZ;
         background: white;
       }
 
-      [open-button] {
-        border-bottom-left-radius: 0px !important;
-        border-bottom-right-radius: 0px !important;
+      [open-button] {
+        border-bottom-left-radius: 0px !important;
+        border-bottom-right-radius: 0px !important;
+      }
+
+      .name {
+        flex-grow: 0;
+      }
+
+      .count {
+        flex-grow: 0;
+        float: right;
+        margin-right: 5px;
+        font-size: 12px;
+        color: var(--paper-grey-500);
+      }
+
+      .hackpadding {
+        /* An obnoxious hack, but I can't get justify-content: space-between to work */
+        flex-grow: 1;
+      }
+    </style>
+  </template>
+  <script>
+    Polymer({
+      is: "tf-collapsable-pane",
+      properties: {
+        opened: {type: Boolean, value: false},
+        restamp: {type: Boolean, value: true},
+        name: {type: String, observer: "hide"},
+        count: {type: Number},
+      },
+      hide: function() {
+        this.opened = false;
+      },
+      togglePane: function() {
+        this.opened = !this.opened;
+      }
+    });
+  </script>
+
+</dom-module>
+
+<dom-module id="scrollbar-style" assetpath="../tf-dashboard-common/">
+  <template>
+    <style>
+      .scrollbar::-webkit-scrollbar-track
+      {
+        visibility: hidden;
+      }
+
+      .scrollbar::-webkit-scrollbar
+      {
+        width: 10px;
+      }
+
+      .scrollbar::-webkit-scrollbar-thumb
+      {
+        border-radius: 10px;
+        -webkit-box-shadow: inset 0 0 2px rgba(0,0,0,.3);
+        background-color: var(--paper-grey-500);
+        color: var(--paper-grey-900);
+      }
+      .scrollbar {
+        box-sizing: border-box;
+      }
+    </style>
+  </template>
+</dom-module>
+
+<dom-module id="tf-dashboard-layout" assetpath="../tf-dashboard-common/">
+  <template>
+    <div id="sidebar">
+      <content select=".sidebar"></content>
+    </div>
+
+    <div id="center" class="scrollbar">
+      <content select=".center"></content>
+    </div>
+    <style include="scrollbar-style"></style>
+    <style>
+      #sidebar {
+        width: inherit;
+        height: 100%;
+        overflow: ellipsis;
+        flex-grow: 0;
+        flex-shrink: 0;
+      }
+
+      #center {
+        height: 100%;
+        overflow-y: auto;
+        flex-grow: 1;
+        flex-shrink: 1;
+      }
+
+      .tf-graph-dashboard #center {
+        background: white;
+      }
+
+      :host {
+        display: flex;
+        flex-direction: row;
+        height: 100%;
+      }
+    </style>
+  </template>
+  <script>
+    Polymer({
+      is: "tf-dashboard-layout",
+    });
+  </script>
+</dom-module>
+<dom-module id="dashboard-style" assetpath="../tf-dashboard-common/">
+  <template>
+    <style>
+      .card {
+        height: 200px;
+        width: 300px;
+        display: flex;
+        flex-direction: column;
+        margin: 5px;
+        padding: 0 30px 30px 0;
+        -webkit-user-select: none;
+        -moz-user-select: none;
+        position: relative;
+      }
+
+      .card .card-title, .card .card-subtitle {
+        flex-grow: 0;
+        flex-shrink: 0;
+        font-size: 14px;
+        text-overflow: ellipsis;
+        overflow: hidden;
+      }
+
+      .card .card-subtitle {
+        font-size: 12px;
+      }
+
+      .card .card-content {
+        flex-grow: 1;
+        flex-shrink: 1;
+        display: flex;
+        margin-top: 10px;
+      }
+      .card .card-bottom-row {
+        position: absolute;
+        left: 50px;
+        bottom: 0;
+        padding-right: 10px;
+      }
+
+      .card.selected {
+        height: 400px;
+        width: 100%;
+      }
+
+      .expand-button {
+        position: absolute;
+        left: 0px;
+        bottom: 0px;
+        color: #2196F3;
+        display: block;
+      }
+
+      #content-container{
+        display: block;
+      }
+
+      .sidebar {
+        display: flex;
+        flex-direction: column;
+        height: 100%;
+        margin-right: 20px;
+      }
+
+      #categorizer {
+        flex-shrink: 0;
+      }
+
+      #xTypeSelector {
+        flex-shrink: 0;
+        margin: 20px 0;
+      }
+
+      #runSelector {
+        flex-shrink: 1;
+        flex-grow: 1;
+      }
+
+      #tooltip-sorting {
+        display: flex;
+        font-size: 14px;
+      }
+
+      #tooltip-sorting-label {
+        margin-top: 13px;
+        margin-left: 28px;
+      }
+
+      #tooltip-sorting paper-dropdown-menu {
+        margin-left: 10px;
+        --paper-input-container-focus-color: var(--tb-orange-strong);
+        width: 105px;
+      }
+
+      .sidebar-section {
+        border-top: solid 1px rgba(0, 0, 0, 0.12);
+        padding: 20px 0px 20px 30px;
       }
 
-      .name {
-        flex-grow: 0;
+      .sidebar-section:first-child {
+        border: none;
       }
 
-      .count {
-        flex-grow: 0;
-        float: right;
-        margin-right: 5px;
-        font-size: 12px;
-        color: var(--paper-grey-500);
+      .sidebar-section:last-child {
+        flex-grow: 1;
+        display: flex;
       }
 
-      .hackpadding {
-        /* An obnoxious hack, but I can't get justify-content: space-between to work */
-        flex-grow: 1;
+      paper-checkbox {
+        --paper-checkbox-checked-color: var(--tb-ui-dark-accent);
+        --paper-checkbox-unchecked-color: var(--tb-ui-dark-accent);
+        font-size: 14px;
+      }
+
+    </style>
+  </template>
+</dom-module>
+
+<dom-module id="tf-downloader" assetpath="../tf-dashboard-common/">
+  <template>
+    <paper-dropdown-menu no-label-float="true" label="run to download" selected-item-label="{{_run}}">
+      <paper-menu class="dropdown-content">
+        <template is="dom-repeat" items="[[_runs]]">
+          <paper-item no-label-float="true">[[item]]</paper-item>
+        </template>
+      </paper-menu>
+    </paper-dropdown-menu>
+    <a download="[[_csvName(_run)]]" href="[[_csvUrl(_run, urlFn)]]">CSV</a>
+    <a download="[[_jsonName(_run)]]" href="[[_jsonUrl(_run, urlFn)]]">JSON</a>
+    <style>
+      :host {
+        display: block;
+      }
+      paper-dropdown-menu {
+        width: 180px;
+        --paper-input-container-label: {
+          font-size: 10px;
+        }
+        --paper-input-container-input: {
+          font-size: 10px;
+        }
+      }
+      a {
+        font-size: 10px;
+        border-radius: 3px;
+        border: 1px solid #EEE;
+      }
+      paper-input {
+        font-size: 22px;
       }
     </style>
   </template>
   <script>
     Polymer({
-      is: "tf-collapsable-pane",
+      is: "tf-downloader",
       properties: {
-        opened: {type: Boolean, value: false},
-        restamp: {type: Boolean, value: true},
-        name: {type: String, observer: "hide"},
-        count: {type: Number},
+        _run: String,
+        _runs: {
+          type: Array,
+          computed: "_computeRuns(runToTag.*, selectedRuns.*)",
+        },
+        selectedRuns: Array,
+        runToTag: Object,
+        tag: String,
+        urlFn: Function,
       },
-      hide: function() {
-        this.opened = false;
+      _computeRuns: function(runToTagChange, selectedRunsChange) {
+        var runToTag = this.runToTag;
+        var tag = this.tag;
+        return this.selectedRuns.filter(function(x) {
+          return runToTag[x].indexOf(tag) !== -1;
+        })
+      },
+      _csvUrl: function(_run, urlFn) {
+        return urlFn(this.tag, _run) + "&format=csv";
+      },
+      _jsonUrl: function(_run, urlFn) {
+        return urlFn(this.tag, _run);
+      },
+      _csvName: function(_run) {
+        return "run_" + _run + ",tag_" + this.tag + ".csv";
+      },
+      _jsonName: function(_run) {
+        return "run-" + _run + "-tag-" + this.tag + ".json";
       },
-      togglePane: function() {
-        this.opened = !this.opened;
-      }
     });
   </script>
-
 </dom-module>
 
-<dom-module id="tf-dashboard-layout" assetpath="../tf-dashboard-common/">
+<dom-module id="tf-no-data-warning" assetpath="../tf-dashboard-common/">
   <template>
-    <div id="sidebar">
-      <content select=".sidebar"></content>
-    </div>
-
-    <div id="center" class="scrollbar">
-      <content select=".center"></content>
-    </div>
-    <style include="scrollbar-style"></style>
-    <style>
-      #sidebar {
-        width: inherit;
-        height: 100%;
-        overflow: ellipsis;
-        flex-grow: 0;
-        flex-shrink: 0;
-      }
-
-      #center {
-        height: 100%;
-        overflow-y: auto;
-        flex-grow: 1;
-        flex-shrink: 1;
-      }
+    <template is="dom-if" if="[[showWarning]]">
+      <div class="warning">
+        <template is="dom-if" if="[[graphMode]]">
+          <h3>
+            No graph definition files were found.
+          </h3>
+          <p>
+            To store a graph, create a
+            <code>tf.train.SummaryWriter</code>
+            and pass the graph either via the constructor, or by calling its
+            <code>add_graph()</code> method.
+            You may want to check out the
+            <a href="https://www.tensorflow.org/versions/master/how_tos/graph_viz/index.html">
+              graph visualizer tutorial
+            </a>
+            .
+          </p>
+        </template>
+        <template is="dom-if" if="[[!graphMode]]">
+          <h3>
+            No <span>[[dataType]]</span> data was found.
+          </h3>
+          <p>
+            Probable causes:
+            </p><ul>
+              <li>
+                You haven't written any <span>[[dataType]]</span> data
+                to your event files.
+              </li>
+              <li>
+                TensorBoard can't find your event files.
+              </li>
+            </ul>
+          <p></p>
+        </template>
+        <p>
+          If you're new to using TensorBoard, and want to find out how to add
+          data and set up your event files, check out the
+          <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/README.md">
+            README
+          </a>
+          and perhaps the
+          <a href="https://www.tensorflow.org/versions/master/how_tos/summaries_and_tensorboard/index.html">
+            TensorBoard tutorial
+          </a>.
+        </p>
 
-      .tf-graph-dashboard #center {
-        background: white;
-      }
+        <p>
+          If you think TensorBoard is configured properly, please see the
+          <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">
+            section of the README devoted to missing data problems
+          </a>
+          and consider filing an issue on GitHub.
+        </p>
 
-      :host {
-        display: flex;
-        flex-direction: row;
-        height: 100%;
+      </div>
+    </template>
+    <style>
+      .warning {
+        max-width: 540px;
+        margin: 80px auto 0 auto;
       }
     </style>
   </template>
+
   <script>
     Polymer({
-      is: "tf-dashboard-layout",
+      is: "tf-no-data-warning",
+      properties: {
+        dataType: String,
+        showWarning: Boolean,
+        graphMode: {type: Boolean, computed: "_isGraph(dataType)"},
+      },
+      _isGraph: function(dataType) {
+        return dataType === "graph";
+      },
     });
   </script>
 </dom-module>
-<dom-module id="dashboard-style" assetpath="../tf-dashboard-common/">
-  <template>
-    <style>
-      .card {
-        height: 200px;
-        width: 300px;
-        display: flex;
-        flex-direction: column;
-        margin: 5px;
-        padding: 0 30px 30px 0;
-        -webkit-user-select: none;
-        -moz-user-select: none;
-        position: relative;
-      }
-
-      .card .card-title, .card .card-subtitle {
-        flex-grow: 0;
-        flex-shrink: 0;
-        font-size: 14px;
-        text-overflow: ellipsis;
-        overflow: hidden;
-      }
-
-      .card .card-subtitle {
-        font-size: 12px;
-      }
+<script>var TF;
+(function (TF) {
+    var Dashboard;
+    (function (Dashboard) {
+        /**
+         * ReloadBehavior: A simple behavior for dashboards where the
+         * frontendReload() function should find every child element with a
+         * given tag name (e.g. "tf-line-chart" or "tf-image-loader")
+         * and call a `reload` method on that child.
+         * May later extend it so it has more sophisticated logic, e.g. reloading
+         * only tags that are in view.
+         */
+        function ReloadBehavior(tagName) {
+            return {
+                properties: {
+                    reloadTag: {
+                        type: String,
+                        value: tagName,
+                    },
+                },
+                frontendReload: function () {
+                    var elements = this.getElementsByTagName(this.reloadTag);
+                    Array.prototype.forEach.call(elements, function (x) { x.reload(); });
+                },
+            };
+        }
+        Dashboard.ReloadBehavior = ReloadBehavior;
+    })(Dashboard = TF.Dashboard || (TF.Dashboard = {}));
+})(TF || (TF = {}));
+</script>
 
-      .card .card-content {
-        flex-grow: 1;
-        flex-shrink: 1;
-        display: flex;
+<dom-module id="tf-option-selector" assetpath="../tf-dashboard-common/">
+  <template>
+    <div id="wrap">
+      <h3>[[name]]</h3>
+      <div class="content-wrapper"><content></content></div>
+    </div>
+    <style>
+      .content-wrapper ::content > * {
+        width: 30%;
+        font-size: 13px;
+        background: none;
         margin-top: 10px;
-      }
-      .card .card-bottom-row {
-        position: absolute;
-        left: 50px;
-        bottom: 0;
-        padding-right: 10px;
+        color: var(--tb-ui-dark-accent);
       }
 
-      .card.selected {
-        height: 400px;
-        width: 100%;
+      .content-wrapper ::content :first-of-type {
+        margin-left: 0;
       }
 
-      .expand-button {
-        position: absolute;
-        left: 0px;
-        bottom: 0px;
-        color: #2196F3;
-        display: block;
+      .content-wrapper ::content .selected {
+        background-color: var(--tb-ui-dark-accent);
+        color: white!important;
       }
 
-      #content-container{
+      h3 {
+        color: var(--paper-grey-800);
+        margin: 0;
+        font-weight: normal;
+        font-size: 14px;
+        margin-bottom: 5px;
         display: block;
+        pointer-events: none;
       }
+    </style>
+  </template>
+  <script>
+    Polymer({
+      is: "tf-option-selector",
+      properties: {
+        name: String,
+        selectedId: {
+          type: String,
+          notify: true,
+          observer: '_selectedIdChanged'
+        }
+      },
+      attached: function() {
+        this.async(function() {
+          this.getEffectiveChildren().forEach(function(node) {
+            this.listen(node, 'tap', '_selectTarget');
+          }.bind(this));
+        });
+      },
+      _selectTarget: function(e) {
+        this.selectedId = e.currentTarget.id;
+      },
+      _selectedIdChanged: function() {
+        var selected = this.queryEffectiveChildren('#' + this.selectedId);
+        if (!selected) {
+          return;
+        }
 
-      .sidebar {
-        display: flex;
-        flex-direction: column;
-        height: 100%;
-        margin-right: 20px;
-      }
-
-      #categorizer {
-        flex-shrink: 0;
-      }
-
-      #xTypeSelector {
-        flex-shrink: 0;
-        margin: 20px 0;
-      }
-
-      #runSelector {
-        flex-shrink: 1;
-        flex-grow: 1;
-      }
-
-      .sidebar-section {
-        border-top: solid 1px rgba(0, 0, 0, 0.12);
-        padding: 20px 0px 20px 30px;
+        this.getEffectiveChildren().forEach(function(node) {
+          node.classList.remove("selected");
+        });
+        selected.classList.add("selected");
       }
+    });
+  </script>
+</dom-module>
+<dom-module id="run-color-style" assetpath="../tf-dashboard-common/">
+  <template>
+    <style>
+    [color-class="light-blue"] paper-checkbox {
+      --paper-checkbox-checked-color: var(--paper-light-blue-500);
+      --paper-checkbox-checked-ink-color: var(--paper-light-blue-500);
+      --paper-checkbox-unchecked-color: var(--paper-light-blue-900);
+      --paper-checkbox-unchecked-ink-color: var(--paper-light-blue-900);
+    }
+    [color-class="red"] paper-checkbox {
+      --paper-checkbox-checked-color: var(--paper-red-500);
+      --paper-checkbox-checked-ink-color: var(--paper-red-500);
+      --paper-checkbox-unchecked-color: var(--paper-red-900);
+      --paper-checkbox-unchecked-ink-color: var(--paper-red-900);
+    }
+    [color-class="green"] paper-checkbox {
+      --paper-checkbox-checked-color: var(--paper-green-500);
+      --paper-checkbox-checked-ink-color: var(--paper-green-500);
+      --paper-checkbox-unchecked-color: var(--paper-green-900);
+      --paper-checkbox-unchecked-ink-color: var(--paper-green-900);
+    }
+    [color-class="purple"] paper-checkbox {
+      --paper-checkbox-checked-color: var(--paper-purple-500);
+      --paper-checkbox-checked-ink-color: var(--paper-purple-500);
+      --paper-checkbox-unchecked-color: var(--paper-purple-900);
+      --paper-checkbox-unchecked-ink-color: var(--paper-purple-900);
+    }
+    [color-class="teal"] paper-checkbox {
+      --paper-checkbox-checked-color: var(--paper-teal-500);
+      --paper-checkbox-checked-ink-color: var(--paper-teal-500);
+      --paper-checkbox-unchecked-color: var(--paper-teal-900);
+      --paper-checkbox-unchecked-ink-color: var(--paper-teal-900);
+    }
+    [color-class="pink"] paper-checkbox {
+      --paper-checkbox-checked-color: var(--paper-pink-500);
+      --paper-checkbox-checked-ink-color: var(--paper-pink-500);
+      --paper-checkbox-unchecked-color: var(--paper-pink-900);
+      --paper-checkbox-unchecked-ink-color: var(--paper-pink-900);
+    }
+    [color-class="orange"] paper-checkbox {
+      --paper-checkbox-checked-color: var(--paper-orange-500);
+      --paper-checkbox-checked-ink-color: var(--paper-orange-500);
+      --paper-checkbox-unchecked-color: var(--paper-orange-900);
+      --paper-checkbox-unchecked-ink-color: var(--paper-orange-900);
+    }
+    [color-class="brown"] paper-checkbox {
+      --paper-checkbox-checked-color: var(--paper-brown-500);
+      --paper-checkbox-checked-ink-color: var(--paper-brown-500);
+      --paper-checkbox-unchecked-color: var(--paper-brown-900);
+      --paper-checkbox-unchecked-ink-color: var(--paper-brown-900);
+    }
+    [color-class="indigo"] paper-checkbox {
+      --paper-checkbox-checked-color: var(--paper-indigo-500);
+      --paper-checkbox-checked-ink-color: var(--paper-indigo-500);
+      --paper-checkbox-unchecked-color: var(--paper-indigo-900);
+      --paper-checkbox-unchecked-ink-color: var(--paper-indigo-900);
+    }
+    </style>
+  </template>
+</dom-module>
 
-      .sidebar-section:first-child {
-        border: none;
-      }
 
-      .sidebar-section:last-child {
-        flex-grow: 1;
-        display: flex;
-      }
+<dom-module id="tf-multi-checkbox" assetpath="../tf-dashboard-common/">
+  <style include="scrollbar-style"></style>
+  <style include="run-color-style"></style>
 
-      paper-checkbox {
-        --paper-checkbox-checked-color: var(--tb-ui-dark-accent);
-        --paper-checkbox-unchecked-color: var(--tb-ui-dark-accent);
+  <template>
+      <paper-input id="runs-regex" no-label-float="" label="Write a regex to filter runs" value="{{regexInput}}"></paper-input>
+    <div id="outer-container" class="scrollbar">
+      <template is="dom-repeat" items="[[namesMatchingRegex]]">
+        <div class="run-row">
+          <div class="checkbox-container vertical-align-container">
+            <paper-checkbox class="checkbox vertical-align-center" name="[[item]]" checked$="[[_isChecked(item, runToIsCheckedMapping.*)]]" on-change="_checkboxChange"></paper-checkbox>
+          </div>
+          <div class="item-label-container">
+            <span>[[item]]</span>
+          </div>
+        </div>
+      </template>
+    </div>
+  <style>
+    paper-input {
+      --paper-input-container-focus-color: var(--tb-orange-strong);
+      --paper-input-container-input: {
         font-size: 14px;
-      }
-
-    </style>
+      };
+      --paper-input-container-label: {
+        font-size: 14px;
+      };
+    }
+    :host {
+      display: flex;
+      flex-direction: column;
+      height: 100%;
+    }
+    #outer-container {
+      overflow-y: auto;
+      overflow-x: hidden;
+      width: 100%;
+      height: 0; /* Quirk to make firefox add scrolling instead of expand div */
+      flex-grow: 1;
+      flex-shrink: 1;
+      word-wrap: break-word;
+    }
+    .run-row {
+      padding-top: 5px;
+      padding-bottom: 5px;
+      display: flex;
+      flex-direction: row;
+      font-size: 13px;
+    }
+    .checkbox-container {
+      flex-grow: 0;
+      flex-shrink: 0;
+    }
+    .checkbox {
+      padding-left: 2px;
+      width: 32px;
+    }
+    .item-label-container {
+      flex-grow: 1;
+      flex-shrink: 1;
+      width: 0px; /* hack to get the flex-grow to work properly */
+    }
+    .tooltip-value-container {
+      display: flex;
+      justify-content: center;
+      flex-grow: 0;
+      flex-shrink: 0;
+      text-align:right;
+      padding-left: 2px;
+    }
+    .vertical-align-container {
+      display: flex;
+      justify-content: center;
+    }
+    .vertical-align-container .vertical-align-center {
+      align-self: center;
+    }
+    .vertical-align-container .vertical-align-top {
+      align-self: start;
+    }
+  </style>
   </template>
-</dom-module>
 
-<dom-module id="tf-downloader" assetpath="../tf-dashboard-common/">
-  <template>
-    <paper-dropdown-menu no-label-float="true" label="run to download" selected-item-label="{{_run}}">
-      <paper-menu class="dropdown-content">
-        <template is="dom-repeat" items="[[_runs]]">
-          <paper-item no-label-float="true">[[item]]</paper-item>
-        </template>
-      </paper-menu>
-    </paper-dropdown-menu>
-    <a download="[[_csvName(_run)]]" href="[[_csvUrl(_run, urlFn)]]">CSV</a>
-    <a download="[[_jsonName(_run)]]" href="[[_jsonUrl(_run, urlFn)]]">JSON</a>
-    <style>
-      :host {
-        display: block;
-      }
-      paper-dropdown-menu {
-        width: 180px;
-        --paper-input-container-label: {
-          font-size: 10px;
-        }
-        --paper-input-container-input: {
-          font-size: 10px;
-        }
-      }
-      a {
-        font-size: 10px;
-        border-radius: 3px;
-        border: 1px solid #EEE;
-      }
-      paper-input {
-        font-size: 22px;
-      }
-    </style>
-  </template>
   <script>
-    Polymer({
-      is: "tf-downloader",
-      properties: {
-        _run: String,
-        _runs: {
-          type: Array,
-          computed: "_computeRuns(runToTag.*, selectedRuns.*)",
-        },
-        selectedRuns: Array,
-        runToTag: Object,
-        tag: String,
-        urlFn: Function,
-      },
-      _computeRuns: function(runToTagChange, selectedRunsChange) {
-        var runToTag = this.runToTag;
-        var tag = this.tag;
-        return this.selectedRuns.filter(function(x) {
-          return runToTag[x].indexOf(tag) !== -1;
-        })
-      },
-      _csvUrl: function(_run, urlFn) {
-        return urlFn(this.tag, _run) + "&format=csv";
-      },
-      _jsonUrl: function(_run, urlFn) {
-        return urlFn(this.tag, _run);
-      },
-      _csvName: function(_run) {
-        return "run_" + _run + ",tag_" + this.tag + ".csv";
+  Polymer({
+    is: "tf-multi-checkbox",
+    properties: {
+      names: Array, // All the runs in consideration
+
+      regexInput: {
+        type: String,
+        value: TF.URIStorage.getStringInitializer("regexInput", ""),
+        observer: "_regexInputObserver"
+      }, // Regex for filtering the runs
+      regex: {
+        type: Object,
+        computed: "makeRegex(regexInput)"
       },
-      _jsonName: function(_run) {
-        return "run-" + _run + "-tag-" + this.tag + ".json";
+      namesMatchingRegex: {
+        type: Array,
+        computed: "computeNamesMatchingRegex(names.*, regex)"
+      }, // Runs that match the regex
+      runToIsCheckedMapping: {
+        type: Object,
+        value: function() {return {};}
+      }, // run name -> Boolean (if its enabled)
+      // (Allows state to persist across regex filtering)
+      outSelected: {
+        type: Array,
+        notify: true,
+        computed: 'computeOutSelected(namesMatchingRegex.*, runToIsCheckedMapping.*)'
       },
-    });
+      colorScale: {
+        type: Object,
+        observer: "synchronizeColors",
+      }, // map from run name to css class
+    },
+    listeners: {
+      'dom-change': 'synchronizeColors',
+    },
+    observers: [
+      "_initializeRunToIsCheckedMapping(names.*)",
+    ],
+    makeRegex: function(regex) {
+      try {
+        return new RegExp(regex)
+      } catch (e) {
+        return null;
+      }
+    },
+    _initializeRunToIsCheckedMapping: function(change) {
+      var runToIsCheckedMapping = _.clone(this.runToIsCheckedMapping);
+
+      this.names.forEach(function(n) {
+        if (runToIsCheckedMapping[n] == null) {
+          // runs default to on
+          runToIsCheckedMapping[n] = true;
+        }
+      });
+      this.runToIsCheckedMapping = runToIsCheckedMapping;
+    },
+    computeNamesMatchingRegex: function(__, ___) {
+      var regex = this.regex;
+      return this.names.filter(function(n) {
+        return regex == null || regex.test(n);
+      });
+    },
+    computeOutSelected: function(__, ___) {
+      var runToIsCheckedMapping = this.runToIsCheckedMapping;
+      return this.namesMatchingRegex.filter(function(n) {
+        return runToIsCheckedMapping[n];
+      });
+    },
+    synchronizeColors: function(e) {
+      if (!this.colorScale) return;
+
+      var checkboxes = Array.prototype.slice.call(this.querySelectorAll("paper-checkbox"));
+      var scale = this.colorScale;
+      checkboxes.forEach(function(p) {
+        var color = scale.scale(p.name);
+        p.customStyle['--paper-checkbox-checked-color'] = color;
+        p.customStyle['--paper-checkbox-checked-ink-color'] = color;
+        p.customStyle['--paper-checkbox-unchecked-color'] = color;
+        p.customStyle['--paper-checkbox-unchecked-ink-color'] = color;
+      });
+      this.updateStyles();
+      // The updateStyles call fails silently if the browser doesn't have focus,
+      // e.g. if TensorBoard was opened into a new tab that isn't visible.
+      // As a workaround... we know requestAnimationFrame won't fire until the
+      // page has focus, so updateStyles again on requestAnimationFrame.
+      var _this = this;
+      window.requestAnimationFrame(function() {_this.updateStyles();});
+    },
+    _checkboxChange: function(e) {
+      var target = e.srcElement || e.target; // Firefox doesn't have srcElement.
+      var name = target.name;
+      var checked = target.checked;
+      this.runToIsCheckedMapping[name] = checked;
+      // n.b. notifyPath won't work because run names may have periods.
+      this.runToIsCheckedMapping = _.clone(this.runToIsCheckedMapping);
+    },
+    _isChecked: function(item, outSelectedChange) {
+      return this.runToIsCheckedMapping[item];
+    },
+    _initializeRuns: function(change) {
+      this.outSelected = change.base.slice();
+    },
+    _regexInputObserver: TF.URIStorage.getStringObserver("regexInput", ""),
+    toggleAll: function() {
+      var _this = this;
+      var allOn = this.namesMatchingRegex
+                    .filter(function(n) {return !_this.runToIsCheckedMapping[n]})
+                    .length === 0;
+      this.namesMatchingRegex.forEach(function(n) {_this.runToIsCheckedMapping[n] = !allOn});
+      this.runToIsCheckedMapping = _.clone(this.runToIsCheckedMapping);
+    },
+  });
   </script>
 </dom-module>
 
-<dom-module id="tf-no-data-warning" assetpath="../tf-dashboard-common/">
+<dom-module id="tf-run-selector" assetpath="../tf-dashboard-common/">
   <template>
-    <template is="dom-if" if="[[showWarning]]">
-      <div class="warning">
-        <template is="dom-if" if="[[graphMode]]">
-          <h3>
-            No graph definition files were found.
-          </h3>
-          <p>
-            To store a graph, create a
-            <code>tf.train.SummaryWriter</code>
-            and pass the graph either via the constructor, or by calling its
-            <code>add_graph()</code> method.
-            You may want to check out the
-            <a href="https://www.tensorflow.org/versions/master/how_tos/graph_viz/index.html">
-              graph visualizer tutorial
-            </a>
-            .
-          </p>
-        </template>
-        <template is="dom-if" if="[[!graphMode]]">
-          <h3>
-            No <span>[[dataType]]</span> data was found.
-          </h3>
-          <p>
-            Probable causes:
-            </p><ul>
-              <li>
-                You haven't written any <span>[[dataType]]</span> data
-                to your event files.
-              </li>
-              <li>
-                TensorBoard can't find your event files.
-              </li>
-            </ul>
-          <p></p>
-        </template>
-        <p>
-          If you're new to using TensorBoard, and want to find out how to add
-          data and set up your event files, check out the
-          <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/README.md">
-            README
-          </a>
-          and perhaps the
-          <a href="https://www.tensorflow.org/versions/master/how_tos/summaries_and_tensorboard/index.html">
-            TensorBoard tutorial
-          </a>.
-        </p>
-
-        <p>
-          If you think TensorBoard is configured properly, please see the
-          <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tensorboard/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">
-            section of the README devoted to missing data problems
-          </a>
-          and consider filing an issue on GitHub.
-        </p>
-
-      </div>
-    </template>
+    <div id="top-text">
+      <h3 id="tooltip-help" class="tooltip-container">
+        Runs
+      </h3>
+    </div>
+    <tf-multi-checkbox id="multiCheckbox" names="[[runs]]" out-selected="{{outSelected}}" color-scale="[[colorScale]]"></tf-multi-checkbox>
+    <paper-button class="x-button" id="toggle-all" on-tap="_toggleAll">
+    Toggle All Runs
+    </paper-button>
     <style>
-      .warning {
-        max-width: 540px;
-        margin: 80px auto 0 auto;
+      :host {
+        display: flex;
+        flex-direction: column;
+        padding-bottom: 10px;
+        box-sizing: border-box;
+      }
+      #top-text {
+        width: 100%;
+        flex-grow: 0;
+        flex-shrink: 0;
+        padding-right: 16px;
+        padding-bottom: 6px;
+        box-sizing: border-box;
+        color: var(--paper-grey-800);
+      }
+      tf-multi-checkbox {
+        display: flex;
+        flex-grow: 1;
+        flex-shrink: 1;
+      }
+      .x-button {
+        font-size: 13px;
+        background-color: var(--tb-ui-light-accent);
+        margin-top: 5px;
+        color: var(--tb-ui-dark-accent);
+      }
+      #tooltip-help {
+        color: var(--paper-grey-800);
+        margin: 0;
+        font-weight: normal;
+        font-size: 14px;
+        margin-bottom: 5px;
+      }
+      paper-button {
+        margin-left: 0;
       }
     </style>
   </template>
-
   <script>
-    Polymer({
-      is: "tf-no-data-warning",
-      properties: {
-        dataType: String,
-        showWarning: Boolean,
-        graphMode: {type: Boolean, computed: "_isGraph(dataType)"},
-      },
-      _isGraph: function(dataType) {
-        return dataType === "graph";
-      },
-    });
+  Polymer({
+    is: "tf-run-selector",
+    properties: {
+      outSelected: {type: Array, notify: true},
+      // runs: an array of strings, representing the run names that may be chosen
+      runs: Array,
+      colorScale: Object, // TF.ColorScale
+    },
+    _toggleAll: function() {
+      this.$.multiCheckbox.toggleAll();
+    },
+  });
   </script>
 </dom-module>
-<script>var TF;
-(function (TF) {
-    var Dashboard;
-    (function (Dashboard) {
-        /**
-         * ReloadBehavior: A simple behavior for dashboards where the
-         * frontendReload() function should find every child element with a
-         * given tag name (e.g. "tf-line-chart" or "tf-image-loader")
-         * and call a `reload` method on that child.
-         * May later extend it so it has more sophisticated logic, e.g. reloading
-         * only tags that are in view.
-         */
-        function ReloadBehavior(tagName) {
-            return {
-                properties: {
-                    reloadTag: {
-                        type: String,
-                        value: tagName,
-                    },
-                },
-                frontendReload: function () {
-                    var elements = this.getElementsByTagName(this.reloadTag);
-                    Array.prototype.forEach.call(elements, function (x) { x.reload(); });
-                },
-            };
-        }
-        Dashboard.ReloadBehavior = ReloadBehavior;
-    })(Dashboard = TF.Dashboard || (TF.Dashboard = {}));
-})(TF || (TF = {}));
-</script>
-<script>/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
 
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-var __extends = (this && this.__extends) || function (d, b) {
-    for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p];
-    function __() { this.constructor = d; }
-    d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
-};
-var TF;
-(function (TF) {
-    var Backend;
-    (function (Backend) {
-        /**
-         * Manages many fetch requests. Launches up to nSimultaneousRequests
-         * simultaneously, and maintains a LIFO queue of requests to process when
-         * more urls are requested than can be handled at once. The queue can be
-         * cleared.
-         *
-         * When a request is made, a Promise is returned which resolves with the
-         * parsed JSON result from the request.
-         */
-        var RequestCancellationError = (function (_super) {
-            __extends(RequestCancellationError, _super);
-            function RequestCancellationError() {
-                _super.apply(this, arguments);
-                this.name = 'RequestCancellationError';
-            }
-            return RequestCancellationError;
-        }(Error));
-        Backend.RequestCancellationError = RequestCancellationError;
-        var RequestNetworkError = (function (_super) {
-            __extends(RequestNetworkError, _super);
-            function RequestNetworkError(req, url) {
-                _super.call(this);
-                this.message = "RequestNetworkError: " + req.status + " at " + url;
-                this.name = 'RequestNetworkError';
-                this.req = req;
-                this.url = url;
-            }
-            return RequestNetworkError;
-        }(Error));
-        Backend.RequestNetworkError = RequestNetworkError;
-        var RequestManager = (function () {
-            function RequestManager(nSimultaneousRequests, maxRetries) {
-                if (nSimultaneousRequests === void 0) { nSimultaneousRequests = 10; }
-                if (maxRetries === void 0) { maxRetries = 3; }
-                this._queue = [];
-                this._nActiveRequests = 0;
-                this._nSimultaneousRequests = nSimultaneousRequests;
-                this._maxRetries = maxRetries;
-            }
-            /* Gives a promise that loads assets from given url (respects queuing) */
-            RequestManager.prototype.request = function (url) {
-                var _this = this;
-                var promise = new Promise(function (resolve, reject) {
-                    var resolver = { resolve: resolve, reject: reject };
-                    _this._queue.push(resolver);
-                    _this.launchRequests();
-                })
-                    .then(function () {
-                    return _this.promiseWithRetries(url, _this._maxRetries);
-                })
-                    .then(function (response) {
-                    // Success - Let's free space for another active
-                    // reqest, and launch it
-                    _this._nActiveRequests--;
-                    _this.launchRequests();
-                    return response;
-                }, function (rejection) {
-                    if (rejection.name === 'RequestNetworkError') {
-                        // If we failed due to network error, we should
-                        // decrement
-                        // _nActiveRequests because this request was
-                        // active
-                        _this._nActiveRequests--;
-                        _this.launchRequests();
-                    }
-                    return Promise.reject(rejection);
-                });
-                return promise;
-            };
-            RequestManager.prototype.clearQueue = function () {
-                while (this._queue.length > 0) {
-                    this._queue.pop().reject(new RequestCancellationError('Request cancelled by clearQueue'));
-                }
-            };
-            /* Return number of currently pending requests */
-            RequestManager.prototype.activeRequests = function () {
-                return this._nActiveRequests;
-            };
-            /* Return total number of outstanding requests (includes queue) */
-            RequestManager.prototype.outstandingRequests = function () {
-                return this._nActiveRequests + this._queue.length;
-            };
-            RequestManager.prototype.launchRequests = function () {
-                while (this._nActiveRequests < this._nSimultaneousRequests &&
-                    this._queue.length > 0) {
-                    this._nActiveRequests++;
-                    this._queue.pop().resolve();
-                }
-            };
-            /**
-             * Try to request a given URL using overwritable _promiseFromUrl method.
-             * If the request fails for any reason, we will retry up to maxRetries
-             * times. In practice, this will help us paper over transient network issues
-             * like '502 Bad Gateway'.
-             * By default, Chrome displays network errors in console, so
-             * the user will be able to tell when the requests are failing. I think this
-             * is a feature, if the request failures and retries are causing any
-             * pain to users, they can see it and file issues.
-             */
-            RequestManager.prototype.promiseWithRetries = function (url, maxRetries) {
-                var _this = this;
-                var success = function (x) { return x; };
-                var failure = function (x) {
-                    if (maxRetries > 0) {
-                        return _this.promiseWithRetries(url, maxRetries - 1);
-                    }
-                    else {
-                        return Promise.reject(x);
-                    }
-                };
-                return this._promiseFromUrl(url).then(success, failure);
-            };
-            /* Actually get promise from url using XMLHttpRequest */
-            RequestManager.prototype._promiseFromUrl = function (url) {
-                return new Promise(function (resolve, reject) {
-                    var req = new XMLHttpRequest();
-                    req.open('GET', url);
-                    req.onload = function () {
-                        if (req.status === 200) {
-                            resolve(JSON.parse(req.responseText));
-                        }
-                        else {
-                            reject(new RequestNetworkError(req, url));
-                        }
-                    };
-                    req.onerror = function () {
-                        reject(new RequestNetworkError(req, url));
-                    };
-                    req.send();
-                });
-            };
-            return RequestManager;
-        }());
-        Backend.RequestManager = RequestManager;
-    })(Backend = TF.Backend || (TF.Backend = {}));
-})(TF || (TF = {}));
-</script>
-<script>/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+<dom-module id="vz-line-chart" assetpath="../vz-line-chart/">
+  <template>
+    <div id="tooltip">
+      <table>
+        <thead>
+          <tr>
+            <th></th>
+            <th>Name</th>
+            <template is="dom-if" if="{{smoothingEnabled}}">
+              <th>Smoothed</th>
+            </template>
+            <th>Value</th>
+            <th>Step</th>
+            <th>Time</th>
+            <th>Relative</th>
+          </tr>
+        </thead>
+        <tbody>
+        </tbody>
+      </table>
+    </div>
+    <svg id="chartsvg"></svg>
+    <style>
+      :host {
+        -webkit-user-select: none;
+        -moz-user-select: none;
+        display: flex;
+        flex-direction: column;
+        flex-grow: 1;
+        flex-shrink: 1;
+        position: relative;
+      }
+      svg {
+        -webkit-user-select: none;
+        -moz-user-select: none;
+        flex-grow: 1;
+        flex-shrink: 1;
+      }
+      td {
+        padding-left: 5px;
+        padding-right: 5px;
+        font-size: 13px;
+        opacity: 1;
+      }
+      #tooltip {
+        pointer-events: none;
+        position: absolute;
+        opacity: 0;
+        box-shadow: 0 1px 4px rgba(0, 0, 0, 0.3);
+        font-size: 14px;
+        background: rgba(0, 0, 0, 0.8);
+        color: white;
+        border-radius: 4px;
+        line-height: 1.4em;
+        padding: 8px;
+        z-index: 5;
+        cursor: none;
+      }
+      .swatch {
+        border-radius: 50%;
+        width: 14px;
+        height: 14px;
+        display: block;
+        border: 2px solid rgba(0,0,0,0);
+      }
+      .closest .swatch {
+        border: 2px solid white;
+      }
+      th {
+        padding-left: 5px;
+        padding-right: 5px;
+        text-align: left;
+      }
+      .distant td {
+        opacity: 0.8;
+      }
 
-Licensed under the Apache License, Version 2.0 (the 'License');
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
+      .distant td.swatch {
+        opacity: 1;
+      }
 
-http://www.apache.org/licenses/LICENSE-2.0
+      .ghost {
+        opacity: 0.2;
+        stroke-width: 1px;
+      }
 
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an 'AS IS' BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-var TF;
-(function (TF) {
-    var Backend;
-    (function (Backend) {
-        Backend.BAD_CHARACTERS = '#%&{}\\/<>*? $!\'":@+`|=() ';
-        /** Cleanup a url so that it can be loaded from a filesystem. */
-        function demoify(s) {
-            // for consistency with python's urllib.urlencode
-            s = s.replace(new RegExp('%20', 'g'), '+');
-            for (var i = 0; i < Backend.BAD_CHARACTERS.length; i++) {
-                var c = Backend.BAD_CHARACTERS[i];
-                s = s.replace(new RegExp('\\' + c, 'g'), '_');
-            }
-            return s;
-        }
-        Backend.demoify = demoify;
-        function queryEncoder(params) {
-            // It's important that the keys be sorted, so we always grab the right file
-            // if we are talking to the backend generated by serialze_tensorboard.py
-            if (params == null) {
-                return '';
-            }
-            var components = _.keys(params)
-                .sort()
-                .filter(function (k) { return params[k] !== undefined; })
-                .map(function (k) { return k + '=' + encodeURIComponent(params[k]); });
-            var result = components.length ? '?' + components.join('&') : '';
-            // Replace parens for consistency with urllib.urlencode
-            return result.replace(/\(/g, '%28').replace(/\)/g, '%29');
-        }
-        Backend.queryEncoder = queryEncoder;
-    })(Backend = TF.Backend || (TF.Backend = {}));
-})(TF || (TF = {}));
-</script>
-<script>/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+    </style>
+  </template>
+  <script>/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the 'License');
 you may not use this file except in compliance with the License.
@@ -3135,566 +2831,955 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
-var TF;
-(function (TF) {
-    var Backend;
-    (function (Backend) {
-        ;
+var __extends = (this && this.__extends) || function (d, b) {
+    for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p];
+    function __() { this.constructor = d; }
+    d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+};
+var Plottable;
+(function (Plottable) {
+    var DragZoomLayer = (function (_super) {
+        __extends(DragZoomLayer, _super);
         /**
-         * The standard router for communicating with the TensorBoard backend
-         * @param dataDir {string} The base prefix for finding data on server.
-         * @param demoMode {boolean} Whether to modify urls for filesystem demo usage.
+         * Constructs a SelectionBoxLayer with an attached DragInteraction and
+         * ClickInteraction. On drag, it triggers an animated zoom into the box
+         * that was dragged. On double click, it zooms back out to the original
+         * view, before any zooming.
+         * The zoom animation uses an easing function (default
+         * d3.ease('cubic-in-out')) and is customizable.
+         * Usage: Construct the selection box layer and attach x and y scales,
+         * and then add the layer over the plot you are zooming on using a
+         * Component Group.
+         * TODO(danmane) - merge this into Plottable
          */
-        function router(dataDir, demoMode) {
-            if (dataDir === void 0) { dataDir = '/data'; }
-            if (demoMode === void 0) { demoMode = false; }
-            var clean = demoMode ? Backend.demoify : function (x) { return x; };
-            if (dataDir[dataDir.length - 1] === '/') {
-                dataDir = dataDir.slice(0, dataDir.length - 1);
+        function DragZoomLayer(xScale, yScale) {
+            _super.call(this);
+            this.isZoomed = false;
+            this.easeFn = d3.ease('cubic-in-out');
+            this._animationTime = 750;
+            this.xScale(xScale);
+            this.yScale(yScale);
+            this._dragInteraction = new Plottable.Interactions.Drag();
+            this._dragInteraction.attachTo(this);
+            this._doubleClickInteraction = new Plottable.Interactions.DoubleClick();
+            this._doubleClickInteraction.attachTo(this);
+            this.setupCallbacks();
+        }
+        /**
+         * Register a method that calls when the DragZoom interaction starts.
+         */
+        DragZoomLayer.prototype.interactionStart = function (cb) { this.onStart = cb; };
+        /**
+         * Register a method that calls when the DragZoom interaction ends.
+         */
+        DragZoomLayer.prototype.interactionEnd = function (cb) { this.onEnd = cb; };
+        DragZoomLayer.prototype.setupCallbacks = function () {
+            var _this = this;
+            var dragging = false;
+            this._dragInteraction.onDragStart(function (startPoint) {
+                _this.bounds({
+                    topLeft: startPoint,
+                    bottomRight: startPoint,
+                });
+                _this.onStart();
+            });
+            this._dragInteraction.onDrag(function (startPoint, endPoint) {
+                _this.bounds({ topLeft: startPoint, bottomRight: endPoint });
+                _this.boxVisible(true);
+                dragging = true;
+            });
+            this._dragInteraction.onDragEnd(function (startPoint, endPoint) {
+                _this.boxVisible(false);
+                _this.bounds({ topLeft: startPoint, bottomRight: endPoint });
+                if (dragging) {
+                    _this.zoom();
+                }
+                else {
+                    _this.onEnd();
+                }
+                dragging = false;
+            });
+            this._doubleClickInteraction.onDoubleClick(this.unzoom.bind(this));
+        };
+        DragZoomLayer.prototype.animationTime = function (animationTime) {
+            if (animationTime == null) {
+                return this._animationTime;
             }
-            function standardRoute(route, demoExtension) {
-                if (demoExtension === void 0) { demoExtension = '.json'; }
-                return function (tag, run) {
-                    var url = dataDir + '/' + route + clean(Backend.queryEncoder({ tag: tag, run: run }));
-                    if (demoMode) {
-                        url += demoExtension;
-                    }
-                    return url;
-                };
+            if (animationTime < 0) {
+                throw new Error('animationTime cannot be negative');
             }
-            function individualImageUrl(query) {
-                var url = dataDir + '/' + clean('individualImage?' + query);
-                if (demoMode) {
-                    url += '.png';
-                }
-                return url;
+            this._animationTime = animationTime;
+            return this;
+        };
+        /**
+         * Set the easing function, which determines how the zoom interpolates
+         * over time.
+         */
+        DragZoomLayer.prototype.ease = function (fn) {
+            if (typeof (fn) !== 'function') {
+                throw new Error('ease function must be a function');
             }
-            function individualAudioUrl(query) {
-                var url = dataDir + '/' + clean('individualAudio?' + query);
-                if (demoMode) {
-                    url += '.wav';
-                }
-                return url;
+            if (fn(0) !== 0 || fn(1) !== 1) {
+                Plottable.Utils.Window.warn('Easing function does not maintain invariant ' +
+                    'f(0)==0 && f(1)==1. Bad behavior may result.');
+            }
+            this.easeFn = fn;
+            return this;
+        };
+        // Zoom into extent of the selection box bounds
+        DragZoomLayer.prototype.zoom = function () {
+            var x0 = this.xExtent()[0].valueOf();
+            var x1 = this.xExtent()[1].valueOf();
+            var y0 = this.yExtent()[1].valueOf();
+            var y1 = this.yExtent()[0].valueOf();
+            if (x0 === x1 || y0 === y1) {
+                return;
+            }
+            if (!this.isZoomed) {
+                this.isZoomed = true;
+            }
+            this.interpolateZoom(x0, x1, y0, y1);
+        };
+        // Restore the scales to their state before any zoom
+        DragZoomLayer.prototype.unzoom = function () {
+            if (!this.isZoomed) {
+                return;
             }
-            function graphUrl(run, limit_attr_size, large_attrs_key) {
-                var query_params = [['run', clean(run)]];
-                if (limit_attr_size != null && !demoMode) {
-                    query_params.push(['limit_attr_size', String(limit_attr_size)]);
-                }
-                if (large_attrs_key != null && !demoMode) {
-                    query_params.push(['large_attrs_key', large_attrs_key]);
+            this.isZoomed = false;
+            // Some Plottable magic follows which ensures that when we un-zoom, we
+            // un-zoom to the current extent of the data; i.e. if new data was loaded
+            // since we zoomed, we should un-zoom to the extent of the new data.
+            // this basically replicates the autoDomain logic in Plottable.
+            // it uses the internal methods to get the same boundaries that autoDomain
+            // would, but allows us to interpolate the zoom with a nice animation.
+            var xScale = this.xScale();
+            var yScale = this.yScale();
+            xScale._domainMin = null;
+            xScale._domainMax = null;
+            yScale._domainMin = null;
+            yScale._domainMax = null;
+            var xDomain = xScale._getExtent();
+            var yDomain = yScale._getExtent();
+            this.interpolateZoom(xDomain[0], xDomain[1], yDomain[0], yDomain[1]);
+        };
+        // If we are zooming, disable interactions, to avoid contention
+        DragZoomLayer.prototype.isZooming = function (isZooming) {
+            this._dragInteraction.enabled(!isZooming);
+            this._doubleClickInteraction.enabled(!isZooming);
+        };
+        DragZoomLayer.prototype.interpolateZoom = function (x0f, x1f, y0f, y1f) {
+            var _this = this;
+            var x0s = this.xScale().domain()[0].valueOf();
+            var x1s = this.xScale().domain()[1].valueOf();
+            var y0s = this.yScale().domain()[0].valueOf();
+            var y1s = this.yScale().domain()[1].valueOf();
+            // Copy a ref to the ease fn, so that changing ease wont affect zooms in
+            // progress.
+            var ease = this.easeFn;
+            var interpolator = function (a, b, p) {
+                return d3.interpolateNumber(a, b)(ease(p));
+            };
+            this.isZooming(true);
+            var start = Date.now();
+            var draw = function () {
+                var now = Date.now();
+                var passed = now - start;
+                var p = _this._animationTime === 0 ?
+                    1 :
+                    Math.min(1, passed / _this._animationTime);
+                var x0 = interpolator(x0s, x0f, p);
+                var x1 = interpolator(x1s, x1f, p);
+                var y0 = interpolator(y0s, y0f, p);
+                var y1 = interpolator(y1s, y1f, p);
+                _this.xScale().domain([x0, x1]);
+                _this.yScale().domain([y0, y1]);
+                if (p < 1) {
+                    Plottable.Utils.DOM.requestAnimationFramePolyfill(draw);
                 }
-                var query = query_params
-                    .map(function (param) {
-                    return param[0] + '=' + encodeURIComponent(param[1]);
-                })
-                    .join('&');
-                var url = dataDir + '/graph' + clean('?' + query);
-                if (demoMode) {
-                    url += '.pbtxt';
+                else {
+                    _this.onEnd();
+                    _this.isZooming(false);
                 }
-                return url;
-            }
-            return {
-                runs: function () { return dataDir + '/runs' + (demoMode ? '.json' : ''); },
-                individualImage: individualImageUrl,
-                individualAudio: individualAudioUrl,
-                graph: graphUrl,
-                scalars: standardRoute('scalars'),
-                histograms: standardRoute('histograms'),
-                compressedHistograms: standardRoute('compressedHistograms'),
-                images: standardRoute('images'),
-                audio: standardRoute('audio'),
-                runMetadata: standardRoute('run_metadata', '.pbtxt'),
             };
-        }
-        Backend.router = router;
-        ;
-    })(Backend = TF.Backend || (TF.Backend = {}));
-})(TF || (TF = {}));
+            draw();
+        };
+        return DragZoomLayer;
+    }(Plottable.Components.SelectionBoxLayer));
+    Plottable.DragZoomLayer = DragZoomLayer;
+})(Plottable || (Plottable = {}));
 </script>
-<script>/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+  <script>/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-var TF;
-(function (TF) {
-    var Backend;
-    (function (Backend_1) {
-        Backend_1.TYPES = [
-            'scalar', 'histogram', 'compressedHistogram', 'graph', 'image', 'audio',
-            'runMetadata'
-        ];
-        /**
-         * The Backend class provides a convenient and typed interface to the backend.
-         *
-         * It provides methods corresponding to the different data sources on the
-         * TensorBoard backend. These methods return a promise containing the data
-         * from the backend. This class does some post-processing on the data; for
-         * example, converting data elements tuples into js objects so that they can
-         * be accessed in a more convenient and clearly-documented fashion.
-         */
-        var Backend = (function () {
-            /**
-             * Construct a Backend instance.
-             * @param router the Router with info on what urls to get data from
-             * @param requestManager The RequestManager, overwritable so you may
-             * manually clear request queue, etc. Defaults to a new RequestManager.
-             */
-            function Backend(r, requestManager) {
-                this.router = r;
-                this.requestManager = requestManager || new Backend_1.RequestManager();
-            }
-            /**
-             * Returns a listing of all the available data in the TensorBoard backend.
-             */
-            Backend.prototype.runs = function () {
-                return this.requestManager.request(this.router.runs());
-            };
-            /**
-             * Return a promise showing the Run-to-Tag mapping for scalar data.
-             */
-            Backend.prototype.scalarRuns = function () {
-                return this.runs().then(function (x) { return _.mapValues(x, 'scalars'); });
-            };
-            /**
-             * Return a promise showing the Run-to-Tag mapping for histogram data.
-             */
-            Backend.prototype.histogramRuns = function () {
-                return this.runs().then(function (x) { return _.mapValues(x, 'histograms'); });
-            };
-            /**
-             * Return a promise showing the Run-to-Tag mapping for image data.
-             */
-            Backend.prototype.imageRuns = function () {
-                return this.runs().then(function (x) { return _.mapValues(x, 'images'); });
-            };
-            /**
-             * Return a promise showing the Run-to-Tag mapping for audio data.
-             */
-            Backend.prototype.audioRuns = function () {
-                return this.runs().then(function (x) { return _.mapValues(x, 'audio'); });
-            };
-            /**
-             * Return a promise showing the Run-to-Tag mapping for compressedHistogram
-             * data.
-             */
-            Backend.prototype.compressedHistogramRuns = function () {
-                return this.runs().then(function (x) { return _.mapValues(x, 'compressedHistograms'); });
-            };
-            /**
-             * Return a promise showing list of runs that contain graphs.
-             */
-            Backend.prototype.graphRuns = function () {
-                return this.runs().then(function (x) { return _.keys(x).filter(function (k) { return x[k].graph; }); });
-            };
-            /**
-             * Return a promise showing the Run-to-Tag mapping for run_metadata objects.
-             */
-            Backend.prototype.runMetadataRuns = function () {
-                return this.runs().then(function (x) { return _.mapValues(x, 'run_metadata'); });
-            };
-            /**
-             * Return a promise of a graph string from the backend.
-             */
-            Backend.prototype.graph = function (tag, limit_attr_size, large_attrs_key) {
-                var url = this.router.graph(tag, limit_attr_size, large_attrs_key);
-                return this.requestManager.request(url);
-            };
-            /**
-             * Return a promise containing ScalarDatums for given run and tag.
-             */
-            Backend.prototype.scalar = function (tag, run) {
-                var p;
-                var url = this.router.scalars(tag, run);
-                p = this.requestManager.request(url);
-                return p.then(map(detupler(createScalar)));
-            };
-            /**
-             * Return a promise containing HistogramDatums for given run and tag.
-             */
-            Backend.prototype.histogram = function (tag, run) {
-                var p;
-                var url = this.router.histograms(tag, run);
-                p = this.requestManager.request(url);
-                return p.then(map(detupler(createHistogram))).then(function (histos) {
-                    // Get the minimum and maximum values across all histograms so that the
-                    // visualization is aligned for all timesteps.
-                    var min = d3.min(histos, function (d) { return d.min; });
-                    var max = d3.max(histos, function (d) { return d.max; });
-                    return histos.map(function (histo, i) {
-                        return {
-                            wall_time: histo.wall_time,
-                            step: histo.step,
-                            bins: convertBins(histo, min, max)
-                        };
-                    });
-                });
-            };
-            /**
-             * Return a promise containing ImageDatums for given run and tag.
-             */
-            Backend.prototype.image = function (tag, run) {
-                var url = this.router.images(tag, run);
-                var p;
-                p = this.requestManager.request(url);
-                return p.then(map(this.createImage.bind(this)));
-            };
-            /**
-             * Return a promise containing AudioDatums for given run and tag.
-             */
-            Backend.prototype.audio = function (tag, run) {
-                var url = this.router.audio(tag, run);
-                var p;
-                p = this.requestManager.request(url);
-                return p.then(map(this.createAudio.bind(this)));
-            };
-            /**
-             * Returns a promise to load the string RunMetadata for given run/tag.
-             */
-            Backend.prototype.runMetadata = function (tag, run) {
-                var url = this.router.runMetadata(tag, run);
-                return this.requestManager.request(url);
-            };
-            /**
-             * Get compressedHistogram data.
-             * Unlike other methods, don't bother reprocessing this data into a nicer
-             * format. This is because we will deprecate this route.
-             */
-            Backend.prototype.compressedHistogram = function (tag, run) {
-                var url = this.router.compressedHistograms(tag, run);
-                var p;
-                p = this.requestManager.request(url);
-                return p.then(map(detupler(function (x) { return x; })));
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+/* tslint:disable:no-namespace variable-name */
+var VZ;
+(function (VZ) {
+    var LineChart = (function () {
+        function LineChart(xType, colorScale, tooltip) {
+            this.seriesNames = [];
+            this.name2datasets = {};
+            this.colorScale = colorScale;
+            this.tooltip = tooltip;
+            this.datasets = [];
+            // lastPointDataset is a dataset that contains just the last point of
+            // every dataset we're currently drawing.
+            this.lastPointsDataset = new Plottable.Dataset();
+            this.nanDataset = new Plottable.Dataset();
+            // need to do a single bind, so we can deregister the callback from
+            // old Plottable.Datasets. (Deregistration is done by identity checks.)
+            this.onDatasetChanged = this._onDatasetChanged.bind(this);
+            this.buildChart(xType);
+        }
+        LineChart.prototype.buildChart = function (xType) {
+            if (this.outer) {
+                this.outer.destroy();
+            }
+            var xComponents = VZ.ChartHelpers.getXComponents(xType);
+            this.xAccessor = xComponents.accessor;
+            this.xScale = xComponents.scale;
+            this.xAxis = xComponents.axis;
+            this.xAxis.margin(0).tickLabelPadding(3);
+            this.yScale = new Plottable.Scales.Linear();
+            this.yAxis = new Plottable.Axes.Numeric(this.yScale, 'left');
+            var yFormatter = VZ.ChartHelpers.multiscaleFormatter(VZ.ChartHelpers.Y_AXIS_FORMATTER_PRECISION);
+            this.yAxis.margin(0).tickLabelPadding(5).formatter(yFormatter);
+            this.yAxis.usesTextWidthApproximation(true);
+            this.dzl = new Plottable.DragZoomLayer(this.xScale, this.yScale);
+            var center = this.buildPlot(this.xAccessor, this.xScale, this.yScale);
+            this.gridlines =
+                new Plottable.Components.Gridlines(this.xScale, this.yScale);
+            this.center =
+                new Plottable.Components.Group([this.gridlines, center, this.dzl]);
+            this.outer = new Plottable.Components.Table([
+                [this.yAxis, this.center],
+                [null, this.xAxis]
+            ]);
+        };
+        LineChart.prototype.buildPlot = function (xAccessor, xScale, yScale) {
+            var _this = this;
+            this.scalarAccessor = function (d) { return d.scalar; };
+            this.smoothedAccessor = function (d) { return d.smoothed; };
+            var linePlot = new Plottable.Plots.Line();
+            linePlot.x(xAccessor, xScale);
+            linePlot.y(this.scalarAccessor, yScale);
+            linePlot.attr('stroke', function (d, i, dataset) {
+                return _this.colorScale.scale(dataset.metadata().name);
+            });
+            this.linePlot = linePlot;
+            var group = this.setupTooltips(linePlot);
+            var smoothLinePlot = new Plottable.Plots.Line();
+            smoothLinePlot.x(xAccessor, xScale);
+            smoothLinePlot.y(this.smoothedAccessor, yScale);
+            smoothLinePlot.attr('stroke', function (d, i, dataset) {
+                return _this.colorScale.scale(dataset.metadata().name);
+            });
+            this.smoothLinePlot = smoothLinePlot;
+            // The scatterPlot will display the last point for each dataset.
+            // This way, if there is only one datum for the series, it is still
+            // visible. We hide it when tooltips are active to keep things clean.
+            var scatterPlot = new Plottable.Plots.Scatter();
+            scatterPlot.x(xAccessor, xScale);
+            scatterPlot.y(this.scalarAccessor, yScale);
+            scatterPlot.attr('fill', function (d) { return _this.colorScale.scale(d.name); });
+            scatterPlot.attr('opacity', 1);
+            scatterPlot.size(VZ.ChartHelpers.TOOLTIP_CIRCLE_SIZE * 2);
+            scatterPlot.datasets([this.lastPointsDataset]);
+            this.scatterPlot = scatterPlot;
+            var nanDisplay = new Plottable.Plots.Scatter();
+            nanDisplay.x(xAccessor, xScale);
+            nanDisplay.y(function (x) { return x.displayY; }, yScale);
+            nanDisplay.attr('fill', function (d) { return _this.colorScale.scale(d.name); });
+            nanDisplay.attr('opacity', 1);
+            nanDisplay.size(VZ.ChartHelpers.NAN_SYMBOL_SIZE * 2);
+            nanDisplay.datasets([this.nanDataset]);
+            nanDisplay.symbol(Plottable.SymbolFactories.triangleUp);
+            this.nanDisplay = nanDisplay;
+            return new Plottable.Components.Group([nanDisplay, scatterPlot, smoothLinePlot, group]);
+        };
+        /** Updates the chart when a dataset changes. Called every time the data of
+         * a dataset changes to update the charts.
+         */
+        LineChart.prototype._onDatasetChanged = function (dataset) {
+            if (this.smoothingEnabled) {
+                this.resmoothDataset(dataset);
+                this.updateSpecialDatasets(this.smoothedAccessor);
+            }
+            else {
+                this.updateSpecialDatasets(this.scalarAccessor);
+            }
+        };
+        /** Constructs special datasets. Each special dataset contains exceptional
+         * values from all of the regular datasets, e.g. last points in series, or
+         * NaN values. Those points will have a `name` and `relative` property added
+         * (since usually those are context in the surrounding dataset).
+         * The accessor will point to the correct data to access.
+         */
+        LineChart.prototype.updateSpecialDatasets = function (accessor) {
+            var lastPointsData = this.datasets
+                .map(function (d) {
+                var datum = null;
+                // filter out NaNs to ensure last point is a clean one
+                var nonNanData = d.data().filter(function (x) { return !isNaN(accessor(x, -1, d)); });
+                if (nonNanData.length > 0) {
+                    var idx = nonNanData.length - 1;
+                    datum = nonNanData[idx];
+                    datum.name = d.metadata().name;
+                    datum.relative =
+                        VZ.ChartHelpers.relativeAccessor(datum, -1, d);
+                }
+                return datum;
+            })
+                .filter(function (x) { return x != null; });
+            this.lastPointsDataset.data(lastPointsData);
+            // Take a dataset, return an array of NaN data points
+            // the NaN points will have a "displayY" property which is the
+            // y-value of a nearby point that was not NaN (0 if all points are NaN)
+            var datasetToNaNData = function (d) {
+                var displayY = null;
+                var data = d.data();
+                var i = 0;
+                while (i < data.length && displayY == null) {
+                    if (!isNaN(accessor(data[i], -1, d))) {
+                        displayY = accessor(data[i], -1, d);
+                    }
+                    i++;
+                }
+                if (displayY == null) {
+                    displayY = 0;
+                }
+                var nanData = [];
+                for (i = 0; i < data.length; i++) {
+                    if (!isNaN(accessor(data[i], -1, d))) {
+                        displayY = accessor(data[i], -1, d);
+                    }
+                    else {
+                        data[i].name = d.metadata().name;
+                        data[i].displayY = displayY;
+                        data[i].relative = VZ.ChartHelpers.relativeAccessor(data[i], -1, d);
+                        nanData.push(data[i]);
+                    }
+                }
+                return nanData;
             };
-            Backend.prototype.createImage = function (x) {
-                return {
-                    width: x.width,
-                    height: x.height,
-                    wall_time: timeToDate(x.wall_time),
-                    step: x.step,
-                    url: this.router.individualImage(x.query),
-                };
+            var nanData = _.flatten(this.datasets.map(datasetToNaNData));
+            this.nanDataset.data(nanData);
+        };
+        LineChart.prototype.setupTooltips = function (plot) {
+            var _this = this;
+            var pi = new Plottable.Interactions.Pointer();
+            pi.attachTo(plot);
+            // PointsComponent is a Plottable Component that will hold the little
+            // circles we draw over the closest data points
+            var pointsComponent = new Plottable.Component();
+            var group = new Plottable.Components.Group([plot, pointsComponent]);
+            var hideTooltips = function () {
+                _this.tooltip.style('opacity', 0);
+                _this.scatterPlot.attr('opacity', 1);
+                pointsComponent.content().selectAll('.point').remove();
             };
-            Backend.prototype.createAudio = function (x) {
-                return {
-                    content_type: x.content_type,
-                    wall_time: timeToDate(x.wall_time),
-                    step: x.step,
-                    url: this.router.individualAudio(x.query),
-                };
+            var enabled = true;
+            var disableTooltips = function () {
+                enabled = false;
+                hideTooltips();
             };
-            return Backend;
-        }());
-        Backend_1.Backend = Backend;
-        /** Given a RunToTag, return sorted array of all runs */
-        function getRuns(r) {
-            return _.keys(r).sort(compareTagNames);
-        }
-        Backend_1.getRuns = getRuns;
-        /** Given a RunToTag, return array of all tags (sorted + dedup'd) */
-        function getTags(r) {
-            return _.union.apply(null, _.values(r)).sort(compareTagNames);
-        }
-        Backend_1.getTags = getTags;
-        /** Compares tag names asciinumerically broken into components. */
-        function compareTagNames(a, b) {
-            var ai = 0;
-            var bi = 0;
-            while (true) {
-                if (ai === a.length)
-                    return bi === b.length ? 0 : -1;
-                if (bi === b.length)
-                    return 1;
-                if (isDigit(a[ai]) && isDigit(b[bi])) {
-                    var ais = ai;
-                    var bis = bi;
-                    ai = consumeNumber(a, ai + 1);
-                    bi = consumeNumber(b, bi + 1);
-                    var an = parseFloat(a.slice(ais, ai));
-                    var bn = parseFloat(b.slice(bis, bi));
-                    if (an < bn)
-                        return -1;
-                    if (an > bn)
-                        return 1;
-                    continue;
+            var enableTooltips = function () { enabled = true; };
+            this.dzl.interactionStart(disableTooltips);
+            this.dzl.interactionEnd(enableTooltips);
+            pi.onPointerMove(function (p) {
+                if (!enabled) {
+                    return;
                 }
-                if (isBreak(a[ai])) {
-                    if (!isBreak(b[bi]))
-                        return -1;
+                var target = {
+                    x: p.x,
+                    y: p.y,
+                    datum: null,
+                    dataset: null,
+                };
+                var centerBBox = _this.gridlines.content().node().getBBox();
+                var points = plot.datasets().map(function (dataset) { return _this.findClosestPoint(target, dataset); });
+                var pointsToCircle = points.filter(function (p) { return p != null &&
+                    Plottable.Utils.DOM.intersectsBBox(p.x, p.y, centerBBox); });
+                var pts = pointsComponent.content().selectAll('.point').data(pointsToCircle, function (p) { return p.dataset.metadata().name; });
+                if (points.length !== 0) {
+                    pts.enter().append('circle').classed('point', true);
+                    pts.attr('r', VZ.ChartHelpers.TOOLTIP_CIRCLE_SIZE)
+                        .attr('cx', function (p) { return p.x; })
+                        .attr('cy', function (p) { return p.y; })
+                        .style('stroke', 'none')
+                        .attr('fill', function (p) { return _this.colorScale.scale(p.dataset.metadata().name); });
+                    pts.exit().remove();
+                    _this.drawTooltips(points, target);
                 }
-                else if (isBreak(b[bi])) {
-                    return 1;
+                else {
+                    hideTooltips();
                 }
-                else if (a[ai] < b[bi]) {
-                    return -1;
+            });
+            pi.onPointerExit(hideTooltips);
+            return group;
+        };
+        LineChart.prototype.drawTooltips = function (points, target) {
+            var _this = this;
+            // Formatters for value, step, and wall_time
+            this.scatterPlot.attr('opacity', 0);
+            var valueFormatter = VZ.ChartHelpers.multiscaleFormatter(VZ.ChartHelpers.Y_TOOLTIP_FORMATTER_PRECISION);
+            var dist = function (p) {
+                return Math.pow(p.x - target.x, 2) + Math.pow(p.y - target.y, 2);
+            };
+            var closestDist = _.min(points.map(dist));
+            var valueSortMethod = this.scalarAccessor;
+            if (this.smoothingEnabled) {
+                valueSortMethod = this.smoothedAccessor;
+            }
+            if (this.tooltipSortingMethod === 'ascending') {
+                points =
+                    _.sortBy(points, function (d) { return valueSortMethod(d.datum, -1, d.dataset); });
+            }
+            else if (this.tooltipSortingMethod === 'descending') {
+                points =
+                    _.sortBy(points, function (d) { return valueSortMethod(d.datum, -1, d.dataset); })
+                        .reverse();
+            }
+            else {
+                points = _.sortBy(points, function (d) { return d.dataset.metadata().name; });
+            }
+            var rows = this.tooltip.select('tbody')
+                .html('')
+                .selectAll('tr')
+                .data(points)
+                .enter()
+                .append('tr');
+            // Grey out the point if any of the following are true:
+            // - The cursor is outside of the x-extent of the dataset
+            // - The point is rendered above or below the screen
+            // - The point's y value is NaN
+            rows.classed('distant', function (d) {
+                var firstPoint = d.dataset.data()[0];
+                var lastPoint = _.last(d.dataset.data());
+                var firstX = _this.xScale.scale(_this.xAccessor(firstPoint, 0, d.dataset));
+                var lastX = _this.xScale.scale(_this.xAccessor(lastPoint, 0, d.dataset));
+                var s = _this.smoothingEnabled ? d.datum.smoothed : d.datum.scalar;
+                var yD = _this.yScale.domain();
+                return target.x < firstX || target.x > lastX || s < yD[0] ||
+                    s > yD[1] || isNaN(s);
+            });
+            rows.classed('closest', function (p) { return dist(p) === closestDist; });
+            // It is a bit hacky that we are manually applying the width to the swatch
+            // and the nowrap property to the text here. The reason is as follows:
+            // the style gets updated asynchronously by Polymer scopeSubtree observer.
+            // Which means we would get incorrect sizing information since the text
+            // would wrap by default. However, we need correct measurements so that
+            // we can stop the text from falling off the edge of the screen.
+            // therefore, we apply the size-critical styles directly.
+            rows.style('white-space', 'nowrap');
+            rows.append('td')
+                .append('span')
+                .classed('swatch', true)
+                .style('background-color', function (d) { return _this.colorScale.scale(d.dataset.metadata().name); });
+            rows.append('td').text(function (d) { return d.dataset.metadata().name; });
+            if (this.smoothingEnabled) {
+                rows.append('td').text(function (d) { return isNaN(d.datum.smoothed) ? 'NaN' :
+                    valueFormatter(d.datum.smoothed); });
+            }
+            rows.append('td').text(function (d) {
+                return isNaN(d.datum.scalar) ? 'NaN' : valueFormatter(d.datum.scalar);
+            });
+            rows.append('td').text(function (d) { return VZ.ChartHelpers.stepFormatter(d.datum.step); });
+            rows.append('td').text(function (d) { return VZ.ChartHelpers.timeFormatter(d.datum.wall_time); });
+            rows.append('td').text(function (d) { return VZ.ChartHelpers.relativeFormatter(VZ.ChartHelpers.relativeAccessor(d.datum, -1, d.dataset)); });
+            // compute left position
+            var documentWidth = document.body.clientWidth;
+            var node = this.tooltip.node();
+            var parentRect = node.parentElement.getBoundingClientRect();
+            var nodeRect = node.getBoundingClientRect();
+            // prevent it from falling off the right side of the screen
+            var left = documentWidth - parentRect.left - nodeRect.width - 60, top = 0;
+            if (this.tooltipPosition === 'right') {
+                left = Math.min(parentRect.width, left);
+            }
+            else {
+                left = Math.min(0, left);
+                top = parentRect.height + VZ.ChartHelpers.TOOLTIP_Y_PIXEL_OFFSET;
+            }
+            this.tooltip.style('transform', 'translate(' + left + 'px,' + top + 'px)');
+            this.tooltip.style('opacity', 1);
+        };
+        LineChart.prototype.findClosestPoint = function (target, dataset) {
+            var _this = this;
+            var points = dataset.data().map(function (d, i) {
+                var x = _this.xAccessor(d, i, dataset);
+                var y = _this.smoothingEnabled ? _this.smoothedAccessor(d, i, dataset) :
+                    _this.scalarAccessor(d, i, dataset);
+                return {
+                    x: _this.xScale.scale(x),
+                    y: _this.yScale.scale(y),
+                    datum: d,
+                    dataset: dataset,
+                };
+            });
+            var idx = _.sortedIndex(points, target, function (p) { return p.x; });
+            if (idx === points.length) {
+                return points[points.length - 1];
+            }
+            else if (idx === 0) {
+                return points[0];
+            }
+            else {
+                var prev = points[idx - 1];
+                var next = points[idx];
+                var prevDist = Math.abs(prev.x - target.x);
+                var nextDist = Math.abs(next.x - target.x);
+                return prevDist < nextDist ? prev : next;
+            }
+        };
+        LineChart.prototype.resmoothDataset = function (dataset) {
+            // When increasing the smoothing window, it smoothes a lot with the first
+            // few points and then starts to gradually smooth slower, so using an
+            // exponential function makes the slider more consistent. 1000^x has a
+            // range of [1, 1000], so subtracting 1 and dividing by 999 results in a
+            // range of [0, 1], which can be used as the percentage of the data, so
+            // that the kernel size can be specified as a percentage instead of a
+            // hardcoded number, what would be bad with multiple series.
+            var factor = (Math.pow(1000, this.smoothingWeight) - 1) / 999;
+            var data = dataset.data();
+            var kernelRadius = Math.floor(data.length * factor / 2);
+            data.forEach(function (d, i) {
+                var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);
+                var start = i - actualKernelRadius;
+                var end = i + actualKernelRadius + 1;
+                // Only smooth finite numbers.
+                if (!_.isFinite(d.scalar)) {
+                    d.smoothed = d.scalar;
                 }
-                else if (a[ai] > b[bi]) {
-                    return 1;
+                else {
+                    d.smoothed = d3.mean(data.slice(start, end).filter(function (d) { return _.isFinite(d.scalar); }), function (d) { return d.scalar; });
                 }
-                ai++;
-                bi++;
+            });
+        };
+        LineChart.prototype.getDataset = function (name) {
+            if (this.name2datasets[name] === undefined) {
+                this.name2datasets[name] = new Plottable.Dataset([], { name: name });
             }
-        }
-        Backend_1.compareTagNames = compareTagNames;
-        function consumeNumber(s, i) {
-            var decimal = false;
-            for (; i < s.length; i++) {
-                if (isDigit(s[i]))
-                    continue;
-                if (!decimal && s[i] === '.') {
-                    decimal = true;
-                    continue;
-                }
-                break;
+            return this.name2datasets[name];
+        };
+        /**
+         * Update the selected series on the chart.
+         */
+        LineChart.prototype.setVisibleSeries = function (names) {
+            var _this = this;
+            this.seriesNames = names;
+            names.reverse(); // draw first series on top
+            this.datasets.forEach(function (d) { return d.offUpdate(_this.onDatasetChanged); });
+            this.datasets = names.map(function (r) { return _this.getDataset(r); });
+            this.datasets.forEach(function (d) { return d.onUpdate(_this.onDatasetChanged); });
+            this.linePlot.datasets(this.datasets);
+            if (this.smoothingEnabled) {
+                this.smoothLinePlot.datasets(this.datasets);
             }
-            return i;
-        }
-        function isDigit(c) { return '0' <= c && c <= '9'; }
-        function isBreak(c) {
-            // TODO(jart): Remove underscore when people stop using it like a slash.
-            return c === '/' || c === '_' || isDigit(c);
-        }
+        };
         /**
-         * Given a RunToTag and an array of runs, return every tag that appears for
-         * at least one run.
-         * Sorted, deduplicated.
+         * Set the data of a series on the chart.
          */
-        function filterTags(r, runs) {
-            var result = [];
-            runs.forEach(function (x) { return result = result.concat(r[x]); });
-            return _.uniq(result).sort();
+        LineChart.prototype.setSeriesData = function (name, data) {
+            this.getDataset(name).data(data);
+        };
+        LineChart.prototype.smoothingUpdate = function (weight) {
+            var _this = this;
+            this.smoothingWeight = weight;
+            this.datasets.forEach(function (d) { return _this.resmoothDataset(d); });
+            if (!this.smoothingEnabled) {
+                this.linePlot.addClass('ghost');
+                this.scatterPlot.y(this.smoothedAccessor, this.yScale);
+                this.smoothingEnabled = true;
+                this.smoothLinePlot.datasets(this.datasets);
+            }
+            this.updateSpecialDatasets(this.smoothedAccessor);
+        };
+        LineChart.prototype.smoothingDisable = function () {
+            if (this.smoothingEnabled) {
+                this.linePlot.removeClass('ghost');
+                this.scatterPlot.y(this.scalarAccessor, this.yScale);
+                this.smoothLinePlot.datasets([]);
+                this.smoothingEnabled = false;
+                this.updateSpecialDatasets(this.scalarAccessor);
+            }
+        };
+        LineChart.prototype.setTooltipSortingMethod = function (method) {
+            this.tooltipSortingMethod = method;
+        };
+        LineChart.prototype.setTooltipPosition = function (position) {
+            this.tooltipPosition = position;
+        };
+        LineChart.prototype.renderTo = function (target) { this.outer.renderTo(target); };
+        LineChart.prototype.redraw = function () { this.outer.redraw(); };
+        LineChart.prototype.destroy = function () { this.outer.destroy(); };
+        return LineChart;
+    }());
+    VZ.LineChart = LineChart;
+})(VZ || (VZ = {}));
+</script>
+  <script>/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+/* tslint:disable:no-namespace variable-name */
+var VZ;
+(function (VZ) {
+    var ChartHelpers;
+    (function (ChartHelpers) {
+        ChartHelpers.Y_TOOLTIP_FORMATTER_PRECISION = 4;
+        ChartHelpers.STEP_FORMATTER_PRECISION = 4;
+        ChartHelpers.Y_AXIS_FORMATTER_PRECISION = 3;
+        ChartHelpers.TOOLTIP_Y_PIXEL_OFFSET = 20;
+        ChartHelpers.TOOLTIP_CIRCLE_SIZE = 4;
+        ChartHelpers.NAN_SYMBOL_SIZE = 6;
+        /* Create a formatter function that will switch between exponential and
+         * regular display depending on the scale of the number being formatted,
+         * and show `digits` significant digits.
+         */
+        function multiscaleFormatter(digits) {
+            return function (v) {
+                var absv = Math.abs(v);
+                if (absv < 1E-15) {
+                    // Sometimes zero-like values get an annoying representation
+                    absv = 0;
+                }
+                var f;
+                if (absv >= 1E4) {
+                    f = d3.format('.' + digits + 'e');
+                }
+                else if (absv > 0 && absv < 0.01) {
+                    f = d3.format('.' + digits + 'e');
+                }
+                else {
+                    f = d3.format('.' + digits + 'g');
+                }
+                return f(v);
+            };
         }
-        Backend_1.filterTags = filterTags;
-        function timeToDate(x) { return new Date(x * 1000); }
-        ;
-        /**  Just a curryable map to make things cute and tidy. */
-        function map(f) {
-            return function (arr) { return arr.map(f); };
+        ChartHelpers.multiscaleFormatter = multiscaleFormatter;
+        function accessorize(key) {
+            return function (d, index, dataset) { return d[key]; };
         }
-        ;
-        /**
-         * This is a higher order function that takes a function that transforms a
-         * T into a G, and returns a function that takes TupleData<T>s and converts
-         * them into the intersection of a G and a Datum.
-         */
-        function detupler(xform) {
-            return function (x) {
-                // Create a G, assert it has type <G & Datum>
-                var obj = xform(x[2]);
-                // ... patch in the properties of datum
-                obj.wall_time = timeToDate(x[0]);
-                obj.step = x[1];
-                return obj;
+        ChartHelpers.accessorize = accessorize;
+        ChartHelpers.stepFormatter = Plottable.Formatters.siSuffix(ChartHelpers.STEP_FORMATTER_PRECISION);
+        function stepX() {
+            var scale = new Plottable.Scales.Linear();
+            var axis = new Plottable.Axes.Numeric(scale, 'bottom');
+            axis.formatter(ChartHelpers.stepFormatter);
+            return {
+                scale: scale,
+                axis: axis,
+                accessor: function (d) { return d.step; },
             };
         }
-        ;
-        function createScalar(x) { return { scalar: x }; }
-        ;
-        function createHistogram(x) {
+        ChartHelpers.stepX = stepX;
+        ChartHelpers.timeFormatter = Plottable.Formatters.time('%a %b %e, %H:%M:%S');
+        function wallX() {
+            var scale = new Plottable.Scales.Time();
             return {
-                min: x[0],
-                max: x[1],
-                nItems: x[2],
-                sum: x[3],
-                sumSquares: x[4],
-                bucketRightEdges: x[5],
-                bucketCounts: x[6],
+                scale: scale,
+                axis: new Plottable.Axes.Time(scale, 'bottom'),
+                accessor: function (d) { return d.wall_time; },
             };
         }
-        ;
-        /**
-         * Takes histogram data as stored by tensorboard backend and converts it to
-         * the standard d3 histogram data format to make it more compatible and easier
-         * to visualize. When visualizing histograms, having the left edge and width
-         * makes things quite a bit easier. The bins are also converted to have an
-         * uniform width, what makes the visualization easier to understand.
-         *
-         * @param histogram A histogram from tensorboard backend.
-         * @param min The leftmost edge. The binning will start on it.
-         * @param max The rightmost edge. The binning will end on it.
-         * @param numBins The number of bins of the converted data. The default of 30
-         * is a sensible default, using more starts to get artifacts because the event
-         * data is stored in buckets, and you start being able to see the aliased
-         * borders between each bucket.
-         * @return A histogram bin. Each bin has an x (left edge), a dx (width),
-         *     and a y (count).
-         *
-         * If given rightedges are inclusive, then these left edges (x) are exclusive.
-         */
-        function convertBins(histogram, min, max, numBins) {
-            if (numBins === void 0) { numBins = 30; }
-            if (histogram.bucketRightEdges.length !== histogram.bucketCounts.length) {
-                throw (new Error('Edges and counts are of different lengths.'));
+        ChartHelpers.wallX = wallX;
+        ChartHelpers.relativeAccessor = function (d, index, dataset) {
+            // We may be rendering the final-point datum for scatterplot.
+            // If so, we will have already provided the 'relative' property
+            if (d.relative != null) {
+                return d.relative;
             }
-            var binWidth = (max - min) / numBins;
-            var bucketLeft = min; // Use the min as the starting point for the bins.
-            var bucketPos = 0;
-            return d3.range(min, max, binWidth).map(function (binLeft) {
-                var binRight = binLeft + binWidth;
-                // Take the count of each existing bucket, multiply it by the proportion
-                // of overlap with the new bin, then sum and store as the count for the
-                // new bin. If no overlap, will add to zero, if 100% overlap, will include
-                // the full count into new bin.
-                var binY = 0;
-                while (bucketPos < histogram.bucketRightEdges.length) {
-                    // Clip the right edge because right-most edge can be infinite-sized.
-                    var bucketRight = Math.min(max, histogram.bucketRightEdges[bucketPos]);
-                    var intersect = Math.min(bucketRight, binRight) - Math.max(bucketLeft, binLeft);
-                    var count = (intersect / (bucketRight - bucketLeft)) *
-                        histogram.bucketCounts[bucketPos];
-                    binY += intersect > 0 ? count : 0;
-                    // If bucketRight is bigger than binRight, than this bin is finished and
-                    // there is data for the next bin, so don't increment bucketPos.
-                    if (bucketRight > binRight) {
-                        break;
-                    }
-                    bucketLeft = Math.max(min, bucketRight);
-                    bucketPos++;
-                }
-                ;
-                return { x: binLeft, dx: binWidth, y: binY };
-            });
-        }
-        Backend_1.convertBins = convertBins;
-    })(Backend = TF.Backend || (TF.Backend = {}));
-})(TF || (TF = {}));
-</script>
-<script>var TF;
-(function (TF) {
-    var Backend;
-    (function (Backend) {
-        Backend.Behavior = {
-            properties: {
-                /** *** Required properties *** */
-                /** Data type. One of TF.Backend.TYPES */
-                dataType: {
-                    type: String,
-                    observer: '_throwErrorOnUnrecognizedType',
-                },
-                /** TF.Backend.Backend for data loading. */
-                backend: {
-                    type: Object,
-                },
-                /** Should it automatically load when configured ready? Default true. */
-                autoLoad: {
-                    type: Boolean,
-                    value: true,
-                },
-                /** *** Component-provided properties *** */
-                /** Every tag available for data type (sorted, dedpulicated) */
-                tags: {
-                    type: Array,
-                    readOnly: true,
-                    notify: true,
-                },
-                /** Every run available for data type (sorted) */
-                runs: {
-                    type: Array,
-                    readOnly: true,
-                    notify: true,
-                },
-                /** Mapping from runs to tags for the data type */
-                run2tag: {
-                    type: Object,
-                    readOnly: true,
-                    notify: true,
-                },
-                /** Promise provider for the data. Useful for passing to subcomponents */
-                dataProvider: { type: Function, computed: '_getDataProvider(dataType, backend)' },
-                /** Has the dashboard loaded yet? */
-                loadState: {
-                    type: String,
-                    value: 'noload',
-                    readOnly: true,
-                },
-                /**
-                 * True if dashboard has loaded, and no tags were found.
-                 * Persists through subsequent reloads (ie. still true while
-                 * next load is pending) so warning won't flash away every reload
-                 * when there is no data.
-                 */
-                dataNotFound: {
-                    type: Boolean,
-                    value: false,
-                    readOnly: true,
-                }
-            },
-            observers: ['_do_autoLoad(dataType, backend, autoLoad)'],
-            /**
-             * Reloading works in two steps:
-             * Backend reload, which gets metadata on available runs, tags, etc from
-             *   the backend.
-             * Frontend reload, which loads new data for each chart or visual display.
-             * Backend reload logic is provided by this behaivor. The frontend reload
-             *   logic should be provided elsewhere, since it is component-specific.
-             * To keep things simple and consistent, we do the backend reload first,
-             *   and the frontend reload afterwards.
-             */
-            reload: function () {
-                var _this = this;
-                return this.backendReload().then(function (x) { return _this.frontendReload(); });
-            },
-            /**
-             * Load data from backend and then set run2tag, tags, runs, and loadState.
-             * Returns a promise that resolves/rejects when data is loaded.
-             */
-            backendReload: function () {
-                var _this = this;
-                if (this.dataType == null) {
-                    throw new Error('TF.Backend.Behavior: Need a dataType to reload.');
-                }
-                if (this.backend == null) {
-                    throw new Error('TF.Backend.Behavior: Need a backend to reload.');
-                }
-                var runsRoute = this.backend[this.dataType + 'Runs'].bind(this.backend);
-                this._setLoadState('pending');
-                return runsRoute().then(function (x) {
-                    _this._setLoadState('loaded');
-                    if (_.isEqual(x, _this.run2tag)) {
-                        // If x and run2tag are equal, let's avoid updating everything
-                        // since that can needlessly trigger run changes, reloads, etc
-                        return x;
-                    }
-                    _this._setRun2tag(x);
-                    var tags = TF.Backend.getTags(x);
-                    _this._setDataNotFound(tags.length === 0);
-                    _this._setTags(tags);
-                    _this._setRuns(TF.Backend.getRuns(x));
-                    return x;
-                }, function (fail) {
-                    _this._setLoadState('failure');
-                    return fail;
-                });
-            },
-            _do_autoLoad: function (type, backend, autoLoad) {
-                if (autoLoad) {
-                    this.reload();
-                }
-                ;
-            },
-            _getDataProvider: function (dataType, backend) {
-                return this.backend[this.dataType].bind(this.backend);
-            },
-            _throwErrorOnUnrecognizedType: function (dataType) {
-                if (TF.Backend.TYPES.indexOf(dataType) === -1) {
-                    throw new Error('TF.Backend.Behavior: Unknown dataType ' + dataType);
-                }
-            },
+            var data = dataset.data();
+            // I can't imagine how this function would be called when the data is
+            // empty (after all, it iterates over the data), but lets guard just
+            // to be safe.
+            var first = data.length > 0 ? +data[0].wall_time : 0;
+            return (+d.wall_time - first) / (60 * 60 * 1000); // ms to hours
+        };
+        ChartHelpers.relativeFormatter = function (n) {
+            // we will always show 2 units of precision, e.g days and hours, or
+            // minutes and seconds, but not hours and minutes and seconds
+            var ret = '';
+            var days = Math.floor(n / 24);
+            n -= (days * 24);
+            if (days) {
+                ret += days + 'd ';
+            }
+            var hours = Math.floor(n);
+            n -= hours;
+            n *= 60;
+            if (hours || days) {
+                ret += hours + 'h ';
+            }
+            var minutes = Math.floor(n);
+            n -= minutes;
+            n *= 60;
+            if (minutes || hours || days) {
+                ret += minutes + 'm ';
+            }
+            var seconds = Math.floor(n);
+            return ret + seconds + 's';
         };
-    })(Backend = TF.Backend || (TF.Backend = {}));
-})(TF || (TF = {}));
+        function relativeX() {
+            var scale = new Plottable.Scales.Linear();
+            return {
+                scale: scale,
+                axis: new Plottable.Axes.Numeric(scale, 'bottom'),
+                accessor: ChartHelpers.relativeAccessor,
+            };
+        }
+        ChartHelpers.relativeX = relativeX;
+        // a very literal definition of NaN: true for NaN for a non-number type
+        // or null, etc. False for Infinity or -Infinity
+        ChartHelpers.isNaN = function (x) { return +x !== x; };
+        function getXComponents(xType) {
+            switch (xType) {
+                case 'step':
+                    return stepX();
+                case 'wall_time':
+                    return wallX();
+                case 'relative':
+                    return relativeX();
+                default:
+                    throw new Error('invalid xType: ' + xType);
+            }
+        }
+        ChartHelpers.getXComponents = getXComponents;
+    })(ChartHelpers = VZ.ChartHelpers || (VZ.ChartHelpers = {}));
+})(VZ || (VZ = {}));
 </script>
+  <script>
+    Polymer({
+      is: "vz-line-chart",
+      properties: {
+        /**
+         * Scale that maps series names to colors. The default colors are from
+         * d3.scale.category10() scale. Use this property to replace the default
+         * line colors with colors of your own choice.
+         * @type {Plottable.Scales.Color}
+         * @required
+         */
+        colorScale: {
+          type: Object,
+          value: function() {
+            return new Plottable.Scales.Color()
+                .range(d3.scale.category10().range());
+          }
+        },
+
+        /**
+         * Whether smoothing is enabled or not. If true, smoothed lines will be
+         * plotted in the chart while the unsmoothed lines will be ghosted in
+         * the background.
+         *
+         * The smoothing algorithm is a simple moving average, which, given a
+         * point p and a window w, replaces p with a simple average of the
+         * points in the [p - floor(w/2), p + floor(w/2)] range.
+         * If there aren't enough points to cover the entire window to the left
+         * and to the right, the window is reduced to fit exactly the amount of
+         * elements available. This means that the smoothed line will be less
+         * smoothed at both extremities and gradually become more smooth until
+         * the desired window is reached.
+         */
+        smoothingEnabled: {
+          type: Boolean,
+          value: false
+        },
+
+        /**
+         * Weight (between 0.0 and 1.0) of the smoothing. This weight controls
+         * the window size, and a weight of 1.0 means using the entire length of
+         * each dataset as the window, while a weight of 0.0 means using a
+         * window of 0 (and thus replacing each point with themselves).
+         *
+         * The growth between 0.0 and 1.0 is not linear though. Because changing
+         * the window from 0% to 30% of the dataset smooths the line a lot more
+         * than changing the window from 70% to 100%, an exponential function is
+         * used instead. This function increases the size of the window slowly
+         * at the beginning and gradually speeds up the growth, but 0.0 still
+         * means a window of 0 and 1.0 still means a window of the dataset's
+         * length.
+         */
+        smoothingWeight: {
+          type: Number,
+          value: 0.6
+        },
+
+        /**
+         * The way to display the X values. Allows:
+         * - "step" - Linear scale using the  "step" property of the datum.
+         * - "wall_time" - Temporal scale using the "wall_time" property of the
+         * datum.
+         * - "relative" - Temporal scale using the "relative" property of the
+         * datum if it is present or calculating from "wall_time" if it isn't.
+         */
+        xType: {
+          type: String,
+          value: 'step'
+        },
+
+        /**
+         * Change how the tooltip is sorted. Allows:
+         * - "name" - Sort the tooltip by series name.
+         * - "ascending" - Sort the tooltip by ascending value.
+         * - "descending" - Sort the tooltip by descending value.
+         */
+        tooltipSortingMethod: {
+          type: String,
+          value: 'name'
+        },
+
+        /**
+         * Change how the tooltip is positioned. Allows:
+         * - "bottom" - Position the tooltip on the bottom of the chart.
+         * - "right" - Position the tooltip to the right of the chart.
+         */
+        tooltipPosition: {
+          type: String,
+          value: 'bottom'
+        },
+
+        _attached: Boolean,
+        _chart: Object,
+        _visibleSeriesCache: {
+          type: Array,
+          value: function() { return [] }
+        },
+        _seriesDataCache: {
+          type: Object,
+          value: function() { return {} }
+        },
+        _makeChartAsyncCallbackId: {
+          type: Number,
+          value: null
+        }
+      },
+      observers: [
+        "_makeChart(xType, colorScale, _attached)",
+        "_reloadFromCache(_chart)",
+        "_smoothingChanged(smoothingEnabled, smoothingWeight, _chart)",
+        "_tooltipSortingMethodChanged(tooltipSortingMethod, _chart)",
+        "_tooltipPositionChanged(tooltipPosition, _chart)"
+      ],
+
+      /**
+       * Sets the series that the chart displays. Series with other names will
+       * not be displayed.
+       *
+       * @param {String[]} names Array with the names of the series to
+       * display.
+       */
+      setVisibleSeries: function(names) {
+        this._visibleSeriesCache = names;
+        if (this._chart) {
+          this._chart.setVisibleSeries(names);
+          this.redraw();
+        }
+      },
+
+      /**
+       * Sets the data of one of the series. Note that to display this series
+       * its name must be in the setVisibleSeries() array.
+       *
+       * @param {String} name Name of the series.
+       * @param {VZ.ChartHelpers.ScalarDatum[]} data Data of the series. This is
+       * an array of objects with at least the following properties:
+       * - step: (Number) - index of the datum.
+       * - wall_time: (Date) - Date object with the datum's time.
+       * - scalar: (Number) - Value of the datum.
+       */
+      setSeriesData: function(name, data) {
+        this._seriesDataCache[name] = data;
+        if (this._chart) {
+          this._chart.setSeriesData(name, data);
+        }
+      },
+
+      /**
+       * Re-renders the chart. Useful if e.g. the container size changed.
+       */
+      redraw: function() {
+        this._chart.redraw();
+      },
+      attached: function() {
+        this._attached = true;
+      },
+      detached: function() {
+        this._attached = false;
+      },
+      ready: function() {
+        this.scopeSubtree(this.$.tooltip, true);
+        this.scopeSubtree(this.$.chartsvg, true);
+      },
+      _makeChart: function(xType, colorScale, _attached) {
+        if (this._makeChartAsyncCallbackId === null) {
+          this.cancelAsync(this._makeChartAsyncCallbackId);
+        }
+
+        this._makeChartAsyncCallbackId = this.async(function() {
+          this._makeChartAsyncCallbackId = null;
+          if (!this._attached) return;
+          if (this._chart) this._chart.destroy();
+          var tooltip = d3.select(this.$.tooltip);
+          var chart = new VZ.LineChart(xType, colorScale, tooltip);
+          var svg = d3.select(this.$.chartsvg);
+          chart.renderTo(svg);
+          this._chart = chart;
+        }, 350);
+      },
+      _reloadFromCache: function() {
+        if(this._chart) {
+          this._chart.setVisibleSeries(this._visibleSeriesCache);
+          this._visibleSeriesCache.forEach(function(name) {
+            this._chart.setSeriesData(name, this._seriesDataCache[name] || []);
+          }.bind(this));
+        }
+      },
+      _smoothingChanged: function() {
+        if(!this._chart) {
+          return;
+        }
+        if(this.smoothingEnabled) {
+          this._chart.smoothingUpdate(this.smoothingWeight);
+        }
+        else {
+          this._chart.smoothingDisable();
+        }
+      },
+      _tooltipSortingMethodChanged: function() {
+        if(this._chart) {
+          this._chart.setTooltipSortingMethod(this.tooltipSortingMethod);
+        }
+      },
+      _tooltipPositionChanged: function() {
+        if (this._chart) {
+          this._chart.setTooltipPosition(this.tooltipPosition);
+        }
+      }
+    });
+  </script>
+</dom-module>
 
 <dom-module id="tf-event-dashboard" assetpath="../tf-event-dashboard/">
   <template>
@@ -3707,7 +3792,19 @@ var TF;
         <div class="sidebar-section">
           <tf-categorizer id="categorizer" tags="[[_visibleTags]]" categories="{{categories}}"></tf-categorizer>
           <paper-checkbox id="download-option" checked="{{_showDownloadLinks}}">Data download links</paper-checkbox>
-        <tf-smoothing-input enabled="{{_smoothingEnabled}}" decay="{{_smoothingDecay}}" step="0.001" min="0" max="1"></tf-smoothing-input>
+          <div id="tooltip-sorting">
+            <div id="tooltip-sorting-label">Tooltip sorting method:</div>
+            <paper-dropdown-menu no-label-float="" selected-item-label="{{_tooltipSortingMethod}}">
+              <paper-menu class="dropdown-content" selected="0">
+                <paper-item>name</paper-item>
+                <paper-item>descending</paper-item>
+                <paper-item>ascending</paper-item>
+              </paper-menu>
+            </paper-dropdown-menu>
+          </div>
+        </div>
+        <div class="sidebar-section">
+          <tf-smoothing-input weight="{{_smoothingWeight}}" step="0.001" min="0" max="1"></tf-smoothing-input>
         </div>
         <div class="sidebar-section">
           <tf-option-selector id="xTypeSelector" name="Horizontal Axis" selected-id="{{_xType}}">
@@ -3730,7 +3827,7 @@ var TF;
                   <span class="card-title">[[tag]]</span>
                   <div class="card-content">
                     <tf-chart-scaffold tag="[[tag]]" data-provider="[[dataProvider]]" visible-series="[[validRuns(tag, selectedRuns.*, run2tag.*)]]">
-                      <vz-line-chart id="chart" x-type="[[_xType]]" color-scale="[[colorScale]]" smoothing-decay="[[_smoothingDecay]]" smoothing-enabled="[[_smoothingEnabled]]" on-keyup="toggleSelected" tabindex="2"></vz-line-chart>
+                      <vz-line-chart id="chart" x-type="[[_xType]]" color-scale="[[colorScale]]" smoothing-enabled="[[_smoothingEnabled]]" smoothing-weight="[[_smoothingWeight]]" tooltip-sorting-method="[[_tooltipSortingMethod]]" on-keyup="toggleSelected" tabindex="2"></vz-line-chart>
                     </tf-chart-scaffold>
                     <paper-icon-button class="expand-button" icon="fullscreen" on-tap="toggleSelected"></paper-icon-button>
                   </div>
@@ -3777,8 +3874,16 @@ var TF;
               false),
           observer: '_showDownloadLinksObserver'
         },
-        _smoothingDecay: Number,
-        _smoothingEnabled: Boolean,
+        _smoothingWeight: {
+          type: Number,
+          notify: true,
+          value: TF.URIStorage.getNumberInitializer('_smoothingWeight', 0.6),
+          observer: '_smoothingWeightObserver'
+        },
+        _smoothingEnabled: {
+          type: Boolean,
+          computed: '_computeSmoothingEnabled(_smoothingWeight)'
+        },
         colorScale: {
           type: Object,
           notify: true,
@@ -3803,6 +3908,11 @@ var TF;
       },
       _showDownloadLinksObserver: TF.URIStorage.getBooleanObserver(
           '_showDownloadLinks', false),
+      _smoothingWeightObserver: TF.URIStorage.getNumberObserver(
+          '_smoothingWeight', 0.6),
+      _computeSmoothingEnabled: function(_smoothingWeight) {
+        return _smoothingWeight > 0;
+      },
       toggleSelected: function(e) {
         var currentTarget = Polymer.dom(e.currentTarget);
         var parentDiv = currentTarget.parentNode.parentNode;
@@ -4283,6 +4393,8 @@ var VZ;
             <rect class="background"></rect>
           </g>
           <g class="x-axis-hover"></g>
+          <g class="y-axis-hover"></g>
+          <g class="y-slice-axis-hover"></g>
         </g>
       </svg>
 
@@ -4325,6 +4437,19 @@ var VZ;
           stroke-width: 1px;
         }
 
+        .hover text {
+          fill: black;
+          opacity: 0;
+        }
+
+        .hover.hover-closest circle {
+          fill: black!important;
+        }
+
+        .hover.hover-closest text {
+          opacity: 1;
+        }
+
         .baseline {
           stroke: black;
           stroke-opacity: 0.1;
@@ -4336,11 +4461,20 @@ var VZ;
           stroke-opacity: 0.5;
         }
 
-        .x-axis-hover {
+        .outline.outline-hover {
+          stroke: black!important;
+          stroke-opacity: 1;
+        }
+
+        .x-axis-hover,
+        .y-axis-hover,
+        .y-slice-axis-hover {
           pointer-events: none;
         }
 
-        .x-axis-hover .label {
+        .x-axis-hover .label,
+        .y-axis-hover .label,
+        .y-slice-axis-hover .label {
           opacity: 0;
           font-weight: bold;
           font-size: 11px;
@@ -4351,15 +4485,21 @@ var VZ;
           text-anchor: middle;
         }
 
-        .x-axis-hover line {
-          stroke: black;
+        .y-axis-hover text,
+        .y-slice-axis-hover text {
+          text-anchor: start;
         }
 
-        .x-axis-hover rect {
-          fill: white;
+        .x-axis-hover line,
+        .y-axis-hover line,
+        .y-slice-axis-hover line {
+          stroke: black;
         }
 
-        .gradient {
+        .x-axis-hover rect,
+        .y-axis-hover rect,
+        .y-slice-axis-hover rect {
+          fill: white;
         }
 
         .axis {
@@ -4653,6 +4793,7 @@ var VZ;
             .scale(ySliceScale)
             .ticks(Math.max(2, height / 15))
             .tickSize(width + 5)
+            .tickFormat(format)
             .orient("right");
 
         var xBinCentroid = function(d) {
@@ -4693,36 +4834,20 @@ var VZ;
         var bisect = d3.bisector(xRightAccessor).left;
         var stage = g.select(".stage")
             .on("mouseover", function() {
-              hoverUpdate.transition().style("opacity", 1);
-              edgeLabelUpdate.transition().style("opacity", 1)
+              hoverUpdate.style("opacity", 1);
+              xAxisHoverUpdate.style("opacity", 1);
+              yAxisHoverUpdate.style("opacity", 1);
+              ySliceAxisHoverUpdate.style("opacity", 1);
             })
             .on("mouseout", function() {
-              hoverUpdate.transition().style("opacity", 0);
-              edgeLabelUpdate.transition().style("opacity", 0)
+              hoverUpdate.style("opacity", 0);
+              xAxisHoverUpdate.style("opacity", 0);
+              yAxisHoverUpdate.style("opacity", 0);
+              ySliceAxisHoverUpdate.style("opacity", 0);
+              hoverUpdate.classed("hover-closest", false);
+              outlineUpdate.classed("outline-hover", false);
             })
-            .on("mousemove", function() {
-              var m = d3.mouse(this),
-                  v = xScale.invert(m[0]);
-
-              function hoverXIndex(d) {
-                return Math.min(d[binsProp].length - 1, bisect(d[binsProp], v));
-              }
-
-              var lastSliceData;
-              hoverUpdate
-                .attr("transform", function(d) {
-                  var index = hoverXIndex(d);
-                  lastSliceData = d;
-                  return "translate(" + xScale(d[binsProp][index][xProp] + d[binsProp][index][dxProp] / 2) + "," + ySliceScale(d[binsProp][index][yProp]) + ")";
-                });
-
-              var index = hoverXIndex(lastSliceData);
-
-              edgeLabelUpdate
-                  .attr("transform", function(d) { return"translate(" + xScale(lastSliceData[binsProp][index][xProp] + lastSliceData[binsProp][index][dxProp] / 2) + ", " + height + ")"; })
-                .select("text")
-                  .text(function(d) { return format(lastSliceData[binsProp][index][xProp] + lastSliceData[binsProp][index][dxProp] / 2); });
-            });
+            .on("mousemove", onMouseMove);
 
         var background = stage.select(".background")
             .attr("transform", "translate(" + -margin.left + "," + -margin.top + ")")
@@ -4736,7 +4861,8 @@ var VZ;
                 .sort(function(a, b) { return timeAccessor(a) - timeAccessor(b); }),
             histogramTransition = gTransition.selectAll(".histogram")
                 .attr("transform", function(d) {
-                  return "translate(0, " + (mode === "offset" ? (yScale(timeAccessor(d)) - sliceHeight) : 0) + ")";
+                  return "translate(0, " +
+                    (mode === "offset" ? (yScale(timeAccessor(d)) - sliceHeight) : 0) + ")";
                 });
 
         var baselineEnter = histogramEnter.append("line").attr("class", "baseline"),
@@ -4752,12 +4878,14 @@ var VZ;
                 .attr("d", function(d) { return path(d[binsProp]); })
                 .style("stroke-width", 1),
             outlineTransition = histogramTransition.select(".outline")
-                .attr("transform", "scale(" + width / outlineCanvasSize + ", " + sliceHeight / outlineCanvasSize + ")")
-                .style("stroke", function(d) { return (mode === "offset" ? "white" : outlineColor(timeAccessor(d))); })
+                .attr("transform", "scale(" + width / outlineCanvasSize + ", " +
+                      sliceHeight / outlineCanvasSize + ")")
+                .style("stroke", function(d) {
+                  return (mode === "offset" ? "white" : outlineColor(timeAccessor(d)));
+                })
                 .style("fill-opacity", function(d) { return (mode === "offset" ? 1 : 0); })
                 .style("fill", function(d) { return outlineColor(timeAccessor(d)); });
 
-
         var hoverEnter = histogramEnter.append("g")
                 .attr("class", "hover")
                 .style("fill", function(d) { return outlineColor(timeAccessor(d)); }),
@@ -4766,35 +4894,69 @@ var VZ;
         hoverEnter.append("circle")
             .attr("r", 2);
 
-        hoverEnter.append("text")
-            .style("display", "none")
-            .style("stroke", "white")
-            .style("stroke-width", 2)
-            .attr("dx", 4);
-
         hoverEnter.append("text")
             .style("display", "none")
             .attr("dx", 4);
 
-        var edgeLabel = g.select(".x-axis-hover").selectAll(".label").data(["x"]),
-            edgeLabelEnter = edgeLabel.enter().append("g").attr("class", "label"),
-            edgeLabelUpdate = edgeLabel;
+        var xAxisHover = g.select(".x-axis-hover").selectAll(".label").data(["x"]),
+            xAxisHoverEnter = xAxisHover.enter().append("g").attr("class", "label"),
+            xAxisHoverUpdate = xAxisHover;
 
-        edgeLabelEnter.append("rect")
+        xAxisHoverEnter.append("rect")
             .attr("x", -20)
             .attr("y", 6)
             .attr("width", 40)
             .attr("height", 14)
 
-        edgeLabelEnter.append("line")
+        xAxisHoverEnter.append("line")
             .attr("x1", 0)
             .attr("x2", 0)
             .attr("y1", 0)
             .attr("y2", 6);
 
-        edgeLabelEnter.append("text")
+        xAxisHoverEnter.append("text")
             .attr("dy", 18);
 
+        var yAxisHover = g.select(".y-axis-hover").selectAll(".label").data(["y"]),
+            yAxisHoverEnter = yAxisHover.enter().append("g").attr("class", "label"),
+            yAxisHoverUpdate = yAxisHover;
+
+        yAxisHoverEnter.append("rect")
+            .attr("x", 8)
+            .attr("y", -6)
+            .attr("width", 40)
+            .attr("height", 14)
+
+        yAxisHoverEnter.append("line")
+            .attr("x1", 0)
+            .attr("x2", 6)
+            .attr("y1", 0)
+            .attr("y2", 0);
+
+        yAxisHoverEnter.append("text")
+            .attr("dx", 8)
+            .attr("dy", 4);
+
+        var ySliceAxisHover = g.select(".y-slice-axis-hover").selectAll(".label").data(["y"]),
+            ySliceAxisHoverEnter = ySliceAxisHover.enter().append("g").attr("class", "label"),
+            ySliceAxisHoverUpdate = ySliceAxisHover;
+
+        ySliceAxisHoverEnter.append("rect")
+            .attr("x", 8)
+            .attr("y", -6)
+            .attr("width", 40)
+            .attr("height", 14)
+
+        ySliceAxisHoverEnter.append("line")
+            .attr("x1", 0)
+            .attr("x2", 6)
+            .attr("y1", 0)
+            .attr("y2", 0);
+
+        ySliceAxisHoverEnter.append("text")
+            .attr("dx", 8)
+            .attr("dy", 4);
+
         gTransition.select(".y.axis.slice")
             .style("opacity", mode === "offset" ? 0 : 1)
             .attr("transform", "translate(0, " + (mode === "offset" ? -sliceHeight : 0) + ")")
@@ -4808,6 +4970,74 @@ var VZ;
             .style("opacity", mode === "offset" ? 1 : 0)
             .attr("transform", "translate(" + width + ", " + (mode === "offset" ? 0 : height) + ")")
             .call(yAxis);
+
+        function onMouseMove() {
+          var m = d3.mouse(this),
+              v = xScale.invert(m[0]),
+              t = yScale.invert(m[1]);
+
+          function hoverXIndex(d) {
+            return Math.min(d[binsProp].length - 1, bisect(d[binsProp], v));
+          }
+          var closestSliceData;
+          var closestSliceDistance = Infinity;
+          var lastSliceData;
+          hoverUpdate
+            .attr("transform", function(d, i) {
+              var index = hoverXIndex(d);
+              lastSliceData = d;
+              var x = xScale(d[binsProp][index][xProp] + d[binsProp][index][dxProp] / 2);
+              var y = ySliceScale(d[binsProp][index][yProp]);
+              var globalY = (mode === "offset" ? yScale(timeAccessor(d)) - (sliceHeight - y) : y);
+              var dist = Math.abs(m[1] - globalY);
+              if (dist < closestSliceDistance) {
+                closestSliceDistance = dist;
+                closestSliceData = d;
+              }
+              return "translate(" + x + "," + y + ")";
+            });
+          hoverUpdate.select("text").text(function(d) {
+            var index = hoverXIndex(d);
+            return d[binsProp][index][yProp];
+          })
+          hoverUpdate.classed("hover-closest", function(d) { return d === closestSliceData; });
+          outlineUpdate.classed("outline-hover", function(d) { return d === closestSliceData; });
+
+          var index = hoverXIndex(lastSliceData);
+
+          xAxisHoverUpdate
+              .attr("transform", function(d) {
+                return "translate(" +
+                  xScale(lastSliceData[binsProp][index][xProp] +
+                         lastSliceData[binsProp][index][dxProp] / 2) + ", " +
+                  height + ")";
+              })
+            .select("text")
+              .text(function(d) { return format(lastSliceData[binsProp][index][xProp] +
+                                                lastSliceData[binsProp][index][dxProp] / 2); });
+
+          var fy = yAxis.tickFormat();
+          yAxisHoverUpdate
+              .attr("transform", function(d) {
+                return "translate(" + width + ", " +
+                  (mode === "offset" ? yScale(timeAccessor(closestSliceData)) : 0) + ")";
+              })
+              .style("display", mode === "offset" ? "" : "none")
+            .select("text")
+              .text(function(d) { return fy(timeAccessor(closestSliceData));});
+
+          var fsy = ySliceAxis.tickFormat();
+          ySliceAxisHoverUpdate
+              .attr("transform", function(d) {
+                return "translate(" + width + ", " +
+                  (mode === "offset" ? 0 : ySliceScale(closestSliceData[binsProp][index][yProp])) +
+                  ")";
+              })
+              .style("display", mode === "offset" ? "none" : "")
+            .select("text")
+              .text(function(d) { return fsy(closestSliceData[binsProp][index][yProp]); });
+
+        }
       }
     });
     </script>
@@ -5059,10 +5289,8 @@ var VZ;
       }
       .image-cell {
         width: 300px;
-        height: 300px;
       }
       .tag-name-cell {
-        height: 300px;
         width: 300px;
         display:flex;
         flex-direction: column;
-- 
GitLab


From cd95f3a7d66833cdb55fa180d5002f2cf686bcef Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 22 Aug 2016 11:56:05 -0800
Subject: [PATCH 008/223] Reduced some unnecessary abstraction layers in worker
 call sending path. Also cleaned up a bunch of boilerplate.

Reduced allocations in Call tag management by inlining Tags into Call.

Reduces CPU usage for an RPC intensive benchmark by ~2%
Change: 130971469
---
 .../core/distributed_runtime/rpc/grpc_call.h  |  93 +++--
 .../rpc/grpc_client_cq_tag.h                  |  22 +-
 .../rpc/grpc_master_service.cc                |   1 -
 .../rpc/grpc_remote_worker.cc                 | 133 ++++---
 .../rpc/grpc_worker_cache.cc                  |   1 -
 .../rpc/grpc_worker_service.cc                |  29 +-
 .../rpc/grpc_worker_service_impl.cc           | 143 ++-----
 .../rpc/grpc_worker_service_impl.h            | 374 +-----------------
 8 files changed, 205 insertions(+), 591 deletions(-)

diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_call.h b/tensorflow/core/distributed_runtime/rpc/grpc_call.h
index 3b6cbf44c2..70627973c7 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_call.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_call.h
@@ -19,6 +19,7 @@ limitations under the License.
 #include "tensorflow/core/platform/macros.h"
 
 #include "grpc++/grpc++.h"
+#include "grpc++/impl/codegen/service_type.h"
 #include "grpc++/server_builder.h"
 
 namespace tensorflow {
@@ -86,13 +87,6 @@ class UntypedCall : public core::RefCounted {
   // otherwise false.
   virtual void RequestReceived(Service* service, bool ok) = 0;
 
-  // This method will be called when the response has been sent by
-  // `service` and the call is no longer used.
-  //
-  // `ok` is true if the response sending completed as a "regular
-  // event", otherwise it is false.
-  void ResponseSent(Service* service, bool ok) {}
-
   // This method will be called either (i) when the server is notified
   // that the request has been cancelled, or (ii) when the request completes
   // normally. The implementation should distinguish these cases by querying
@@ -100,27 +94,31 @@ class UntypedCall : public core::RefCounted {
   virtual void RequestCancelled(Service* service, bool ok) = 0;
 
   // Associates a tag in a `::grpc::CompletionQueue` with a callback
-  // for an incoming RPC.  A Tag owns a reference on the corresponding
+  // for an incoming RPC.  An active Tag owns a reference on the corresponding
   // Call object.
   class Tag {
    public:
-    using Callback = void (UntypedCall::*)(Service*, bool);
-
-    // Creates a new `Tag` for the given `UntypedCall`. When the
-    // request associated with this tag is complete, `callback` will
-    // be called.
-    Tag(UntypedCall* call, Callback callback)
-        : call_(call), callback_(callback) {
-      call_->Ref();
-    }
+    // One enum value per supported callback.
+    enum Callback { kRequestReceived, kResponseSent, kCancelled };
 
-    ~Tag() { call_->Unref(); }
+    Tag(UntypedCall* call, Callback cb) : call_(call), callback_(cb) {}
 
     // Calls the callback associated with this tag.
     //
     // The callback takes ownership of `this->call_`.
     void OnCompleted(Service* service, bool ok) {
-      (call_->*callback_)(service, ok);
+      switch (callback_) {
+        case kRequestReceived:
+          call_->RequestReceived(service, ok);
+          break;
+        case kResponseSent:
+          // No special handling needed apart from the Unref below.
+          break;
+        case kCancelled:
+          call_->RequestCancelled(service, ok);
+          break;
+      }
+      call_->Unref();  // Ref acquired when tag handed to grpc.
     }
 
    private:
@@ -161,9 +159,8 @@ class Call : public UntypedCall<Service> {
   }
 
   void SendResponse(::grpc::Status status) {
-    responder_.Finish(response, status,
-                      new typename UntypedCall<Service>::Tag(
-                          this, &UntypedCall<Service>::ResponseSent));
+    this->Ref();  // Ref for grpc; released in Tag callback.
+    responder_.Finish(response, status, &response_sent_tag_);
     this->Unref();
   }
 
@@ -174,9 +171,6 @@ class Call : public UntypedCall<Service> {
         cancel_callback_();
       }
     }
-    // NOTE(mrry): This can be called before or after RequestReceived, so we
-    // release `cancel_tag_` (in order to allow the event loop to free it).
-    cancel_tag_.release();
   }
 
   // Registers `callback` as the function that should be called if and when this
@@ -208,11 +202,31 @@ class Call : public UntypedCall<Service> {
       call->RegisterCancellationHandler();
     }
 
-    (grpc_service->*enqueue_function)(
-        &call->ctx_, &call->request, &call->responder_, cq, cq,
-        new typename UntypedCall<Service>::Tag(
-            call, &UntypedCall<Service>::RequestReceived));
-    call->Unref();
+    // Initial ref for call handed to grpc; released in Tag callback.
+    (grpc_service->*enqueue_function)(&call->ctx_, &call->request,
+                                      &call->responder_, cq, cq,
+                                      &call->request_received_tag_);
+  }
+
+  // Enqueues a new request for the given service on the given
+  // completion queue, using the given `method_id`.
+  //
+  // The request will be handled with the given
+  // `handle_request_function`.
+  static void EnqueueRequestForMethod(
+      GrpcService* grpc_service, ::grpc::ServerCompletionQueue* cq,
+      int method_id, HandleRequestFunction handle_request_function,
+      bool supports_cancel) {
+    auto call = new Call<Service, GrpcService, RequestMessage, ResponseMessage>(
+        handle_request_function);
+    if (supports_cancel) {
+      call->RegisterCancellationHandler();
+    }
+
+    // Initial ref for call handed to grpc; released in Tag callback.
+    grpc_service->RequestAsyncUnary(method_id, &call->ctx_, &call->request,
+                                    &call->responder_, cq, cq,
+                                    &call->request_received_tag_);
   }
 
   RequestMessage request;
@@ -223,22 +237,23 @@ class Call : public UntypedCall<Service> {
   // NOTE: This method must be called before this call is enqueued on a
   // completion queue.
   void RegisterCancellationHandler() {
-    cancel_tag_.reset(new typename UntypedCall<Service>::Tag(
-        this, &UntypedCall<Service>::RequestCancelled));
-    ctx_.AsyncNotifyWhenDone(cancel_tag_.get());
+    this->Ref();  // Ref for grpc; released in Tag callback.
+    ctx_.AsyncNotifyWhenDone(&cancelled_tag_);
   }
 
   HandleRequestFunction handle_request_function_;
   ::grpc::ServerContext ctx_;
   ::grpc::ServerAsyncResponseWriter<ResponseMessage> responder_;
+
+  // Used as void* completion markers from grpc to indicate different
+  // events of interest for a Call.
+  using typename UntypedCall<Service>::Tag;
+  Tag request_received_tag_{this, Tag::kRequestReceived};
+  Tag response_sent_tag_{this, Tag::kResponseSent};
+  Tag cancelled_tag_{this, Tag::kCancelled};
+
   mutex mu_;
   std::function<void()> cancel_callback_ GUARDED_BY(mu_);
-
-  // This tag is initially owned by `*this` and borrowed by
-  // `ctx_->AsyncNotifyWhenDone()`. Ownership is transferred to the
-  // appropriate service's completion queue after
-  // `this->RequestReceived(..., true)` is called.
-  std::unique_ptr<typename UntypedCall<Service>::Tag> cancel_tag_;
 };
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_client_cq_tag.h b/tensorflow/core/distributed_runtime/rpc/grpc_client_cq_tag.h
index b305ab44fe..95c2c935f0 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_client_cq_tag.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_client_cq_tag.h
@@ -28,26 +28,14 @@ namespace tensorflow {
 // stored in a `grpc::CompletionQueue`.
 class GrpcClientCQTag {
  public:
-  GrpcClientCQTag(::grpc::ClientContext* context, StatusCallback cb)
-      : context_(context), cb_(std::move(cb)) {}
-  ~GrpcClientCQTag() { delete context_; }
+  GrpcClientCQTag() {}
+  virtual ~GrpcClientCQTag() {}
 
-  void OnCompleted(bool ok) {
-    if (!ok) {
-      VLOG(2) << "Call returned with non-ok status: "
-              << status_.error_message();
-    }
-    cb_(FromGrpcStatus(status_));
-  }
-
-  ::grpc::ClientContext* context() { return context_; }
-  ::grpc::Status* status() { return &status_; }
+  // OnCompleted is invoked when the RPC has finished.
+  // Implementations of OnCompleted must delete *this.
+  virtual void OnCompleted(bool ok) = 0;
 
  private:
-  ::grpc::ClientContext* context_;
-  ::grpc::Status status_;
-  StatusCallback cb_;
-
   TF_DISALLOW_COPY_AND_ASSIGN(GrpcClientCQTag);
 };
 
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc b/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc
index 9823980e83..c8a0892842 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_master_service.cc
@@ -118,7 +118,6 @@ class GrpcMasterService : public AsyncServiceInterface {
           static_cast<UntypedCall<GrpcMasterService>::Tag*>(tag);
       if (callback_tag) {
         callback_tag->OnCompleted(this, ok);
-        delete callback_tag;
       } else {
         // NOTE(mrry): A null `callback_tag` indicates that this is
         // the shutdown alarm.
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc b/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc
index 14ad1e0355..79d3b3e2f6 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_remote_worker.cc
@@ -37,8 +37,17 @@ class GrpcRemoteWorker : public WorkerInterface {
   explicit GrpcRemoteWorker(SharedGrpcChannelPtr channel,
                             ::grpc::CompletionQueue* completion_queue,
                             WorkerCacheLogger* logger)
-      : stub_(grpc::WorkerService::NewStub(channel)),
+      : channel_(channel),
         cq_(completion_queue),
+        getstatus_(Method(GrpcWorkerMethod::kGetStatus)),
+        registergraph_(Method(GrpcWorkerMethod::kRegisterGraph)),
+        deregistergraph_(Method(GrpcWorkerMethod::kDeregisterGraph)),
+        rungraph_(Method(GrpcWorkerMethod::kRunGraph)),
+        cleanupgraph_(Method(GrpcWorkerMethod::kCleanupGraph)),
+        cleanupall_(Method(GrpcWorkerMethod::kCleanupAll)),
+        recvtensor_(Method(GrpcWorkerMethod::kRecvTensor)),
+        logging_(Method(GrpcWorkerMethod::kLogging)),
+        tracing_(Method(GrpcWorkerMethod::kTracing)),
         logger_(logger) {}
 
   ~GrpcRemoteWorker() override {}
@@ -46,45 +55,36 @@ class GrpcRemoteWorker : public WorkerInterface {
   void GetStatusAsync(const GetStatusRequest* request,
                       GetStatusResponse* response,
                       StatusCallback done) override {
-    IssueRequest(request, response, &grpc::WorkerService::Stub::AsyncGetStatus,
-                 std::move(done));
+    IssueRequest(request, response, getstatus_, std::move(done));
   }
 
   void RegisterGraphAsync(const RegisterGraphRequest* request,
                           RegisterGraphResponse* response,
                           StatusCallback done) override {
-    IssueRequest(request, response,
-                 &grpc::WorkerService::Stub::AsyncRegisterGraph,
-                 std::move(done));
+    IssueRequest(request, response, registergraph_, std::move(done));
   }
 
   void DeregisterGraphAsync(const DeregisterGraphRequest* request,
                             DeregisterGraphResponse* response,
                             StatusCallback done) override {
-    IssueRequest(request, response,
-                 &grpc::WorkerService::Stub::AsyncDeregisterGraph,
-                 std::move(done));
+    IssueRequest(request, response, deregistergraph_, std::move(done));
   }
 
   void RunGraphAsync(CallOptions* call_opts, const RunGraphRequest* request,
                      RunGraphResponse* response, StatusCallback done) override {
-    IssueRequest(request, response, &grpc::WorkerService::Stub::AsyncRunGraph,
-                 std::move(done), call_opts);
+    IssueRequest(request, response, rungraph_, std::move(done), call_opts);
   }
 
   void CleanupGraphAsync(const CleanupGraphRequest* request,
                          CleanupGraphResponse* response,
                          StatusCallback done) override {
-    IssueRequest(request, response,
-                 &grpc::WorkerService::Stub::AsyncCleanupGraph,
-                 std::move(done));
+    IssueRequest(request, response, cleanupgraph_, std::move(done));
   }
 
   void CleanupAllAsync(const CleanupAllRequest* request,
                        CleanupAllResponse* response,
                        StatusCallback done) override {
-    IssueRequest(request, response, &grpc::WorkerService::Stub::AsyncCleanupAll,
-                 std::move(done));
+    IssueRequest(request, response, cleanupall_, std::move(done));
   }
 
   void RecvTensorAsync(CallOptions* call_opts, const RecvTensorRequest* request,
@@ -156,59 +156,98 @@ class GrpcRemoteWorker : public WorkerInterface {
       cb_to_use = &wrapper_done;
     }
 
-    IssueRequest(req_copy ? req_copy : request, response,
-                 &grpc::WorkerService::Stub::AsyncRecvTensor,
+    IssueRequest(req_copy ? req_copy : request, response, recvtensor_,
                  std::move(*cb_to_use), call_opts);
   }
 
   void LoggingAsync(const LoggingRequest* request, LoggingResponse* response,
                     StatusCallback done) override {
-    IssueRequest(request, response, &grpc::WorkerService::Stub::AsyncLogging,
-                 done);
+    IssueRequest(request, response, logging_, done);
   }
 
   void TracingAsync(const TracingRequest* request, TracingResponse* response,
                     StatusCallback done) override {
-    IssueRequest(request, response, &grpc::WorkerService::Stub::AsyncTracing,
-                 done);
+    IssueRequest(request, response, tracing_, done);
   }
 
  private:
+  // Object allocated per active RPC.
   template <class RequestMessage, class ResponseMessage>
-  using AsyncMethod =
-      std::unique_ptr<::grpc::ClientAsyncResponseReader<ResponseMessage>> (
-          grpc::WorkerService::Stub::*)(::grpc::ClientContext*,
-                                        const RequestMessage&,
-                                        ::grpc::CompletionQueue*);
+  class RPCState final : public GrpcClientCQTag {
+   public:
+    RPCState(::grpc::ChannelInterface* channel, ::grpc::CompletionQueue* cq,
+             const ::grpc::RpcMethod& method, const RequestMessage& request,
+             StatusCallback done, CallOptions* call_opts)
+        : call_opts_(call_opts),
+          reader_(channel, cq, method, InitContext(call_opts), request),
+          done_(std::move(done)) {}
+
+    ~RPCState() override {}
+
+    void StartRPC(ResponseMessage* response) {
+      reader_.Finish(response, &status_, this);
+    }
+
+    void OnCompleted(bool ok) override {
+      if (!ok) {
+        VLOG(2) << "Call returned with non-ok status: "
+                << status_.error_message();
+      }
+      if (call_opts_) {
+        call_opts_->ClearCancelCallback();
+      }
+      done_(FromGrpcStatus(status_));
+      delete this;
+    }
+
+   private:
+    CallOptions* call_opts_;
+    ::grpc::ClientContext context_;
+    ::grpc::ClientAsyncResponseReader<ResponseMessage> reader_;
+    ::grpc::Status status_;
+    StatusCallback done_;
+
+    ::grpc::ClientContext* InitContext(CallOptions* call_opts) {
+      // The initialization and recovery protocols rely on blocking
+      // until we get a response.
+      context_.set_fail_fast(false);
+      if (call_opts) {
+        call_opts->SetCancelCallback([this]() { context_.TryCancel(); });
+      }
+      return &context_;
+    }
+  };
 
   // Utility method for issuing a generic asynchronous request. The
   // given callback, `done`, will be called when the RPC completes.
   template <class RequestMessage, class ResponseMessage>
   void IssueRequest(const RequestMessage* request, ResponseMessage* response,
-                    AsyncMethod<RequestMessage, ResponseMessage> async_method,
-                    StatusCallback done, CallOptions* call_opts = nullptr) {
-    ::grpc::ClientContext* context = new ::grpc::ClientContext;
-    // The initialization and recovery protocols rely on blocking
-    // until we get a response.
-    context->set_fail_fast(false);
-    if (call_opts) {
-      call_opts->SetCancelCallback([context]() { context->TryCancel(); });
-    }
-    auto rpc = (stub_.get()->*async_method)(context, *request, cq_).release();
-    GrpcClientCQTag* tag =
-        new GrpcClientCQTag(context, [rpc, done, call_opts](Status s) {
-          if (call_opts) {
-            call_opts->ClearCancelCallback();
-          }
-          delete rpc;
-          done(s);
-        });
-    rpc->Finish(response, tag->status(), tag);
+                    const ::grpc::RpcMethod& method, StatusCallback done,
+                    CallOptions* call_opts = nullptr) {
+    auto state = new RPCState<RequestMessage, ResponseMessage>(
+        channel_.get(), cq_, method, *request, std::move(done), call_opts);
+    state->StartRPC(response);
   }
 
-  std::unique_ptr<grpc::WorkerService::Stub> stub_;
+  // Helper function for initializing the RpcMethod objects below.
+  ::grpc::RpcMethod Method(GrpcWorkerMethod id) {
+    return ::grpc::RpcMethod(GrpcWorkerMethodName(id),
+                             ::grpc::RpcMethod::NORMAL_RPC, channel_);
+  }
+
+  SharedGrpcChannelPtr channel_;
   ::grpc::CompletionQueue* cq_;
 
+  const ::grpc::RpcMethod getstatus_;
+  const ::grpc::RpcMethod registergraph_;
+  const ::grpc::RpcMethod deregistergraph_;
+  const ::grpc::RpcMethod rungraph_;
+  const ::grpc::RpcMethod cleanupgraph_;
+  const ::grpc::RpcMethod cleanupall_;
+  const ::grpc::RpcMethod recvtensor_;
+  const ::grpc::RpcMethod logging_;
+  const ::grpc::RpcMethod tracing_;
+
   // Support for logging.
   WorkerCacheLogger* logger_;
   bool retry_unavailable_;
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc b/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc
index 485ed14c0f..0c0c80117a 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.cc
@@ -38,7 +38,6 @@ class GrpcWorkerCache : public WorkerCachePartial {
           while (completion_queue_.Next(&tag, &ok)) {
             GrpcClientCQTag* callback_tag = static_cast<GrpcClientCQTag*>(tag);
             callback_tag->OnCompleted(ok);
-            delete callback_tag;
           }
         });
   }
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc
index 9f7d009a36..9a87bbda1b 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service.cc
@@ -85,7 +85,7 @@ class GrpcWorkerService : public AsyncServiceInterface {
   }
 
 // This macro creates a new request for the given RPC method name
-// (e.g., `ENQUEUE_REQUEST(GetStatus);`), and enqueues it on
+// (e.g., `ENQUEUE_REQUEST(GetStatus, false);`), and enqueues it on
 // `this->cq_`.
 //
 // This macro is invoked one or more times for each RPC method to
@@ -95,17 +95,17 @@ class GrpcWorkerService : public AsyncServiceInterface {
 // The implementation of the request handler for each RPC method
 // must ensure that it calls ENQUEUE_REQUEST() for that RPC method,
 // to keep accepting new requests.
-#define ENQUEUE_REQUEST(method, supports_cancel)                              \
-  do {                                                                        \
-    mutex_lock l(shutdown_mu_);                                               \
-    if (!is_shutdown_) {                                                      \
-      Call<GrpcWorkerService, grpc::WorkerService::AsyncService,              \
-           method##Request, method##Response>::                               \
-          EnqueueRequest(&worker_service_, cq_,                               \
-                         &grpc::WorkerService::AsyncService::Request##method, \
-                         &GrpcWorkerService::method##Handler,                 \
-                         (supports_cancel));                                  \
-    }                                                                         \
+#define ENQUEUE_REQUEST(method, supports_cancel)                       \
+  do {                                                                 \
+    mutex_lock l(shutdown_mu_);                                        \
+    if (!is_shutdown_) {                                               \
+      Call<GrpcWorkerService, grpc::WorkerService::AsyncService,       \
+           method##Request, method##Response>::                        \
+          EnqueueRequestForMethod(                                     \
+              &worker_service_, cq_,                                   \
+              static_cast<int>(GrpcWorkerMethod::k##method),           \
+              &GrpcWorkerService::method##Handler, (supports_cancel)); \
+    }                                                                  \
   } while (0)
 
   // This method blocks forever handling requests from the completion queue.
@@ -145,7 +145,6 @@ class GrpcWorkerService : public AsyncServiceInterface {
           static_cast<UntypedCall<GrpcWorkerService>::Tag*>(tag);
       if (callback_tag) {
         callback_tag->OnCompleted(this, ok);
-        delete callback_tag;
       } else {
         // NOTE(mrry): A null `callback_tag` indicates that this is
         // the shutdown alarm.
@@ -267,9 +266,9 @@ class GrpcWorkerService : public AsyncServiceInterface {
     if (!is_shutdown_) {
       Call<GrpcWorkerService, grpc::WorkerService::AsyncService,
            RecvTensorRequest, ::grpc::ByteBuffer>::
-          EnqueueRequest(
+          EnqueueRequestForMethod(
               &worker_service_, cq_,
-              &grpc::WorkerService::AsyncService::RequestRecvTensorRaw,
+              static_cast<int>(GrpcWorkerMethod::kRecvTensor),
               &GrpcWorkerService::RecvTensorHandlerRaw,
               true /* supports cancel*/);
     }
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.cc b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.cc
index 9f5e50f90e..a3ba22a95d 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.cc
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.cc
@@ -26,127 +26,36 @@ limitations under the License.
 
 namespace tensorflow {
 
-namespace grpc {
-
-static const char* grpcWorkerService_method_names[] = {
-    "/tensorflow.WorkerService/GetStatus",
-    "/tensorflow.WorkerService/RegisterGraph",
-    "/tensorflow.WorkerService/DeregisterGraph",
-    "/tensorflow.WorkerService/RunGraph",
-    "/tensorflow.WorkerService/CleanupGraph",
-    "/tensorflow.WorkerService/CleanupAll",
-    "/tensorflow.WorkerService/RecvTensor",
-    "/tensorflow.WorkerService/Logging",
-    "/tensorflow.WorkerService/Tracing",
-};
-
-std::unique_ptr<WorkerService::Stub> WorkerService::NewStub(
-    const std::shared_ptr< ::grpc::ChannelInterface>& channel,
-    const ::grpc::StubOptions& options) {
-  std::unique_ptr<WorkerService::Stub> stub(new WorkerService::Stub(channel));
-  return stub;
-}
-
-WorkerService::Stub::Stub(
-    const std::shared_ptr< ::grpc::ChannelInterface>& channel)
-    : channel_(channel),
-      rpcmethod_GetStatus_(grpcWorkerService_method_names[0],
-                           ::grpc::RpcMethod::NORMAL_RPC, channel),
-      rpcmethod_RegisterGraph_(grpcWorkerService_method_names[1],
-                               ::grpc::RpcMethod::NORMAL_RPC, channel),
-      rpcmethod_DeregisterGraph_(grpcWorkerService_method_names[2],
-                                 ::grpc::RpcMethod::NORMAL_RPC, channel),
-      rpcmethod_RunGraph_(grpcWorkerService_method_names[3],
-                          ::grpc::RpcMethod::NORMAL_RPC, channel),
-      rpcmethod_CleanupGraph_(grpcWorkerService_method_names[4],
-                              ::grpc::RpcMethod::NORMAL_RPC, channel),
-      rpcmethod_CleanupAll_(grpcWorkerService_method_names[5],
-                            ::grpc::RpcMethod::NORMAL_RPC, channel),
-      rpcmethod_RecvTensor_(grpcWorkerService_method_names[6],
-                            ::grpc::RpcMethod::NORMAL_RPC, channel),
-      rpcmethod_Logging_(grpcWorkerService_method_names[7],
-                         ::grpc::RpcMethod::NORMAL_RPC, channel),
-      rpcmethod_Tracing_(grpcWorkerService_method_names[8],
-                         ::grpc::RpcMethod::NORMAL_RPC, channel) {}
-
-::grpc::ClientAsyncResponseReader<GetStatusResponse>*
-WorkerService::Stub::AsyncGetStatusRaw(::grpc::ClientContext* context,
-                                       const GetStatusRequest& request,
-                                       ::grpc::CompletionQueue* cq) {
-  return new ::grpc::ClientAsyncResponseReader<GetStatusResponse>(
-      channel_.get(), cq, rpcmethod_GetStatus_, context, request);
-}
-
-::grpc::ClientAsyncResponseReader<RegisterGraphResponse>*
-WorkerService::Stub::AsyncRegisterGraphRaw(::grpc::ClientContext* context,
-                                           const RegisterGraphRequest& request,
-                                           ::grpc::CompletionQueue* cq) {
-  return new ::grpc::ClientAsyncResponseReader<RegisterGraphResponse>(
-      channel_.get(), cq, rpcmethod_RegisterGraph_, context, request);
-}
-
-::grpc::ClientAsyncResponseReader<DeregisterGraphResponse>*
-WorkerService::Stub::AsyncDeregisterGraphRaw(
-    ::grpc::ClientContext* context, const DeregisterGraphRequest& request,
-    ::grpc::CompletionQueue* cq) {
-  return new ::grpc::ClientAsyncResponseReader<DeregisterGraphResponse>(
-      channel_.get(), cq, rpcmethod_DeregisterGraph_, context, request);
-}
-
-::grpc::ClientAsyncResponseReader<RunGraphResponse>*
-WorkerService::Stub::AsyncRunGraphRaw(::grpc::ClientContext* context,
-                                      const RunGraphRequest& request,
-                                      ::grpc::CompletionQueue* cq) {
-  return new ::grpc::ClientAsyncResponseReader<RunGraphResponse>(
-      channel_.get(), cq, rpcmethod_RunGraph_, context, request);
-}
-
-::grpc::ClientAsyncResponseReader<CleanupGraphResponse>*
-WorkerService::Stub::AsyncCleanupGraphRaw(::grpc::ClientContext* context,
-                                          const CleanupGraphRequest& request,
-                                          ::grpc::CompletionQueue* cq) {
-  return new ::grpc::ClientAsyncResponseReader<CleanupGraphResponse>(
-      channel_.get(), cq, rpcmethod_CleanupGraph_, context, request);
-}
-
-::grpc::ClientAsyncResponseReader<CleanupAllResponse>*
-WorkerService::Stub::AsyncCleanupAllRaw(::grpc::ClientContext* context,
-                                        const CleanupAllRequest& request,
-                                        ::grpc::CompletionQueue* cq) {
-  return new ::grpc::ClientAsyncResponseReader<CleanupAllResponse>(
-      channel_.get(), cq, rpcmethod_CleanupAll_, context, request);
-}
-
-::grpc::ClientAsyncResponseReader<TensorResponse>*
-WorkerService::Stub::AsyncRecvTensorRaw(::grpc::ClientContext* context,
-                                        const RecvTensorRequest& request,
-                                        ::grpc::CompletionQueue* cq) {
-  return new ::grpc::ClientAsyncResponseReader<TensorResponse>(
-      channel_.get(), cq, rpcmethod_RecvTensor_, context, request);
-}
-
-::grpc::ClientAsyncResponseReader<LoggingResponse>*
-WorkerService::Stub::AsyncLoggingRaw(::grpc::ClientContext* context,
-                                     const LoggingRequest& request,
-                                     ::grpc::CompletionQueue* cq) {
-  return new ::grpc::ClientAsyncResponseReader<LoggingResponse>(
-      channel_.get(), cq, rpcmethod_Logging_, context, request);
+const char* GrpcWorkerMethodName(GrpcWorkerMethod id) {
+  switch (id) {
+    case GrpcWorkerMethod::kGetStatus:
+      return "/tensorflow.WorkerService/GetStatus";
+    case GrpcWorkerMethod::kRegisterGraph:
+      return "/tensorflow.WorkerService/RegisterGraph";
+    case GrpcWorkerMethod::kDeregisterGraph:
+      return "/tensorflow.WorkerService/DeregisterGraph";
+    case GrpcWorkerMethod::kRunGraph:
+      return "/tensorflow.WorkerService/RunGraph";
+    case GrpcWorkerMethod::kCleanupGraph:
+      return "/tensorflow.WorkerService/CleanupGraph";
+    case GrpcWorkerMethod::kCleanupAll:
+      return "/tensorflow.WorkerService/CleanupAll";
+    case GrpcWorkerMethod::kRecvTensor:
+      return "/tensorflow.WorkerService/RecvTensor";
+    case GrpcWorkerMethod::kLogging:
+      return "/tensorflow.WorkerService/Logging";
+    case GrpcWorkerMethod::kTracing:
+      return "/tensorflow.WorkerService/Tracing";
+  }
 }
 
-::grpc::ClientAsyncResponseReader<TracingResponse>*
-WorkerService::Stub::AsyncTracingRaw(::grpc::ClientContext* context,
-                                     const TracingRequest& request,
-                                     ::grpc::CompletionQueue* cq) {
-  return new ::grpc::ClientAsyncResponseReader<TracingResponse>(
-      channel_.get(), cq, rpcmethod_Tracing_, context, request);
-}
+namespace grpc {
 
 WorkerService::AsyncService::AsyncService() {
-  (void)grpcWorkerService_method_names;
-  for (int i = 0; i < TF_ARRAYSIZE(grpcWorkerService_method_names); ++i) {
-    AddMethod(new ::grpc::RpcServiceMethod(grpcWorkerService_method_names[i],
-                                           ::grpc::RpcMethod::NORMAL_RPC,
-                                           nullptr));
+  for (int i = 0; i < kGrpcNumWorkerMethods; ++i) {
+    AddMethod(new ::grpc::RpcServiceMethod(
+        GrpcWorkerMethodName(static_cast<GrpcWorkerMethod>(i)),
+        ::grpc::RpcMethod::NORMAL_RPC, nullptr));
     ::grpc::Service::MarkMethodAsync(i);
   }
 }
diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h
index 0513bc6894..f3aac795ca 100644
--- a/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h
+++ b/tensorflow/core/distributed_runtime/rpc/grpc_worker_service_impl.h
@@ -106,6 +106,23 @@ class SerializationTraits<tensorflow::TensorResponse>
 
 namespace tensorflow {
 
+// Names of worker methods.
+enum class GrpcWorkerMethod {
+  kGetStatus,
+  kRegisterGraph,
+  kDeregisterGraph,
+  kRunGraph,
+  kCleanupGraph,
+  kCleanupAll,
+  kRecvTensor,
+  kLogging,
+  kTracing,
+};
+static const int kGrpcNumWorkerMethods =
+    static_cast<int>(GrpcWorkerMethod::kTracing) + 1;
+
+const char* GrpcWorkerMethodName(GrpcWorkerMethod id);
+
 namespace grpc {
 
 // Implementation of `tensorflow.WorkerService`, based on the
@@ -114,364 +131,13 @@ namespace grpc {
 // See the proto file for the definition of methods and messages.
 class WorkerService GRPC_FINAL {
  public:
-  class StubInterface {
-   public:
-    virtual ~StubInterface() {}
-    std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::GetStatusResponse>>
-    AsyncGetStatus(::grpc::ClientContext* context,
-                   const ::tensorflow::GetStatusRequest& request,
-                   ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-          ::tensorflow::GetStatusResponse>>(
-          AsyncGetStatusRaw(context, request, cq));
-    }
-    std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::RegisterGraphResponse>>
-    AsyncRegisterGraph(::grpc::ClientContext* context,
-                       const ::tensorflow::RegisterGraphRequest& request,
-                       ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-          ::tensorflow::RegisterGraphResponse>>(
-          AsyncRegisterGraphRaw(context, request, cq));
-    }
-    std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::DeregisterGraphResponse>>
-    AsyncDeregisterGraph(::grpc::ClientContext* context,
-                         const ::tensorflow::DeregisterGraphRequest& request,
-                         ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-          ::tensorflow::DeregisterGraphResponse>>(
-          AsyncDeregisterGraphRaw(context, request, cq));
-    }
-    std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::RunGraphResponse>>
-    AsyncRunGraph(::grpc::ClientContext* context,
-                  const ::tensorflow::RunGraphRequest& request,
-                  ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-          ::tensorflow::RunGraphResponse>>(
-          AsyncRunGraphRaw(context, request, cq));
-    }
-    std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::CleanupGraphResponse>>
-    AsyncCleanupGraph(::grpc::ClientContext* context,
-                      const ::tensorflow::CleanupGraphRequest& request,
-                      ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-          ::tensorflow::CleanupGraphResponse>>(
-          AsyncCleanupGraphRaw(context, request, cq));
-    }
-    std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::CleanupAllResponse>>
-    AsyncCleanupAll(::grpc::ClientContext* context,
-                    const ::tensorflow::CleanupAllRequest& request,
-                    ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-          ::tensorflow::CleanupAllResponse>>(
-          AsyncCleanupAllRaw(context, request, cq));
-    }
-    std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::TensorResponse>>
-    AsyncRecvTensor(::grpc::ClientContext* context,
-                    const ::tensorflow::RecvTensorRequest& request,
-                    ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-          ::tensorflow::TensorResponse>>(
-          AsyncRecvTensorRaw(context, request, cq));
-    }
-    std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::LoggingResponse>>
-    AsyncLogging(::grpc::ClientContext* context,
-                 const ::tensorflow::LoggingRequest& request,
-                 ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-          ::tensorflow::LoggingResponse>>(
-          AsyncLoggingRaw(context, request, cq));
-    }
-    std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::TracingResponse>>
-    AsyncTracing(::grpc::ClientContext* context,
-                 const ::tensorflow::TracingRequest& request,
-                 ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<::grpc::ClientAsyncResponseReaderInterface<
-          ::tensorflow::TracingResponse>>(
-          AsyncTracingRaw(context, request, cq));
-    }
-
-   private:
-    virtual ::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::GetStatusResponse>*
-    AsyncGetStatusRaw(::grpc::ClientContext* context,
-                      const ::tensorflow::GetStatusRequest& request,
-                      ::grpc::CompletionQueue* cq) = 0;
-    virtual ::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::RegisterGraphResponse>*
-    AsyncRegisterGraphRaw(::grpc::ClientContext* context,
-                          const ::tensorflow::RegisterGraphRequest& request,
-                          ::grpc::CompletionQueue* cq) = 0;
-    virtual ::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::DeregisterGraphResponse>*
-    AsyncDeregisterGraphRaw(::grpc::ClientContext* context,
-                            const ::tensorflow::DeregisterGraphRequest& request,
-                            ::grpc::CompletionQueue* cq) = 0;
-    virtual ::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::RunGraphResponse>*
-    AsyncRunGraphRaw(::grpc::ClientContext* context,
-                     const ::tensorflow::RunGraphRequest& request,
-                     ::grpc::CompletionQueue* cq) = 0;
-    virtual ::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::CleanupGraphResponse>*
-    AsyncCleanupGraphRaw(::grpc::ClientContext* context,
-                         const ::tensorflow::CleanupGraphRequest& request,
-                         ::grpc::CompletionQueue* cq) = 0;
-    virtual ::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::CleanupAllResponse>*
-    AsyncCleanupAllRaw(::grpc::ClientContext* context,
-                       const ::tensorflow::CleanupAllRequest& request,
-                       ::grpc::CompletionQueue* cq) = 0;
-    virtual ::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::TensorResponse>*
-    AsyncRecvTensorRaw(::grpc::ClientContext* context,
-                       const ::tensorflow::RecvTensorRequest& request,
-                       ::grpc::CompletionQueue* cq) = 0;
-    virtual ::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::LoggingResponse>*
-    AsyncLoggingRaw(::grpc::ClientContext* context,
-                    const ::tensorflow::LoggingRequest& request,
-                    ::grpc::CompletionQueue* cq) = 0;
-    virtual ::grpc::ClientAsyncResponseReaderInterface<
-        ::tensorflow::TracingResponse>*
-    AsyncTracingRaw(::grpc::ClientContext* context,
-                    const ::tensorflow::TracingRequest& request,
-                    ::grpc::CompletionQueue* cq) = 0;
-  };
-  class Stub GRPC_FINAL : public StubInterface {
-   public:
-    Stub(const std::shared_ptr<::grpc::ChannelInterface>& channel);
-    std::unique_ptr<
-        ::grpc::ClientAsyncResponseReader<::tensorflow::GetStatusResponse>>
-    AsyncGetStatus(::grpc::ClientContext* context,
-                   const ::tensorflow::GetStatusRequest& request,
-                   ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<
-          ::grpc::ClientAsyncResponseReader<::tensorflow::GetStatusResponse>>(
-          AsyncGetStatusRaw(context, request, cq));
-    }
-    std::unique_ptr<
-        ::grpc::ClientAsyncResponseReader<::tensorflow::RegisterGraphResponse>>
-    AsyncRegisterGraph(::grpc::ClientContext* context,
-                       const ::tensorflow::RegisterGraphRequest& request,
-                       ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<::grpc::ClientAsyncResponseReader<
-          ::tensorflow::RegisterGraphResponse>>(
-          AsyncRegisterGraphRaw(context, request, cq));
-    }
-    std::unique_ptr<::grpc::ClientAsyncResponseReader<
-        ::tensorflow::DeregisterGraphResponse>>
-    AsyncDeregisterGraph(::grpc::ClientContext* context,
-                         const ::tensorflow::DeregisterGraphRequest& request,
-                         ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<::grpc::ClientAsyncResponseReader<
-          ::tensorflow::DeregisterGraphResponse>>(
-          AsyncDeregisterGraphRaw(context, request, cq));
-    }
-    std::unique_ptr<
-        ::grpc::ClientAsyncResponseReader<::tensorflow::RunGraphResponse>>
-    AsyncRunGraph(::grpc::ClientContext* context,
-                  const ::tensorflow::RunGraphRequest& request,
-                  ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<
-          ::grpc::ClientAsyncResponseReader<::tensorflow::RunGraphResponse>>(
-          AsyncRunGraphRaw(context, request, cq));
-    }
-    std::unique_ptr<
-        ::grpc::ClientAsyncResponseReader<::tensorflow::CleanupGraphResponse>>
-    AsyncCleanupGraph(::grpc::ClientContext* context,
-                      const ::tensorflow::CleanupGraphRequest& request,
-                      ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<::grpc::ClientAsyncResponseReader<
-          ::tensorflow::CleanupGraphResponse>>(
-          AsyncCleanupGraphRaw(context, request, cq));
-    }
-    std::unique_ptr<
-        ::grpc::ClientAsyncResponseReader<::tensorflow::CleanupAllResponse>>
-    AsyncCleanupAll(::grpc::ClientContext* context,
-                    const ::tensorflow::CleanupAllRequest& request,
-                    ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<
-          ::grpc::ClientAsyncResponseReader<::tensorflow::CleanupAllResponse>>(
-          AsyncCleanupAllRaw(context, request, cq));
-    }
-    std::unique_ptr<
-        ::grpc::ClientAsyncResponseReader<::tensorflow::TensorResponse>>
-    AsyncRecvTensor(::grpc::ClientContext* context,
-                    const ::tensorflow::RecvTensorRequest& request,
-                    ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<
-          ::grpc::ClientAsyncResponseReader<::tensorflow::TensorResponse>>(
-          AsyncRecvTensorRaw(context, request, cq));
-    }
-    std::unique_ptr<
-        ::grpc::ClientAsyncResponseReader<::tensorflow::LoggingResponse>>
-    AsyncLogging(::grpc::ClientContext* context,
-                 const ::tensorflow::LoggingRequest& request,
-                 ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<
-          ::grpc::ClientAsyncResponseReader<::tensorflow::LoggingResponse>>(
-          AsyncLoggingRaw(context, request, cq));
-    }
-    std::unique_ptr<
-        ::grpc::ClientAsyncResponseReader<::tensorflow::TracingResponse>>
-    AsyncTracing(::grpc::ClientContext* context,
-                 const ::tensorflow::TracingRequest& request,
-                 ::grpc::CompletionQueue* cq) {
-      return std::unique_ptr<
-          ::grpc::ClientAsyncResponseReader<::tensorflow::TracingResponse>>(
-          AsyncTracingRaw(context, request, cq));
-    }
-
-   private:
-    std::shared_ptr<::grpc::ChannelInterface> channel_;
-    ::grpc::ClientAsyncResponseReader<::tensorflow::GetStatusResponse>*
-    AsyncGetStatusRaw(::grpc::ClientContext* context,
-                      const ::tensorflow::GetStatusRequest& request,
-                      ::grpc::CompletionQueue* cq) GRPC_OVERRIDE;
-    ::grpc::ClientAsyncResponseReader<::tensorflow::RegisterGraphResponse>*
-    AsyncRegisterGraphRaw(::grpc::ClientContext* context,
-                          const ::tensorflow::RegisterGraphRequest& request,
-                          ::grpc::CompletionQueue* cq) GRPC_OVERRIDE;
-    ::grpc::ClientAsyncResponseReader<::tensorflow::DeregisterGraphResponse>*
-    AsyncDeregisterGraphRaw(::grpc::ClientContext* context,
-                            const ::tensorflow::DeregisterGraphRequest& request,
-                            ::grpc::CompletionQueue* cq) GRPC_OVERRIDE;
-    ::grpc::ClientAsyncResponseReader<::tensorflow::RunGraphResponse>*
-    AsyncRunGraphRaw(::grpc::ClientContext* context,
-                     const ::tensorflow::RunGraphRequest& request,
-                     ::grpc::CompletionQueue* cq) GRPC_OVERRIDE;
-    ::grpc::ClientAsyncResponseReader<::tensorflow::CleanupGraphResponse>*
-    AsyncCleanupGraphRaw(::grpc::ClientContext* context,
-                         const ::tensorflow::CleanupGraphRequest& request,
-                         ::grpc::CompletionQueue* cq) GRPC_OVERRIDE;
-    ::grpc::ClientAsyncResponseReader<::tensorflow::CleanupAllResponse>*
-    AsyncCleanupAllRaw(::grpc::ClientContext* context,
-                       const ::tensorflow::CleanupAllRequest& request,
-                       ::grpc::CompletionQueue* cq) GRPC_OVERRIDE;
-    ::grpc::ClientAsyncResponseReader<::tensorflow::TensorResponse>*
-    AsyncRecvTensorRaw(::grpc::ClientContext* context,
-                       const ::tensorflow::RecvTensorRequest& request,
-                       ::grpc::CompletionQueue* cq) GRPC_OVERRIDE;
-    ::grpc::ClientAsyncResponseReader<::tensorflow::LoggingResponse>*
-    AsyncLoggingRaw(::grpc::ClientContext* context,
-                    const ::tensorflow::LoggingRequest& request,
-                    ::grpc::CompletionQueue* cq) GRPC_OVERRIDE;
-    ::grpc::ClientAsyncResponseReader<::tensorflow::TracingResponse>*
-    AsyncTracingRaw(::grpc::ClientContext* context,
-                    const ::tensorflow::TracingRequest& request,
-                    ::grpc::CompletionQueue* cq) GRPC_OVERRIDE;
-    const ::grpc::RpcMethod rpcmethod_GetStatus_;
-    const ::grpc::RpcMethod rpcmethod_RegisterGraph_;
-    const ::grpc::RpcMethod rpcmethod_DeregisterGraph_;
-    const ::grpc::RpcMethod rpcmethod_RunGraph_;
-    const ::grpc::RpcMethod rpcmethod_CleanupGraph_;
-    const ::grpc::RpcMethod rpcmethod_CleanupAll_;
-    const ::grpc::RpcMethod rpcmethod_RecvTensor_;
-    const ::grpc::RpcMethod rpcmethod_Logging_;
-    const ::grpc::RpcMethod rpcmethod_Tracing_;
-  };
-  static std::unique_ptr<Stub> NewStub(
-      const std::shared_ptr<::grpc::ChannelInterface>& channel,
-      const ::grpc::StubOptions& options = ::grpc::StubOptions());
-
   class AsyncService : public ::grpc::Service {
    public:
     AsyncService();
     virtual ~AsyncService();
-    void RequestGetStatus(
-        ::grpc::ServerContext* context, ::tensorflow::GetStatusRequest* request,
-        ::grpc::ServerAsyncResponseWriter<::tensorflow::GetStatusResponse>*
-            response,
-        ::grpc::CompletionQueue* new_call_cq,
-        ::grpc::ServerCompletionQueue* notification_cq, void* tag) {
-      ::grpc::Service::RequestAsyncUnary(0, context, request, response,
-                                         new_call_cq, notification_cq, tag);
-    }
-    void RequestRegisterGraph(
-        ::grpc::ServerContext* context,
-        ::tensorflow::RegisterGraphRequest* request,
-        ::grpc::ServerAsyncResponseWriter<::tensorflow::RegisterGraphResponse>*
-            response,
-        ::grpc::CompletionQueue* new_call_cq,
-        ::grpc::ServerCompletionQueue* notification_cq, void* tag) {
-      ::grpc::Service::RequestAsyncUnary(1, context, request, response,
-                                         new_call_cq, notification_cq, tag);
-    }
-    void RequestDeregisterGraph(
-        ::grpc::ServerContext* context,
-        ::tensorflow::DeregisterGraphRequest* request,
-        ::grpc::ServerAsyncResponseWriter<
-            ::tensorflow::DeregisterGraphResponse>* response,
-        ::grpc::CompletionQueue* new_call_cq,
-        ::grpc::ServerCompletionQueue* notification_cq, void* tag) {
-      ::grpc::Service::RequestAsyncUnary(2, context, request, response,
-                                         new_call_cq, notification_cq, tag);
-    }
-    void RequestRunGraph(
-        ::grpc::ServerContext* context, ::tensorflow::RunGraphRequest* request,
-        ::grpc::ServerAsyncResponseWriter<::tensorflow::RunGraphResponse>*
-            response,
-        ::grpc::CompletionQueue* new_call_cq,
-        ::grpc::ServerCompletionQueue* notification_cq, void* tag) {
-      ::grpc::Service::RequestAsyncUnary(3, context, request, response,
-                                         new_call_cq, notification_cq, tag);
-    }
-    void RequestCleanupGraph(
-        ::grpc::ServerContext* context,
-        ::tensorflow::CleanupGraphRequest* request,
-        ::grpc::ServerAsyncResponseWriter<::tensorflow::CleanupGraphResponse>*
-            response,
-        ::grpc::CompletionQueue* new_call_cq,
-        ::grpc::ServerCompletionQueue* notification_cq, void* tag) {
-      ::grpc::Service::RequestAsyncUnary(4, context, request, response,
-                                         new_call_cq, notification_cq, tag);
-    }
-    void RequestCleanupAll(
-        ::grpc::ServerContext* context,
-        ::tensorflow::CleanupAllRequest* request,
-        ::grpc::ServerAsyncResponseWriter<::tensorflow::CleanupAllResponse>*
-            response,
-        ::grpc::CompletionQueue* new_call_cq,
-        ::grpc::ServerCompletionQueue* notification_cq, void* tag) {
-      ::grpc::Service::RequestAsyncUnary(5, context, request, response,
-                                         new_call_cq, notification_cq, tag);
-    }
-    void RequestRecvTensorRaw(
-        ::grpc::ServerContext* context,
-        ::tensorflow::RecvTensorRequest* request,
-        ::grpc::ServerAsyncResponseWriter<::grpc::ByteBuffer>* response,
-        ::grpc::CompletionQueue* new_call_cq,
-        ::grpc::ServerCompletionQueue* notification_cq, void* tag) {
-      ::grpc::Service::RequestAsyncUnary(6, context, request, response,
-                                         new_call_cq, notification_cq, tag);
-    }
-    void RequestLogging(
-        ::grpc::ServerContext* context, ::tensorflow::LoggingRequest* request,
-        ::grpc::ServerAsyncResponseWriter<::tensorflow::LoggingResponse>*
-            response,
-        ::grpc::CompletionQueue* new_call_cq,
-        ::grpc::ServerCompletionQueue* notification_cq, void* tag) {
-      ::grpc::Service::RequestAsyncUnary(7, context, request, response,
-                                         new_call_cq, notification_cq, tag);
-    }
-    void RequestTracing(
-        ::grpc::ServerContext* context, ::tensorflow::TracingRequest* request,
-        ::grpc::ServerAsyncResponseWriter<::tensorflow::TracingResponse>*
-            response,
-        ::grpc::CompletionQueue* new_call_cq,
-        ::grpc::ServerCompletionQueue* notification_cq, void* tag) {
-      ::grpc::Service::RequestAsyncUnary(8, context, request, response,
-                                         new_call_cq, notification_cq, tag);
-    }
+
+    // Make RequestAsyncUnary public for grpc_call.h
+    using ::grpc::Service::RequestAsyncUnary;
   };
 };
 
-- 
GitLab


From d1abf00728cb42ac341a3614b2038aad89f8279e Mon Sep 17 00:00:00 2001
From: Justine Tunney <jart@google.com>
Date: Mon, 22 Aug 2016 13:03:29 -0800
Subject: [PATCH 009/223] Use asciinumerical sort for tooltips and more

In order to accomplish this, I refactored the new sorting algorithm
out into a separate tf-sorting component.
Change: 130979195
---
 .../components/tf-backend/backend.ts          | 58 +-------------
 .../tf-backend/test/backendTests.ts           | 52 -------------
 .../components/tf-backend/test/index.html     |  1 +
 .../components/tf-backend/tf-backend.html     |  1 +
 .../tf-dashboard-common/categorizer.ts        |  4 +-
 .../tf-dashboard-common/test/index.html       |  1 +
 .../tf-dashboard-common/tf-categorizer.html   |  1 +
 .../tf-event-dashboard.html                   |  2 +-
 .../components/tf-globals/tf-globals.html     |  2 +-
 .../vz-line-chart/vz-line-chart.html          |  4 +-
 .../components/vz-line-chart/vz-line-chart.ts |  7 +-
 .../components/vz-sorting/sorting.ts          | 78 +++++++++++++++++++
 .../components/vz-sorting/test/index.html     | 22 ++++++
 .../vz-sorting/test/sortingTests.ts           | 71 +++++++++++++++++
 .../components/vz-sorting/vz-sorting.html     | 17 ++++
 15 files changed, 206 insertions(+), 115 deletions(-)
 create mode 100644 tensorflow/tensorboard/components/vz-sorting/sorting.ts
 create mode 100644 tensorflow/tensorboard/components/vz-sorting/test/index.html
 create mode 100644 tensorflow/tensorboard/components/vz-sorting/test/sortingTests.ts
 create mode 100644 tensorflow/tensorboard/components/vz-sorting/vz-sorting.html

diff --git a/tensorflow/tensorboard/components/tf-backend/backend.ts b/tensorflow/tensorboard/components/tf-backend/backend.ts
index 5c75d79010..39c967b239 100644
--- a/tensorflow/tensorboard/components/tf-backend/backend.ts
+++ b/tensorflow/tensorboard/components/tf-backend/backend.ts
@@ -260,64 +260,12 @@ module TF.Backend {
 
   /** Given a RunToTag, return sorted array of all runs */
   export function getRuns(r: RunToTag): string[] {
-    return _.keys(r).sort(compareTagNames);
+    return _.keys(r).sort(VZ.Sorting.compareTagNames);
   }
 
   /** Given a RunToTag, return array of all tags (sorted + dedup'd) */
   export function getTags(r: RunToTag): string[] {
-    return _.union.apply(null, _.values(r)).sort(compareTagNames);
-  }
-
-  /** Compares tag names asciinumerically broken into components. */
-  export function compareTagNames(a, b: string): number {
-    let ai = 0;
-    let bi = 0;
-    while (true) {
-      if (ai === a.length) return bi === b.length ? 0 : -1;
-      if (bi === b.length) return 1;
-      if (isDigit(a[ai]) && isDigit(b[bi])) {
-        let ais = ai;
-        let bis = bi;
-        ai = consumeNumber(a, ai + 1);
-        bi = consumeNumber(b, bi + 1);
-        let an = parseFloat(a.slice(ais, ai));
-        let bn = parseFloat(b.slice(bis, bi));
-        if (an < bn) return -1;
-        if (an > bn) return 1;
-        continue;
-      }
-      if (isBreak(a[ai])) {
-        if (!isBreak(b[bi])) return -1;
-      } else if (isBreak(b[bi])) {
-        return 1;
-      } else if (a[ai] < b[bi]) {
-        return -1;
-      } else if (a[ai] > b[bi]) {
-        return 1;
-      }
-      ai++;
-      bi++;
-    }
-  }
-
-  function consumeNumber(s: string, i: number): number {
-    let decimal = false;
-    for (; i < s.length; i++) {
-      if (isDigit(s[i])) continue;
-      if (!decimal && s[i] === '.') {
-        decimal = true;
-        continue;
-      }
-      break;
-    }
-    return i;
-  }
-
-  function isDigit(c: string): boolean { return '0' <= c && c <= '9'; }
-
-  function isBreak(c: string): boolean {
-    // TODO(jart): Remove underscore when people stop using it like a slash.
-    return c === '/' || c === '_' || isDigit(c);
+    return _.union.apply(null, _.values(r)).sort(VZ.Sorting.compareTagNames);
   }
 
   /**
@@ -328,7 +276,7 @@ module TF.Backend {
   export function filterTags(r: RunToTag, runs: string[]): string[] {
     var result = [];
     runs.forEach((x) => result = result.concat(r[x]));
-    return _.uniq(result).sort();
+    return _.uniq(result).sort(VZ.Sorting.compareTagNames);
   }
 
   function timeToDate(x: number): Date { return new Date(x * 1000); };
diff --git a/tensorflow/tensorboard/components/tf-backend/test/backendTests.ts b/tensorflow/tensorboard/components/tf-backend/test/backendTests.ts
index 15cdd0bf95..9df5399db5 100644
--- a/tensorflow/tensorboard/components/tf-backend/test/backendTests.ts
+++ b/tensorflow/tensorboard/components/tf-backend/test/backendTests.ts
@@ -277,56 +277,4 @@ module TF.Backend {
          assertHistogramEquality(newHistogram, histogram);
        });
   });
-
-  describe('sortTagNames', () => {
-
-    let sortTagNames = (a) => a.sort(compareTagNames);
-
-    it('is asciibetical', () => {
-      assert.deepEqual(sortTagNames(['a', 'b']), ['a', 'b']);
-      assert.deepEqual(sortTagNames(['a', 'B']), ['B', 'a']);
-    });
-
-    it('sorts integer portions', () => {
-      assert.deepEqual(['03', '1'].sort(), ['03', '1']);
-      assert.deepEqual(sortTagNames(['03', '1']), ['1', '03']);
-      assert.deepEqual(sortTagNames(['a03', 'a1']), ['a1', 'a03']);
-      assert.deepEqual(sortTagNames(['a03', 'b1']), ['a03', 'b1']);
-      assert.deepEqual(sortTagNames(['x0a03', 'x0a1']), ['x0a1', 'x0a03']);
-      assert.deepEqual(sortTagNames(['a/b/03', 'a/b/1']), ['a/b/1', 'a/b/03']);
-    });
-
-    it('sorts floating point portions', () => {
-      assert.deepEqual(sortTagNames(['a0.1', 'a0.01']), ['a0.01', 'a0.1']);
-    });
-
-    it('is componentized by slash', () => {
-      assert.deepEqual(['a+/a', 'a/a', 'ab/a'].sort(), ['a+/a', 'a/a', 'ab/a']);
-      assert.deepEqual(
-          sortTagNames(['a+/a', 'a/a', 'ab/a']), ['a/a', 'a+/a', 'ab/a']);
-    });
-
-    it('is componentized by underscore', () => {
-      assert.deepEqual(
-          sortTagNames(['a+_a', 'a_a', 'ab_a']), ['a_a', 'a+_a', 'ab_a']);
-      assert.deepEqual(
-          sortTagNames(['a+/a', 'a_a', 'ab_a']), ['a_a', 'a+/a', 'ab_a']);
-    });
-
-    it('is componentized by number boundaries', () => {
-      assert.deepEqual(
-          sortTagNames(['a+0a', 'a0a', 'ab0a']), ['a0a', 'a+0a', 'ab0a']);
-    });
-
-    it('empty comes first', () => {
-      assert.deepEqual(
-          sortTagNames(['a', '//', '/', '']), ['', '/', '//', 'a']);
-    });
-
-    it('decimal parsed correctly', () => {
-      assert.deepEqual(sortTagNames(['0.2', '0.03']), ['0.03', '0.2']);
-      assert.deepEqual(sortTagNames(['0..2', '0..03']), ['0..2', '0..03']);
-      assert.deepEqual(sortTagNames(['.2', '.03']), ['.2', '.03']);
-    });
-  });
 }
diff --git a/tensorflow/tensorboard/components/tf-backend/test/index.html b/tensorflow/tensorboard/components/tf-backend/test/index.html
index c97873f46a..a1ee080b0e 100644
--- a/tensorflow/tensorboard/components/tf-backend/test/index.html
+++ b/tensorflow/tensorboard/components/tf-backend/test/index.html
@@ -20,6 +20,7 @@ limitations under the License.
   <script src="../../web-component-tester/browser.js"></script>
   <link rel="import" href="../../polymer/polymer.html">
   <link rel="import" href="../../tf-imports/d3.html">
+  <link rel="import" href="../../vz-sorting/vz-sorting.html">
 </head>
 <body>
   <test-fixture id="testElementFixture">
diff --git a/tensorflow/tensorboard/components/tf-backend/tf-backend.html b/tensorflow/tensorboard/components/tf-backend/tf-backend.html
index bc4f16a5f7..d416787e3f 100644
--- a/tensorflow/tensorboard/components/tf-backend/tf-backend.html
+++ b/tensorflow/tensorboard/components/tf-backend/tf-backend.html
@@ -1,6 +1,7 @@
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-imports/lodash.html">
 <link rel="import" href="../tf-imports/d3.html">
+<link rel="import" href="../vz-sorting/vz-sorting.html">
 
 <script src="requestManager.js"></script>
 <script src="urlPathHelpers.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/categorizer.ts b/tensorflow/tensorboard/components/tf-dashboard-common/categorizer.ts
index d9c477e744..5131b108e4 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/categorizer.ts
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/categorizer.ts
@@ -81,7 +81,7 @@ module Categorizer {
       if (tags.length === 0) {
         return [];
       }
-      var sortedTags = tags.slice().sort();
+      var sortedTags = tags.slice().sort(VZ.Sorting.compareTagNames);
       var categories: Category[] = [];
       var currentCategory = {
         name: extractor(sortedTags[0]),
@@ -134,7 +134,7 @@ module Categorizer {
             tags.push(t);
           }
         });
-        var cat = { name: def.name, tags: tags.sort() };
+        var cat = {name: def.name, tags: tags.sort(VZ.Sorting.compareTagNames)};
         return cat;
       });
       var defaultCategories = fallback(remaining.values());
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/test/index.html b/tensorflow/tensorboard/components/tf-dashboard-common/test/index.html
index fd4a097708..6336c876b8 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/test/index.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/test/index.html
@@ -5,6 +5,7 @@
   <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
   <script src="../../web-component-tester/browser.js"></script>
   <link rel="import" href="../../tf-imports/d3.html">
+  <link rel="import" href="../../vz-sorting/vz-sorting.html">
 </head>
 <body>
   <script src="../categorizer.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/tf-categorizer.html b/tensorflow/tensorboard/components/tf-dashboard-common/tf-categorizer.html
index 1c3a3f811e..9eeef71a23 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/tf-categorizer.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/tf-categorizer.html
@@ -3,6 +3,7 @@
 <link rel="import" href="../tf-storage/tf-storage.html">
 <link rel="import" href="tf-regex-group.html">
 <link rel="import" href="tensorboard-color.html">
+<link rel="import" href="../vz-sorting/vz-sorting.html">
 
 <!--
 `tf-categorizer` turns an array of tags into an array of categories
diff --git a/tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html b/tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html
index d3000e4616..db9089280e 100644
--- a/tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html
+++ b/tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html
@@ -60,7 +60,7 @@ The #center div contains tf-line-charts embedded inside tf-collapsable-panes.
               selected-item-label="{{_tooltipSortingMethod}}"
             >
               <paper-menu class="dropdown-content" selected="0">
-                <paper-item>name</paper-item>
+                <paper-item>default</paper-item>
                 <paper-item>descending</paper-item>
                 <paper-item>ascending</paper-item>
               </paper-menu>
diff --git a/tensorflow/tensorboard/components/tf-globals/tf-globals.html b/tensorflow/tensorboard/components/tf-globals/tf-globals.html
index 3195518f37..a76f21d96c 100644
--- a/tensorflow/tensorboard/components/tf-globals/tf-globals.html
+++ b/tensorflow/tensorboard/components/tf-globals/tf-globals.html
@@ -14,6 +14,6 @@ limitations under the License.
 =============================================================================-->
 
 <dom-module id="tf-globals">
- <script src="globals.js"></script>
+  <script src="globals.js"></script>
 </dom-module>
 
diff --git a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html
index 9ca063c082..1d68cc90f7 100644
--- a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html
+++ b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html
@@ -174,13 +174,13 @@ such as different X scales (linear and temporal), tooltips and smoothing.
 
         /**
          * Change how the tooltip is sorted. Allows:
-         * - "name" - Sort the tooltip by series name.
+         * - "default" - Sort the tooltip by input order.
          * - "ascending" - Sort the tooltip by ascending value.
          * - "descending" - Sort the tooltip by descending value.
          */
         tooltipSortingMethod: {
           type: String,
-          value: 'name'
+          value: 'default'
         },
 
         /**
diff --git a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts
index 8e15200011..6fde2706f1 100644
--- a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts
+++ b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts
@@ -304,8 +304,11 @@ module VZ {
         points =
             _.sortBy(points, (d) => valueSortMethod(d.datum, -1, d.dataset))
                 .reverse();
-      } else {  // Sort by 'name'
-        points = _.sortBy(points, (d) => d.dataset.metadata().name);
+      } else {
+        // The 'default' sorting method maintains the order of names passed to
+        // setVisibleSeries(). However we reverse that order when defining the
+        // datasets. So we must call reverse again to restore the order.
+        points = points.slice(0).reverse();
       }
 
       let rows = this.tooltip.select('tbody')
diff --git a/tensorflow/tensorboard/components/vz-sorting/sorting.ts b/tensorflow/tensorboard/components/vz-sorting/sorting.ts
new file mode 100644
index 0000000000..53dd349e9d
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-sorting/sorting.ts
@@ -0,0 +1,78 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+module VZ.Sorting {
+
+  /**
+   * Compares tag names asciinumerically broken into components.
+   *
+   * <p>This is the comparison function used for sorting most string values in
+   * TensorBoard. Unlike the standard asciibetical comparator, this function
+   * knows that 'a10b' > 'a2b'. Numbers with decimal points are supported. It
+   * also splits the input by slash and underscore and performs an array
+   * sort. Therefore it knows that 'a/a' < 'a+/a' even though '+' < '/' in the
+   * ASCII table.
+   */
+  export function compareTagNames(a, b: string): number {
+    let ai = 0;
+    let bi = 0;
+    while (true) {
+      if (ai === a.length) return bi === b.length ? 0 : -1;
+      if (bi === b.length) return 1;
+      if (isDigit(a[ai]) && isDigit(b[bi])) {
+        const ais = ai;
+        const bis = bi;
+        ai = consumeNumber(a, ai + 1);
+        bi = consumeNumber(b, bi + 1);
+        const an = parseFloat(a.slice(ais, ai));
+        const bn = parseFloat(b.slice(bis, bi));
+        if (an < bn) return -1;
+        if (an > bn) return 1;
+        continue;
+      }
+      if (isBreak(a[ai])) {
+        if (!isBreak(b[bi])) return -1;
+      } else if (isBreak(b[bi])) {
+        return 1;
+      } else if (a[ai] < b[bi]) {
+        return -1;
+      } else if (a[ai] > b[bi]) {
+        return 1;
+      }
+      ai++;
+      bi++;
+    }
+  }
+
+  function consumeNumber(s: string, i: number): number {
+    let decimal = false;
+    for (; i < s.length; i++) {
+      if (isDigit(s[i])) continue;
+      if (!decimal && s[i] === '.') {
+        decimal = true;
+        continue;
+      }
+      break;
+    }
+    return i;
+  }
+
+  function isDigit(c: string): boolean { return '0' <= c && c <= '9'; }
+
+  function isBreak(c: string): boolean {
+    // TODO(jart): Remove underscore when people stop using it like a slash.
+    return c === '/' || c === '_' || isDigit(c);
+  }
+}
diff --git a/tensorflow/tensorboard/components/vz-sorting/test/index.html b/tensorflow/tensorboard/components/vz-sorting/test/index.html
new file mode 100644
index 0000000000..9ba04e0136
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-sorting/test/index.html
@@ -0,0 +1,22 @@
+<!doctype html>
+<!--
+  Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+-->
+
+<meta charset="utf-8">
+<script src="../../web-component-tester/browser.js"></script>
+<body>
+<script src="../sorting.js"></script>
+<script src="sortingTests.js"></script>
diff --git a/tensorflow/tensorboard/components/vz-sorting/test/sortingTests.ts b/tensorflow/tensorboard/components/vz-sorting/test/sortingTests.ts
new file mode 100644
index 0000000000..0a24463d8a
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-sorting/test/sortingTests.ts
@@ -0,0 +1,71 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+module VZ.Sorting {
+
+  const assert = chai.assert;
+
+  describe('compareTagNames', () => {
+
+    const sortTagNames = (a) => a.sort(compareTagNames);
+
+    it('is asciibetical', () => {
+      assert.deepEqual(sortTagNames(['a', 'b']), ['a', 'b']);
+      assert.deepEqual(sortTagNames(['a', 'B']), ['B', 'a']);
+    });
+
+    it('sorts integer portions', () => {
+      assert.deepEqual(['03', '1'].sort(), ['03', '1']);
+      assert.deepEqual(sortTagNames(['03', '1']), ['1', '03']);
+      assert.deepEqual(sortTagNames(['a03', 'a1']), ['a1', 'a03']);
+      assert.deepEqual(sortTagNames(['a03', 'b1']), ['a03', 'b1']);
+      assert.deepEqual(sortTagNames(['x0a03', 'x0a1']), ['x0a1', 'x0a03']);
+      assert.deepEqual(sortTagNames(['a/b/03', 'a/b/1']), ['a/b/1', 'a/b/03']);
+    });
+
+    it('sorts floating point portions', () => {
+      assert.deepEqual(sortTagNames(['a0.1', 'a0.01']), ['a0.01', 'a0.1']);
+    });
+
+    it('is componentized by slash', () => {
+      assert.deepEqual(['a+/a', 'a/a', 'ab/a'].sort(), ['a+/a', 'a/a', 'ab/a']);
+      assert.deepEqual(
+          sortTagNames(['a+/a', 'a/a', 'ab/a']), ['a/a', 'a+/a', 'ab/a']);
+    });
+
+    it('is componentized by underscore', () => {
+      assert.deepEqual(
+          sortTagNames(['a+_a', 'a_a', 'ab_a']), ['a_a', 'a+_a', 'ab_a']);
+      assert.deepEqual(
+          sortTagNames(['a+/a', 'a_a', 'ab_a']), ['a_a', 'a+/a', 'ab_a']);
+    });
+
+    it('is componentized by number boundaries', () => {
+      assert.deepEqual(
+          sortTagNames(['a+0a', 'a0a', 'ab0a']), ['a0a', 'a+0a', 'ab0a']);
+    });
+
+    it('empty comes first', () => {
+      assert.deepEqual(
+          sortTagNames(['a', '//', '/', '']), ['', '/', '//', 'a']);
+    });
+
+    it('decimal parsed correctly', () => {
+      assert.deepEqual(sortTagNames(['0.2', '0.03']), ['0.03', '0.2']);
+      assert.deepEqual(sortTagNames(['0..2', '0..03']), ['0..2', '0..03']);
+      assert.deepEqual(sortTagNames(['.2', '.03']), ['.2', '.03']);
+    });
+  });
+}
diff --git a/tensorflow/tensorboard/components/vz-sorting/vz-sorting.html b/tensorflow/tensorboard/components/vz-sorting/vz-sorting.html
new file mode 100644
index 0000000000..9376663dae
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-sorting/vz-sorting.html
@@ -0,0 +1,17 @@
+<!--
+  Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+  Licensed under the Apache License, Version 2.0 (the "License");
+  you may not use this file except in compliance with the License.
+  You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+  Unless required by applicable law or agreed to in writing, software
+  distributed under the License is distributed on an "AS IS" BASIS,
+  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  See the License for the specific language governing permissions and
+  limitations under the License.
+-->
+
+<script src="sorting.js"></script>
-- 
GitLab


From aade4f25c2e2db90199394c5dd81c807341b0654 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 22 Aug 2016 13:15:46 -0800
Subject: [PATCH 010/223] Specifies individual tensor ranks in
 sparse_placeholder. Change: 130980786

---
 tensorflow/python/ops/array_ops.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 7e9a3cb115..f54aec50fb 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -1317,15 +1317,17 @@ def sparse_placeholder(dtype, shape=None, name=None):
   """
   if shape is None:
     shape = placeholder(
-        dtypes.int64, name=(name + "/shape") if name is not None else None)
+        dtypes.int64, shape=[None],
+        name=(name + "/shape") if name is not None else None)
   else:
     shape = ops.convert_to_tensor(
         shape, name=(name + "/shape") if name is not None else None)
   return ops.SparseTensor(
       values=placeholder(
-          dtype, name=(name + "/values") if name is not None else None),
+          dtype, shape=[None],
+          name=(name + "/values") if name is not None else None),
       indices=placeholder(
-          dtypes.int64,
+          dtypes.int64, shape=[None, None],
           name=(name + "/indices") if name is not None else None),
       shape=shape
   )
-- 
GitLab


From d72ef0239427978ac2c6e7c02d0b9803bd2d542c Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Mon, 22 Aug 2016 13:22:10 -0800
Subject: [PATCH 011/223] Implement ShapeManager, which will be used to perform
 graph-level shape inference in C++ using the C++ shape functions.

The interface for this class follows the way Graph building works: you
pass a Node at a time, and it creates an InferenceContext per Node.
On every call to AddNode, it requests the shapes from the
InferenceContexts of all the inputs, and it creates the
InferenceContext and runs the shape function in C++ to generate the
new node's InferenceContext data.  One possible use of this class
will be to perform a ReverseDFS (data-dep order) traversal of a Graph,
calling AddNode each time, while another use of this function will be
to add nodes as they are created in python, etc -- the only
requirement is that the nodes be added in the data dependency order,
so that a node's input shapes have been computed before the node.

As a start for doing as effectively as python does, we try to
materialize the input_tensor values by statically analyzing the graph
in the same way python's constant_value works, but I've only
implemented "Const" for now.  I'll do the rest in a follow up CL to
bring parity to the C++ implementation.

Since we do not know which inputs are needed before the shape function
runs, we run the inference function once to find out which tensors are
needed, and then we call it again once we materialize the tensors.

(I tried to get rid of the existing InferenceContext constructor,
 but it's difficult: moving the creation of shapes from strings
 relies on an InferenceContext in the first place! There's probably
 a way to do this, but this can be an internal refactor).

This is only a small step towards where we want to be with C++ Shape
inference.  This does not yet implement lazy-running of the graphs to
materialize as much of the graph as is computable, and does not yet
support iterative shape inference (forward and backward inference
until convergence).  These features will be built incrementally as we
discover any subtle nuances that make these features challenging.
Change: 130981592
---
 tensorflow/core/BUILD                        |   3 +
 tensorflow/core/framework/shape_inference.cc | 129 +++++++-----
 tensorflow/core/framework/shape_inference.h  |  37 +++-
 tensorflow/core/graph/shape_inferer.cc       | 129 ++++++++++++
 tensorflow/core/graph/shape_inferer.h        |  70 +++++++
 tensorflow/core/graph/shape_inferer_test.cc  | 194 +++++++++++++++++++
 6 files changed, 515 insertions(+), 47 deletions(-)
 create mode 100644 tensorflow/core/graph/shape_inferer.cc
 create mode 100644 tensorflow/core/graph/shape_inferer.h
 create mode 100644 tensorflow/core/graph/shape_inferer_test.cc

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 224badc7d3..c60ad1ef7e 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -427,6 +427,7 @@ tf_cuda_library(
         "graph/graph_constructor.h",
         "graph/graph_def_builder.h",
         "graph/node_builder.h",
+        "graph/shape_inferer.h",
         "graph/validate.h",
         "public/session.h",
         "public/session_options.h",
@@ -1452,6 +1453,7 @@ tf_cc_tests(
         "graph/node_builder_test.cc",
         "graph/optimizer_cse_test.cc",
         "graph/quantize_training_test.cc",
+        "graph/shape_inferer_test.cc",
         "graph/subgraph_test.cc",
         "graph/tensor_id_test.cc",
         "graph/validate_test.cc",
@@ -1486,6 +1488,7 @@ tf_cc_tests(
         ":test_main",
         ":testlib",
         "//tensorflow/cc:cc_ops",
+        "//tensorflow/cc:scope",
         "//tensorflow/cc:sendrecv_ops",
         "//tensorflow/core/kernels:ops_util",
         "//third_party/eigen3",
diff --git a/tensorflow/core/framework/shape_inference.cc b/tensorflow/core/framework/shape_inference.cc
index b4cf712019..4300784ffe 100644
--- a/tensorflow/core/framework/shape_inference.cc
+++ b/tensorflow/core/framework/shape_inference.cc
@@ -31,54 +31,62 @@ InferenceContext::InferenceContext(
     const NodeDef* node_def, const OpDef& op_def,
     const std::vector<string>& input_shapes,
     const std::vector<const Tensor*>& input_tensors)
-    : input_tensors_(input_tensors), node_def_(*CHECK_NOTNULL(node_def)) {
+    : node_def_(*CHECK_NOTNULL(node_def)) {
+  PreInputInit(op_def, input_tensors);
+
+  for (const string& spec : input_shapes) {
+    const Shape* shape;
+    construction_status_.Update(MakeShapeFromString(spec, &shape));
+    if (!construction_status_.ok()) {
+      return;
+    }
+    inputs_.push_back(shape);
+  }
+
+  PostInputInit();
+}
+
+InferenceContext::InferenceContext(
+    const NodeDef* node_def, const OpDef& op_def,
+    const std::vector<string>& input_shapes_string,
+    const std::vector<const Shape*>& input_shapes,
+    const std::vector<const Tensor*>& input_tensors)
+    : node_def_(*CHECK_NOTNULL(node_def)) {
+  PreInputInit(op_def, input_tensors);
+  if (!construction_status_.ok()) return;
+  inputs_ = input_shapes;
+  PostInputInit();
+}
+
+InferenceContext::~InferenceContext() {
+  for (auto* s : all_shapes_) delete s;
+  for (auto* d : all_dims_) delete d;
+}
+
+void InferenceContext::PreInputInit(
+    const OpDef& op_def, const std::vector<const Tensor*>& input_tensors) {
+  input_tensors_ = input_tensors;
+
   construction_status_ =
-      NameRangesForNode(*node_def, op_def, &input_name_map_, &output_name_map_);
+      NameRangesForNode(node_def_, op_def, &input_name_map_, &output_name_map_);
   if (!construction_status_.ok()) return;
 
   int num_outputs = 0;
   for (const auto& e : output_name_map_) {
     num_outputs = std::max(num_outputs, e.second.second);
   }
+  for (int i = 0; i < num_outputs; ++i) {
+    outputs_.push_back(nullptr);
+  }
+}
+
+void InferenceContext::PostInputInit() {
   int num_inputs_from_node_def = 0;
   for (const auto& e : input_name_map_) {
     num_inputs_from_node_def =
         std::max(num_inputs_from_node_def, e.second.second);
   }
 
-  for (const string& spec : input_shapes) {
-    if (spec == "?") {
-      inputs_.push_back(UnknownShape());
-    } else {
-      std::vector<const Dimension*> dims;
-      strings::Scanner scanner(spec);
-      scanner.OneLiteral("[");
-      while (scanner.Peek() != ']') {
-        if (scanner.Peek() == '?') {
-          scanner.OneLiteral("?");
-          dims.push_back(UnknownDim());
-        } else {
-          scanner.RestartCapture().Many(strings::Scanner::DIGIT);
-          StringPiece match;
-          int64 dim_size = 0;
-          CHECK(scanner.GetResult(nullptr, &match) &&
-                strings::safe_strto64(match, &dim_size))
-              << spec;
-          dims.push_back(MakeDim(dim_size));
-        }
-
-        if (scanner.Peek() == ',') {
-          scanner.OneLiteral(",");
-        } else if (scanner.Peek() != ']') {
-          construction_status_ = errors::InvalidArgument(
-              "Invalid input spec (] not found in dim shape): ", spec);
-          return;
-        }
-      }
-      CHECK(scanner.OneLiteral("]").Eos().GetResult());
-      inputs_.push_back(MakeShape(dims));
-    }
-  }
   if (inputs_.size() != num_inputs_from_node_def) {
     construction_status_ = errors::InvalidArgument(
         "Wrong number of inputs passed: ", inputs_.size(), " while ",
@@ -86,17 +94,9 @@ InferenceContext::InferenceContext(
     return;
   }
 
-  CHECK_LE(input_tensors_.size(), input_shapes.size());
-  input_tensors_.resize(input_shapes.size());
-
-  for (int i = 0; i < num_outputs; ++i) {
-    outputs_.push_back(UnknownShape());
-  }
-}
-
-InferenceContext::~InferenceContext() {
-  for (auto* s : all_shapes_) delete s;
-  for (auto* d : all_dims_) delete d;
+  CHECK_LE(input_tensors_.size(), inputs_.size());
+  input_tensors_.resize(inputs_.size());
+  requested_input_tensor_.resize(inputs_.size());
 }
 
 bool InferenceContext::FullyDefined(const Shape* s) {
@@ -652,5 +652,42 @@ Status InferenceContext::Max(const Dimension* first, DimensionOrConstant second,
   return Status::OK();
 }
 
+Status InferenceContext::MakeShapeFromString(const string& spec,
+                                             const Shape** output) {
+  if (spec == "?") {
+    *output = UnknownShape();
+    return Status::OK();
+  }
+
+  std::vector<const Dimension*> dims;
+  strings::Scanner scanner(spec);
+  scanner.OneLiteral("[");
+  while (scanner.Peek() != ']') {
+    if (scanner.Peek() == '?') {
+      scanner.OneLiteral("?");
+      dims.push_back(UnknownDim());
+    } else {
+      scanner.RestartCapture().Many(strings::Scanner::DIGIT);
+      StringPiece match;
+      int64 dim_size = 0;
+      CHECK(scanner.GetResult(nullptr, &match) &&
+            strings::safe_strto64(match, &dim_size))
+          << spec;
+      dims.push_back(MakeDim(dim_size));
+    }
+
+    if (scanner.Peek() == ',') {
+      scanner.OneLiteral(",");
+    } else if (scanner.Peek() != ']') {
+      return errors::InvalidArgument(
+          "Invalid input spec (] not found in dim shape): ", spec);
+    }
+  }
+  CHECK(scanner.OneLiteral("]").Eos().GetResult());
+  *output = MakeShape(dims);
+
+  return Status::OK();
+}
+
 }  // namespace shape_inference
 }  // namespace tensorflow
diff --git a/tensorflow/core/framework/shape_inference.h b/tensorflow/core/framework/shape_inference.h
index cc7b604150..b08ffd369e 100644
--- a/tensorflow/core/framework/shape_inference.h
+++ b/tensorflow/core/framework/shape_inference.h
@@ -89,6 +89,17 @@ class InferenceContext {
   static constexpr int64 kUnknownDim = -1;
   static constexpr int32 kUnknownRank = -1;
 
+  // <input_tensors> is NULL-padded to be the same size as <input_shapes>.
+  //
+  // REQUIRES: <node_def> is not NULL, and must outlive the InferenceContext.
+  //
+  // TODO(vrv): Remove 'input_shapes_string' once we can move the
+  // creation of Shapes from strings out of this class (or hide it).
+  InferenceContext(const NodeDef* node_def, const OpDef& op_def,
+                   const std::vector<string>& input_shapes_string,
+                   const std::vector<const Shape*>& input_shapes,
+                   const std::vector<const Tensor*>& input_tensors);
+
   // This is a temporary constructor used for initial testing.
   //
   // TODO(cwhipkey): remove this temporary constructor.
@@ -106,6 +117,7 @@ class InferenceContext {
   InferenceContext(const NodeDef* node_def, const OpDef& op_def,
                    const std::vector<string>& input_shapes,
                    const std::vector<const Tensor*>& input_tensors);
+
   ~InferenceContext();
 
   const Shape* input(int idx) const { return inputs_[idx]; }
@@ -113,7 +125,20 @@ class InferenceContext {
 
   // Returns the input tensor at index <idx>, or nullptr if the input tensor is
   // not available at the time of shape inference.
-  const Tensor* input_tensor(int idx) const { return input_tensors_[idx]; }
+  const Tensor* input_tensor(int idx) {
+    // Mark that this idx was requested.
+    requested_input_tensor_[idx] = true;
+    return input_tensors_[idx];
+  }
+
+  // Returns true iff input_tensor(idx) was called by the shape function.
+  bool requested_input_tensor(int idx) const {
+    return requested_input_tensor_[idx];
+  }
+
+  void set_input_tensors(const std::vector<const Tensor*>& input_tensors) {
+    input_tensors_ = input_tensors;
+  }
 
   void set_output(int idx, const Shape* shape) { outputs_[idx] = shape; }
   int num_outputs() const { return outputs_.size(); }
@@ -351,6 +376,15 @@ class InferenceContext {
   }
 
  private:
+  // Shared initialization across the two constructors.  Remove
+  // once we get rid of one of them.
+  void PreInputInit(const OpDef& op_def,
+                    const std::vector<const Tensor*>& input_tensors);
+  void PostInputInit();
+
+  // Returns a shape from 'shape_string'.
+  Status MakeShapeFromString(const string& shape_string, const Shape** output);
+
   const Dimension* GetDimension(const DimensionOrConstant& d);
 
   Status ReturnUnknownShape(const Shape** out) {
@@ -369,6 +403,7 @@ class InferenceContext {
   // inputs_ and outputs_ refer to values from all_shapes_.
   std::vector<const Shape*> inputs_;
   std::vector<const Tensor*> input_tensors_;
+  std::vector<bool> requested_input_tensor_;
   std::vector<const Shape*> outputs_;
 
   const NodeDef& node_def_;
diff --git a/tensorflow/core/graph/shape_inferer.cc b/tensorflow/core/graph/shape_inferer.cc
new file mode 100644
index 0000000000..d27c83d8d5
--- /dev/null
+++ b/tensorflow/core/graph/shape_inferer.cc
@@ -0,0 +1,129 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/core/graph/shape_inferer.h"
+
+#include <memory>
+#include <vector>
+
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/gtl/stl_util.h"
+
+namespace tensorflow {
+
+ShapeInferer::ShapeInferer() {}
+ShapeInferer::~ShapeInferer() { gtl::STLDeleteValues(&node_to_context_); }
+
+Status ShapeInferer::AddNode(const Node* node) {
+  // For each 'input' of this node, fetch the corresponding shape
+  // from 'input's InferenceContext, and store into a vector
+  // indexed by 'node's input.
+  std::vector<const Node*> input_nodes(node->num_inputs());
+  std::vector<const shape_inference::Shape*> input_shapes(node->num_inputs());
+  for (const Edge* e : node->in_edges()) {
+    if (e->IsControlEdge()) continue;
+
+    const Node* input = e->src();
+    auto it = node_to_context_.find(input);
+    if (it == node_to_context_.end()) {
+      return errors::FailedPrecondition(
+          "Input ", e->dst_input(), " ('", input->name(), "') for '",
+          node->name(), "' was not previously added to ShapeInferer.");
+    }
+
+    shape_inference::InferenceContext* c = it->second;
+    DCHECK_GE(e->dst_input(), 0);
+    input_nodes[e->dst_input()] = input;
+    input_shapes[e->dst_input()] = c->output(e->src_output());
+  }
+
+  // Get the shape function for this node
+  const OpRegistrationData* op_reg_data;
+  // TODO(vrv): Take in the OpRegistryInterface* instead of taking
+  // the global one.
+  TF_RETURN_IF_ERROR(
+      OpRegistry::Global()->LookUp(node->type_string(), &op_reg_data));
+  if (op_reg_data->shape_inference_fn == nullptr) {
+    return errors::InvalidArgument(
+        "No shape inference function exists for op '", node->type_string(),
+        "', did you forget to define it?");
+  }
+
+  // This needs to be filled in with real data in a second pass.
+  std::vector<const Tensor*> input_tensors(node->num_inputs());
+  std::vector<Tensor> real_tensors(node->num_inputs());
+  std::vector<bool> attempted_materialization(node->num_inputs());
+
+  // Create the inference context for this node with the existing input shapes.
+  std::unique_ptr<shape_inference::InferenceContext> c(
+      new shape_inference::InferenceContext(&node->def(), node->op_def(),
+                                            {} /* input_shapes_string */,
+                                            input_shapes, input_tensors));
+  if (!c->construction_status().ok()) {
+    return c->construction_status();
+  }
+
+  // Run the shape inference function, and return if there was an error.
+  TF_RETURN_IF_ERROR(op_reg_data->shape_inference_fn(c.get()));
+
+  // We must run the shape function repeatedly, in case users write
+  // shape functions where they only conditionally call input_tensor()
+  // based on the values of another input tensor.
+  bool rerun_shape_fn;
+  do {
+    // If the result of running shape inference would have benefitted
+    // from knowing the values of input tensors, try to materialize
+    // the results of those tensors, and then run the shape inference
+    // function again using those known tensors.
+    rerun_shape_fn = false;
+    for (int i = 0; i < c->num_inputs(); ++i) {
+      // Check if we have not already filled in the requested input,
+      // and if not, try to materialize the tensors.
+      if (c->requested_input_tensor(i) && !attempted_materialization[i]) {
+        rerun_shape_fn = true;
+        attempted_materialization[i] = true;
+
+        // For now, we do a simple static analysis of the graph to
+        // materialize those tensors, but in the future, we should try to do
+        // a partial evaluation of the graph.
+        const Node* input_node = input_nodes[i];
+
+        // TODO(vrv): Handle other types of nodes, like we do in python:
+        // Shape, Size, Rank, Range, Cast, Concat, Pack.  Some of these
+        // require recursively accessing the input's inputs, while some
+        // can be computed using local information.
+        if (input_node->IsConstant()) {
+          TF_RETURN_IF_ERROR(
+              GetNodeAttr(input_node->def(), "value", &real_tensors[i]));
+          input_tensors[i] = &real_tensors[i];
+        }
+      }
+    }
+
+    if (rerun_shape_fn) {
+      // We have more information about the shapes on this pass,
+      // so re-run shape inference.
+      c->set_input_tensors(input_tensors);
+      TF_RETURN_IF_ERROR(op_reg_data->shape_inference_fn(c.get()));
+    }
+  } while (rerun_shape_fn);
+
+  // Store the resulting InferenceContext object in the map.
+  node_to_context_[node] = c.release();
+
+  return Status::OK();
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/graph/shape_inferer.h b/tensorflow/core/graph/shape_inferer.h
new file mode 100644
index 0000000000..6f2df6aa74
--- /dev/null
+++ b/tensorflow/core/graph/shape_inferer.h
@@ -0,0 +1,70 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_GRAPH_SHAPE_INFERER_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_GRAPH_SHAPE_INFERER_H_
+
+#include <vector>
+
+#include "tensorflow/core/framework/shape_inference.h"
+#include "tensorflow/core/graph/graph.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/macros.h"
+
+namespace tensorflow {
+
+// ShapeInferer performs shape inference for TensorFlow Graphs.  It is
+// responsible for instantiating InferenceContext objects for each
+// Node in the Graph, and providing/storing the 'input_tensor' Tensors
+// used by Shape Inference functions, when available at graph
+// construction time.
+class ShapeInferer {
+ public:
+  ShapeInferer();
+  ~ShapeInferer();
+
+  // Performs validation of 'node' and runs 'node's shape function,
+  // storing its shape outputs.
+  //
+  // All inputs of 'node' must be added to ShapeInferer prior to
+  // adding 'node'.
+  //
+  // Returns an error if:
+  //  - the shape function for 'node' was not registered.
+  //  - 'node' was added before its inputs.
+  //  - The shape inference function returns an error.
+  Status AddNode(const Node* node);
+
+  // Returns the InferenceContext for 'node', if present.
+  shape_inference::InferenceContext* GetContext(const Node* node) {
+    auto it = node_to_context_.find(node);
+    if (it == node_to_context_.end()) {
+      return nullptr;
+    }
+    return it->second;
+  }
+
+ private:
+  // Stores a map from a node to its InferenceContext.
+  //
+  // Owns values.
+  std::unordered_map<const Node*, shape_inference::InferenceContext*>
+      node_to_context_;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(ShapeInferer);
+};
+
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_GRAPH_SHAPE_INFERER_H_
diff --git a/tensorflow/core/graph/shape_inferer_test.cc b/tensorflow/core/graph/shape_inferer_test.cc
new file mode 100644
index 0000000000..86fc46d9c1
--- /dev/null
+++ b/tensorflow/core/graph/shape_inferer_test.cc
@@ -0,0 +1,194 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/graph/shape_inferer.h"
+
+#include "tensorflow/cc/framework/scope.h"
+#include "tensorflow/cc/ops/standard_ops.h"
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/graph/node_builder.h"
+#include "tensorflow/core/graph/testlib.h"
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace {
+
+#define EXPECT_SHAPE(EXPECTED, M, OP, IDX)                            \
+  do {                                                                \
+    shape_inference::InferenceContext* ctx = M.GetContext(OP.node()); \
+    EXPECT_EQ(EXPECTED, ctx->DebugString(ctx->output(IDX)));          \
+  } while (0);
+
+TEST(ShapeInfererTest, Constant) {
+  // Create a constant node and validate that adding it is successful
+  // and that its shape is correct.
+  Scope root = Scope::NewRootScope();
+  auto c = ops::Const(root, 42.0f);
+  ShapeInferer m;
+  TF_ASSERT_OK(m.AddNode(c.node()));
+
+  EXPECT_SHAPE("[]", m, c, 0);
+}
+
+TEST(ShapeInfererTest, MatMul) {
+  ShapeInferer m;
+
+  Scope root = Scope::NewRootScope();
+  auto a = ops::Const(root, {{1.0f}, {2.0f}});
+  auto b = ops::Const(root, {{1.0f, 2.0f}});
+  auto mm = ops::MatMul(root, a, b);
+
+  TF_ASSERT_OK(m.AddNode(a.node()));
+  TF_ASSERT_OK(m.AddNode(b.node()));
+  TF_ASSERT_OK(m.AddNode(mm.node()));
+
+  EXPECT_SHAPE("[2,1]", m, a, 0);
+  EXPECT_SHAPE("[1,2]", m, b, 0);
+  EXPECT_SHAPE("[2,2]", m, mm, 0);
+}
+
+TEST(ShapeInfererTest, InvalidOrder) {
+  ShapeInferer m;
+  Scope root = Scope::NewRootScope();
+  auto a = ops::Const(root, {{1.0f}, {2.0f}});
+  auto b = ops::Const(root, {{1.0f, 2.0f}});
+  auto mm = ops::MatMul(root, a, b);
+
+  Status s = m.AddNode(mm.node());
+  ASSERT_FALSE(s.ok());
+  ASSERT_EQ(
+      "Input 0 ('Const') for 'MatMul' was not previously added to "
+      "ShapeInferer.",
+      s.error_message());
+}
+
+TEST(ShapeInfererTest, BadShapes) {
+  ShapeInferer m;
+  Scope root = Scope::NewRootScope();
+  auto a = ops::Const(root, {{1.0f}, {2.0f}});
+  auto b = ops::Const(root, {{1.0f}, {2.0f}});
+  auto mm = ops::MatMul(root, a, b);
+
+  TF_ASSERT_OK(m.AddNode(a.node()));
+  TF_ASSERT_OK(m.AddNode(b.node()));
+  // The shape of the inputs are not compatible, so we should expect
+  // an error.
+  Status s = m.AddNode(mm.node());
+  ASSERT_FALSE(s.ok());
+  ASSERT_EQ("Dimensions must be equal, but are 1 and 2", s.error_message());
+}
+
+TEST(ShapeInfererTest, PropagateConstants) {
+  // Reduction dimension is a variable, so we don't know its value.
+  // So the output shape value is unknown (though its rank is known).
+  {
+    Scope root = Scope::NewRootScope();
+    // 3x2 input
+    auto input = ops::Const(root, {{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}});
+    // Reduce along unspecified dimension
+    auto dim = ops::Variable(root, {}, DT_INT32);
+
+    auto am = ops::ArgMax(root, input, dim);
+    ShapeInferer m;
+    TF_ASSERT_OK(m.AddNode(input.node()));
+    TF_ASSERT_OK(m.AddNode(dim.node()));
+    TF_ASSERT_OK(m.AddNode(am.node()));
+    EXPECT_SHAPE("[?]", m, am, 0);
+  }
+
+  // Constant is used as dimension, which can be materialized,
+  // so the shape function can be more precise about the output.
+  {
+    Scope root = Scope::NewRootScope();
+    // 3x2 input
+    auto input = ops::Const(root, {{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}});
+    // Reduce along 2nd dimension
+    auto dim = ops::Const(root, 1);
+
+    auto am = ops::ArgMax(root, input, dim);
+    ShapeInferer m;
+    TF_ASSERT_OK(m.AddNode(input.node()));
+    TF_ASSERT_OK(m.AddNode(dim.node()));
+    TF_ASSERT_OK(m.AddNode(am.node()));
+    EXPECT_SHAPE("[3]", m, am, 0);
+  }
+
+  // Reduce along known first dimension.
+  {
+    Scope root = Scope::NewRootScope();
+    // 3x2 input
+    auto input = ops::Const(root, {{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}});
+    // Reduce along 1st dimension
+    auto dim = ops::Const(root, 0);
+
+    auto am = ops::ArgMax(root, input, dim);
+    ShapeInferer m;
+    TF_ASSERT_OK(m.AddNode(input.node()));
+    TF_ASSERT_OK(m.AddNode(dim.node()));
+    TF_ASSERT_OK(m.AddNode(am.node()));
+    EXPECT_SHAPE("[2]", m, am, 0);
+  }
+}
+
+namespace {
+
+// An op with a shape function whose outputs depend in a complex
+// way on whether input tensors are available.
+REGISTER_OP("TestOp")
+    .Input("a: float")
+    .Input("b: float")
+    .Output("o: float")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      if (c->input_tensor(0)) {
+        if (c->input_tensor(1)) {
+          c->set_output(0, c->Matrix(10, 10));
+          return Status::OK();
+        }
+        return shape_inference::ScalarShape(c);
+      }
+      return shape_inference::UnknownShape(c);
+    });
+
+}  // namespace
+
+TEST(ShapeInfererTest, InputTensorDependencies) {
+  ShapeInferer m;
+  Graph graph(OpRegistry::Global());
+  Node* node;
+
+  Tensor a(DT_FLOAT, TensorShape({}));
+  a.scalar<float>()() = 0.0;
+
+  Tensor b(DT_FLOAT, TensorShape({}));
+  b.scalar<float>()() = 0.0;
+
+  Node* input_a = test::graph::Constant(&graph, a);
+  Node* input_b = test::graph::Constant(&graph, b);
+  TF_ASSERT_OK(NodeBuilder("Test", "TestOp")
+                   .Input(input_a)
+                   .Input(input_b)
+                   .Finalize(&graph, &node));
+
+  TF_ASSERT_OK(m.AddNode(input_a));
+  TF_ASSERT_OK(m.AddNode(input_b));
+  TF_ASSERT_OK(m.AddNode(node));
+  shape_inference::InferenceContext* ctx = m.GetContext(node);
+  EXPECT_EQ("[10,10]", ctx->DebugString(ctx->output(0)));
+}
+
+}  // namespace
+}  // namespace tensorflow
-- 
GitLab


From 368b9d409272f8dad01a953c57c99d2abce4d6f4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 22 Aug 2016 13:22:42 -0800
Subject: [PATCH 012/223] Remove missing `keep_keys` arg from pydoc. Change:
 130981665

---
 .../learn/python/learn/learn_io/graph_io.py        | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py b/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py
index 5f502f0108..9becd7b97b 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/graph_io.py
@@ -74,8 +74,7 @@ def read_batch_examples(file_pattern, batch_size, reader,
     name: Name of resulting op.
 
   Returns:
-    String `Tensor` of batched `Example` proto. If `keep_keys` is True, then
-    returns tuple of string `Tensor`s, where first value is the key.
+    String `Tensor` of batched `Example` proto.
 
   Raises:
     ValueError: for invalid inputs.
@@ -127,8 +126,9 @@ def read_keyed_batch_examples(
     name: Name of resulting op.
 
   Returns:
-    String `Tensor` of batched `Example` proto. If `keep_keys` is True, then
-    returns tuple of string `Tensor`s, where first value is the key.
+    Returns tuple of:
+    - `Tensor` of string keys.
+    - String `Tensor` of batched `Example` proto.
 
   Raises:
     ValueError: for invalid inputs.
@@ -272,8 +272,9 @@ def read_keyed_batch_features(file_pattern,
     name: Name of resulting op.
 
   Returns:
-    A dict of `Tensor` or `SparseTensor` objects for each in `features`.
-    If `keep_keys` is `True`, returns tuple of string `Tensor` and above dict.
+    Returns tuple of:
+    - `Tensor` of string keys.
+    - A dict of `Tensor` or `SparseTensor` objects for each in `features`.
 
   Raises:
     ValueError: for invalid inputs.
@@ -392,7 +393,6 @@ def read_batch_features(file_pattern, batch_size, features, reader,
 
   Returns:
     A dict of `Tensor` or `SparseTensor` objects for each in `features`.
-    If `keep_keys` is `True`, returns tuple of string `Tensor` and above dict.
 
   Raises:
     ValueError: for invalid inputs.
-- 
GitLab


From 92527942d7d55d5e20f16f94d58483178f377157 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 22 Aug 2016 13:28:35 -0800
Subject: [PATCH 013/223] Split NodeDef out of graph.proto into node_def.proto.
  Needed so we can use NodeDef in FunctionDef. Change: 130982373

---
 tensorflow/c/c_api_test.cc                    |  1 +
 .../copy_graph/python/util/copy_elements.py   |  2 +-
 tensorflow/core/framework/graph.proto         | 62 ++----------------
 tensorflow/core/framework/node_def.proto      | 65 +++++++++++++++++++
 tensorflow/core/framework/node_def_util.h     |  2 +-
 .../g3doc/how_tos/tool_developers/index.md    |  7 +-
 tensorflow/python/__init__.py                 |  1 +
 tensorflow/python/framework/errors.py         |  4 +-
 tensorflow/python/framework/graph_util.py     |  9 +--
 tensorflow/python/framework/ops.py            | 13 ++--
 .../python/ops/control_flow_ops_test.py       |  3 +-
 tensorflow/python/training/device_setter.py   |  4 +-
 tensorflow/tensorflow.bzl                     |  1 +
 13 files changed, 96 insertions(+), 78 deletions(-)
 create mode 100644 tensorflow/core/framework/node_def.proto

diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc
index b9c5256ec2..589f001b14 100644
--- a/tensorflow/c/c_api_test.cc
+++ b/tensorflow/c/c_api_test.cc
@@ -17,6 +17,7 @@ limitations under the License.
 
 #include <vector>
 #include "tensorflow/core/framework/graph.pb_text.h"
+#include "tensorflow/core/framework/node_def.pb_text.h"
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/lib/core/error_codes.pb.h"
diff --git a/tensorflow/contrib/copy_graph/python/util/copy_elements.py b/tensorflow/contrib/copy_graph/python/util/copy_elements.py
index 62a8d784ee..3c80a17633 100644
--- a/tensorflow/contrib/copy_graph/python/util/copy_elements.py
+++ b/tensorflow/contrib/copy_graph/python/util/copy_elements.py
@@ -198,7 +198,7 @@ def copy_op_to_graph(org_instance, to_graph, variables,
                       for x in op.inputs]
 
         #Make a new node_def based on that of the original.
-        #An instance of tensorflow.core.framework.graph_pb2.NodeDef, it
+        #An instance of tensorflow.core.framework.node_def_pb2.NodeDef, it
         #stores String-based info such as name, device and type of the op.
         #Unique to every Operation instance.
         new_node_def = deepcopy(op._node_def)
diff --git a/tensorflow/core/framework/graph.proto b/tensorflow/core/framework/graph.proto
index 1d3e7c9e76..7c669db319 100644
--- a/tensorflow/core/framework/graph.proto
+++ b/tensorflow/core/framework/graph.proto
@@ -6,7 +6,9 @@ option java_outer_classname = "GraphProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
 
-import "tensorflow/core/framework/attr_value.proto";
+// Temporarily include node_def.proto here as a public import until
+// users can be migrated to reference node_def.proto to get NodeDef.
+import public "tensorflow/core/framework/node_def.proto";
 import "tensorflow/core/framework/function.proto";
 import "tensorflow/core/framework/versions.proto";
 
@@ -34,7 +36,7 @@ message GraphDef {
   //     different orgs. E.g.,
   //     { "/google/nn", { ... }},
   //     { "/google/vision", { ... }}
-  //     { "/org_foo/module_bar", {...}}
+  //     { "/org_foo/module_bar", { ... }}
   //     map<string, FunctionDefLib> named_lib;
   //   * If node[i].op is the name of one function in "library",
   //     node[i] is deemed as a function call. Otherwise, node[i].op
@@ -54,59 +56,3 @@ message GraphDef {
   //     function are ready.
   FunctionDefLibrary library = 2;
 };
-
-message NodeDef {
-  // The name given to this operator. Used for naming inputs,
-  // logging, visualization, etc.  Unique within a single GraphDef.
-  // Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
-  string name = 1;
-
-  // The operation name.  There may be custom parameters in attrs.
-  // Op names starting with an underscore are reserved for internal use.
-  string op = 2;
-
-  // Each input is "node:src_output" with "node" being a string name and
-  // "src_output" indicating which output tensor to use from "node". If
-  // "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
-  // may optionally be followed by control inputs that have the format
-  // "^node".
-  repeated string input = 3;
-
-  // A (possibly partial) specification for the device on which this
-  // node should be placed.
-  // The expected syntax for this string is as follows:
-  //
-  // DEVICE_SPEC ::= COLOCATED_NODE | PARTIAL_SPEC
-  //
-  // COLOCATED_NODE ::= "@" NODE_NAME  // See NodeDef.name above.
-  // PARTIAL_SPEC ::= ("/" CONSTRAINT) *
-  // CONSTRAINT ::= ("job:" JOB_NAME)
-  //              | ("replica:" [1-9][0-9]*)
-  //              | ("task:" [1-9][0-9]*)
-  //              | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") )
-  //
-  // Valid values for this string include:
-  // * "@other/node"                         (colocate with "other/node")
-  // * "/job:worker/replica:0/task:1/gpu:3"  (full specification)
-  // * "/job:worker/gpu:3"                   (partial specification)
-  // * ""                                    (no specification)
-  //
-  // If the constraints do not resolve to a single device (or if this
-  // field is empty or not present), the runtime will attempt to
-  // choose a device automatically.
-  string device = 4;
-
-  // Operation-specific graph-construction-time configuration.
-  // Note that this should include all attrs defined in the
-  // corresponding OpDef, including those with a value matching
-  // the default -- this allows the default to change and makes
-  // NodeDefs easier to interpret on their own.  However, if
-  // an attr with a default is not specified in this list, the
-  // default will be used.
-  // The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
-  // one of the names from the corresponding OpDef's attr field).
-  // The values must have a type matching the corresponding OpDef
-  // attr's type field.
-  // TODO(josh11b): Add some examples here showing best practices.
-  map<string, AttrValue> attr = 5;
-};
diff --git a/tensorflow/core/framework/node_def.proto b/tensorflow/core/framework/node_def.proto
new file mode 100644
index 0000000000..8d3811582a
--- /dev/null
+++ b/tensorflow/core/framework/node_def.proto
@@ -0,0 +1,65 @@
+syntax = "proto3";
+
+package tensorflow;
+option cc_enable_arenas = true;
+option java_outer_classname = "NodeProto";
+option java_multiple_files = true;
+option java_package = "org.tensorflow.framework";
+
+import "tensorflow/core/framework/attr_value.proto";
+
+message NodeDef {
+  // The name given to this operator. Used for naming inputs,
+  // logging, visualization, etc.  Unique within a single GraphDef.
+  // Must match the regexp "[A-Za-z0-9.][A-Za-z0-9_./]*".
+  string name = 1;
+
+  // The operation name.  There may be custom parameters in attrs.
+  // Op names starting with an underscore are reserved for internal use.
+  string op = 2;
+
+  // Each input is "node:src_output" with "node" being a string name and
+  // "src_output" indicating which output tensor to use from "node". If
+  // "src_output" is 0 the ":0" suffix can be omitted.  Regular inputs
+  // may optionally be followed by control inputs that have the format
+  // "^node".
+  repeated string input = 3;
+
+  // A (possibly partial) specification for the device on which this
+  // node should be placed.
+  // The expected syntax for this string is as follows:
+  //
+  // DEVICE_SPEC ::= COLOCATED_NODE | PARTIAL_SPEC
+  //
+  // COLOCATED_NODE ::= "@" NODE_NAME  // See NodeDef.name above.
+  // PARTIAL_SPEC ::= ("/" CONSTRAINT) *
+  // CONSTRAINT ::= ("job:" JOB_NAME)
+  //              | ("replica:" [1-9][0-9]*)
+  //              | ("task:" [1-9][0-9]*)
+  //              | ( ("gpu" | "cpu") ":" ([1-9][0-9]* | "*") )
+  //
+  // Valid values for this string include:
+  // * "@other/node"                         (colocate with "other/node")
+  // * "/job:worker/replica:0/task:1/gpu:3"  (full specification)
+  // * "/job:worker/gpu:3"                   (partial specification)
+  // * ""                                    (no specification)
+  //
+  // If the constraints do not resolve to a single device (or if this
+  // field is empty or not present), the runtime will attempt to
+  // choose a device automatically.
+  string device = 4;
+
+  // Operation-specific graph-construction-time configuration.
+  // Note that this should include all attrs defined in the
+  // corresponding OpDef, including those with a value matching
+  // the default -- this allows the default to change and makes
+  // NodeDefs easier to interpret on their own.  However, if
+  // an attr with a default is not specified in this list, the
+  // default will be used.
+  // The "names" (keys) must match the regexp "[a-z][a-z0-9_]+" (and
+  // one of the names from the corresponding OpDef's attr field).
+  // The values must have a type matching the corresponding OpDef
+  // attr's type field.
+  // TODO(josh11b): Add some examples here showing best practices.
+  map<string, AttrValue> attr = 5;
+};
diff --git a/tensorflow/core/framework/node_def_util.h b/tensorflow/core/framework/node_def_util.h
index d74431b679..dfc7b9b907 100644
--- a/tensorflow/core/framework/node_def_util.h
+++ b/tensorflow/core/framework/node_def_util.h
@@ -21,7 +21,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/framework/attr_value_util.h"
-#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/op_def.pb.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
diff --git a/tensorflow/g3doc/how_tos/tool_developers/index.md b/tensorflow/g3doc/how_tos/tool_developers/index.md
index 71f81de82a..94eb182a52 100644
--- a/tensorflow/g3doc/how_tos/tool_developers/index.md
+++ b/tensorflow/g3doc/how_tos/tool_developers/index.md
@@ -90,9 +90,10 @@ of nodes stored in the node member. Here's the code that loops through those:
 for node in graph_def.node
 ```
 
-Each node is a `NodeDef` object, also defined in graph.proto. These are the
-fundamental building blocks of TensorFlow graphs, with each one defining a
-single operation along with its input connections. Here are the members of a
+Each node is a `NodeDef` object, defined in
+[tensorflow/core/framework/node_def.proto](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/node_def.proto). These
+are the fundamental building blocks of TensorFlow graphs, with each one defining
+a single operation along with its input connections. Here are the members of a
 `NodeDef`, and what they mean.
 
 ### `name`
diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py
index df57b2fc17..78da74268b 100644
--- a/tensorflow/python/__init__.py
+++ b/tensorflow/python/__init__.py
@@ -57,6 +57,7 @@ please exit the tensorflow source tree, and relaunch your python interpreter
 from there.""" % traceback.format_exc()
   raise ImportError(msg)
 
+from tensorflow.core.framework.node_def_pb2 import *
 from tensorflow.core.framework.summary_pb2 import *
 from tensorflow.core.framework.attr_value_pb2 import *
 from tensorflow.core.protobuf.config_pb2 import *
diff --git a/tensorflow/python/framework/errors.py b/tensorflow/python/framework/errors.py
index cdc515df06..db21f5895c 100644
--- a/tensorflow/python/framework/errors.py
+++ b/tensorflow/python/framework/errors.py
@@ -41,8 +41,8 @@ class OpError(Exception):
     """Creates a new `OpError` indicating that a particular op failed.
 
     Args:
-      node_def: The `graph_pb2.NodeDef` proto representing the op that failed,
-        if known; otherwise None.
+      node_def: The `node_def_pb2.NodeDef` proto representing the op that
+        failed, if known; otherwise None.
       op: The `ops.Operation` that failed, if known; otherwise None.
       message: The message string describing the failure.
       error_code: The `error_codes_pb2.Code` describing the error.
diff --git a/tensorflow/python/framework/graph_util.py b/tensorflow/python/framework/graph_util.py
index f2c086ed7f..ba693503c2 100644
--- a/tensorflow/python/framework/graph_util.py
+++ b/tensorflow/python/framework/graph_util.py
@@ -24,6 +24,7 @@ import re
 
 from tensorflow.core.framework import attr_value_pb2
 from tensorflow.core.framework import graph_pb2
+from tensorflow.core.framework import node_def_pb2
 from tensorflow.python.framework import device as pydev
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
@@ -81,7 +82,7 @@ def must_run_on_cpu(node, pin_variables_on_cpu=False):
   if isinstance(node, ops.Operation):
     node_def = node.node_def
   else:
-    assert isinstance(node, graph_pb2.NodeDef)
+    assert isinstance(node, node_def_pb2.NodeDef)
     node_def = node
 
   # If the op is a variable-related op, should we pin it on CPU?
@@ -235,7 +236,7 @@ def convert_variables_to_constants(sess, input_graph_def, output_node_names,
   output_graph_def = graph_pb2.GraphDef()
   how_many_converted = 0
   for input_node in inference_graph.node:
-    output_node = graph_pb2.NodeDef()
+    output_node = node_def_pb2.NodeDef()
     if input_node.name in found_variables:
       output_node.op = "Const"
       output_node.name = input_node.name
@@ -283,7 +284,7 @@ def remove_training_nodes(input_graph):
   for node in input_nodes:
     if node.name in names_to_remove:
       continue
-    new_node = graph_pb2.NodeDef()
+    new_node = node_def_pb2.NodeDef()
     new_node.CopyFrom(node)
     input_before_removal = node.input
     del new_node.input[:]
@@ -312,7 +313,7 @@ def remove_training_nodes(input_graph):
   for node in nodes_after_removal:
     if node.name in names_to_splice:
       continue
-    new_node = graph_pb2.NodeDef()
+    new_node = node_def_pb2.NodeDef()
     new_node.CopyFrom(node)
     input_before_removal = node.input
     del new_node.input[:]
diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py
index 0189acc97f..bc635c4d33 100644
--- a/tensorflow/python/framework/ops.py
+++ b/tensorflow/python/framework/ops.py
@@ -32,6 +32,7 @@ import six
 from tensorflow.core.framework import attr_value_pb2
 from tensorflow.core.framework import function_pb2
 from tensorflow.core.framework import graph_pb2
+from tensorflow.core.framework import node_def_pb2
 from tensorflow.core.framework import versions_pb2
 from tensorflow.python.framework import device as pydev
 from tensorflow.python.framework import dtypes
@@ -1103,9 +1104,9 @@ def _NodeDef(op_type, name, device=None, attrs=None):
       AttrValue).
 
   Returns:
-    A graph_pb2.NodeDef protocol buffer.
+    A node_def_pb2.NodeDef protocol buffer.
   """
-  node_def = graph_pb2.NodeDef()
+  node_def = node_def_pb2.NodeDef()
   node_def.op = compat.as_bytes(op_type)
   node_def.name = compat.as_bytes(name)
   if attrs is not None:
@@ -1170,8 +1171,8 @@ class Operation(object):
         [A-Za-z0-9.][A-Za-z0-9_.\\-/]*
 
     Args:
-      node_def: `graph_pb2.NodeDef`.  `NodeDef` for the `Operation`.
-        Used for attributes of `graph_pb2.NodeDef`, typically `name`,
+      node_def: `node_def_pb2.NodeDef`.  `NodeDef` for the `Operation`.
+        Used for attributes of `node_def_pb2.NodeDef`, typically `name`,
         `op`, and `device`.  The `input` attribute is irrelevant here
         as it will be computed when generating the model.
       g: `Graph`. The parent graph.
@@ -1199,7 +1200,7 @@ class Operation(object):
         or if `inputs` and `input_types` are incompatible.
       ValueError: if the `node_def` name is not valid.
     """
-    if not isinstance(node_def, graph_pb2.NodeDef):
+    if not isinstance(node_def, node_def_pb2.NodeDef):
       raise TypeError("node_def needs to be a NodeDef: %s" % node_def)
     if node_def.ByteSize() >= (1 << 31) or node_def.ByteSize() < 0:
       raise ValueError(
@@ -1501,7 +1502,7 @@ class Operation(object):
 
     Returns:
       A
-      [`NodeDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto)
+      [`NodeDef`](https://www.tensorflow.org/code/tensorflow/core/framework/node_def.proto)
       protocol buffer.
     """
     return self._node_def
diff --git a/tensorflow/python/ops/control_flow_ops_test.py b/tensorflow/python/ops/control_flow_ops_test.py
index 05d0bf46fe..8c80318054 100644
--- a/tensorflow/python/ops/control_flow_ops_test.py
+++ b/tensorflow/python/ops/control_flow_ops_test.py
@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.core.framework import graph_pb2
+from tensorflow.core.framework import node_def_pb2
 from tensorflow.python.framework import ops
 from tensorflow.python.framework.test_util import TensorFlowTestCase
 from tensorflow.python.ops import control_flow_ops
@@ -31,7 +32,7 @@ from tensorflow.python.training import momentum
 class GroupTestCase(TensorFlowTestCase):
 
   def _StripNode(self, nd):
-    snode = graph_pb2.NodeDef(name=nd.name, op=nd.op, input=nd.input)
+    snode = node_def_pb2.NodeDef(name=nd.name, op=nd.op, input=nd.input)
     if nd.device:
       snode.device = nd.device
     return snode
diff --git a/tensorflow/python/training/device_setter.py b/tensorflow/python/training/device_setter.py
index 4c723f55dc..c7a8af85d1 100644
--- a/tensorflow/python/training/device_setter.py
+++ b/tensorflow/python/training/device_setter.py
@@ -17,7 +17,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.core.framework import graph_pb2
+from tensorflow.core.framework import node_def_pb2
 from tensorflow.python.framework import device as pydev
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training import server_lib
@@ -76,7 +76,7 @@ class _ReplicaDeviceChooser(object):
     current_device = pydev.DeviceSpec.from_string(op.device or "")
     spec = pydev.DeviceSpec()
     if self._ps_tasks and self._ps_device:
-      node_def = op if isinstance(op, graph_pb2.NodeDef) else op.node_def
+      node_def = op if isinstance(op, node_def_pb2.NodeDef) else op.node_def
       if node_def.op in self._ps_ops:
         device_string = "%s/task:%d" % (self._ps_device, self._next_task())
         if self._merge_devices:
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index acbe96ad50..8776b96f4f 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -55,6 +55,7 @@ def tf_android_core_proto_sources_relative():
         "framework/graph.proto",
         "framework/kernel_def.proto",
         "framework/log_memory.proto",
+        "framework/node_def.proto",
         "framework/op_def.proto",
         "framework/step_stats.proto",
         "framework/summary.proto",
-- 
GitLab


From 42ec11bd167d4069a8b9653dd5aa9d955fc4c368 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 22 Aug 2016 13:34:51 -0800
Subject: [PATCH 014/223] Update generated Python Op docs. Change: 130983143

---
 tensorflow/g3doc/api_docs/python/client.md                  | 4 ++--
 tensorflow/g3doc/api_docs/python/contrib.learn.md           | 4 +---
 tensorflow/g3doc/api_docs/python/framework.md               | 6 +++---
 .../shard4/tf.contrib.learn.read_batch_features.md          | 1 -
 .../python/functions_and_classes/shard5/tf.OpError.md       | 4 ++--
 .../shard6/tf.contrib.learn.read_batch_examples.md          | 3 +--
 .../python/functions_and_classes/shard7/tf.Operation.md     | 6 +++---
 7 files changed, 12 insertions(+), 16 deletions(-)

diff --git a/tensorflow/g3doc/api_docs/python/client.md b/tensorflow/g3doc/api_docs/python/client.md
index 1490bbaec7..7a38444daf 100644
--- a/tensorflow/g3doc/api_docs/python/client.md
+++ b/tensorflow/g3doc/api_docs/python/client.md
@@ -454,8 +454,8 @@ Creates a new `OpError` indicating that a particular op failed.
 ##### Args:
 
 
-*  <b>`node_def`</b>: The `graph_pb2.NodeDef` proto representing the op that failed,
-    if known; otherwise None.
+*  <b>`node_def`</b>: The `node_def_pb2.NodeDef` proto representing the op that
+    failed, if known; otherwise None.
 *  <b>`op`</b>: The `ops.Operation` that failed, if known; otherwise None.
 *  <b>`message`</b>: The message string describing the failure.
 *  <b>`error_code`</b>: The `error_codes_pb2.Code` describing the error.
diff --git a/tensorflow/g3doc/api_docs/python/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md
index cc4484493a..668ca09b07 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.learn.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md
@@ -4818,8 +4818,7 @@ Use `parse_fn` if you need to do parsing / processing on single examples.
 
 ##### Returns:
 
-  String `Tensor` of batched `Example` proto. If `keep_keys` is True, then
-  returns tuple of string `Tensor`s, where first value is the key.
+  String `Tensor` of batched `Example` proto.
 
 ##### Raises:
 
@@ -4869,7 +4868,6 @@ All ops are added to the default graph.
 ##### Returns:
 
   A dict of `Tensor` or `SparseTensor` objects for each in `features`.
-  If `keep_keys` is `True`, returns tuple of string `Tensor` and above dict.
 
 ##### Raises:
 
diff --git a/tensorflow/g3doc/api_docs/python/framework.md b/tensorflow/g3doc/api_docs/python/framework.md
index 794f9d8023..0da2a8ec24 100644
--- a/tensorflow/g3doc/api_docs/python/framework.md
+++ b/tensorflow/g3doc/api_docs/python/framework.md
@@ -1028,8 +1028,8 @@ regular expression:
 ##### Args:
 
 
-*  <b>`node_def`</b>: `graph_pb2.NodeDef`.  `NodeDef` for the `Operation`.
-    Used for attributes of `graph_pb2.NodeDef`, typically `name`,
+*  <b>`node_def`</b>: `node_def_pb2.NodeDef`.  `NodeDef` for the `Operation`.
+    Used for attributes of `node_def_pb2.NodeDef`, typically `name`,
     `op`, and `device`.  The `input` attribute is irrelevant here
     as it will be computed when generating the model.
 *  <b>`g`</b>: `Graph`. The parent graph.
@@ -1076,7 +1076,7 @@ Returns a serialized `NodeDef` representation of this operation.
 ##### Returns:
 
   A
-  [`NodeDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto)
+  [`NodeDef`](https://www.tensorflow.org/code/tensorflow/core/framework/node_def.proto)
   protocol buffer.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.read_batch_features.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.read_batch_features.md
index 7c3dddb26a..9c40083f50 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.read_batch_features.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.read_batch_features.md
@@ -38,7 +38,6 @@ All ops are added to the default graph.
 ##### Returns:
 
   A dict of `Tensor` or `SparseTensor` objects for each in `features`.
-  If `keep_keys` is `True`, returns tuple of string `Tensor` and above dict.
 
 ##### Raises:
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.OpError.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.OpError.md
index c23014ad17..d28bd7ee14 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.OpError.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.OpError.md
@@ -39,8 +39,8 @@ Creates a new `OpError` indicating that a particular op failed.
 ##### Args:
 
 
-*  <b>`node_def`</b>: The `graph_pb2.NodeDef` proto representing the op that failed,
-    if known; otherwise None.
+*  <b>`node_def`</b>: The `node_def_pb2.NodeDef` proto representing the op that
+    failed, if known; otherwise None.
 *  <b>`op`</b>: The `ops.Operation` that failed, if known; otherwise None.
 *  <b>`message`</b>: The message string describing the failure.
 *  <b>`error_code`</b>: The `error_codes_pb2.Code` describing the error.
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.read_batch_examples.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.read_batch_examples.md
index c4a52d71b8..be7d043bd6 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.read_batch_examples.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.learn.read_batch_examples.md
@@ -36,8 +36,7 @@ Use `parse_fn` if you need to do parsing / processing on single examples.
 
 ##### Returns:
 
-  String `Tensor` of batched `Example` proto. If `keep_keys` is True, then
-  returns tuple of string `Tensor`s, where first value is the key.
+  String `Tensor` of batched `Example` proto.
 
 ##### Raises:
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.Operation.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.Operation.md
index a9e21fb29e..4cbcf8af3e 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.Operation.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.Operation.md
@@ -151,8 +151,8 @@ regular expression:
 ##### Args:
 
 
-*  <b>`node_def`</b>: `graph_pb2.NodeDef`.  `NodeDef` for the `Operation`.
-    Used for attributes of `graph_pb2.NodeDef`, typically `name`,
+*  <b>`node_def`</b>: `node_def_pb2.NodeDef`.  `NodeDef` for the `Operation`.
+    Used for attributes of `node_def_pb2.NodeDef`, typically `name`,
     `op`, and `device`.  The `input` attribute is irrelevant here
     as it will be computed when generating the model.
 *  <b>`g`</b>: `Graph`. The parent graph.
@@ -199,7 +199,7 @@ Returns a serialized `NodeDef` representation of this operation.
 ##### Returns:
 
   A
-  [`NodeDef`](https://www.tensorflow.org/code/tensorflow/core/framework/graph.proto)
+  [`NodeDef`](https://www.tensorflow.org/code/tensorflow/core/framework/node_def.proto)
   protocol buffer.
 
 
-- 
GitLab


From 59df941dbec8c1a97941bdd3a7a0fb11a951263b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 22 Aug 2016 14:51:29 -0800
Subject: [PATCH 015/223] Fix up documentation for sample_n which allows for
 `n` to be int64. Change: 130992111

---
 tensorflow/contrib/distributions/python/ops/bernoulli.py     | 3 ++-
 tensorflow/contrib/distributions/python/ops/beta.py          | 5 +++--
 tensorflow/contrib/distributions/python/ops/categorical.py   | 3 ++-
 tensorflow/contrib/distributions/python/ops/dirichlet.py     | 3 ++-
 tensorflow/contrib/distributions/python/ops/distribution.py  | 3 ++-
 tensorflow/contrib/distributions/python/ops/exponential.py   | 3 ++-
 tensorflow/contrib/distributions/python/ops/gamma.py         | 4 ++--
 tensorflow/contrib/distributions/python/ops/inverse_gamma.py | 4 ++--
 tensorflow/contrib/distributions/python/ops/laplace.py       | 5 +++--
 tensorflow/contrib/distributions/python/ops/mvn.py           | 5 +++--
 tensorflow/contrib/distributions/python/ops/normal.py        | 5 +++--
 tensorflow/contrib/distributions/python/ops/student_t.py     | 3 ++-
 .../distributions/python/ops/transformed_distribution.py     | 3 ++-
 tensorflow/contrib/distributions/python/ops/uniform.py       | 3 ++-
 tensorflow/contrib/distributions/python/ops/wishart.py       | 3 ++-
 15 files changed, 34 insertions(+), 21 deletions(-)

diff --git a/tensorflow/contrib/distributions/python/ops/bernoulli.py b/tensorflow/contrib/distributions/python/ops/bernoulli.py
index e4dbaba638..148e8b8562 100644
--- a/tensorflow/contrib/distributions/python/ops/bernoulli.py
+++ b/tensorflow/contrib/distributions/python/ops/bernoulli.py
@@ -174,7 +174,8 @@ class Bernoulli(distribution.Distribution):
     """Generate `n` samples.
 
     Args:
-      n: scalar.  Number of samples to draw from each distribution.
+      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+        observations to sample.
       seed: Python integer seed for RNG.
       name: name to give to the op.
 
diff --git a/tensorflow/contrib/distributions/python/ops/beta.py b/tensorflow/contrib/distributions/python/ops/beta.py
index 5b1bdcc5d6..cc6beb110a 100644
--- a/tensorflow/contrib/distributions/python/ops/beta.py
+++ b/tensorflow/contrib/distributions/python/ops/beta.py
@@ -359,7 +359,8 @@ class Beta(distribution.Distribution):
     """Sample `n` observations from the Beta Distributions.
 
     Args:
-      n: `Scalar`, type int32, the number of observations to sample.
+      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+        observations to sample.
       seed: Python integer, the random seed.
       name: The name to give this op.
 
@@ -371,7 +372,7 @@ class Beta(distribution.Distribution):
       with ops.name_scope(name, values=[self.a, self.b, n]):
         a = array_ops.ones_like(self._a_b_sum, dtype=self.dtype) * self.a
         b = array_ops.ones_like(self._a_b_sum, dtype=self.dtype) * self.b
-        n = ops.convert_to_tensor(n)
+        n = ops.convert_to_tensor(n, name="n")
 
         gamma1_sample = random_ops.random_gamma(
             [n,], a, dtype=self.dtype, seed=seed)
diff --git a/tensorflow/contrib/distributions/python/ops/categorical.py b/tensorflow/contrib/distributions/python/ops/categorical.py
index 4380637d49..02e74e1d54 100644
--- a/tensorflow/contrib/distributions/python/ops/categorical.py
+++ b/tensorflow/contrib/distributions/python/ops/categorical.py
@@ -170,7 +170,8 @@ class Categorical(distribution.Distribution):
     """Sample `n` observations from the Categorical distribution.
 
     Args:
-      n: 0-D.  Number of independent samples to draw for each distribution.
+      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+        observations to sample.
       seed: Random seed (optional).
       name: A name for this operation (optional).
 
diff --git a/tensorflow/contrib/distributions/python/ops/dirichlet.py b/tensorflow/contrib/distributions/python/ops/dirichlet.py
index 7081053db0..2b27954124 100644
--- a/tensorflow/contrib/distributions/python/ops/dirichlet.py
+++ b/tensorflow/contrib/distributions/python/ops/dirichlet.py
@@ -354,7 +354,8 @@ class Dirichlet(distribution.Distribution):
     """Sample `n` observations from the distributions.
 
     Args:
-      n: `Scalar`, type int32, the number of observations to sample.
+      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+        observations to sample.
       seed: Python integer, the random seed.
       name: The name to give this op.
 
diff --git a/tensorflow/contrib/distributions/python/ops/distribution.py b/tensorflow/contrib/distributions/python/ops/distribution.py
index 63be0ad79b..1115d30a4c 100644
--- a/tensorflow/contrib/distributions/python/ops/distribution.py
+++ b/tensorflow/contrib/distributions/python/ops/distribution.py
@@ -64,7 +64,8 @@ class BaseDistribution(object):
     """Generate `n` samples.
 
     Args:
-      n: scalar. Number of samples to draw.
+      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+        observations to sample.
       seed: Python integer seed for RNG
       name: name to give to the op.
 
diff --git a/tensorflow/contrib/distributions/python/ops/exponential.py b/tensorflow/contrib/distributions/python/ops/exponential.py
index e0d2dba7a0..95aedc88a9 100644
--- a/tensorflow/contrib/distributions/python/ops/exponential.py
+++ b/tensorflow/contrib/distributions/python/ops/exponential.py
@@ -84,7 +84,8 @@ class Exponential(gamma.Gamma):
     """Sample `n` observations from the Exponential Distributions.
 
     Args:
-      n: `Scalar`, type int32, the number of observations to sample.
+      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+        observations to sample.
       seed: Python integer, the random seed.
       name: The name to give this op.
 
diff --git a/tensorflow/contrib/distributions/python/ops/gamma.py b/tensorflow/contrib/distributions/python/ops/gamma.py
index c9a6c286f0..4363a1b7c5 100644
--- a/tensorflow/contrib/distributions/python/ops/gamma.py
+++ b/tensorflow/contrib/distributions/python/ops/gamma.py
@@ -338,8 +338,8 @@ class Gamma(distribution.Distribution):
     See the doc for tf.random_gamma for further detail.
 
     Args:
-      n: Python integer, the number of observations to sample from each
-        distribution.
+      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+        observations to sample.
       seed: Python integer, the random seed for this operation.
       name: Optional name for the operation.
 
diff --git a/tensorflow/contrib/distributions/python/ops/inverse_gamma.py b/tensorflow/contrib/distributions/python/ops/inverse_gamma.py
index b0c1eb3c85..869b61bd3e 100644
--- a/tensorflow/contrib/distributions/python/ops/inverse_gamma.py
+++ b/tensorflow/contrib/distributions/python/ops/inverse_gamma.py
@@ -366,8 +366,8 @@ class InverseGamma(distribution.Distribution):
     See the doc for tf.random_gamma for further details on sampling strategy.
 
     Args:
-      n: Python integer, the number of observations to sample from each
-        distribution.
+      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+        observations to sample.
       seed: Python integer, the random seed for this operation.
       name: Optional name for the operation.
 
diff --git a/tensorflow/contrib/distributions/python/ops/laplace.py b/tensorflow/contrib/distributions/python/ops/laplace.py
index 6f6980b5a2..bc8e4fc38c 100644
--- a/tensorflow/contrib/distributions/python/ops/laplace.py
+++ b/tensorflow/contrib/distributions/python/ops/laplace.py
@@ -284,7 +284,8 @@ class Laplace(distribution.Distribution):
     """Sample `n` observations from the Laplace Distributions.
 
     Args:
-      n: `Scalar`, type int32, the number of observations to sample.
+      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+        observations to sample.
       seed: Python integer, the random seed.
       name: The name to give this op.
 
@@ -294,7 +295,7 @@ class Laplace(distribution.Distribution):
     """
     with ops.name_scope(self.name):
       with ops.name_scope(name, values=[self._loc, self._scale, n]):
-        n = ops.convert_to_tensor(n)
+        n = ops.convert_to_tensor(n, name="n")
         n_val = tensor_util.constant_value(n)
         shape = array_ops.concat(0, ([n], self.batch_shape()))
         # Sample uniformly-at-random from the open-interval (-1, 1).
diff --git a/tensorflow/contrib/distributions/python/ops/mvn.py b/tensorflow/contrib/distributions/python/ops/mvn.py
index f957ad1c6c..0546733cb9 100644
--- a/tensorflow/contrib/distributions/python/ops/mvn.py
+++ b/tensorflow/contrib/distributions/python/ops/mvn.py
@@ -348,7 +348,8 @@ class _MultivariateNormalOperatorPD(distribution.Distribution):
     """Sample `n` observations from the Multivariate Normal Distributions.
 
     Args:
-      n: `Scalar`, type int32, the number of observations to sample.
+      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+        observations to sample.
       seed: Python integer, the random seed.
       name: The name to give this op.
 
@@ -360,7 +361,7 @@ class _MultivariateNormalOperatorPD(distribution.Distribution):
       with ops.name_scope(name, values=[self._mu, n] + self._cov.inputs):
         # Recall _check_mu ensures mu and self._cov have same batch shape.
         broadcast_shape = self.mu.get_shape()
-        n = ops.convert_to_tensor(n)
+        n = ops.convert_to_tensor(n, name="n")
 
         shape = array_ops.concat(0, [self._cov.vector_shape(), [n]])
         white_samples = random_ops.random_normal(shape=shape,
diff --git a/tensorflow/contrib/distributions/python/ops/normal.py b/tensorflow/contrib/distributions/python/ops/normal.py
index 54813ad990..d5444991b1 100644
--- a/tensorflow/contrib/distributions/python/ops/normal.py
+++ b/tensorflow/contrib/distributions/python/ops/normal.py
@@ -329,7 +329,8 @@ class Normal(distribution.Distribution):
     """Sample `n` observations from the Normal Distributions.
 
     Args:
-      n: `Scalar`, type int32, the number of observations to sample.
+      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+        observations to sample.
       seed: Python integer, the random seed.
       name: The name to give this op.
 
@@ -341,7 +342,7 @@ class Normal(distribution.Distribution):
       with ops.name_scope(name, values=[self._mu, self._sigma, n]):
         broadcast_shape = common_shapes.broadcast_shape(
             self._mu.get_shape(), self._sigma.get_shape())
-        n = ops.convert_to_tensor(n)
+        n = ops.convert_to_tensor(n, name="n")
         shape = array_ops.concat(0, ([n], array_ops.shape(self.mean())))
         sampled = random_ops.random_normal(
             shape=shape, mean=0, stddev=1, dtype=self._mu.dtype, seed=seed)
diff --git a/tensorflow/contrib/distributions/python/ops/student_t.py b/tensorflow/contrib/distributions/python/ops/student_t.py
index fd02e1d2ec..dfcc2f2c3f 100644
--- a/tensorflow/contrib/distributions/python/ops/student_t.py
+++ b/tensorflow/contrib/distributions/python/ops/student_t.py
@@ -338,7 +338,8 @@ class StudentT(distribution.Distribution):
     """Sample `n` observations from the Student t Distributions.
 
     Args:
-      n: `Scalar`, type int32, the number of observations to sample.
+      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+        observations to sample.
       seed: Python integer, the random seed.
       name: The name to give this op.
 
diff --git a/tensorflow/contrib/distributions/python/ops/transformed_distribution.py b/tensorflow/contrib/distributions/python/ops/transformed_distribution.py
index 24429332c0..657700e9ed 100644
--- a/tensorflow/contrib/distributions/python/ops/transformed_distribution.py
+++ b/tensorflow/contrib/distributions/python/ops/transformed_distribution.py
@@ -224,7 +224,8 @@ class TransformedDistribution(distribution.Distribution):
     Samples from the base distribution and then passes through the transform.
 
     Args:
-      n: scalar, type int32, the number of observations to sample.
+      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+        observations to sample.
       seed: Python integer, the random seed.
       name: The name to give this op.
 
diff --git a/tensorflow/contrib/distributions/python/ops/uniform.py b/tensorflow/contrib/distributions/python/ops/uniform.py
index 3174cf2549..f5d4e3e48f 100644
--- a/tensorflow/contrib/distributions/python/ops/uniform.py
+++ b/tensorflow/contrib/distributions/python/ops/uniform.py
@@ -218,7 +218,8 @@ class Uniform(distribution.Distribution):
     """Sample `n` observations from the Uniform Distributions.
 
     Args:
-      n: `Scalar`, type int32, the number of observations to sample.
+      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+        observations to sample.
       seed: Python integer, the random seed.
       name: The name to give this op.
 
diff --git a/tensorflow/contrib/distributions/python/ops/wishart.py b/tensorflow/contrib/distributions/python/ops/wishart.py
index 6fb61bcad9..0c82720283 100644
--- a/tensorflow/contrib/distributions/python/ops/wishart.py
+++ b/tensorflow/contrib/distributions/python/ops/wishart.py
@@ -338,7 +338,8 @@ class _WishartOperatorPD(distribution.Distribution):
     https://en.wikipedia.org/wiki/Chi-squared_distribution#Gamma.2C_exponential.2C_and_related_distributions).
 
     Args:
-      n: Scalar. Number of samples to draw from each distribution.
+      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+        observations to sample.
       seed: Python integer; random number generator seed.
       name: The name of this op.
 
-- 
GitLab


From b3690899bbd4e2738e330624327761db6f1b6de2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 22 Aug 2016 15:04:29 -0800
Subject: [PATCH 016/223] Update generated Python Op docs. Change: 130993625

---
 .../api_docs/python/contrib.distributions.md  | 63 ++++++++++++-------
 .../tf.contrib.distributions.Bernoulli.md     |  3 +-
 .../tf.contrib.distributions.Dirichlet.md     |  3 +-
 ...istributions.MultivariateNormalCholesky.md |  3 +-
 ...ib.distributions.MultivariateNormalDiag.md |  3 +-
 .../tf.contrib.distributions.StudentT.md      |  3 +-
 ...b.distributions.TransformedDistribution.md |  3 +-
 .../tf.contrib.distributions.Categorical.md   |  3 +-
 .../shard2/tf.contrib.distributions.Chi2.md   |  4 +-
 .../tf.contrib.distributions.Uniform.md       |  3 +-
 ...f.contrib.distributions.WishartCholesky.md |  3 +-
 .../tf.contrib.distributions.Exponential.md   |  3 +-
 .../shard3/tf.contrib.distributions.Gamma.md  |  4 +-
 .../tf.contrib.distributions.InverseGamma.md  |  4 +-
 ...ibutions.MultivariateNormalDiagPlusVDVT.md |  3 +-
 .../shard6/tf.contrib.distributions.Beta.md   |  3 +-
 .../tf.contrib.distributions.Laplace.md       |  3 +-
 ....contrib.distributions.BaseDistribution.md |  3 +-
 ...ib.distributions.MultivariateNormalFull.md |  3 +-
 .../shard7/tf.contrib.distributions.Normal.md |  3 +-
 .../tf.contrib.distributions.WishartFull.md   |  3 +-
 21 files changed, 80 insertions(+), 46 deletions(-)

diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.md
index e8c68402c1..148058d014 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.distributions.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.md
@@ -1337,7 +1337,8 @@ Generate `n` samples.
 ##### Args:
 
 
-*  <b>`n`</b>: scalar.  Number of samples to draw from each distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer seed for RNG.
 *  <b>`name`</b>: name to give to the op.
 
@@ -1851,7 +1852,8 @@ Sample `n` observations from the Beta Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
@@ -2220,7 +2222,8 @@ Sample `n` observations from the Categorical distribution.
 ##### Args:
 
 
-*  <b>`n`</b>: 0-D.  Number of independent samples to draw for each distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Random seed (optional).
 *  <b>`name`</b>: A name for this operation (optional).
 
@@ -2697,8 +2700,8 @@ See the doc for tf.random_gamma for further detail.
 ##### Args:
 
 
-*  <b>`n`</b>: Python integer, the number of observations to sample from each
-    distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed for this operation.
 *  <b>`name`</b>: Optional name for the operation.
 
@@ -3175,7 +3178,8 @@ Sample `n` observations from the Exponential Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
@@ -3671,8 +3675,8 @@ See the doc for tf.random_gamma for further detail.
 ##### Args:
 
 
-*  <b>`n`</b>: Python integer, the number of observations to sample from each
-    distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed for this operation.
 *  <b>`name`</b>: Optional name for the operation.
 
@@ -4178,8 +4182,8 @@ See the doc for tf.random_gamma for further details on sampling strategy.
 ##### Args:
 
 
-*  <b>`n`</b>: Python integer, the number of observations to sample from each
-    distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed for this operation.
 *  <b>`name`</b>: Optional name for the operation.
 
@@ -4642,7 +4646,8 @@ Sample `n` observations from the Laplace Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
@@ -5122,7 +5127,8 @@ Sample `n` observations from the Normal Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
@@ -6014,7 +6020,8 @@ Sample `n` observations from the Student t Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
@@ -6454,7 +6461,8 @@ Sample `n` observations from the Uniform Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
@@ -6908,7 +6916,8 @@ Sample `n` observations from the Multivariate Normal Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
@@ -7363,7 +7372,8 @@ Sample `n` observations from the Multivariate Normal Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
@@ -7827,7 +7837,8 @@ Sample `n` observations from the Multivariate Normal Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
@@ -8383,7 +8394,8 @@ Sample `n` observations from the distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
@@ -9871,7 +9883,8 @@ https://en.wikipedia.org/wiki/Chi-squared_distribution#Gamma.2C_exponential.2C_a
 ##### Args:
 
 
-*  <b>`n`</b>: Scalar. Number of samples to draw from each distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer; random number generator seed.
 *  <b>`name`</b>: The name of this op.
 
@@ -10356,7 +10369,8 @@ https://en.wikipedia.org/wiki/Chi-squared_distribution#Gamma.2C_exponential.2C_a
 ##### Args:
 
 
-*  <b>`n`</b>: Scalar. Number of samples to draw from each distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer; random number generator seed.
 *  <b>`name`</b>: The name of this op.
 
@@ -10850,7 +10864,8 @@ Samples from the base distribution and then passes through the transform.
 ##### Args:
 
 
-*  <b>`n`</b>: scalar, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
@@ -11131,7 +11146,8 @@ Generate `n` samples.
 ##### Args:
 
 
-*  <b>`n`</b>: scalar. Number of samples to draw.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
@@ -11585,7 +11601,8 @@ Sample `n` observations from the Multivariate Normal Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md
index ce41346bee..98ba7f6071 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md
@@ -377,7 +377,8 @@ Generate `n` samples.
 ##### Args:
 
 
-*  <b>`n`</b>: scalar.  Number of samples to draw from each distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer seed for RNG.
 *  <b>`name`</b>: name to give to the op.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md
index a99e3f0568..9964b0115a 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md
@@ -444,7 +444,8 @@ Sample `n` observations from the distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md
index ff0218d03b..c34b12543a 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md
@@ -411,7 +411,8 @@ Sample `n` observations from the Multivariate Normal Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md
index 99cbd331df..fb6fff957b 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md
@@ -410,7 +410,8 @@ Sample `n` observations from the Multivariate Normal Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md
index 81ef29de73..427199e5f1 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md
@@ -409,7 +409,8 @@ Sample `n` observations from the Student t Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md
index 6b1f752686..cbc87efd29 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md
@@ -419,7 +419,8 @@ Samples from the base distribution and then passes through the transform.
 ##### Args:
 
 
-*  <b>`n`</b>: scalar, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md
index 037e5f05d3..701ab200ed 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md
@@ -330,7 +330,8 @@ Sample `n` observations from the Categorical distribution.
 ##### Args:
 
 
-*  <b>`n`</b>: 0-D.  Number of independent samples to draw for each distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Random seed (optional).
 *  <b>`name`</b>: A name for this operation (optional).
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md
index e1e7a63c22..fb4d719a40 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md
@@ -440,8 +440,8 @@ See the doc for tf.random_gamma for further detail.
 ##### Args:
 
 
-*  <b>`n`</b>: Python integer, the number of observations to sample from each
-    distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed for this operation.
 *  <b>`name`</b>: Optional name for the operation.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md
index f0d6fe0dbc..457b15e39c 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md
@@ -373,7 +373,8 @@ Sample `n` observations from the Uniform Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.WishartCholesky.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.WishartCholesky.md
index de410d5eae..e7dd32ef8b 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.WishartCholesky.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.WishartCholesky.md
@@ -417,7 +417,8 @@ https://en.wikipedia.org/wiki/Chi-squared_distribution#Gamma.2C_exponential.2C_a
 ##### Args:
 
 
-*  <b>`n`</b>: Scalar. Number of samples to draw from each distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer; random number generator seed.
 *  <b>`name`</b>: The name of this op.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md
index 5878273ca0..9ce971c182 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md
@@ -438,7 +438,8 @@ Sample `n` observations from the Exponential Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md
index a026df5d0a..173ad43326 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md
@@ -457,8 +457,8 @@ See the doc for tf.random_gamma for further detail.
 ##### Args:
 
 
-*  <b>`n`</b>: Python integer, the number of observations to sample from each
-    distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed for this operation.
 *  <b>`name`</b>: Optional name for the operation.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md
index 8956e3f890..d9dd80bcee 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md
@@ -467,8 +467,8 @@ See the doc for tf.random_gamma for further details on sampling strategy.
 ##### Args:
 
 
-*  <b>`n`</b>: Python integer, the number of observations to sample from each
-    distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed for this operation.
 *  <b>`name`</b>: Optional name for the operation.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.md
index e4aac2549f..17b9b0a145 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.md
@@ -437,7 +437,8 @@ Sample `n` observations from the Multivariate Normal Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md
index 341a185e52..de784991b2 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md
@@ -455,7 +455,8 @@ Sample `n` observations from the Beta Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md
index 52cba8fcb5..eca8801302 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md
@@ -411,7 +411,8 @@ Sample `n` observations from the Laplace Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.BaseDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.BaseDistribution.md
index 81699a03b5..92c712e827 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.BaseDistribution.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.BaseDistribution.md
@@ -55,7 +55,8 @@ Generate `n` samples.
 ##### Args:
 
 
-*  <b>`n`</b>: scalar. Number of samples to draw.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md
index f8dfe3f7e5..5a9b3c686f 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md
@@ -402,7 +402,8 @@ Sample `n` observations from the Multivariate Normal Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md
index eed175c094..2962678742 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md
@@ -434,7 +434,8 @@ Sample `n` observations from the Normal Distributions.
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar`, type int32, the number of observations to sample.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer, the random seed.
 *  <b>`name`</b>: The name to give this op.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.WishartFull.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.WishartFull.md
index 99e26f5066..d4dfd7719b 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.WishartFull.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.WishartFull.md
@@ -413,7 +413,8 @@ https://en.wikipedia.org/wiki/Chi-squared_distribution#Gamma.2C_exponential.2C_a
 ##### Args:
 
 
-*  <b>`n`</b>: Scalar. Number of samples to draw from each distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer; random number generator seed.
 *  <b>`name`</b>: The name of this op.
 
-- 
GitLab


From 3410be13a67e8cee35b72313a5f4a76bb305dc2e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 22 Aug 2016 15:04:42 -0800
Subject: [PATCH 017/223] Update makefiles for recent change splitting NodeDef
 out of graph.proto into node_def.proto. Change: 130993653

---
 tensorflow/contrib/cmake/tf_core_framework.cmake | 1 +
 tensorflow/contrib/makefile/tf_proto_files.txt   | 1 +
 2 files changed, 2 insertions(+)

diff --git a/tensorflow/contrib/cmake/tf_core_framework.cmake b/tensorflow/contrib/cmake/tf_core_framework.cmake
index c482882355..0dbe3c194d 100644
--- a/tensorflow/contrib/cmake/tf_core_framework.cmake
+++ b/tensorflow/contrib/cmake/tf_core_framework.cmake
@@ -95,6 +95,7 @@ set(tf_proto_text_srcs
     "tensorflow/core/framework/graph.proto"
     "tensorflow/core/framework/kernel_def.proto"
     "tensorflow/core/framework/log_memory.proto"
+    "tensorflow/core/framework/node_def.proto"
     "tensorflow/core/framework/op_def.proto"
     "tensorflow/core/framework/step_stats.proto"
     "tensorflow/core/framework/summary.proto"
diff --git a/tensorflow/contrib/makefile/tf_proto_files.txt b/tensorflow/contrib/makefile/tf_proto_files.txt
index 0490cebf16..a40bb046b9 100644
--- a/tensorflow/contrib/makefile/tf_proto_files.txt
+++ b/tensorflow/contrib/makefile/tf_proto_files.txt
@@ -22,6 +22,7 @@ tensorflow/core/framework/op_def.proto
 tensorflow/core/framework/log_memory.proto
 tensorflow/core/framework/kernel_def.proto
 tensorflow/core/framework/graph.proto
+tensorflow/core/framework/node_def.proto
 tensorflow/core/framework/function.proto
 tensorflow/core/framework/device_attributes.proto
 tensorflow/core/framework/cost_graph.proto
-- 
GitLab


From 626808e4e4a83aafbb3809a30db57bb78e839040 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Mon, 22 Aug 2016 15:35:25 -0800
Subject: [PATCH 018/223] Bugfixes for softplus

* Fix underflow issues in softplus
* Fix overflow issues in softplus for float16s.
Change: 130997105
---
 tensorflow/core/kernels/softplus_op.h         | 21 +++++++++++++---
 .../python/kernel_tests/softplus_op_test.py   | 24 ++++++++++++++++---
 2 files changed, 39 insertions(+), 6 deletions(-)

diff --git a/tensorflow/core/kernels/softplus_op.h b/tensorflow/core/kernels/softplus_op.h
index f019837f16..e17e175d41 100644
--- a/tensorflow/core/kernels/softplus_op.h
+++ b/tensorflow/core/kernels/softplus_op.h
@@ -33,9 +33,24 @@ struct Softplus {
   // activations: same shape as "features".
   void operator()(const Device& d, typename TTypes<T>::ConstTensor features,
                   typename TTypes<T>::Tensor activations) {
-    activations.device(d) =
-        (features > features.constant(T(30)))
-            .select(features, (features.exp() + features.constant(T(1))).log());
+    // Choose a threshold on x below which exp(x) may underflow
+    // when added to 1, but for which exp(x) is always within epsilon of the
+    // true softplus(x).  Offset of 2 from machine epsilon checked
+    // experimentally for float16, float32, float64.  Checked against
+    // softplus implemented with numpy's log1p and numpy's logaddexp.
+    static const T threshold =
+        Eigen::numext::log(Eigen::NumTraits<T>::epsilon()) + T(2);
+    // Value above which exp(x) may overflow, but softplus(x) == x
+    // is within machine epsilon.
+    auto too_large = features > features.constant(-threshold);
+    // Value below which exp(x) may underflow, but softplus(x) == exp(x)
+    // is within machine epsilon.
+    auto too_small = features < features.constant(threshold);
+    auto features_exp = features.exp();
+    activations.device(d) = too_large.select(
+        features,                       // softplus(x) ~= x for x large
+        too_small.select(features_exp,  // softplus(x) ~= exp(x) for x small
+                         (features_exp + features.constant(T(1))).log()));
   }
 };
 
diff --git a/tensorflow/python/kernel_tests/softplus_op_test.py b/tensorflow/python/kernel_tests/softplus_op_test.py
index 348e898343..6223cacf61 100644
--- a/tensorflow/python/kernel_tests/softplus_op_test.py
+++ b/tensorflow/python/kernel_tests/softplus_op_test.py
@@ -25,24 +25,42 @@ import tensorflow as tf
 class SoftplusTest(tf.test.TestCase):
 
   def _npSoftplus(self, np_features):
-    return np.log(1 + np.exp(np_features))
+    np_features = np.asarray(np_features)
+    zero = np.asarray(0).astype(np_features.dtype)
+    return np.logaddexp(zero, np_features)
 
   def _testSoftplus(self, np_features, use_gpu=False):
     np_softplus = self._npSoftplus(np_features)
     with self.test_session(use_gpu=use_gpu):
       softplus = tf.nn.softplus(np_features)
       tf_softplus = softplus.eval()
-    self.assertAllClose(np_softplus, tf_softplus)
+    self.assertAllCloseAccordingToType(np_softplus, tf_softplus)
+    self.assertTrue(np.all(tf_softplus > 0))
     self.assertShapeEqual(np_softplus, softplus)
 
   def testNumbers(self):
-    for t in [np.float, np.double]:
+    for t in [np.float16, np.float32, np.float64]:
       self._testSoftplus(
           np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
           use_gpu=False)
       self._testSoftplus(
           np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
           use_gpu=True)
+      log_eps = np.log(np.finfo(t).eps)
+      one = t(1)
+      ten = t(10)
+      self._testSoftplus(
+          [log_eps, log_eps - one, log_eps + one,
+           log_eps - ten, log_eps + ten,
+           -log_eps, -log_eps - one, -log_eps + one,
+           -log_eps - ten, -log_eps + ten],
+          use_gpu=False)
+      self._testSoftplus(
+          [log_eps, log_eps - one, log_eps + one,
+           log_eps - ten, log_eps + ten
+           -log_eps, -log_eps - one, -log_eps + one,
+           -log_eps - ten, -log_eps + ten],
+          use_gpu=True)
 
   def testGradient(self):
     with self.test_session():
-- 
GitLab


From 8a34264272b6107cd1dd8640830597599a0b11ee Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Mon, 22 Aug 2016 15:39:36 -0800
Subject: [PATCH 019/223] RNN API updates and reduce_min/reduce_max bugfixes.

* Expose dynamic_bidirectional_rnn and raw_rnn to the public api.
* Add GPU ops for reduce_max/reduce_min, so rnn code will work even with hard device placement.
Change: 130997558
---
 tensorflow/core/kernels/reduction_ops_max.cc | 13 +++++++++++++
 tensorflow/core/kernels/reduction_ops_min.cc | 13 +++++++++++++
 tensorflow/python/ops/nn.py                  |  2 ++
 3 files changed, 28 insertions(+)

diff --git a/tensorflow/core/kernels/reduction_ops_max.cc b/tensorflow/core/kernels/reduction_ops_max.cc
index 758a237c59..6d3feeb666 100644
--- a/tensorflow/core/kernels/reduction_ops_max.cc
+++ b/tensorflow/core/kernels/reduction_ops_max.cc
@@ -35,6 +35,19 @@ TF_CALL_REAL_NUMBER_TYPES(REGISTER_CPU_KERNELS);
       ReductionOp<GPUDevice, type, Eigen::internal::MaxReducer<type>>);
 REGISTER_GPU_KERNELS(float);
 REGISTER_GPU_KERNELS(double);
+
+// A special GPU kernel for int32.
+// TODO(b/25387198): Also enable int32 in device memory. This kernel
+// registration requires all int32 inputs and outputs to be in host memory.
+REGISTER_KERNEL_BUILDER(
+    Name("Max")
+        .Device(DEVICE_GPU)
+        .HostMemory("reduction_indices")
+        .HostMemory("input")
+        .HostMemory("output")
+        .TypeConstraint<int32>("T"),
+    ReductionOp<CPUDevice, int32, Eigen::internal::MaxReducer<int32>>);
+
 #undef REGISTER_GPU_KERNELS
 
 #endif
diff --git a/tensorflow/core/kernels/reduction_ops_min.cc b/tensorflow/core/kernels/reduction_ops_min.cc
index 2582356b92..c567aca0b0 100644
--- a/tensorflow/core/kernels/reduction_ops_min.cc
+++ b/tensorflow/core/kernels/reduction_ops_min.cc
@@ -35,6 +35,19 @@ TF_CALL_REAL_NUMBER_TYPES(REGISTER_CPU_KERNELS);
       ReductionOp<GPUDevice, type, Eigen::internal::MinReducer<type>>);
 REGISTER_GPU_KERNELS(float);
 REGISTER_GPU_KERNELS(double);
+
+// A special GPU kernel for int32.
+// TODO(b/25387198): Also enable int32 in device memory. This kernel
+// registration requires all int32 inputs and outputs to be in host memory.
+REGISTER_KERNEL_BUILDER(
+    Name("Min")
+        .Device(DEVICE_GPU)
+        .HostMemory("reduction_indices")
+        .HostMemory("input")
+        .HostMemory("output")
+        .TypeConstraint<int32>("T"),
+    ReductionOp<CPUDevice, int32, Eigen::internal::MinReducer<int32>>);
+
 #undef REGISTER_GPU_KERNELS
 
 #endif
diff --git a/tensorflow/python/ops/nn.py b/tensorflow/python/ops/nn.py
index fd4eedcfcc..1837ea5a2a 100644
--- a/tensorflow/python/ops/nn.py
+++ b/tensorflow/python/ops/nn.py
@@ -220,7 +220,9 @@ Neural Networks.  Most accept an `RNNCell`-subclassed object
 @@dynamic_rnn
 @@rnn
 @@state_saving_rnn
+@@bidirectional_dynamic_rnn
 @@bidirectional_rnn
+@@raw_rnn
 
 ## Conectionist Temporal Classification (CTC)
 
-- 
GitLab


From 6a9c92002a9a1462effd17766a343a7cf04b5dd5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 22 Aug 2016 15:49:13 -0800
Subject: [PATCH 020/223] Update generated Python Op docs. Change: 130998646

---
 .../shard8/tf.nn.bidirectional_dynamic_rnn.md |  86 +++++++
 .../shard9/tf.nn.raw_rnn.md                   | 150 +++++++++++
 tensorflow/g3doc/api_docs/python/index.md     |   2 +
 tensorflow/g3doc/api_docs/python/nn.md        | 242 ++++++++++++++++++
 4 files changed, 480 insertions(+)
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.bidirectional_dynamic_rnn.md
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.raw_rnn.md

diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.bidirectional_dynamic_rnn.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.bidirectional_dynamic_rnn.md
new file mode 100644
index 0000000000..1252efa34d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.nn.bidirectional_dynamic_rnn.md
@@ -0,0 +1,86 @@
+### `tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, initial_state_fw=None, initial_state_bw=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None)` {#bidirectional_dynamic_rnn}
+
+Creates a dynamic version of bidirectional recurrent neural network.
+
+Similar to the unidirectional case above (rnn) but takes input and builds
+independent forward and backward RNNs. The input_size of forward and
+backward cell must match. The initial state for both directions is zero by
+default (but can be set optionally) and no intermediate states are ever
+returned -- the network is fully unrolled for the given (passed in)
+length(s) of the sequence(s) or completely unrolled if length(s) is not
+given.
+
+##### Args:
+
+
+*  <b>`cell_fw`</b>: An instance of RNNCell, to be used for forward direction.
+*  <b>`cell_bw`</b>: An instance of RNNCell, to be used for backward direction.
+*  <b>`inputs`</b>: The RNN inputs.
+    If time_major == False (default), this must be a tensor of shape:
+      `[batch_size, max_time, input_size]`.
+    If time_major == True, this must be a tensor of shape:
+      `[max_time, batch_size, input_size]`.
+    [batch_size, input_size].
+*  <b>`sequence_length`</b>: An int32/int64 vector, size `[batch_size]`,
+    containing the actual lengths for each of the sequences.
+*  <b>`initial_state_fw`</b>: (optional) An initial state for the forward RNN.
+    This must be a tensor of appropriate type and shape
+    `[batch_size x cell_fw.state_size]`.
+    If `cell_fw.state_size` is a tuple, this should be a tuple of
+    tensors having shapes `[batch_size, s] for s in cell_fw.state_size`.
+*  <b>`initial_state_bw`</b>: (optional) Same as for `initial_state_fw`, but using
+    the corresponding properties of `cell_bw`.
+*  <b>`dtype`</b>: (optional) The data type for the initial states and expected output.
+    Required if initial_states are not provided or RNN states have a
+    heterogeneous dtype.
+*  <b>`parallel_iterations`</b>: (Default: 32).  The number of iterations to run in
+    parallel.  Those operations which do not have any temporal dependency
+    and can be run in parallel, will be.  This parameter trades off
+    time for space.  Values >> 1 use more memory but take less time,
+    while smaller values use less memory but computations take longer.
+*  <b>`swap_memory`</b>: Transparently swap the tensors produced in forward inference
+    but needed for back prop from GPU to CPU.  This allows training RNNs
+    which would typically not fit on a single GPU, with very minimal (or no)
+    performance penalty.
+*  <b>`time_major`</b>: The shape format of the `inputs` and `outputs` Tensors.
+    If true, these `Tensors` must be shaped `[max_time, batch_size, depth]`.
+    If false, these `Tensors` must be shaped `[batch_size, max_time, depth]`.
+    Using `time_major = True` is a bit more efficient because it avoids
+    transposes at the beginning and end of the RNN calculation.  However,
+    most TensorFlow data is batch-major, so by default this function
+    accepts input and emits output in batch-major form.
+*  <b>`dtype`</b>: (optional) The data type for the initial state.  Required if
+    initial_state is not provided.
+*  <b>`sequence_length`</b>: An int32/int64 vector, size `[batch_size]`,
+    containing the actual lengths for each of the sequences.
+    either of the initial states are not provided.
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "BiRNN"
+
+##### Returns:
+
+  A tuple (outputs, output_states) where:
+
+*  <b>`outputs`</b>: A tuple (output_fw, output_bw) containing the forward and
+      the backward rnn output `Tensor`.
+      If time_major == False (default),
+        output_fw will be a `Tensor` shaped:
+        `[batch_size, max_time, cell_fw.output_size]`
+        and output_bw will be a `Tensor` shaped:
+        `[batch_size, max_time, cell_bw.output_size]`.
+      If time_major == True,
+        output_fw will be a `Tensor` shaped:
+        `[max_time, batch_size, cell_fw.output_size]`
+        and output_bw will be a `Tensor` shaped:
+        `[max_time, batch_size, cell_bw.output_size]`.
+      It returns a tuple instead of a single concatenated `Tensor`, unlike
+      in the `bidirectional_rnn`. If the concatenated one is preferred,
+      the forward and backward outputs can be concatenated as
+      `tf.concat(2, outputs)`.
+*  <b>`output_states`</b>: A tuple (output_state_fw, output_state_bw) containing
+      the forward and the backward final states of bidirectional rnn.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.raw_rnn.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.raw_rnn.md
new file mode 100644
index 0000000000..d0055a8a43
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.raw_rnn.md
@@ -0,0 +1,150 @@
+### `tf.nn.raw_rnn(cell, loop_fn, initial_state, parallel_iterations=None, swap_memory=False, scope=None)` {#raw_rnn}
+
+Creates an `RNN` specified by RNNCell `cell` and loop function `loop_fn`.
+
+**NOTE: This method is still in testing, and the API may change.**
+
+This function is a more primitive version of `dynamic_rnn` that provides
+more direct access to the inputs each iteration.  It also provides more
+control over when to start and finish reading the sequence, and
+what to emit for the output.
+
+For example, it can be used to implement the dynamic decoder of a seq2seq
+model.
+
+Instead of working with `Tensor` objects, most operations work with
+`TensorArray` objects directly.
+
+The operation of `raw_rnn`, in pseudo-code, is basically the following:
+```
+emit_ta = TensorArray(dynamic_size=True, dtype=initial_state.dtype)
+time = tf.constant(0, dtype=tf.int32)
+(finished, next_input, _, loop_state) = loop_fn(
+    time=time, cell_output=None, loop_state=None)
+state = initial_state
+while not all(finished):
+  (output, next_state) = cell(next_input, state)
+  (next_finished, next_input, emit, loop_state) = loop_fn(
+      time=time + 1, cell_output=output, loop_state=loop_state)
+  # Emit zeros and copy forward state for minibatch entries that are finished.
+  state = tf.select(finished, state, next_state)
+  emit = tf.select(finished, tf.zeros_like(emit), emit)
+  emit_ta = emit_ta.write(time, emit)
+  # If any new minibatch entries are marked as finished, mark these
+  finished = tf.logical_or(finished, next_finished)
+  time += 1
+return (emit_ta, state, loop_state)
+```
+
+with the additional properties that output and state may be (possibly nested)
+tuples, as determined by `cell.output_size` and `cell.state_size`, and
+as a result the final `state` and `emit_ta` may themselves be tuples.
+
+A simple implementation of `dynamic_rnn` via `raw_rnn` looks like this:
+
+```python
+inputs = tf.placeholder(shape=(max_time, batch_size, input_depth),
+                        dtype=tf.float32)
+sequence_length = tf.placeholder(shape=(batch_size,), dtype=tf.int32)
+inputs_ta = tf.TensorArray(dtype=tf.float32, size=max_time)
+inputs_ta = inputs_ta.unpack(inputs)
+
+def loop_fn(time, cell_output, loop_state):
+  emit_output = cell_output  # == None for time == 0
+  elements_finished = (time >= sequence_length)
+  finished = tf.reduce_all(elements_finished)
+  next_input = tf.cond(
+      finished,
+      lambda: tf.zeros([batch_size, input_depth], dtype=tf.float32),
+      lambda: inputs_ta.read(time))
+  next_loop_state = None
+  return (elements_finished, next_input, emit_output, next_loop_state)
+
+cell = tf.nn.rnn_cell.LSTMCell(num_units, state_is_tuple=True)
+initial_state = cell.zero_state(batch_size, tf.float32)
+outputs_ta, final_state, _ = raw_rnn(cell, loop_fn, initial_state)
+outputs = outputs_ta.pack()
+```
+
+##### Args:
+
+
+*  <b>`cell`</b>: An instance of RNNCell.
+*  <b>`loop_fn`</b>: A callable that takes inputs `(time, cell_output, loop_state)` and
+    returns the tuple `(finished, next_input, emit_output, next_loop_state)`.
+    Here `time` is an int32 scalar `Tensor`, `cell_output` is a
+    `Tensor` or (possibly nested) tuple of tensors as determined by
+    `cell.output_size`.  In addition, `finished` is a boolean `Tensor` of
+    shape `[batch_size]`, `next_input` is the next input to feed to `cell`,
+    and `emit_output` is the output to store for this iteration.  Note that
+    `emit_output` should be a `Tensor` or (possibly nested) tuple of tensors
+    with shapes and structure matching `cell.output_size` and `cell_output`
+    above.  The parameter `loop_state` and output `next_loop_state` may be
+    either a single or (possibly nested) tuple of tensors.  This paramter
+    may be ignored by `loop_fn` and the return value may be `None`.  If it
+    is not `None`, then the `loop_state` will be propagated through the RNN
+    loop, for use purely by `loop_fn` to keep track of its own state.
+    The `next_loop_state` parameter returned may be `None`.
+
+    The first call to `loop_fn` will be `time = 0`, `cell_output = None`,
+    and `loop_state = None`.  Its `emit_output` value in this case may be
+    either `None` or a (possibly nested) tuple structure of Tensors, e.g.,
+    `(tf.zeros(shape_0, dtype=dtype_0), tf.zeros(shape_1, dtype=dtype_1))`.
+    If this first `emit_output` return value is `None`,
+    then the `emit_ta` result of `raw_rnn` will have the same structure and
+    dtypes as `cell.output_size`.  Otherwise `emit_ta` will have the same
+    structure, shapes (prepended with a `batch_size` dimension), and dtypes
+    as `emit_output`.  The actual values returned for `emit_output` at this
+    initializing call are ignored.  Note, this emit structure must be
+    consistent across all time steps.
+
+
+*  <b>`initial_state`</b>: An initial state for the RNN.
+    If `cell.state_size` is an integer, this must be
+    a `Tensor` of appropriate type and shape `[batch_size, cell.state_size]`.
+    If `cell.state_size` is a `TensorShape`, this must be a `Tensor` of
+    appropriate type and shape `[batch_size] + cell.state_size`.
+    If `cell.state_size` is a (possibly nested) tuple of ints or
+    `TensorShape`, this will be a tuple having the corresponding shapes.
+*  <b>`parallel_iterations`</b>: (Default: 32).  The number of iterations to run in
+    parallel.  Those operations which do not have any temporal dependency
+    and can be run in parallel, will be.  This parameter trades off
+    time for space.  Values >> 1 use more memory but take less time,
+    while smaller values use less memory but computations take longer.
+*  <b>`swap_memory`</b>: Transparently swap the tensors produced in forward inference
+    but needed for back prop from GPU to CPU.  This allows training RNNs
+    which would typically not fit on a single GPU, with very minimal (or no)
+    performance penalty.
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "RNN".
+
+##### Returns:
+
+  A tuple `(emit_ta, final_state, final_loop_state)` where:
+
+    `emit_ta`: The RNN output `TensorArray`.
+       If `loop_fn` returns a (possibly nested) set of Tensors for
+       `emit_output` during initialization, (inputs `time = 0`,
+       `cell_output = None`, and `loop_state = None`), then `emit_ta` will
+       have the same structure, dtypes, and shapes as `emit_output` instead.
+       If `loop_fn` returns `emit_output = None` during this call,
+       the structure of `cell.output_size` is used:
+
+       If `cell.output_size` is a (possibly nested) tuple of integers
+       or `TensorShape` objects, then `emit_ta` will be a tuple having the
+       same structure as `cell.output_size`, containing TensorArrays whose
+       elements' shapes correspond to the shape data in `cell.output_size`.
+
+    `final_state`: The final cell state.  If `cell.state_size` is an int, this
+      will be shaped `[batch_size, cell.state_size]`.  If it is a
+      `TensorShape`, this will be shaped `[batch_size] + cell.state_size`.
+      If it is a (possibly nested) tuple of ints or `TensorShape`, this will
+      be a tuple having the corresponding shapes.
+
+    `final_loop_state`: The final loop state as returned by `loop_fn`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `cell` is not an instance of RNNCell, or `loop_fn` is not
+    a `callable`.
+
diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md
index c93190c139..8ab445b732 100644
--- a/tensorflow/g3doc/api_docs/python/index.md
+++ b/tensorflow/g3doc/api_docs/python/index.md
@@ -445,6 +445,7 @@
   * [`avg_pool3d`](../../api_docs/python/nn.md#avg_pool3d)
   * [`batch_normalization`](../../api_docs/python/nn.md#batch_normalization)
   * [`bias_add`](../../api_docs/python/nn.md#bias_add)
+  * [`bidirectional_dynamic_rnn`](../../api_docs/python/nn.md#bidirectional_dynamic_rnn)
   * [`bidirectional_rnn`](../../api_docs/python/nn.md#bidirectional_rnn)
   * [`compute_accidental_hits`](../../api_docs/python/nn.md#compute_accidental_hits)
   * [`conv1d`](../../api_docs/python/nn.md#conv1d)
@@ -478,6 +479,7 @@
   * [`moments`](../../api_docs/python/nn.md#moments)
   * [`nce_loss`](../../api_docs/python/nn.md#nce_loss)
   * [`normalize_moments`](../../api_docs/python/nn.md#normalize_moments)
+  * [`raw_rnn`](../../api_docs/python/nn.md#raw_rnn)
   * [`relu`](../../api_docs/python/nn.md#relu)
   * [`relu6`](../../api_docs/python/nn.md#relu6)
   * [`rnn`](../../api_docs/python/nn.md#rnn)
diff --git a/tensorflow/g3doc/api_docs/python/nn.md b/tensorflow/g3doc/api_docs/python/nn.md
index ed25ee474e..1e8a37186d 100644
--- a/tensorflow/g3doc/api_docs/python/nn.md
+++ b/tensorflow/g3doc/api_docs/python/nn.md
@@ -1766,6 +1766,95 @@ RNN that accepts a state saver for time-truncated RNN calculation.
    type of `state_name` does not match that of `cell.state_size`.
 
 
+- - -
+
+### `tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, initial_state_fw=None, initial_state_bw=None, dtype=None, parallel_iterations=None, swap_memory=False, time_major=False, scope=None)` {#bidirectional_dynamic_rnn}
+
+Creates a dynamic version of bidirectional recurrent neural network.
+
+Similar to the unidirectional case above (rnn) but takes input and builds
+independent forward and backward RNNs. The input_size of forward and
+backward cell must match. The initial state for both directions is zero by
+default (but can be set optionally) and no intermediate states are ever
+returned -- the network is fully unrolled for the given (passed in)
+length(s) of the sequence(s) or completely unrolled if length(s) is not
+given.
+
+##### Args:
+
+
+*  <b>`cell_fw`</b>: An instance of RNNCell, to be used for forward direction.
+*  <b>`cell_bw`</b>: An instance of RNNCell, to be used for backward direction.
+*  <b>`inputs`</b>: The RNN inputs.
+    If time_major == False (default), this must be a tensor of shape:
+      `[batch_size, max_time, input_size]`.
+    If time_major == True, this must be a tensor of shape:
+      `[max_time, batch_size, input_size]`.
+    [batch_size, input_size].
+*  <b>`sequence_length`</b>: An int32/int64 vector, size `[batch_size]`,
+    containing the actual lengths for each of the sequences.
+*  <b>`initial_state_fw`</b>: (optional) An initial state for the forward RNN.
+    This must be a tensor of appropriate type and shape
+    `[batch_size x cell_fw.state_size]`.
+    If `cell_fw.state_size` is a tuple, this should be a tuple of
+    tensors having shapes `[batch_size, s] for s in cell_fw.state_size`.
+*  <b>`initial_state_bw`</b>: (optional) Same as for `initial_state_fw`, but using
+    the corresponding properties of `cell_bw`.
+*  <b>`dtype`</b>: (optional) The data type for the initial states and expected output.
+    Required if initial_states are not provided or RNN states have a
+    heterogeneous dtype.
+*  <b>`parallel_iterations`</b>: (Default: 32).  The number of iterations to run in
+    parallel.  Those operations which do not have any temporal dependency
+    and can be run in parallel, will be.  This parameter trades off
+    time for space.  Values >> 1 use more memory but take less time,
+    while smaller values use less memory but computations take longer.
+*  <b>`swap_memory`</b>: Transparently swap the tensors produced in forward inference
+    but needed for back prop from GPU to CPU.  This allows training RNNs
+    which would typically not fit on a single GPU, with very minimal (or no)
+    performance penalty.
+*  <b>`time_major`</b>: The shape format of the `inputs` and `outputs` Tensors.
+    If true, these `Tensors` must be shaped `[max_time, batch_size, depth]`.
+    If false, these `Tensors` must be shaped `[batch_size, max_time, depth]`.
+    Using `time_major = True` is a bit more efficient because it avoids
+    transposes at the beginning and end of the RNN calculation.  However,
+    most TensorFlow data is batch-major, so by default this function
+    accepts input and emits output in batch-major form.
+*  <b>`dtype`</b>: (optional) The data type for the initial state.  Required if
+    initial_state is not provided.
+*  <b>`sequence_length`</b>: An int32/int64 vector, size `[batch_size]`,
+    containing the actual lengths for each of the sequences.
+    either of the initial states are not provided.
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "BiRNN"
+
+##### Returns:
+
+  A tuple (outputs, output_states) where:
+
+*  <b>`outputs`</b>: A tuple (output_fw, output_bw) containing the forward and
+      the backward rnn output `Tensor`.
+      If time_major == False (default),
+        output_fw will be a `Tensor` shaped:
+        `[batch_size, max_time, cell_fw.output_size]`
+        and output_bw will be a `Tensor` shaped:
+        `[batch_size, max_time, cell_bw.output_size]`.
+      If time_major == True,
+        output_fw will be a `Tensor` shaped:
+        `[max_time, batch_size, cell_fw.output_size]`
+        and output_bw will be a `Tensor` shaped:
+        `[max_time, batch_size, cell_bw.output_size]`.
+      It returns a tuple instead of a single concatenated `Tensor`, unlike
+      in the `bidirectional_rnn`. If the concatenated one is preferred,
+      the forward and backward outputs can be concatenated as
+      `tf.concat(2, outputs)`.
+*  <b>`output_states`</b>: A tuple (output_state_fw, output_state_bw) containing
+      the forward and the backward final states of bidirectional rnn.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `cell_fw` or `cell_bw` is not an instance of `RNNCell`.
+
+
 - - -
 
 ### `tf.nn.bidirectional_rnn(cell_fw, cell_bw, inputs, initial_state_fw=None, initial_state_bw=None, dtype=None, sequence_length=None, scope=None)` {#bidirectional_rnn}
@@ -1816,6 +1905,159 @@ length(s) of the sequence(s) or completely unrolled if length(s) is not given.
 *  <b>`ValueError`</b>: If inputs is None or an empty list.
 
 
+- - -
+
+### `tf.nn.raw_rnn(cell, loop_fn, initial_state, parallel_iterations=None, swap_memory=False, scope=None)` {#raw_rnn}
+
+Creates an `RNN` specified by RNNCell `cell` and loop function `loop_fn`.
+
+**NOTE: This method is still in testing, and the API may change.**
+
+This function is a more primitive version of `dynamic_rnn` that provides
+more direct access to the inputs each iteration.  It also provides more
+control over when to start and finish reading the sequence, and
+what to emit for the output.
+
+For example, it can be used to implement the dynamic decoder of a seq2seq
+model.
+
+Instead of working with `Tensor` objects, most operations work with
+`TensorArray` objects directly.
+
+The operation of `raw_rnn`, in pseudo-code, is basically the following:
+```
+emit_ta = TensorArray(dynamic_size=True, dtype=initial_state.dtype)
+time = tf.constant(0, dtype=tf.int32)
+(finished, next_input, _, loop_state) = loop_fn(
+    time=time, cell_output=None, loop_state=None)
+state = initial_state
+while not all(finished):
+  (output, next_state) = cell(next_input, state)
+  (next_finished, next_input, emit, loop_state) = loop_fn(
+      time=time + 1, cell_output=output, loop_state=loop_state)
+  # Emit zeros and copy forward state for minibatch entries that are finished.
+  state = tf.select(finished, state, next_state)
+  emit = tf.select(finished, tf.zeros_like(emit), emit)
+  emit_ta = emit_ta.write(time, emit)
+  # If any new minibatch entries are marked as finished, mark these
+  finished = tf.logical_or(finished, next_finished)
+  time += 1
+return (emit_ta, state, loop_state)
+```
+
+with the additional properties that output and state may be (possibly nested)
+tuples, as determined by `cell.output_size` and `cell.state_size`, and
+as a result the final `state` and `emit_ta` may themselves be tuples.
+
+A simple implementation of `dynamic_rnn` via `raw_rnn` looks like this:
+
+```python
+inputs = tf.placeholder(shape=(max_time, batch_size, input_depth),
+                        dtype=tf.float32)
+sequence_length = tf.placeholder(shape=(batch_size,), dtype=tf.int32)
+inputs_ta = tf.TensorArray(dtype=tf.float32, size=max_time)
+inputs_ta = inputs_ta.unpack(inputs)
+
+def loop_fn(time, cell_output, loop_state):
+  emit_output = cell_output  # == None for time == 0
+  elements_finished = (time >= sequence_length)
+  finished = tf.reduce_all(elements_finished)
+  next_input = tf.cond(
+      finished,
+      lambda: tf.zeros([batch_size, input_depth], dtype=tf.float32),
+      lambda: inputs_ta.read(time))
+  next_loop_state = None
+  return (elements_finished, next_input, emit_output, next_loop_state)
+
+cell = tf.nn.rnn_cell.LSTMCell(num_units, state_is_tuple=True)
+initial_state = cell.zero_state(batch_size, tf.float32)
+outputs_ta, final_state, _ = raw_rnn(cell, loop_fn, initial_state)
+outputs = outputs_ta.pack()
+```
+
+##### Args:
+
+
+*  <b>`cell`</b>: An instance of RNNCell.
+*  <b>`loop_fn`</b>: A callable that takes inputs `(time, cell_output, loop_state)` and
+    returns the tuple `(finished, next_input, emit_output, next_loop_state)`.
+    Here `time` is an int32 scalar `Tensor`, `cell_output` is a
+    `Tensor` or (possibly nested) tuple of tensors as determined by
+    `cell.output_size`.  In addition, `finished` is a boolean `Tensor` of
+    shape `[batch_size]`, `next_input` is the next input to feed to `cell`,
+    and `emit_output` is the output to store for this iteration.  Note that
+    `emit_output` should be a `Tensor` or (possibly nested) tuple of tensors
+    with shapes and structure matching `cell.output_size` and `cell_output`
+    above.  The parameter `loop_state` and output `next_loop_state` may be
+    either a single or (possibly nested) tuple of tensors.  This paramter
+    may be ignored by `loop_fn` and the return value may be `None`.  If it
+    is not `None`, then the `loop_state` will be propagated through the RNN
+    loop, for use purely by `loop_fn` to keep track of its own state.
+    The `next_loop_state` parameter returned may be `None`.
+
+    The first call to `loop_fn` will be `time = 0`, `cell_output = None`,
+    and `loop_state = None`.  Its `emit_output` value in this case may be
+    either `None` or a (possibly nested) tuple structure of Tensors, e.g.,
+    `(tf.zeros(shape_0, dtype=dtype_0), tf.zeros(shape_1, dtype=dtype_1))`.
+    If this first `emit_output` return value is `None`,
+    then the `emit_ta` result of `raw_rnn` will have the same structure and
+    dtypes as `cell.output_size`.  Otherwise `emit_ta` will have the same
+    structure, shapes (prepended with a `batch_size` dimension), and dtypes
+    as `emit_output`.  The actual values returned for `emit_output` at this
+    initializing call are ignored.  Note, this emit structure must be
+    consistent across all time steps.
+
+
+*  <b>`initial_state`</b>: An initial state for the RNN.
+    If `cell.state_size` is an integer, this must be
+    a `Tensor` of appropriate type and shape `[batch_size, cell.state_size]`.
+    If `cell.state_size` is a `TensorShape`, this must be a `Tensor` of
+    appropriate type and shape `[batch_size] + cell.state_size`.
+    If `cell.state_size` is a (possibly nested) tuple of ints or
+    `TensorShape`, this will be a tuple having the corresponding shapes.
+*  <b>`parallel_iterations`</b>: (Default: 32).  The number of iterations to run in
+    parallel.  Those operations which do not have any temporal dependency
+    and can be run in parallel, will be.  This parameter trades off
+    time for space.  Values >> 1 use more memory but take less time,
+    while smaller values use less memory but computations take longer.
+*  <b>`swap_memory`</b>: Transparently swap the tensors produced in forward inference
+    but needed for back prop from GPU to CPU.  This allows training RNNs
+    which would typically not fit on a single GPU, with very minimal (or no)
+    performance penalty.
+*  <b>`scope`</b>: VariableScope for the created subgraph; defaults to "RNN".
+
+##### Returns:
+
+  A tuple `(emit_ta, final_state, final_loop_state)` where:
+
+    `emit_ta`: The RNN output `TensorArray`.
+       If `loop_fn` returns a (possibly nested) set of Tensors for
+       `emit_output` during initialization, (inputs `time = 0`,
+       `cell_output = None`, and `loop_state = None`), then `emit_ta` will
+       have the same structure, dtypes, and shapes as `emit_output` instead.
+       If `loop_fn` returns `emit_output = None` during this call,
+       the structure of `cell.output_size` is used:
+
+       If `cell.output_size` is a (possibly nested) tuple of integers
+       or `TensorShape` objects, then `emit_ta` will be a tuple having the
+       same structure as `cell.output_size`, containing TensorArrays whose
+       elements' shapes correspond to the shape data in `cell.output_size`.
+
+    `final_state`: The final cell state.  If `cell.state_size` is an int, this
+      will be shaped `[batch_size, cell.state_size]`.  If it is a
+      `TensorShape`, this will be shaped `[batch_size] + cell.state_size`.
+      If it is a (possibly nested) tuple of ints or `TensorShape`, this will
+      be a tuple having the corresponding shapes.
+
+    `final_loop_state`: The final loop state as returned by `loop_fn`.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: If `cell` is not an instance of RNNCell, or `loop_fn` is not
+    a `callable`.
+
+
 
 ## Conectionist Temporal Classification (CTC)
 
-- 
GitLab


From 208c2a977a546bcdca2a3215fe1f2dc771f5287f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 22 Aug 2016 16:15:26 -0800
Subject: [PATCH 021/223] Fixing spacing issue in docs for tf.meshgrid as per
 b/30845368. Change: 131001422

---
 tensorflow/python/ops/array_ops.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index f54aec50fb..c556e8e213 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -1415,11 +1415,14 @@ def meshgrid(*args, **kwargs):
   Examples:
 
   Calling `X, Y = meshgrid(x, y)` with the tensors
+
   ```prettyprint
     x = [1, 2, 3]
     y = [4, 5, 6]
   ```
+
   results in
+
   ```prettyprint
     X = [[1, 1, 1],
          [2, 2, 2],
-- 
GitLab


From 85922ce40b46333a92ffb443e2ff20bd0731749c Mon Sep 17 00:00:00 2001
From: Jingtian Peng <pjt73651@gmail.com>
Date: Tue, 23 Aug 2016 08:29:59 +0800
Subject: [PATCH 022/223] Add PriorityQueue to the list

Add the class PriorityQueue to the list and it will automatically generate the corresponding documentation with the new description of PriorityQueue
---
 tensorflow/python/ops/io_ops.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/python/ops/io_ops.py b/tensorflow/python/ops/io_ops.py
index 0483e0e7aa..5be32bb7cb 100644
--- a/tensorflow/python/ops/io_ops.py
+++ b/tensorflow/python/ops/io_ops.py
@@ -79,6 +79,7 @@ Queues](../../how_tos/threading_and_queues/index.md).
 @@FIFOQueue
 @@PaddingFIFOQueue
 @@RandomShuffleQueue
+@@PriorityQueue
 
 ## Dealing with the filesystem
 
-- 
GitLab


From 554f897b07760af8a50548590ba8b347b20c0a89 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 22 Aug 2016 16:35:30 -0800
Subject: [PATCH 023/223] Update generated Python Op docs. Change: 131003245

---
 tensorflow/g3doc/api_docs/python/array_ops.md                  | 3 +++
 .../python/functions_and_classes/shard9/tf.meshgrid.md         | 3 +++
 2 files changed, 6 insertions(+)

diff --git a/tensorflow/g3doc/api_docs/python/array_ops.md b/tensorflow/g3doc/api_docs/python/array_ops.md
index 52a6b99555..e0bf98a39d 100644
--- a/tensorflow/g3doc/api_docs/python/array_ops.md
+++ b/tensorflow/g3doc/api_docs/python/array_ops.md
@@ -477,11 +477,14 @@ instructions for the first two dimensions are swapped.
 Examples:
 
 Calling `X, Y = meshgrid(x, y)` with the tensors
+
 ```prettyprint
   x = [1, 2, 3]
   y = [4, 5, 6]
 ```
+
 results in
+
 ```prettyprint
   X = [[1, 1, 1],
        [2, 2, 2],
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.meshgrid.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.meshgrid.md
index cb859eebbb..673a5c0717 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.meshgrid.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.meshgrid.md
@@ -14,11 +14,14 @@ instructions for the first two dimensions are swapped.
 Examples:
 
 Calling `X, Y = meshgrid(x, y)` with the tensors
+
 ```prettyprint
   x = [1, 2, 3]
   y = [4, 5, 6]
 ```
+
 results in
+
 ```prettyprint
   X = [[1, 1, 1],
        [2, 2, 2],
-- 
GitLab


From b21c69a266b9058133827d12f8915829ae07cd9a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 22 Aug 2016 17:13:47 -0800
Subject: [PATCH 024/223] Exceptions thrown in the session destructor get
 ignored and reported to STDERR. For example calling session.Reset causes the
 session destructor to throw an error on sess.close() which gets reported in
 STDERR as:

"Exception google3.third_party.tensorflow.python.framework.errors.AbortedError: AbortedError() in <bound method Session.__del__ of <google3.third_party.tensorflow.python.client.session.Session object at 0x7f7f020e1750>> ignored".

Since the exception is always ignored, catching and passing on the exception in the destructor will make for clean STDERR logs.
Change: 131006510
---
 tensorflow/python/client/session.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py
index 0c2edcb227..6eccb4a65f 100644
--- a/tensorflow/python/client/session.py
+++ b/tensorflow/python/client/session.py
@@ -519,10 +519,17 @@ class BaseSession(SessionInterface):
           tf_session.TF_CloseSession(self._session, status)
 
   def __del__(self):
-    self.close()
+    # cleanly ignore all exceptions
+    try:
+      self.close()
+    except Exception:  # pylint: disable=broad-except
+      pass
     if self._session is not None:
-      with errors.raise_exception_on_not_ok_status() as status:
+      try:
+        status = tf_session.TF_NewStatus()
         tf_session.TF_DeleteSession(self._session, status)
+      finally:
+        tf_session.TF_DeleteStatus(status)
       self._session = None
 
   @property
-- 
GitLab


From 946ef5dbcf6becffc29a8028956d740ab6e9cc51 Mon Sep 17 00:00:00 2001
From: Justine Tunney <jart@google.com>
Date: Mon, 22 Aug 2016 17:21:26 -0800
Subject: [PATCH 025/223] Support engineering notation in sort algorithm
 Change: 131007072

---
 .../components/vz-sorting/sorting.ts          | 39 +++++++++++++------
 .../vz-sorting/test/sortingTests.ts           | 12 +++++-
 2 files changed, 39 insertions(+), 12 deletions(-)

diff --git a/tensorflow/tensorboard/components/vz-sorting/sorting.ts b/tensorflow/tensorboard/components/vz-sorting/sorting.ts
index 53dd349e9d..d80f805f45 100644
--- a/tensorflow/tensorboard/components/vz-sorting/sorting.ts
+++ b/tensorflow/tensorboard/components/vz-sorting/sorting.ts
@@ -14,16 +14,15 @@ limitations under the License.
 ==============================================================================*/
 
 module VZ.Sorting {
-
   /**
    * Compares tag names asciinumerically broken into components.
    *
    * <p>This is the comparison function used for sorting most string values in
    * TensorBoard. Unlike the standard asciibetical comparator, this function
-   * knows that 'a10b' > 'a2b'. Numbers with decimal points are supported. It
-   * also splits the input by slash and underscore and performs an array
-   * sort. Therefore it knows that 'a/a' < 'a+/a' even though '+' < '/' in the
-   * ASCII table.
+   * knows that 'a10b' > 'a2b'. Fixed point and engineering notation are
+   * supported. This function also splits the input by slash and underscore to
+   * perform array comparison. Therefore it knows that 'a/a' < 'a+/a' even
+   * though '+' < '/' in the ASCII table.
    */
   export function compareTagNames(a, b: string): number {
     let ai = 0;
@@ -57,14 +56,32 @@ module VZ.Sorting {
   }
 
   function consumeNumber(s: string, i: number): number {
-    let decimal = false;
+    enum State { NATURAL, REAL, EXPONENT_SIGN, EXPONENT }
+    let state = State.NATURAL;
     for (; i < s.length; i++) {
-      if (isDigit(s[i])) continue;
-      if (!decimal && s[i] === '.') {
-        decimal = true;
-        continue;
+      if (state === State.NATURAL) {
+        if (s[i] === '.') {
+          state = State.REAL;
+        } else if (s[i] === 'e' || s[i] === 'E') {
+          state = State.EXPONENT_SIGN;
+        } else if (!isDigit(s[i])) {
+          break;
+        }
+      } else if (state === State.REAL) {
+        if (s[i] === 'e' || s[i] === 'E') {
+          state = State.EXPONENT_SIGN;
+        } else if (!isDigit(s[i])) {
+          break;
+        }
+      } else if (state === State.EXPONENT_SIGN) {
+        if (isDigit(s[i]) || s[i] === '+' || s[i] === '-') {
+          state = State.EXPONENT;
+        } else {
+          break;
+        }
+      } else if (state === State.EXPONENT) {
+        if (!isDigit(s[i])) break;
       }
-      break;
     }
     return i;
   }
diff --git a/tensorflow/tensorboard/components/vz-sorting/test/sortingTests.ts b/tensorflow/tensorboard/components/vz-sorting/test/sortingTests.ts
index 0a24463d8a..4dba3e35b9 100644
--- a/tensorflow/tensorboard/components/vz-sorting/test/sortingTests.ts
+++ b/tensorflow/tensorboard/components/vz-sorting/test/sortingTests.ts
@@ -35,10 +35,20 @@ module VZ.Sorting {
       assert.deepEqual(sortTagNames(['a/b/03', 'a/b/1']), ['a/b/1', 'a/b/03']);
     });
 
-    it('sorts floating point portions', () => {
+    it('sorts fixed point numbers', () => {
       assert.deepEqual(sortTagNames(['a0.1', 'a0.01']), ['a0.01', 'a0.1']);
     });
 
+    it('sorts engineering notation', () => {
+      assert.deepEqual(sortTagNames(['a1e9', 'a9e8']), ['a9e8', 'a1e9']);
+      assert.deepEqual(sortTagNames(['a1e+9', 'a9e+8']), ['a9e+8', 'a1e+9']);
+      assert.deepEqual(sortTagNames(['a1e+5', 'a9e-6']), ['a9e-6', 'a1e+5']);
+      assert.deepEqual(
+          sortTagNames(['a1.0e9', 'a9.0e8']), ['a9.0e8', 'a1.0e9']);
+      assert.deepEqual(
+          sortTagNames(['a1.0e+9', 'a9.0e+8']), ['a9.0e+8', 'a1.0e+9']);
+    });
+
     it('is componentized by slash', () => {
       assert.deepEqual(['a+/a', 'a/a', 'ab/a'].sort(), ['a+/a', 'a/a', 'ab/a']);
       assert.deepEqual(
-- 
GitLab


From f4c030e15f779f6944bc14ff9b286a84bde8e0ae Mon Sep 17 00:00:00 2001
From: Jennifer Guo <jennifer.jinyuan.guo@gmail.com>
Date: Mon, 22 Aug 2016 18:49:54 -0700
Subject: [PATCH 026/223] update instructions for Raspberry Pi

When installing on a fresh Raspberry Pi, I noticed that I also had to install a couple additional packages. These two lines were taken from the Linux instructions.
---
 tensorflow/contrib/makefile/README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/makefile/README.md b/tensorflow/contrib/makefile/README.md
index acfcf8f220..c0a68470ba 100644
--- a/tensorflow/contrib/makefile/README.md
+++ b/tensorflow/contrib/makefile/README.md
@@ -260,15 +260,17 @@ For other variations of valid optimization flags, see [clang optimization levels
 ## Raspberry Pi
 
 Building on the Raspberry Pi is similar to a normal Linux system. First
-download the dependencies and build protobuf:
+download the dependencies, install the required packages and build protobuf:
 
 ```bash
 tensorflow/contrib/makefile/download_dependencies.sh
+sudo apt-get install autoconf automake libtool
 cd tensorflow/contrib/makefile/downloads/protobuf/
 ./autogen.sh
 ./configure
 make
 sudo make install
+sudo ldconfig  # refresh shared library cache
 cd ../../../../..
 ```
 
-- 
GitLab


From e058f2d1382aae39c88b6d3a71f25ec6114ffb31 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Mon, 22 Aug 2016 18:01:49 -0800
Subject: [PATCH 027/223] Begin transition to use NodeDef in FunctionDef
 instead of FunctionDef.Node. Change: 131009401

---
 tensorflow/core/common_runtime/function.cc    |  31 +-
 .../core/common_runtime/function_test.cc      |  51 ++-
 tensorflow/core/framework/attr_value_util.cc  |   2 +-
 tensorflow/core/framework/function.cc         | 427 +++++++++++++++---
 tensorflow/core/framework/function.h          |  35 +-
 tensorflow/core/framework/function.proto      |  53 +++
 tensorflow/core/framework/function_test.cc    |  84 +++-
 .../core/framework/op_compatibility_test.cc   |  60 ++-
 tensorflow/core/framework/op_def_util.cc      |  45 +-
 tensorflow/core/public/version.h              |   3 +-
 tensorflow/python/framework/function.py       |   5 +-
 11 files changed, 669 insertions(+), 127 deletions(-)

diff --git a/tensorflow/core/common_runtime/function.cc b/tensorflow/core/common_runtime/function.cc
index 9f76830f1f..2b0a3b3c08 100644
--- a/tensorflow/core/common_runtime/function.cc
+++ b/tensorflow/core/common_runtime/function.cc
@@ -44,7 +44,11 @@ static const char* const kRetOp = "_Retval";
 static const char* const kGradientOp = "SymbolicGradient";
 static const char* const kNodeLabel = "Func";
 static const char* const kFuncAttr = "f";
-static const char* const kNoinlineAttr = "noinline";
+// kNoinlineAttr must start with an "_" to avoid collisions with
+// user-specified attrs.
+static const char* const kNoinlineAttr = "_noinline";
+// Old graphs use no "_".
+static const char* const kOldNoinlineAttr = "noinline";
 
 // Represents the index-th output of a node.
 struct Endpoint {
@@ -357,6 +361,8 @@ Status FunctionLibraryRuntimeImpl::InstantiateSymbolicGradient(
                                      func.name());
     }
     FunctionDef grad_fdef;
+    // TODO(josh11b): Should filter out the attrs from func that aren't used
+    // by the gradient function.
     TF_RETURN_IF_ERROR(creator(AttrSlice(&func.attr()), &grad_fdef));
     TF_RETURN_IF_ERROR(FunctionDefToBody(grad_fdef, func.attr(), g_body));
   } else {
@@ -867,12 +873,16 @@ static void InlineFunctionBody(Graph* g, Node* caller,
 }
 
 // Given a node's NodeDef, returns false iff the node explicitly
-// specified noinline. This gives ExpandInlineFunctions a heuristic to
+// specified _noinline. This gives ExpandInlineFunctions a heuristic to
 // decide whether to inline the function.
-bool ShouldInline(const NodeDef& ndef) {
+// `old` is true for GraphDef versions older than 12, when the
+// `noinline` attr was renamed to `_noinline` to avoid conflicts with
+// user-specified attrs.
+bool ShouldInline(const NodeDef& ndef, bool old) {
   bool noinline = false;
-  if (GetNodeAttr(ndef, kNoinlineAttr, &noinline).ok()) {
-    // If the node specifies attribute 'noinlne', returns accordingly.
+  const char* const attr = old ? kOldNoinlineAttr : kNoinlineAttr;
+  if (GetNodeAttr(ndef, attr, &noinline).ok()) {
+    // If the node specifies attribute '_noinline', returns accordingly.
     return !noinline;
   }
   if (ndef.op() != kGradientOp) {
@@ -881,7 +891,7 @@ bool ShouldInline(const NodeDef& ndef) {
     return true;
   }
   // If the node is a SymbolicGradient, we use the forward
-  // function's attribute 'noinline' instead.
+  // function's attribute '_noinline' instead.
   const NameAttrList* forward_func_attrs;
   Status s =
       GetNodeAttr(AttrSlice(&ndef.attr()), kFuncAttr, &forward_func_attrs);
@@ -890,10 +900,9 @@ bool ShouldInline(const NodeDef& ndef) {
     // continue and the runtime will error out.
     return false;
   }
-  s = GetNodeAttr(AttrSlice(&forward_func_attrs->attr()), kNoinlineAttr,
-                  &noinline);
+  s = GetNodeAttr(AttrSlice(&forward_func_attrs->attr()), attr, &noinline);
   if (!s.ok()) {
-    // The forward function doesn't specify 'noinline' attr, we should
+    // The forward function doesn't specify '_noinline' attr, we should
     // be free to decide.
     return true;
   }
@@ -903,9 +912,11 @@ bool ShouldInline(const NodeDef& ndef) {
 
 bool ExpandInlineFunctions(FunctionLibraryRuntime* lib, Graph* graph) {
   std::vector<std::pair<Node*, const FunctionBody*>> candidates;
+  // Identify old graphs before the 'noinline' attr was renamed '_noinline'.
+  const bool old_inline_attr = graph->versions().producer() < 12;
   for (Node* node : graph->nodes()) {
     VLOG(3) << "Expanding " << node->DebugString();
-    if (!ShouldInline(node->def())) {
+    if (!ShouldInline(node->def(), old_inline_attr)) {
       VLOG(3) << "noinline: " << node->DebugString();
       continue;
     }
diff --git a/tensorflow/core/common_runtime/function_test.cc b/tensorflow/core/common_runtime/function_test.cc
index c4d1bb59e8..2f5507a0c5 100644
--- a/tensorflow/core/common_runtime/function_test.cc
+++ b/tensorflow/core/common_runtime/function_test.cc
@@ -257,7 +257,7 @@ TEST_F(FunctionLibraryRuntimeTest, ExpandInlineFunctions) {
   Init({test::function::XTimesTwo(), test::function::XTimesFour(),
         test::function::XTimes16()});
   Graph* g = GetFuncBody("XTimes16", {{"T", DT_FLOAT}});
-  CHECK(g);
+  ASSERT_TRUE(g != nullptr);
   const char* e0 = R"P(
 (n2:float) -> (n4:float) {
   n3 = XTimesFour[T=float](n2)
@@ -342,7 +342,7 @@ TEST_F(FunctionLibraryRuntimeTest, OptimizeGraph) {
   Init({test::function::XTimesTwo(), test::function::XTimesFour(),
         test::function::XTimes16()});
   Graph* g = GetFuncBody("XTimes16", {{"T", DT_FLOAT}});
-  CHECK(g);
+  ASSERT_TRUE(g != nullptr);
   ExpandInlineFunctions(lib_, g);
   OptimizeGraph(lib_, &g);
   const char* e0 = R"P(
@@ -358,8 +358,8 @@ TEST_F(FunctionLibraryRuntimeTest, OptimizeGraph) {
   delete g;
 }
 
-TEST_F(FunctionLibraryRuntimeTest, ManySwaps) {
-  auto func = FDH::Define(
+TEST_F(FunctionLibraryRuntimeTest, ManySwapsOld) {
+  auto func = FDH::Define(  // Creates a FunctionDef using FunctionDef::Nodes
       // Name
       "ManySwapsFirst",
       // Args
@@ -377,8 +377,41 @@ TEST_F(FunctionLibraryRuntimeTest, ManySwaps) {
        {{"a5", "b5"}, "Swap", {"a4", "b4"}, {{"T", DT_FLOAT}}},
        {{"o"}, "Identity", {"a5"}, {{"T", DT_FLOAT}}}});
   Init({test::function::Swap(), func});
-  Graph* g = GetFuncBody("ManySwapsFirst", {{"T", DT_FLOAT}});
-  CHECK(g);
+  Graph* g = GetFuncBody("ManySwapsFirst", {});
+  ASSERT_TRUE(g != nullptr);
+  OptimizeGraph(lib_, &g);
+  const char* e0 = R"P(
+(n3:float, n2:float) -> (n3:float) {
+}
+)P";
+  EXPECT_EQ(e0, DebugString(g));
+  delete g;
+}
+
+// Like the above test, but using NodeDefs in the FunctionDef.
+TEST_F(FunctionLibraryRuntimeTest, ManySwapsNodeDef) {
+  auto func = FDH::Create(  // Creates a FunctionDef using NodeDefs
+      // Name
+      "ManySwapsNodeDef",
+      // Input
+      {"x: float", "y: float"},
+      // Output
+      {"o: float"},
+      // Attr
+      {},
+      // Nodes
+      {{{"a"}, "Swap", {"x", "y"}, {{"T", DT_FLOAT}}},
+       {{"b"}, "Swap", {"a:o0", "a:o1"}, {{"T", DT_FLOAT}}},
+       {{"c"}, "Swap", {"b:o0", "b:o1"}, {{"T", DT_FLOAT}}},
+       {{"d"}, "Swap", {"c:o0", "c:o1"}, {{"T", DT_FLOAT}}},
+       {{"e"}, "Swap", {"d:o0", "d:o1"}, {{"T", DT_FLOAT}}},
+       {{"f"}, "Swap", {"e:o0", "e:o1"}, {{"T", DT_FLOAT}}},
+       {{"g"}, "Identity", {"f:o0"}, {{"T", DT_FLOAT}}}},
+      // Return
+      {{"o", "g:output"}});
+  Init({test::function::Swap(), func});
+  Graph* g = GetFuncBody("ManySwapsNodeDef", {});
+  ASSERT_TRUE(g != nullptr);
   OptimizeGraph(lib_, &g);
   const char* e0 = R"P(
 (n3:float, n2:float) -> (n3:float) {
@@ -410,8 +443,8 @@ TEST_F(FunctionLibraryRuntimeTest, ControlDeps) {
        {{"y2"}, "Mul", {"y", "y"}, {{"T", DT_FLOAT}}, {"a1"}},
        {{"o"}, "Add", {"x2", "y2"}, {{"T", DT_FLOAT}}}});
   Init({test::function::Swap(), func});
-  Graph* g = GetFuncBody("ManySwapsFirst", {{"T", DT_FLOAT}});
-  CHECK(g);
+  Graph* g = GetFuncBody("ManySwapsFirst", {});
+  ASSERT_TRUE(g != nullptr);
   OptimizeGraph(lib_, &g);
 
   // NOTE: We can remove n8, n9, n10, n11 with a control edge n8->n5.
@@ -588,7 +621,7 @@ TEST_F(FunctionLibraryRuntimeTest, Gradient_AddSum) {
   Init({test, grad});
 
   Graph* g = GetFuncBody("TestGrad", {});
-  CHECK(g);
+  ASSERT_TRUE(g != nullptr);
   const char* e0 = R"P(
 (n4:float, n3:float) -> (n8:float, n6:float) {
   n2 = Const[dtype=float, value=Tensor<type: float shape: [] values: 1>]()
diff --git a/tensorflow/core/framework/attr_value_util.cc b/tensorflow/core/framework/attr_value_util.cc
index 57c2801ac9..36c42db800 100644
--- a/tensorflow/core/framework/attr_value_util.cc
+++ b/tensorflow/core/framework/attr_value_util.cc
@@ -167,7 +167,7 @@ Status AttrValueHasType(const AttrValue& attr_value, StringPiece type) {
 
   if (attr_value.value_case() == AttrValue::kPlaceholder) {
     return errors::InvalidArgument(
-        "AttrValue had value with unexpected type 'placeholder");
+        "AttrValue had value with unexpected type 'placeholder'");
   }
 
   // If the attr type is 'list', we expect attr_value.has_list() to be
diff --git a/tensorflow/core/framework/function.cc b/tensorflow/core/framework/function.cc
index 5fade36016..f2ca053df4 100644
--- a/tensorflow/core/framework/function.cc
+++ b/tensorflow/core/framework/function.cc
@@ -106,17 +106,40 @@ Status ValidateSignatureWithAttrs(const OpDef& sig,
                                   const InstantiateAttrValueMap& attr_values) {
   // attr_values should specify all attrs defined in fdef.
   for (const auto& a : sig.attr()) {
-    if (attr_values.find(a.name()) == attr_values.end()) {
-      return errors::NotFound("Attr ", a.name(), " is not found.");
+    auto const iter = attr_values.find(a.name());
+    if (iter == attr_values.end()) {
+      return errors::NotFound("Attr ", a.name(), " is not found from ",
+                              SummarizeOpDef(sig));
+    }
+    Status status = AttrValueHasType(iter->second, a.type());
+    if (!status.ok()) {
+      errors::AppendToMessage(&status, "for attr '", iter->first, "'");
+      return status;
     }
   }
 
-  for (const auto& p : attr_values) {
-    if (HasPlaceHolder(p.second)) {
-      return errors::InvalidArgument(p.first,
-                                     " in attr_values is still a placeholder.");
+// TODO(josh11b): Enable this code once it works with function gradients.
+// Right now the C++ function gradient code assumes it can pass
+// all the attrs of the function to the gradient, and any attrs that
+// the gradient doesn't care about will be ignored.
+#if 0
+  if (attr_values.size() != sig.attr_size()) {
+    for (const auto& a : attr_values) {
+      // TODO(josh11b): Possibly should ignore attrs that start with "_" here?
+      bool found = false;
+      for (const auto& s : sig.attr()) {
+        if (a.first == s.name()) {
+          found = true;
+          break;
+        }
+      }
+      if (!found) {
+        return errors::NotFound("Attr ", a.first, " is not found in ",
+                                SummarizeOpDef(sig));
+      }
     }
   }
+#endif
 
   return Status::OK();
 }
@@ -225,6 +248,38 @@ Status BuildNodeOutputIndex(const FunctionDef::Node& node,
   return Status::OK();
 }
 
+Status BuildNodeOutputIndex(const NodeDef& node,
+                            const InstantiateAttrValueMap& attrs,
+                            GetFunctionSignature get_function,
+                            const int arg_index, NameInfoIndex* name_info) {
+  const OpDef* node_sig = nullptr;
+  TF_RETURN_IF_ERROR(get_function(node.op(), &node_sig));
+  if (node_sig->output_arg_size() == 0) {
+    return AddRetName(name_info, node.name(), {false, arg_index, 0, false, {}});
+  }
+  const int num_retval = node_sig->output_arg_size();
+  int start = 0;
+  bool is_type_list;
+  DataTypeVector dtypes;
+  for (int i = 0; i < num_retval; ++i) {
+    TF_RETURN_IF_ERROR(
+        ArgNumType(attrs, node_sig->output_arg(i), &is_type_list, &dtypes));
+    // Note that we rely on the backwards-compatibility test enforcing
+    // that output_arg(*).name() doesn't change here.
+    const string base_name =
+        strings::StrCat(node.name(), ":", node_sig->output_arg(i).name());
+    TF_RETURN_IF_ERROR(AddRetName(
+        name_info, base_name, {false, arg_index, start, is_type_list, dtypes}));
+    for (int j = 0; j < static_cast<int>(dtypes.size()); ++j) {
+      TF_RETURN_IF_ERROR(
+          AddRetName(name_info, strings::StrCat(base_name, ":", j),
+                     {false, arg_index, start + j, false, {dtypes[j]}}));
+    }
+    start += dtypes.size();
+  }
+  return Status::OK();
+}
+
 Status InstantiateNode(const FunctionDef::Node& fnode,
                        const InstantiateAttrValueMap& attrs,
                        GetFunctionSignature get_function,
@@ -252,7 +307,7 @@ Status InstantiateNode(const FunctionDef::Node& fnode,
       }
       if (dtypes != item->dtypes) {
         return errors::InvalidArgument("Invalid arg(", i,
-                                       ") for function arg: ", " ",
+                                       ") for function arg: ",
                                        DataTypeSliceString(dtypes), " vs. ",
                                        DataTypeSliceString(item->dtypes), ".");
       }
@@ -274,7 +329,7 @@ Status InstantiateNode(const FunctionDef::Node& fnode,
         }
         if (item->dtypes.size() != 1 || (item->dtypes[0] != dtypes[j])) {
           return errors::InvalidArgument(
-              "Invalid typelist arg(", i + j, ") for function arg: ", " ",
+              "Invalid typelist arg(", i + j, ") for function arg: ",
               DataTypeSliceString(dtypes), " vs. ",
               DataTypeSliceString(item->dtypes), ".");
         }
@@ -306,6 +361,105 @@ Status InstantiateNode(const FunctionDef::Node& fnode,
   return Status::OK();
 }
 
+Status InstantiateNode(const NodeDef& fnode,
+                       const InstantiateAttrValueMap& attrs,
+                       GetFunctionSignature get_function,
+                       const NameInfoIndex& name_info, GraphDef* gdef) {
+  const OpDef* fnode_sig = nullptr;
+  TF_CHECK_OK(get_function(fnode.op(), &fnode_sig));
+  NodeDef* gnode = gdef->add_node();
+  gnode->set_name(Name(gdef->node_size() - 1));
+  gnode->set_op(fnode.op());
+
+  // Input
+  const int num_args = fnode_sig->input_arg_size();
+  bool is_type_list;
+  DataTypeVector dtypes;
+  int fnode_arg_index = 0;
+  for (int i = 0; i < num_args; ++i) {
+    TF_RETURN_IF_ERROR(
+        ArgNumType(attrs, fnode_sig->input_arg(i), &is_type_list, &dtypes));
+    if (!is_type_list) {
+      if (fnode_arg_index >= fnode.input_size()) {
+        return errors::InvalidArgument("Attempt to access beyond input size: ",
+                                       fnode_arg_index, " >= ",
+                                       fnode.input_size());
+      }
+      const NameInfoItem* item =
+          gtl::FindOrNull(name_info, fnode.input(fnode_arg_index));
+      if (item == nullptr) {
+        return errors::InvalidArgument("input[", i, "] == '",
+                                       fnode.input(fnode_arg_index),
+                                       "' is not found");
+      }
+      if (dtypes != item->dtypes) {
+        return errors::InvalidArgument("Invalid type of input(", i,
+                                       ") for function node: ",
+                                       DataTypeSliceString(dtypes), " vs. ",
+                                       DataTypeSliceString(item->dtypes), ".");
+      }
+      for (size_t j = 0; j < dtypes.size(); ++j) {
+        if (item->is_func_arg) {
+          gnode->add_input(Name(item->nid + j));
+        } else {
+          gnode->add_input(Name(item->nid, item->idx + j));
+        }
+      }
+      ++fnode_arg_index;
+    } else {
+      for (size_t j = 0; j < dtypes.size(); ++j) {
+        if (fnode_arg_index + j >= fnode.input_size()) {
+          return errors::InvalidArgument(
+              "Attempt to access beyond input size: ", fnode_arg_index + j,
+              " >= ", fnode.input_size());
+        }
+        const NameInfoItem* item =
+            gtl::FindOrNull(name_info, fnode.input(fnode_arg_index + j));
+        if (item == nullptr) {
+          return errors::InvalidArgument("input[", i + j, "] is not found: ",
+                                         SummarizeNodeDef(fnode));
+        }
+        if (item->dtypes.size() != 1 || (item->dtypes[0] != dtypes[j])) {
+          return errors::InvalidArgument(
+              "Invalid typelist input(", i + j, ") for function arg: ", " ",
+              DataTypeSliceString(dtypes), " vs. ",
+              DataTypeSliceString(item->dtypes), ".");
+        }
+        if (item->is_func_arg) {
+          gnode->add_input(Name(item->nid));
+        } else {
+          gnode->add_input(Name(item->nid, item->idx));
+        }
+      }
+      fnode_arg_index += dtypes.size();
+    }
+  }
+  // Control deps.
+  for (int i = fnode_arg_index; i < fnode.input_size(); ++i) {
+    const string& input = fnode.input(i);
+    if (input.empty() || input[0] != '^') {
+      return errors::InvalidArgument("Expected input[", i, "] == '", input,
+                                     "' to be a control input.");
+    }
+    const NameInfoItem* item = gtl::FindOrNull(name_info, input.substr(1));
+    if (item == nullptr) {
+      return errors::InvalidArgument("input[", i, "] == '", input,
+                                     "', is not found.");
+    }
+    gnode->add_input(Dep(item->nid));
+  }
+
+  // Attrs.
+  for (const auto& p : attrs) {
+    (*gnode->mutable_attr())[p.first] = p.second;
+  }
+
+  AddDefaultsToNodeDef(*fnode_sig, gnode);
+
+  return Status::OK();
+}
+
+// FunctionDef::Node version
 Status AddReturnNode(const OpDef::ArgDef& ret_def,
                      const InstantiateAttrValueMap& attrs,
                      const NameInfoIndex& name_info, int* ret_index,
@@ -336,6 +490,43 @@ Status AddReturnNode(const OpDef::ArgDef& ret_def,
   return Status::OK();
 }
 
+// NodeDef version
+Status AddReturnNode(const OpDef::ArgDef& ret_def,
+                     const InstantiateAttrValueMap& attrs,
+                     const ::tensorflow::protobuf::Map<string, string>& ret_map,
+                     const NameInfoIndex& name_info, int* ret_index,
+                     InstantiationResult* result) {
+  auto ret_iter = ret_map.find(ret_def.name());
+  if (ret_iter == ret_map.end()) {
+    return errors::InvalidArgument("Return ", ret_def.name(), " missing.");
+  }
+  bool is_type_list;
+  DataTypeVector dtypes;
+  TF_RETURN_IF_ERROR(ArgNumType(attrs, ret_def, &is_type_list, &dtypes));
+  CHECK_GE(dtypes.size(), size_t{1});
+  const NameInfoItem* item = gtl::FindOrNull(name_info, ret_iter->second);
+  if (item == nullptr) {
+    return errors::InvalidArgument("Return ", ret_def.name(), " -> ",
+                                   ret_iter->second, " is not found.");
+  }
+  if (dtypes != item->dtypes) {
+    return errors::InvalidArgument("Invalid ret types ", ret_def.name(), " : ",
+                                   DataTypeVectorString(dtypes), " vs. ",
+                                   DataTypeVectorString(item->dtypes));
+  }
+  GraphDef* gdef = &result->gdef;
+  for (size_t i = 0; i < dtypes.size(); ++i) {
+    NodeDef* gnode = gdef->add_node();
+    gnode->set_name(Name(gdef->node_size() - 1));
+    gnode->set_op("_Retval");
+    gnode->add_input(Name(item->nid, item->idx + i));
+    AddAttr("T", dtypes[i], gnode);
+    AddAttr("index", (*ret_index)++, gnode);
+    result->ret_types.push_back(dtypes[i]);
+  }
+  return Status::OK();
+}
+
 // Various helpers Print(proto) to print relevant protos to ascii.
 string Print(const OpDef::ArgDef& arg) {
   string out;
@@ -353,6 +544,7 @@ string Print(const OpDef::ArgDef& arg) {
   return out;
 }
 
+// TODO(josh11b): Merge this with SummarizeAttrValue().
 string Print(const AttrValue& attr_value) {
   if (attr_value.value_case() == AttrValue::kType) {
     return DataTypeString(attr_value.type());
@@ -412,6 +604,35 @@ string Print(const FunctionDef::Node& node) {
   return out;
 }
 
+// TODO(josh11b): Merge this with SummarizeNodeDef().
+string Print(const NodeDef& n) {
+  string out;
+  strings::StrAppend(&out, n.name(), " = ", n.op());
+  if (n.attr_size() > 0) {
+    std::vector<string> entries;
+    for (auto& a : n.attr()) {
+      entries.push_back(strings::StrCat(a.first, "=", Print(a.second)));
+    }
+    sort(entries.begin(), entries.end());
+    strings::StrAppend(&out, "[", str_util::Join(entries, ", "), "]");
+  }
+  strings::StrAppend(&out, "(");
+  std::vector<StringPiece> dat;
+  std::vector<string> dep;
+  for (StringPiece s : n.input()) {
+    if (s.Consume("^")) {
+      dep.push_back(s.ToString());
+    } else {
+      dat.push_back(s);
+    }
+  }
+  strings::StrAppend(&out, str_util::Join(dat, ", "), ")");
+  if (!dep.empty()) {
+    strings::StrAppend(&out, " @ ", str_util::Join(dep, ", "));
+  }
+  return out;
+}
+
 string Print(const FunctionDef& fdef) {
   string out;
   const OpDef& sig = fdef.signature();
@@ -440,38 +661,19 @@ string Print(const FunctionDef& fdef) {
     strings::StrAppend(&out, Print(sig.output_arg(i)));
   }
   strings::StrAppend(&out, ") {\n");
-  for (const auto& n : fdef.node()) {
-    strings::StrAppend(&out, "  ", Print(n), "\n");
-  }
-  strings::StrAppend(&out, "}\n");
-  return out;
-}
-
-string Print(const NodeDef& n) {
-  string out;
-  strings::StrAppend(&out, n.name(), " = ", n.op());
-  if (n.attr_size() > 0) {
-    std::vector<string> entries;
-    for (auto& a : n.attr()) {
-      entries.push_back(strings::StrCat(a.first, "=", Print(a.second)));
+  if (fdef.node_def_size() > 0) {
+    for (const auto& n : fdef.node_def()) {
+      strings::StrAppend(&out, "  ", Print(n), "\n");
     }
-    sort(entries.begin(), entries.end());
-    strings::StrAppend(&out, "[", str_util::Join(entries, ", "), "]");
-  }
-  strings::StrAppend(&out, "(");
-  std::vector<StringPiece> dat;
-  std::vector<string> dep;
-  for (StringPiece s : n.input()) {
-    if (s.Consume("^")) {
-      dep.push_back(s.ToString());
-    } else {
-      dat.push_back(s);
+    for (const auto& r : fdef.ret()) {
+      strings::StrAppend(&out, "  return ", r.first, " = ", r.second, "\n");
+    }
+  } else {  // TODO(josh11b): Eventually remove this case.
+    for (const auto& n : fdef.node()) {
+      strings::StrAppend(&out, "  ", Print(n), "\n");
     }
   }
-  strings::StrAppend(&out, str_util::Join(dat, ", "), ")");
-  if (!dep.empty()) {
-    strings::StrAppend(&out, " @ ", str_util::Join(dep, ", "));
-  }
+  strings::StrAppend(&out, "}\n");
   return out;
 }
 
@@ -554,14 +756,27 @@ Status InstantiateFunction(const FunctionDef& fdef,
   // Makes a copy of all attrs in fdef and substitutes placeholders.
   // After this step, every attr is bound to a concrete value.
   std::vector<InstantiateAttrValueMap> node_attrs;
-  node_attrs.resize(fdef.node_size());
-  for (int i = 0; i < fdef.node_size(); ++i) {
-    for (auto attr : fdef.node(i).attr()) {
-      if (!SubstitutePlaceholders(substitute, &attr.second)) {
-        return errors::InvalidArgument("Failed to bind all placeholders in ",
-                                       SummarizeAttrValue(attr.second));
+  if (fdef.node_def_size() > 0) {
+    node_attrs.resize(fdef.node_def_size());
+    for (int i = 0; i < fdef.node_def_size(); ++i) {
+      for (auto attr : fdef.node_def(i).attr()) {
+        if (!SubstitutePlaceholders(substitute, &attr.second)) {
+          return errors::InvalidArgument("Failed to bind all placeholders in ",
+                                         SummarizeAttrValue(attr.second));
+        }
+        CHECK(node_attrs[i].insert(attr).second);
+      }
+    }
+  } else {  // TODO(josh11b): Eventually remove this case.
+    node_attrs.resize(fdef.node_size());
+    for (int i = 0; i < fdef.node_size(); ++i) {
+      for (auto attr : fdef.node(i).attr()) {
+        if (!SubstitutePlaceholders(substitute, &attr.second)) {
+          return errors::InvalidArgument("Failed to bind all placeholders in ",
+                                         SummarizeAttrValue(attr.second));
+        }
+        CHECK(node_attrs[i].insert(attr).second);
       }
-      CHECK(node_attrs[i].insert(attr).second);
     }
   }
 
@@ -570,36 +785,66 @@ Status InstantiateFunction(const FunctionDef& fdef,
   for (const OpDef::ArgDef& arg_def : sig.input_arg()) {
     s = BuildInputArgIndex(arg_def, attr_values, &name_info, result);
     if (!s.ok()) {
-      errors::AppendToMessage(&s, " In ", Print(arg_def));
+      errors::AppendToMessage(&s, "In ", Print(arg_def));
       return s;
     }
   }
-  for (int i = 0; i < fdef.node_size(); ++i) {
-    s = BuildNodeOutputIndex(fdef.node(i), node_attrs[i], get_function,
-                             gdef->node_size() + i, &name_info);
-    if (!s.ok()) {
-      errors::AppendToMessage(&s, " In ", Print(fdef.node(i)));
-      return s;
+  if (fdef.node_def_size() > 0) {
+    for (int i = 0; i < fdef.node_def_size(); ++i) {
+      s = BuildNodeOutputIndex(fdef.node_def(i), node_attrs[i], get_function,
+                               gdef->node_size() + i, &name_info);
+      if (!s.ok()) {
+        errors::AppendToMessage(&s, "In ", SummarizeNodeDef(fdef.node_def(i)));
+        return s;
+      }
+    }
+    // Emits one gdef.node for each fdef.node_def.
+    for (int i = 0; i < fdef.node_def_size(); ++i) {
+      s = InstantiateNode(fdef.node_def(i), node_attrs[i], get_function,
+                          name_info, gdef);
+      if (!s.ok()) {
+        errors::AppendToMessage(&s, "In ", SummarizeNodeDef(fdef.node_def(i)));
+        return s;
+      }
     }
-  }
 
-  // Emits one gdef.node for each fdef.node.
-  for (int i = 0; i < fdef.node_size(); ++i) {
-    s = InstantiateNode(fdef.node(i), node_attrs[i], get_function, name_info,
-                        gdef);
-    if (!s.ok()) {
-      errors::AppendToMessage(&s, " In ", Print(fdef.node(i)));
-      return s;
+    // Emits nodes for the function's return values.
+    int ret_index = 0;
+    for (const OpDef::ArgDef& ret_def : sig.output_arg()) {
+      s = AddReturnNode(ret_def, attr_values, fdef.ret(), name_info, &ret_index,
+                        result);
+      if (!s.ok()) {
+        errors::AppendToMessage(&s, "In function output ", Print(ret_def));
+        return s;
+      }
+    }
+  } else {  // TODO(josh11b): Eventually remove this case.
+    for (int i = 0; i < fdef.node_size(); ++i) {
+      s = BuildNodeOutputIndex(fdef.node(i), node_attrs[i], get_function,
+                               gdef->node_size() + i, &name_info);
+      if (!s.ok()) {
+        errors::AppendToMessage(&s, "In ", Print(fdef.node(i)));
+        return s;
+      }
+    }
+    // Emits one gdef.node for each fdef.node.
+    for (int i = 0; i < fdef.node_size(); ++i) {
+      s = InstantiateNode(fdef.node(i), node_attrs[i], get_function, name_info,
+                          gdef);
+      if (!s.ok()) {
+        errors::AppendToMessage(&s, "In ", Print(fdef.node(i)));
+        return s;
+      }
     }
-  }
 
-  // Emits nodes for the function's return values.
-  int ret_index = 0;
-  for (const OpDef::ArgDef& ret_def : sig.output_arg()) {
-    s = AddReturnNode(ret_def, attr_values, name_info, &ret_index, result);
-    if (!s.ok()) {
-      errors::AppendToMessage(&s, " In ", Print(ret_def));
-      return s;
+    // Emits nodes for the function's return values.
+    int ret_index = 0;
+    for (const OpDef::ArgDef& ret_def : sig.output_arg()) {
+      s = AddReturnNode(ret_def, attr_values, name_info, &ret_index, result);
+      if (!s.ok()) {
+        errors::AppendToMessage(&s, "In function output ", Print(ret_def));
+        return s;
+      }
     }
   }
 
@@ -844,7 +1089,53 @@ FunctionDef::Node FunctionDefHelper::Node::ToProto() const {
   return n;
 }
 
-/*  static */
+NodeDef FunctionDefHelper::Node::ToNodeDef() const {
+  NodeDef n;
+  n.set_op(this->op);
+  n.set_name(this->ret[0]);
+  for (const string& a : arg) {
+    n.add_input(a);
+  }
+  for (const auto& a : this->attr) {
+    n.mutable_attr()->insert({a.first, a.second.proto});
+  }
+  for (const string& d : dep) {
+    n.add_input(strings::StrCat("^", d));
+  }
+  return n;
+}
+
+/* static */
+FunctionDef FunctionDefHelper::Create(
+    const string& function_name, gtl::ArraySlice<string> in_def,
+    gtl::ArraySlice<string> out_def, gtl::ArraySlice<string> attr_def,
+    gtl::ArraySlice<Node> node_def,
+    gtl::ArraySlice<std::pair<string, string>> ret_def) {
+  FunctionDef fdef;
+
+  // Signature
+  OpDefBuilder b(function_name);
+  for (const auto& i : in_def) b.Input(i);
+  for (const auto& o : out_def) b.Output(o);
+  for (const auto& a : attr_def) b.Attr(a);
+
+  OpRegistrationData op_reg_data;
+  TF_CHECK_OK(b.Finalize(&op_reg_data));
+  fdef.mutable_signature()->Swap(&op_reg_data.op_def);
+
+  // Function body
+  for (const auto& n : node_def) {
+    *(fdef.add_node_def()) = n.ToNodeDef();
+  }
+
+  // Returns
+  for (const auto& r : ret_def) {
+    fdef.mutable_ret()->insert({r.first, r.second});
+  }
+  return fdef;
+}
+
+/* static */
 FunctionDef FunctionDefHelper::Define(const string& name,
                                       gtl::ArraySlice<string> arg_def,
                                       gtl::ArraySlice<string> ret_def,
diff --git a/tensorflow/core/framework/function.h b/tensorflow/core/framework/function.h
index c8f4952495..4a9c62f08c 100644
--- a/tensorflow/core/framework/function.h
+++ b/tensorflow/core/framework/function.h
@@ -37,9 +37,22 @@ class Node;
 class OpKernel;
 class ResourceMgr;
 
-// FunctionDefHelper::Define is a convenient helper to construct a
+// FunctionDefHelper::Create is a convenient helper to construct a
 // FunctionDef proto.
+// E.g.,
+//   FunctionDef my_func = FunctionDefHelper::Create(
+//     "my_func_name",
+//     {"x:T", "y:T" /* one string per argument */},
+//     {"z:T" /* one string per return value */},
+//     {"T: {float, double}" /* one string per attribute  */},
+//     {
+//        {{"o"}, "Mul", {"x", "y"}, {{"T", "$T"}}}
+//        /* one entry per function node */
+//     },
+//     /* Mapping between function returns and function node outputs. */
+//     {{"z", "o:z"}});
 //
+// For the old Function::Node approach, use FunctionDefHelper::Define()
 // E.g.,
 //   FunctionDef my_func = FunctionDefHelper::Define(
 //     "my_func_name",
@@ -49,10 +62,7 @@ class ResourceMgr;
 //     {
 //        {{"z"}, "Mul", {"x", "y"}, {{"T", "$T"}}}
 //        /* one entry per function node */
-//     })
-//
-// NOTE: When we have a TFLang parser, we can add another helper:
-//   FunctionDef FunctionDefHelper::Define(const string& tf_func);
+//     });
 class FunctionDefHelper {
  public:
   // AttrValueWrapper has copy constructors for the type T so that
@@ -90,6 +100,8 @@ class FunctionDefHelper {
   // lists. E.g.,
   //  Node n = {{"z"}, "Mul", {"x", "y"}, {{"T", "$T"}}};  // z = x * y
   struct Node {
+    // When constructing a NodeDef, the first entry in ret is used as
+    // the node name, the remaining values are ignored.
     std::vector<string> ret;
     string op;
     std::vector<string> arg;
@@ -97,8 +109,21 @@ class FunctionDefHelper {
     std::vector<string> dep;
 
     FunctionDef::Node ToProto() const;
+    NodeDef ToNodeDef() const;
   };
 
+  // The Create() function uses the new NodeDef field.  `ret_def`
+  // holds a mapping from the function output names from `out_def` to
+  // the node outputs from `node_def`.
+  static FunctionDef Create(const string& function_name,
+                            gtl::ArraySlice<string> in_def,
+                            gtl::ArraySlice<string> out_def,
+                            gtl::ArraySlice<string> attr_def,
+                            gtl::ArraySlice<Node> node_def,
+                            gtl::ArraySlice<std::pair<string, string>> ret_def);
+
+  // The two Define() functions use the old FunctionDef::Node field.
+  // TODO(josh11b): Get rid of these and transition to the one above.
   static FunctionDef Define(const string& function_name,
                             gtl::ArraySlice<string> arg_def,
                             gtl::ArraySlice<string> ret_def,
diff --git a/tensorflow/core/framework/function.proto b/tensorflow/core/framework/function.proto
index 4003943a85..4a1ad7a125 100644
--- a/tensorflow/core/framework/function.proto
+++ b/tensorflow/core/framework/function.proto
@@ -7,6 +7,7 @@ option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
 
 import "tensorflow/core/framework/attr_value.proto";
+import "tensorflow/core/framework/node_def.proto";
 import "tensorflow/core/framework/op_def.proto";
 
 // A library is a set of named functions.
@@ -26,6 +27,8 @@ message FunctionDef {
   // attrs etc.
   OpDef signature = 1;
 
+  // TO BE REPLACED
+
   // The body of the function.
   repeated Node node = 2;  // function.node.ret[*] are unique.
 
@@ -69,6 +72,56 @@ message FunctionDef {
     // signature.
     map<string, AttrValue> attr = 5;
   }
+
+  // WILL REPLACE THE ABOVE
+
+  // If node_def is present, and the consumer is at GraphDef version
+  // >= 12, then these fields are used and `node` is ignored.  If the
+  // consumer's GraphDef version is < 12 or this field is empty, then
+  // `node` is used.  This allows producers to fill both fields to
+  // remain compatible with old consumers.  At some future GraphDef
+  // version, `node` will be ignored even if `node_def` is empty.
+  // TODO(josh11b): Finish this transition.
+
+  // In both of the following fields, there is the need to specify an
+  // output that is used as either the input to another node (in
+  // `node_def`) or as a return value of the function (in `ret`).
+  // Unlike the NodeDefs in GraphDef, we need to be able to specify a
+  // list in some cases (instead of just single outputs).  Also, we
+  // need to be able to deal with lists of unknown length (so the
+  // output index may not be known at function definition time).  So
+  // we use the following format instead:
+  // * "fun_in" where "fun_in" is the name of a function input arg in
+  //   the `signature` field above.  This represents that input, whether
+  //   it is a single tensor or a list.
+  // * "fun_in:0" gives the first element of a function input arg (a
+  //   non-list input is considered a list of length 1 for these
+  //   purposes).
+  // * "node:out" where "node" is the name of a node in `node_def` and
+  //   "out" is the name one of its op's output arguments (the name
+  //   comes from the OpDef of the node's op). This represents that
+  //   node's output, whether it is a single tensor or a list.
+  //   Note: We enforce that an op's output arguments are never
+  //   renamed in the backwards-compatibility test.
+  // * "node:out:0" gives the first element of a node output arg (a
+  //   non-list output is considered a list of length 1 for these
+  //   purposes).
+  //
+  // NOT CURRENTLY SUPPORTED (but may be in the future):
+  // * "node:out:-1" gives last element in a node output list
+  // * "node:out:1:" gives a list with all but the first element in a
+  //   node output list
+  // * "node:out::-1" gives a list with all but the last element in a
+  //   node output list
+
+  // The body of the function.  Unlike the NodeDefs in a GraphDef, attrs
+  // may have values of type `placeholder` and the `input` field uses
+  // the "output" format above.
+  repeated NodeDef node_def = 3;
+
+  // A mapping from the output arg names from `signature` to the
+  // outputs from `node_def` that should be returned by the function.
+  map<string, string> ret = 4;
 }
 
 // GradientDef defines the gradient function of a function defined in
diff --git a/tensorflow/core/framework/function_test.cc b/tensorflow/core/framework/function_test.cc
index 82fbe75988..23fd5f7247 100644
--- a/tensorflow/core/framework/function_test.cc
+++ b/tensorflow/core/framework/function_test.cc
@@ -48,8 +48,8 @@ y: A scalar in type T.
 
 static InstantiateAttrValueMap kNoAttrs;
 
-TEST(TFunc, SquarePlusOne) {
-  auto fdef = FDH::Define(
+TEST(TFunc, SquarePlusOneOld) {
+  auto fdef = FDH::Define(  // Create a FunctionDef using Function::Nodes.
       // Name
       "SquarePlusOne",
       // Args
@@ -78,7 +78,53 @@ SquarePlusOne[T:{float, double, int32, int64}](x:T) -> (y:T) {
 
   // Instantiate one with T=float
   InstantiationResult result;
-  TF_CHECK_OK(InstantiateFunction(fdef, {{"T", DT_FLOAT}}, GetOpSig, &result));
+  TF_ASSERT_OK(InstantiateFunction(fdef, {{"T", DT_FLOAT}}, GetOpSig, &result));
+  const char* e2 = R"P(
+(n0:float) -> (n3:float) {
+  n1 = Square[T=float](n0)
+  n2 = One[T=float]()
+  n3 = Add[T=float](n1, n2)
+}
+)P";
+  EXPECT_EQ(result.arg_types, DataTypeVector({DT_FLOAT}));
+  EXPECT_EQ(result.ret_types, DataTypeVector({DT_FLOAT}));
+  EXPECT_EQ(DebugString(result.gdef), e2);
+}
+
+TEST(TFunc, SquarePlusOneNodeDef) {
+  auto fdef = FDH::Create(  // Create a FunctionDef using NodeDefs.
+      // Name
+      "SquarePlusOne",
+      // Inputs
+      {"x: T"},
+      // Outputs
+      {"y: T"},
+      // Attrs
+      {"T: {float, double, int32, int64}"},
+      // Nodes
+      {// a = Square<T>(x)
+       {{"a"}, "Square", {"x"}, {{"T", "$T"}}},
+       // o = One<T>()
+       // NOTE: We can also have a Cast<Tin, Tout>(x) instead.
+       {{"o"}, "One", {}, {{"T", "$T"}}},
+       // y = Add<T>(a, o)
+       {{"y"}, "Add", {"a:y", "o:y"}, {{"T", "$T"}}}},
+      // Returns
+      {{"y", "y:z:0"}});
+
+  const char* e = R"P(
+SquarePlusOne[T:{float, double, int32, int64}](x:T) -> (y:T) {
+  a = Square[T=$T](x)
+  o = One[T=$T]()
+  y = Add[T=$T](a:y, o:y)
+  return y = y:z:0
+}
+)P";
+  EXPECT_EQ(DebugString(fdef), e);
+
+  // Instantiate one with T=float
+  InstantiationResult result;
+  TF_ASSERT_OK(InstantiateFunction(fdef, {{"T", DT_FLOAT}}, GetOpSig, &result));
   const char* e2 = R"P(
 (n0:float) -> (n3:float) {
   n1 = Square[T=float](n0)
@@ -141,8 +187,8 @@ AddSquared[N:int, T:{float, double, int32, int64}](x:N*T) -> (y:T) {
 
   // Instantiate one with T=float
   InstantiationResult result;
-  TF_CHECK_OK(InstantiateFunction(fdef, {{"N", 3}, {"T", DT_FLOAT}}, GetOpSig,
-                                  &result));
+  TF_ASSERT_OK(InstantiateFunction(fdef, {{"N", 3}, {"T", DT_FLOAT}}, GetOpSig,
+                                   &result));
   const char* e2 = R"P(
 (n0:float, n1:float, n2:float) -> (n4:float) {
   n3 = Map[N=3, T=float, U=float, func=Square[T=float]](n0, n1, n2)
@@ -184,7 +230,7 @@ ControlDeps(x:float) -> () {
   EXPECT_EQ(DebugString(fdef), e);
 
   InstantiationResult result;
-  TF_CHECK_OK(InstantiateFunction(fdef, kNoAttrs, GetOpSig, &result));
+  TF_ASSERT_OK(InstantiateFunction(fdef, kNoAttrs, GetOpSig, &result));
   const char* e2 = R"P(
 (n0:float) -> () {
   n1 = One[T=float]() @ n0
@@ -264,7 +310,7 @@ Test(i:float) -> (o:float) {
   EXPECT_EQ(DebugString(fdef), e);
 
   InstantiationResult result;
-  TF_CHECK_OK(InstantiateFunction(fdef, kNoAttrs, GetOpSig, &result));
+  TF_ASSERT_OK(InstantiateFunction(fdef, kNoAttrs, GetOpSig, &result));
   const char* e2 = R"P(
 (n0:float) -> (n7:float) {
   n1 = Const[dtype=int32, value=Tensor<type: int32 shape: [] values: 0>]()
@@ -336,7 +382,7 @@ MySelect(x:float) -> (z:float) {
   EXPECT_EQ(DebugString(fdef), e);
 
   InstantiationResult result;
-  TF_CHECK_OK(InstantiateFunction(fdef, kNoAttrs, GetOpSig, &result));
+  TF_ASSERT_OK(InstantiateFunction(fdef, kNoAttrs, GetOpSig, &result));
   const char* e2 = R"P(
 (n0:float) -> (n2:float) {
   n1 = Cond[Tin={float}, cond=MyCond, else_branch=MyElse, out_types={float}, then_branch=MyThen](n0)
@@ -350,7 +396,7 @@ MySelect(x:float) -> (z:float) {
 
 static void HasError(const Status& s, const string& substr) {
   EXPECT_TRUE(StringPiece(s.ToString()).contains(substr))
-      << s << ", expected substring " << substr;
+      << ">>" << s << "<<, expected substring >>" << substr << "<<";
 }
 
 TEST(InstantiateErrors, Not_Sufficient_Attrs) {
@@ -358,15 +404,27 @@ TEST(InstantiateErrors, Not_Sufficient_Attrs) {
       FDH::Define("nop", {}, {}, {"T:{float, double, int32, int64}"}, {});
   InstantiationResult result;
   HasError(InstantiateFunction(fdef, {{"U", DT_FLOAT}}, GetOpSig, &result),
-           "T is not found");
+           "Attr T is not found from ");
+}
+
+#if 0  // TODO(josh11b): Enable this test once having an extra attr is an error.
+TEST(InstantiateErrors, Too_Many_Attrs) {
+  auto fdef =
+      FDH::Define("nop", {}, {}, {"T:{float, double, int32, int64}"}, {});
+  InstantiationResult result;
+  HasError(InstantiateFunction(fdef, {{"T", DT_INT32}, {"U", DT_FLOAT}},
+                               GetOpSig, &result),
+           "Attr U is not found in ");
 }
+#endif
 
 TEST(InstantiateErrors, AttrValue_Value_Placeholder) {
   auto fdef =
       FDH::Define("nop", {}, {}, {"T:{float, double, int32, int64}"}, {});
   InstantiationResult result;
-  HasError(InstantiateFunction(fdef, {{"T", "$bad"}}, GetOpSig, &result),
-           "T in attr_values is still a placeholder");
+  HasError(
+      InstantiateFunction(fdef, {{"T", "$bad"}}, GetOpSig, &result),
+      "AttrValue had value with unexpected type 'placeholder'\n\tfor attr 'T'");
 }
 
 TEST(InstantiateErrors, Unbounded_Attr) {
@@ -475,7 +533,7 @@ TEST(InstantiateErrors, FuncRet_Mismatch) {
                           });
   InstantiationResult result;
   HasError(InstantiateFunction(fdef, kNoAttrs, GetOpSig, &result),
-           "Invalid ret types y : float vs. double\n\t In y");
+           "Invalid ret types y : float vs. double\n\tIn function output y");
 }
 
 TEST(InstantiateErrors, TypeList_Missing_Retval_Attr) {
diff --git a/tensorflow/core/framework/op_compatibility_test.cc b/tensorflow/core/framework/op_compatibility_test.cc
index 3d85c97332..ae2fdae379 100644
--- a/tensorflow/core/framework/op_compatibility_test.cc
+++ b/tensorflow/core/framework/op_compatibility_test.cc
@@ -150,6 +150,19 @@ class OpCompatibilityTest : public OpsTestBase {
 
     ExpectIncompatible(old_op_def, *new_op_def, compatibility_error);
   }
+
+  void ExpectRenameFailure(const OpDef& old_op_def,
+                           const string& compatibility_error) {
+    // This should be all that is needed to get compatibility.
+    const OpDef* new_op_def = RegisteredOpDef();
+    AddDefaultsToNodeDef(*new_op_def, node_def());
+
+    // Validate that the NodeDef is valid.  This will ignore
+    // problems caused by output name changes for functions.
+    TF_ASSERT_OK(ValidateNodeDef(*node_def(), *new_op_def));
+
+    ExpectIncompatible(old_op_def, *new_op_def, compatibility_error);
+  }
 };
 
 // Should be compatible if the Op hasn't changed (sanity check).
@@ -986,7 +999,7 @@ TEST_F(OpCompatibilityTest, InputAddRefFails) {
 
 REGISTER_OP("OutputRemoveRef").Output("o: int32");
 
-TEST_F(OpCompatibilityTest, OutputRemoveRef) {
+TEST_F(OpCompatibilityTest, OutputRemoveRefFails) {
   OpRegistrationData old_op;
   TF_ASSERT_OK(OpDefBuilder("OutputRemoveRef")
                    .Output("o: Ref(int32)")
@@ -996,6 +1009,51 @@ TEST_F(OpCompatibilityTest, OutputRemoveRef) {
   ExpectTypeMismatch(old_op.op_def, "Output 0 changed from ref to non-ref");
 }
 
+// Can't rename an output, to avoid problems in FunctionDefs.
+
+REGISTER_OP("RenameOutput").Output("new: int32");
+
+TEST_F(OpCompatibilityTest, RenameOutputFails) {
+  OpRegistrationData old_op;
+  TF_ASSERT_OK(
+      OpDefBuilder("RenameOutput").Output("old: int32").Finalize(&old_op));
+  TF_ASSERT_OK(
+      NodeDefBuilder("rename_output", &old_op.op_def).Finalize(node_def()));
+  ExpectRenameFailure(old_op.op_def,
+                      "Output signature mismatch 'old:int32' vs. 'new:int32'");
+}
+
+REGISTER_OP("RenameNOutputs").Output("new: N*int32").Attr("N: int");
+
+TEST_F(OpCompatibilityTest, RenameNOutputsFails) {
+  OpRegistrationData old_op;
+  TF_ASSERT_OK(OpDefBuilder("RenameNOutputs")
+                   .Output("old: N*int32")
+                   .Attr("N: int")
+                   .Finalize(&old_op));
+  TF_ASSERT_OK(NodeDefBuilder("rename_n_outputs", &old_op.op_def)
+                   .Attr("N", 2)
+                   .Finalize(node_def()));
+  ExpectRenameFailure(
+      old_op.op_def,
+      "Output signature mismatch 'old:N * int32' vs. 'new:N * int32'");
+}
+
+REGISTER_OP("RenameOutputList").Output("new: T").Attr("T: list(type)");
+
+TEST_F(OpCompatibilityTest, RenameOutputListFails) {
+  OpRegistrationData old_op;
+  TF_ASSERT_OK(OpDefBuilder("RenameOutputList")
+                   .Output("old: T")
+                   .Attr("T: list(type)")
+                   .Finalize(&old_op));
+  TF_ASSERT_OK(NodeDefBuilder("rename_output_list", &old_op.op_def)
+                   .Attr("T", {DT_INT32, DT_FLOAT})
+                   .Finalize(node_def()));
+  ExpectRenameFailure(old_op.op_def,
+                      "Output signature mismatch 'old:T' vs. 'new:T'");
+}
+
 // Changing an attr's default is not technically illegal, but should
 // be forbidden if it the attr ever didn't exist since it likely
 // affects semantics.
diff --git a/tensorflow/core/framework/op_def_util.cc b/tensorflow/core/framework/op_def_util.cc
index d92a1d47ac..5717488b1c 100644
--- a/tensorflow/core/framework/op_def_util.cc
+++ b/tensorflow/core/framework/op_def_util.cc
@@ -471,6 +471,13 @@ void AddComma(string* s, bool* add_comma) {
   }
 }
 
+// Will add the `name` from arg if name is true.
+void AddName(string* s, bool name, const OpDef::ArgDef& arg) {
+  if (name) {
+    strings::StrAppend(s, arg.name(), ":");
+  }
+}
+
 // Compute a signature for either inputs or outputs that will be the
 // same for both the old and new OpDef if they are compatible.  We
 // assume that new_attrs is a superset of old_attrs, and that any attr
@@ -484,8 +491,8 @@ void AddComma(string* s, bool* add_comma) {
 // old_attrs, or substituting the default value from new_attrs.
 string ComputeArgSignature(
     const protobuf::RepeatedPtrField<OpDef::ArgDef>& args,
-    const AttrMap& old_attrs, const AttrMap& new_attrs,
-    std::vector<bool>* ref) {
+    const AttrMap& old_attrs, const AttrMap& new_attrs, std::vector<bool>* ref,
+    bool names) {
   string s;
   bool add_comma = false;
   for (const OpDef::ArgDef& arg : args) {
@@ -495,6 +502,7 @@ string ComputeArgSignature(
       if (old_attr) {
         // Both old and new have the list(type) attr, so can use it directly.
         AddComma(&s, &add_comma);
+        AddName(&s, names, arg);
         strings::StrAppend(&s, arg.type_list_attr());
         ref->push_back(arg.is_ref());
       } else {
@@ -506,6 +514,7 @@ string ComputeArgSignature(
         if (type_list.empty()) continue;
         for (int i = 0; i < type_list.size(); ++i) {
           AddComma(&s, &add_comma);
+          AddName(&s, names, arg);
           strings::StrAppend(
               &s, DataTypeString(static_cast<DataType>(type_list.Get(i))));
           ref->push_back(arg.is_ref());
@@ -513,15 +522,15 @@ string ComputeArgSignature(
       }
     } else {
       int num = 1;  // How many input/outputs does this represent?
+      string type;  // What is the type of this arg?
+      AddName(&type, names, arg);
       if (!arg.number_attr().empty()) {
         // N * type case.
         const OpDef::AttrDef* old_attr =
             gtl::FindPtrOrNull(old_attrs, arg.number_attr());
         if (old_attr) {
           // Both old and new have the number attr, so can use it directly.
-          AddComma(&s, &add_comma);
-          strings::StrAppend(&s, arg.number_attr(), " * ");
-          add_comma = false;  // Don't add another comma before the type.
+          strings::StrAppend(&type, arg.number_attr(), " * ");
         } else {
           // Missing the number attr in the old, so use the default
           // value for the attr from new instead.
@@ -531,22 +540,22 @@ string ComputeArgSignature(
         }
       }
 
-      string type;  // What is the type of this arg?
       if (arg.type() != DT_INVALID) {
         // int32, float, etc. case
-        type = DataTypeString(arg.type());
+        strings::StrAppend(&type, DataTypeString(arg.type()));
       } else {
         const OpDef::AttrDef* old_attr =
             gtl::FindPtrOrNull(old_attrs, arg.type_attr());
         if (old_attr) {
           // Both old and new have the type attr, so can use it directly.
-          type = arg.type_attr();
+          strings::StrAppend(&type, arg.type_attr());
         } else {
           // Missing the type attr in the old, so use the default
           // value for the attr from new instead.
           const OpDef::AttrDef* new_attr =
               gtl::FindPtrOrNull(new_attrs, arg.type_attr());
-          type = DataTypeString(new_attr->default_value().type());
+          strings::StrAppend(&type,
+                             DataTypeString(new_attr->default_value().type()));
         }
       }
 
@@ -600,10 +609,10 @@ Status OpDefCompatible(const OpDef& old_op, const OpDef& new_op) {
   }
 
   std::vector<bool> old_in_ref, new_in_ref, old_out_ref, new_out_ref;
-  const string old_in_sig = ComputeArgSignature(old_op.input_arg(), old_attrs,
-                                                new_attrs, &old_in_ref);
-  const string new_in_sig = ComputeArgSignature(new_op.input_arg(), old_attrs,
-                                                new_attrs, &new_in_ref);
+  const string old_in_sig = ComputeArgSignature(
+      old_op.input_arg(), old_attrs, new_attrs, &old_in_ref, false /* names */);
+  const string new_in_sig = ComputeArgSignature(
+      new_op.input_arg(), old_attrs, new_attrs, &new_in_ref, false /* names */);
   VALIDATE(old_in_sig == new_in_sig, "Input signature mismatch '", old_in_sig,
            "' vs. '", new_in_sig, "'");
   VALIDATE(old_in_ref.size() == new_in_ref.size(),  // Should not happen
@@ -614,10 +623,12 @@ Status OpDefCompatible(const OpDef& old_op, const OpDef& new_op) {
              " changed from non-ref to ref");
   }
 
-  const string old_out_sig = ComputeArgSignature(old_op.output_arg(), old_attrs,
-                                                 new_attrs, &old_out_ref);
-  const string new_out_sig = ComputeArgSignature(new_op.output_arg(), old_attrs,
-                                                 new_attrs, &new_out_ref);
+  const string old_out_sig =
+      ComputeArgSignature(old_op.output_arg(), old_attrs, new_attrs,
+                          &old_out_ref, true /* names */);
+  const string new_out_sig =
+      ComputeArgSignature(new_op.output_arg(), old_attrs, new_attrs,
+                          &new_out_ref, true /* names */);
   VALIDATE(old_out_sig == new_out_sig, "Output signature mismatch '",
            old_out_sig, "' vs. '", new_out_sig, "'");
   VALIDATE(old_out_ref.size() == new_out_ref.size(),  // Should not happen
diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h
index 3f6fa3826a..a9b63ea1f4 100644
--- a/tensorflow/core/public/version.h
+++ b/tensorflow/core/public/version.h
@@ -65,9 +65,10 @@ limitations under the License.
 // 9. Deprecate batch_norm_with_global_normalization (16feb2016).
 // 10. Deprecate conv3d_backprop_{filter,input} (10jun2016).
 // 11. Deprecate {batch}_self_adjoint_eig (3aug2016).
+// 12. Graph consumers understand the node_def field of FunctionDef (22aug2016).
 #define TF_GRAPH_DEF_VERSION_MIN_PRODUCER 0
 #define TF_GRAPH_DEF_VERSION_MIN_CONSUMER 0
-#define TF_GRAPH_DEF_VERSION 11
+#define TF_GRAPH_DEF_VERSION 12
 
 // Checkpoint compatibility versions (the versions field in SavedSliceMeta).
 //
diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py
index 8556b68439..c7ccdf96c3 100644
--- a/tensorflow/python/framework/function.py
+++ b/tensorflow/python/framework/function.py
@@ -234,7 +234,8 @@ def call_function(func_def, *inputs, **kwargs):
   Args:
     func_def: A `FunctionDef` protocol buffer.
     *inputs: A list of tensors
-    **kwargs: Optional keyword arguments.  Can only contain 'name'.
+    **kwargs: Optional keyword arguments.  Can only contain 'name' or
+        'noinline'.
 
   Returns:
     A list of tensors representing the outputs of the call to `func_def`.
@@ -248,7 +249,7 @@ def call_function(func_def, *inputs, **kwargs):
     attrs = None
   else:
     attrs = {}
-    attrs["noinline"] = attr_value_pb2.AttrValue(b=bool(noinline))
+    attrs["_noinline"] = attr_value_pb2.AttrValue(b=bool(noinline))
   if kwargs:
     raise ValueError("Unknown keyword arguments: %s" % kwargs.keys())
   func_name = func_def.signature.name
-- 
GitLab


From 9ff98045712ee3bb14e3285096652a06a9b3b74f Mon Sep 17 00:00:00 2001
From: Gunhan Gulsoy <gunan@google.com>
Date: Mon, 22 Aug 2016 19:07:04 -0800
Subject: [PATCH 028/223] Edit random ops test to minimize the use of use_gpu
 arg in session. Change: 131012545

---
 .../python/kernel_tests/random_ops_test.py    | 160 ++++++++----------
 1 file changed, 75 insertions(+), 85 deletions(-)

diff --git a/tensorflow/python/kernel_tests/random_ops_test.py b/tensorflow/python/kernel_tests/random_ops_test.py
index e9f2dde6eb..046a23acc5 100644
--- a/tensorflow/python/kernel_tests/random_ops_test.py
+++ b/tensorflow/python/kernel_tests/random_ops_test.py
@@ -40,18 +40,17 @@ class RandomNormalTest(tf.test.TestCase):
   # to see the same sequence of values. Will catch buggy
   # implementations which uses the same random number seed.
   def testDistinct(self):
-    for use_gpu in [False, True]:
-      for dt in tf.float16, tf.float32, tf.float64:
-        sampler = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=use_gpu)
-        x = sampler()
-        y = sampler()
-        # Number of different samples.
-        count = (x == y).sum()
-        if count >= 10:
-          print("x = ", x)
-          print("y = ", y)
-          print("count = ", count)
-        self.assertTrue(count < 10)
+    for dt in tf.float16, tf.float32, tf.float64:
+      sampler = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=True)
+      x = sampler()
+      y = sampler()
+      # Number of different samples.
+      count = (x == y).sum()
+      if count >= 10:
+        print("x = ", x)
+        print("y = ", y)
+        print("count = ", count)
+      self.assertTrue(count < 10)
 
   # Checks that the CPU and GPU implementation returns the same results,
   # given the same random seed
@@ -67,11 +66,10 @@ class RandomNormalTest(tf.test.TestCase):
         self.assertAllClose(results[False], results[True], rtol=1e-6, atol=1e-6)
 
   def testSeed(self):
-    for use_gpu in [False, True]:
-      for dt in tf.float16, tf.float32, tf.float64:
-        sx = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=use_gpu, seed=345)
-        sy = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=use_gpu, seed=345)
-        self.assertAllEqual(sx(), sy())
+    for dt in tf.float16, tf.float32, tf.float64:
+      sx = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=True, seed=345)
+      sy = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=True, seed=345)
+      self.assertAllEqual(sx(), sy())
 
   def testNoCSE(self):
     for use_gpu in [False, True]:
@@ -101,9 +99,9 @@ class TruncatedNormalTest(tf.test.TestCase):
   # implementations which uses the same random number seed.
   def testDistinct(self):
     # NOTE: TruncatedNormal on GPU is not supported.
-    for use_gpu in [False]:
+    if not tf.test.is_gpu_available():
       for dt in tf.float16, tf.float32, tf.float64:
-        sampler = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=use_gpu)
+        sampler = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=False)
         x = sampler()
         y = sampler()
         # Number of different samples.
@@ -131,31 +129,28 @@ class TruncatedNormalTest(tf.test.TestCase):
         self.assertAllClose(results[False], results[True], rtol=1e-6, atol=1e-6)
 
   def testSeed(self):
-    for use_gpu in [False, True]:
-      for dt in tf.float16, tf.float32, tf.float64:
-        sx = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=use_gpu, seed=345)
-        sy = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=use_gpu, seed=345)
-        self.assertAllEqual(sx(), sy())
+    for dt in tf.float16, tf.float32, tf.float64:
+      sx = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=True, seed=345)
+      sy = self._Sampler(1000, 0.0, 1.0, dt, use_gpu=True, seed=345)
+      self.assertAllEqual(sx(), sy())
 
   # The effective standard deviation of truncated normal is 85% of the
   # requested one.
   def testStdDev(self):
-    for use_gpu in [False, True]:
-      for dt in tf.float16, tf.float32, tf.float64:
-        stddev = 3.0
-        sampler = self._Sampler(100000, 0.0, stddev, dt, use_gpu=use_gpu)
-        x = sampler()
-        print("std(x)", np.std(x), abs(np.std(x) / stddev - 0.85))
-        self.assertTrue(abs(np.std(x) / stddev - 0.85) < 0.04)
+    for dt in tf.float16, tf.float32, tf.float64:
+      stddev = 3.0
+      sampler = self._Sampler(100000, 0.0, stddev, dt, use_gpu=True)
+      x = sampler()
+      print("std(x)", np.std(x), abs(np.std(x) / stddev - 0.85))
+      self.assertTrue(abs(np.std(x) / stddev - 0.85) < 0.04)
 
   def testNoCSE(self):
-    for use_gpu in [False, True]:
-      with self.test_session(use_gpu=use_gpu):
-        shape = [2, 3, 4]
-        rnd1 = tf.truncated_normal(shape, 0.0, 1.0, tf.float32)
-        rnd2 = tf.truncated_normal(shape, 0.0, 1.0, tf.float32)
-        diff = rnd2 - rnd1
-        self.assertTrue(np.linalg.norm(diff.eval()) > 0.1)
+    with self.test_session(use_gpu=True):
+      shape = [2, 3, 4]
+      rnd1 = tf.truncated_normal(shape, 0.0, 1.0, tf.float32)
+      rnd2 = tf.truncated_normal(shape, 0.0, 1.0, tf.float32)
+      diff = rnd2 - rnd1
+      self.assertTrue(np.linalg.norm(diff.eval()) > 0.1)
 
 
 class RandomUniformTest(tf.test.TestCase):
@@ -172,32 +167,30 @@ class RandomUniformTest(tf.test.TestCase):
     return func
 
   def testRange(self):
-    for use_gpu in False, True:
-      for dt in tf.float16, tf.float32, tf.float64, tf.int32, tf.int64:
-        sampler = self._Sampler(1000, minv=-2, maxv=8, dtype=dt,
-                                use_gpu=use_gpu)
-        x = sampler()
-        self.assertTrue(-2 <= np.min(x))
-        self.assertTrue(np.max(x) < 8)
+    for dt in tf.float16, tf.float32, tf.float64, tf.int32, tf.int64:
+      sampler = self._Sampler(1000, minv=-2, maxv=8, dtype=dt,
+                              use_gpu=True)
+      x = sampler()
+      self.assertTrue(-2 <= np.min(x))
+      self.assertTrue(np.max(x) < 8)
 
   # Asserts that different trials (1000 samples per trial) is unlikely
   # to see the same sequence of values. Will catch buggy
   # implementations which uses the same random number seed.
   def testDistinct(self):
-    for use_gpu in False, True:
-      for dt in tf.float16, tf.float32, tf.float64, tf.int32, tf.int64:
-        maxv = 1.0 if dt.is_floating else 1 << 30
-        sampler = self._Sampler(1000, minv=0, maxv=maxv, dtype=dt,
-                                use_gpu=use_gpu)
-        x = sampler()
-        y = sampler()
-        count = (x == y).sum()
-        count_limit = 50 if dt == tf.float16 else 10
-        if count >= count_limit:
-          print("x = ", x)
-          print("y = ", y)
-          print("count = ", count)
-        self.assertTrue(count < count_limit)
+    for dt in tf.float16, tf.float32, tf.float64, tf.int32, tf.int64:
+      maxv = 1.0 if dt.is_floating else 1 << 30
+      sampler = self._Sampler(1000, minv=0, maxv=maxv, dtype=dt,
+                              use_gpu=True)
+      x = sampler()
+      y = sampler()
+      count = (x == y).sum()
+      count_limit = 50 if dt == tf.float16 else 10
+      if count >= count_limit:
+        print("x = ", x)
+        print("y = ", y)
+        print("count = ", count)
+      self.assertTrue(count < count_limit)
 
   # Check that uniform ints actually follow a uniform distribution.
   def testUniformInts(self):
@@ -208,19 +201,18 @@ class RandomUniformTest(tf.test.TestCase):
     # The counts should follow an (n, p) binomial distribution.
     mean = p * n
     std = np.sqrt(n * p * (1 - p))
-    for use_gpu in False, True:
-      for dt in tf.int32, tf.int64:
-        # Use a fixed seed here to make the test deterministic.
-        # Without the fixed seed, the 5 * std bound will (very rarely) fail.
-        sampler = self._Sampler(n // 10, minv=minv, maxv=maxv, dtype=dt,
-                                use_gpu=use_gpu, seed=17)
-        x = sampler().ravel()
-        self.assertEqual(x.shape, (n,))
-        counts, _ = np.histogram(x, bins=maxv - minv)
-        self.assertEqual(counts.shape, (maxv - minv,))
-        self.assertEqual(counts.sum(), n)
-        error = np.abs(counts - mean)
-        self.assertLess(error.max(), 5 * std)
+    for dt in tf.int32, tf.int64:
+      # Use a fixed seed here to make the test deterministic.
+      # Without the fixed seed, the 5 * std bound will (very rarely) fail.
+      sampler = self._Sampler(n // 10, minv=minv, maxv=maxv, dtype=dt,
+                              use_gpu=True, seed=17)
+      x = sampler().ravel()
+      self.assertEqual(x.shape, (n,))
+      counts, _ = np.histogram(x, bins=maxv - minv)
+      self.assertEqual(counts.shape, (maxv - minv,))
+      self.assertEqual(counts.sum(), n)
+      error = np.abs(counts - mean)
+      self.assertLess(error.max(), 5 * std)
 
   # Checks that the CPU and GPU implementation returns the same results,
   # given the same random seed
@@ -235,22 +227,20 @@ class RandomUniformTest(tf.test.TestCase):
       self.assertAllEqual(results[False], results[True])
 
   def testSeed(self):
-    for use_gpu in False, True:
-      for dt in tf.float16, tf.float32, tf.float64, tf.int32, tf.int64:
-          for seed in [345, 2**100, -2**100]:
-            sx = self._Sampler(1000, 0, 17, dtype=dt, use_gpu=use_gpu, seed=seed)
-            sy = self._Sampler(1000, 0, 17, dtype=dt, use_gpu=use_gpu, seed=seed)
-            self.assertAllEqual(sx(), sy())
+    for dt in tf.float16, tf.float32, tf.float64, tf.int32, tf.int64:
+      for seed in [345, 2**100, -2**100]:
+        sx = self._Sampler(1000, 0, 17, dtype=dt, use_gpu=True, seed=seed)
+        sy = self._Sampler(1000, 0, 17, dtype=dt, use_gpu=True, seed=seed)
+        self.assertAllEqual(sx(), sy())
 
   def testNoCSE(self):
     shape = [2, 3, 4]
-    for use_gpu in False, True:
-      for dtype in tf.float16, tf.float32, tf.int32:
-        with self.test_session(use_gpu=use_gpu):
-          rnd1 = tf.random_uniform(shape, 0, 17, dtype=dtype)
-          rnd2 = tf.random_uniform(shape, 0, 17, dtype=dtype)
-          diff = (rnd2 - rnd1).eval()
-          self.assertTrue(np.linalg.norm(diff) > 0.1)
+    for dtype in tf.float16, tf.float32, tf.int32:
+      with self.test_session(use_gpu=True):
+        rnd1 = tf.random_uniform(shape, 0, 17, dtype=dtype)
+        rnd2 = tf.random_uniform(shape, 0, 17, dtype=dtype)
+        diff = (rnd2 - rnd1).eval()
+        self.assertTrue(np.linalg.norm(diff) > 0.1)
 
 
 class RandomShapeTest(tf.test.TestCase):
-- 
GitLab


From dd56e71952b73f4ab1158f8343812cbccd5c1c8d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 02:05:14 -0800
Subject: [PATCH 029/223] Fixed bug in _PartitionedVariable that prevents it
 from being created within cond()/while_loop() and then subsequently used
 outside it. The error is: InvalidArgumentError: All inputs to node add must
 be from the same frame. Change: 131036071

---
 .../partitioned_variables_test.py             | 45 +++++++++++++++++++
 tensorflow/python/ops/variables.py            | 32 +++++++++----
 2 files changed, 69 insertions(+), 8 deletions(-)

diff --git a/tensorflow/python/kernel_tests/partitioned_variables_test.py b/tensorflow/python/kernel_tests/partitioned_variables_test.py
index a16ac20cc0..77feacad19 100644
--- a/tensorflow/python/kernel_tests/partitioned_variables_test.py
+++ b/tensorflow/python/kernel_tests/partitioned_variables_test.py
@@ -456,6 +456,51 @@ class PartitionedVariablesTestCase(tf.test.TestCase):
         tf.create_partitioned_variables(
             [10, 43], [1, 50], rnd.initialized_value())
 
+  def testControlDepsNone(self):
+    with self.test_session() as session:
+      c = tf.constant(1.0)
+      with tf.control_dependencies([c]):
+        # d get the control dependency.
+        d = tf.constant(2.0)
+        # Partitioned variables do not.
+        var_x = tf.get_variable(
+            "x",
+            initializer=tf.ones_initializer([2]),
+            partitioner=tf.variable_axis_size_partitioner(4))
+
+        ops_before_read = session.graph.get_operations()
+        var_x.as_tensor()  # Caches the ops for subsequent reads.
+        reading_ops = [op for op in session.graph.get_operations()
+                       if op not in ops_before_read]
+
+      self.assertEqual([c.op], d.op.control_inputs)
+      # Tests that no control dependencies are added to reading a partitioned
+      # variable which is similar to reading a variable.
+      for op in reading_ops:
+        self.assertEqual([], op.control_inputs)
+
+  def testConcat(self):
+    with self.test_session() as session:
+      var_x = tf.get_variable(
+          "x",
+          initializer=tf.constant([1., 2.]),
+          partitioner=tf.variable_axis_size_partitioner(4))
+
+      c = tf.constant(1.0)
+      with tf.control_dependencies([c]):
+        ops_before_concat = session.graph.get_operations()
+        value = var_x.concat()
+        concat_ops = [op for op in session.graph.get_operations()
+                      if op not in ops_before_concat]
+
+      concat_control_inputs = [ci for op in concat_ops
+                               for ci in op.control_inputs]
+      self.assertTrue(
+          c.op in concat_control_inputs,
+          "var_x.concat() should get control dependencies from its scope.")
+      tf.initialize_all_variables().run()
+      self.assertAllClose(value.eval(), var_x.as_tensor().eval())
+
 
 if __name__ == "__main__":
   tf.test.main()
diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py
index 47ced90acd..a720d7d26a 100644
--- a/tensorflow/python/ops/variables.py
+++ b/tensorflow/python/ops/variables.py
@@ -772,31 +772,47 @@ class _PartitionedVariable(object):
     self._partitions = partitions
     self._as_tensor = None
 
-  def as_tensor(self):
+  def concat(self):
     """Returns the overall concatenated value as a `Tensor`.
 
+    This is different from using the partitioned variable directly as a tensor
+    (through tensor conversion and `as_tensor`) in that it creates a new set of
+    operations that keeps the control dependencies from its scope.
+
     Returns:
       `Tensor` containing the concatenated value.
     """
-    if self._as_tensor is not None:
-      return self._as_tensor
-
     if len(self._variable_list) == 1:
       with ops.name_scope(None):
-        self._as_tensor = array_ops.identity(
-            self._variable_list[0], name=self._name)
-        return self._as_tensor
+        return array_ops.identity(self._variable_list[0], name=self._name)
 
     if all([p < 2 for p in self._partitions]):
       partition_ix = 0
     else:
       partition_ix = [i for i, p in enumerate(self._partitions) if p > 1][0]
+
     with ops.name_scope(self._name + "/ConcatPartitions/"):
       concatenated = array_ops.concat(partition_ix, self._variable_list)
+
     with ops.name_scope(None):
+      return array_ops.identity(concatenated, name=self._name)
+
+  def as_tensor(self):
+    """Returns the overall concatenated value as a `Tensor`.
+
+    The returned tensor will not inherit the control dependencies from the scope
+    where the value is used, which is similar to getting the value of
+    `Variable`.
+
+    Returns:
+      `Tensor` containing the concatenated value.
+    """
+    if self._as_tensor is None:
       # Be sure to cache the concatenated tensor to not do extraneous
       # computations.
-      self._as_tensor = array_ops.identity(concatenated, name=self._name)
+      with ops.control_dependencies(None):
+        self._as_tensor = self.concat()
+
     return self._as_tensor
 
   @staticmethod
-- 
GitLab


From a58c688e1c5b0c8d9887dd5db17fb78e9f238dd7 Mon Sep 17 00:00:00 2001
From: David Soergel <soergel@google.com>
Date: Tue, 23 Aug 2016 05:08:26 -0800
Subject: [PATCH 030/223] Wire num_threads from in_memory_source to
 enqueue_data Change: 131047076

---
 .../python/learn/dataframe/transforms/in_memory_source.py      | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py
index d96d53468a..ee4bdb1502 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py
@@ -27,7 +27,7 @@ class BaseInMemorySource(transform.Transform):
 
   def __init__(self,
                data,
-               num_threads=1,
+               num_threads=None,
                enqueue_size=None,
                batch_size=None,
                queue_capacity=None,
@@ -94,6 +94,7 @@ class BaseInMemorySource(transform.Transform):
                                            self.queue_capacity,
                                            self.shuffle,
                                            self.min_after_dequeue,
+                                           num_threads=self.num_threads,
                                            seed=self.seed,
                                            name=self.data_name,
                                            enqueue_size=self.enqueue_size)
-- 
GitLab


From 44c0237eafaea616707bb3e8f9ec27402b4b33ef Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 06:48:52 -0800
Subject: [PATCH 031/223] Added config property to BaseEstimator to allow
 retrieval of RunConfig. Change: 131054755

---
 .../learn/python/learn/estimators/estimator.py   |  6 ++++++
 .../contrib/learn/python/learn/experiment.py     | 10 ++++++----
 .../learn/python/learn/tests/experiment_test.py  | 16 ++++++++++------
 3 files changed, 22 insertions(+), 10 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator.py b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
index b60c4e91e6..c6c894542c 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/estimator.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
@@ -20,6 +20,7 @@ from __future__ import division
 from __future__ import print_function
 
 import abc
+import copy
 import inspect
 import itertools
 import os
@@ -224,6 +225,11 @@ class BaseEstimator(
 
     self._graph = None
 
+  @property
+  def config(self):
+    # TODO(wicke): make RunConfig immutable, and then return it without a copy.
+    return copy.deepcopy(self._config)
+
   def fit(self, x=None, y=None, input_fn=None, steps=None, batch_size=None,
           monitors=None, max_steps=None):
     # pylint: disable=g-doc-args,g-doc-return-or-yield
diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index 0f96b70fae..f26a194bb4 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -89,6 +89,10 @@ class Experiment(object):
     self._eval_delay_secs = eval_delay_secs
     self._continuous_eval_throttle_secs = continuous_eval_throttle_secs
 
+  @property
+  def estimator(self):
+    return self._estimator
+
   def train(self, delay_secs=None):
     """Fit the estimator using the training data.
 
@@ -102,10 +106,8 @@ class Experiment(object):
       The trained estimator.
     """
     if delay_secs is None:
-      task_id = 0
-      if hasattr(FLAGS, "task"):
-        task_id = FLAGS.task
-      delay_secs = min(60, task_id*5)
+      task_id = self._estimator.config.task or 0
+      delay_secs = min(60, task_id * 5)
 
     if delay_secs:
       logging.info("Waiting %d secs before starting training.", delay_secs)
diff --git a/tensorflow/contrib/learn/python/learn/tests/experiment_test.py b/tensorflow/contrib/learn/python/learn/tests/experiment_test.py
index 11feab7c48..7980881ea6 100644
--- a/tensorflow/contrib/learn/python/learn/tests/experiment_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/experiment_test.py
@@ -19,16 +19,20 @@ from __future__ import print_function
 import time
 
 import tensorflow as tf
-# importing to get flags.
-from tensorflow.contrib.learn.python.learn import learn_runner  # pylint: disable=unused-import
+from tensorflow.contrib.learn.python.learn import run_config
 
 
 class TestEstimator(tf.contrib.learn.Evaluable, tf.contrib.learn.Trainable):
 
-  def __init__(self):
+  def __init__(self, config=None):
     self.eval_count = 0
     self.fit_count = 0
     self.monitors = []
+    self._config = config or run_config.RunConfig()
+
+  @property
+  def config(self):
+    return self._config
 
   def evaluate(self, **kwargs):
     tf.logging.info('evaluate called with args: %s' % kwargs)
@@ -72,14 +76,14 @@ class ExperimentTest(tf.test.TestCase):
       self.assertAlmostEqual(duration, delay, delta=0.5)
 
   def test_train_default_delay(self):
-    est = TestEstimator()
+    config = run_config.RunConfig()
+    est = TestEstimator(config)
     ex = tf.contrib.learn.Experiment(est,
                                      train_input_fn='train_input',
                                      eval_input_fn='eval_input')
-    tf.flags.DEFINE_integer('task', 0, 'task')
     for task in [0, 1, 3]:
       start = time.time()
-      tf.flags.FLAGS.task = task
+      config.task = task
       ex.train()
       duration = time.time() - start
       self.assertAlmostEqual(duration, task*5, delta=0.5)
-- 
GitLab


From 9b980557a7b60de06c9b291abbd821d09c0ab02b Mon Sep 17 00:00:00 2001
From: David Soergel <soergel@google.com>
Date: Tue, 23 Aug 2016 06:50:43 -0800
Subject: [PATCH 032/223] Allow limiting epochs for memory sources Change:
 131054894

---
 .../dataframe/queues/feeding_functions.py     | 74 ++++++++++++++++---
 .../dataframe/transforms/in_memory_source.py  |  3 +-
 2 files changed, 65 insertions(+), 12 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/dataframe/queues/feeding_functions.py b/tensorflow/contrib/learn/python/learn/dataframe/queues/feeding_functions.py
index ae925b606d..f1fcba0b04 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/queues/feeding_functions.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/queues/feeding_functions.py
@@ -23,6 +23,7 @@ import numpy as np
 
 from tensorflow.contrib.learn.python.learn.dataframe.queues import feeding_queue_runner as fqr
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import data_flow_ops
@@ -47,7 +48,8 @@ class _ArrayFeedFn(object):
                array,
                batch_size,
                random_start=False,
-               seed=None):
+               seed=None,
+               num_epochs=None):
     if len(placeholders) != 2:
       raise ValueError("_array_feed_fn expects 2 placeholders; got {}.".format(
           len(placeholders)))
@@ -55,13 +57,26 @@ class _ArrayFeedFn(object):
     self._array = array
     self._max = len(array)
     self._batch_size = batch_size
+    self._num_epochs = num_epochs
+    self._epoch = 0
     random.seed(seed)
     self._trav = random.randrange(self._max) if random_start else 0
+    self._epoch_end = (self._trav - 1) % self._max
 
   def __call__(self):
+    if self._num_epochs and self._epoch >= self._num_epochs:
+      raise errors.OutOfRangeError(None, None,
+                                   "Already emitted %s epochs." % self._epoch)
+
     integer_indexes = [j % self._max
                        for j in range(self._trav, self._trav + self._batch_size)
                       ]
+
+    if self._epoch_end in integer_indexes:
+      # after this batch we will have processed self._epoch epochs, possibly
+      # overshooting a bit to fill out a batch.
+      self._epoch += 1
+
     self._trav = (integer_indexes[-1] + 1) % self._max
     return {self._placeholders[0]: integer_indexes,
             self._placeholders[1]: self._array[integer_indexes]}
@@ -75,7 +90,8 @@ class _PandasFeedFn(object):
                dataframe,
                batch_size,
                random_start=False,
-               seed=None):
+               seed=None,
+               num_epochs=None):
     if len(placeholders) != len(dataframe.columns) + 1:
       raise ValueError("Expected {} placeholders; got {}.".format(
           len(dataframe.columns), len(placeholders)))
@@ -84,13 +100,30 @@ class _PandasFeedFn(object):
     self._dataframe = dataframe
     self._max = len(dataframe)
     self._batch_size = batch_size
+    self._num_epochs = num_epochs
+    self._epoch = 0
     random.seed(seed)
     self._trav = random.randrange(self._max) if random_start else 0
+    self._epoch_end = (self._trav - 1) % self._max
 
   def __call__(self):
+    if self._num_epochs and self._epoch >= self._num_epochs:
+      raise errors.OutOfRangeError(None, None,
+                                   "Already emitted %s epochs." % self._epoch)
+
     integer_indexes = [j % self._max
                        for j in range(self._trav, self._trav + self._batch_size)
                       ]
+
+    if self._epoch_end in integer_indexes:
+      # after this batch we will have processed self._epoch epochs, possibly
+      # overshooting a bit to fill out a batch.
+      self._epoch += 1
+      if self._epoch == self._num_epochs:
+        # trim this batch, so as not to overshoot the last epoch.
+        batch_end_inclusive = integer_indexes.index(self._epoch_end)
+        integer_indexes = integer_indexes[:(batch_end_inclusive+1)]
+
     self._trav = (integer_indexes[-1] + 1) % self._max
     result = self._dataframe.iloc[integer_indexes]
     cols = [result[col].values for col in result.columns]
@@ -106,7 +139,8 @@ def enqueue_data(data,
                  num_threads=1,
                  seed=None,
                  name="enqueue_input",
-                 enqueue_size=1):
+                 enqueue_size=1,
+                 num_epochs=None):
   """Creates a queue filled from a numpy array or pandas `DataFrame`.
 
     Returns a queue filled with the rows of the given array or `DataFrame`. In
@@ -126,6 +160,7 @@ def enqueue_data(data,
     seed: used to seed shuffling and reader starting points.
     name: a scope name identifying the data.
     enqueue_size: the number of rows to enqueue per step.
+    num_epochs: limit enqueuing to a specified number of epochs, if provided.
 
   Returns:
     A queue filled with the rows of the given array or `DataFrame`.
@@ -148,6 +183,29 @@ def enqueue_data(data,
           "data must be either a numpy array or pandas DataFrame if pandas is "
           "installed; got {}".format(type(data).__name__))
 
+    # TODO(jamieas): TensorBoard warnings for all warnings below once available.
+
+    if num_threads > 1 and num_epochs is not None:
+      logging.warning(
+          "enqueue_data was called with num_epochs and num_threads > 1. "
+          "num_epochs is applied per thread, so this will produce more "
+          "epochs than you probably intend. "
+          "If you want to limit epochs, use one thread.")
+
+    if shuffle and num_threads > 1 and num_epochs is not None:
+      logging.warning(
+          "enqueue_data was called with shuffle=True, num_threads > 1, and "
+          "num_epochs. This will create multiple threads, all reading the "
+          "array/dataframe in order adding to the same shuffling queue; the "
+          "results will likely not be sufficiently shuffled.")
+
+    if not shuffle and num_threads > 1:
+      logging.warning(
+          "enqueue_data was called with shuffle=False and num_threads > 1. "
+          "This will create multiple threads, all reading the "
+          "array/dataframe in order. If you want examples read in order, use"
+          " one thread; if you want multiple threads, enable shuffling.")
+
     if shuffle:
       min_after_dequeue = int(capacity / 4 if min_after_dequeue is None else
                               min_after_dequeue)
@@ -157,13 +215,6 @@ def enqueue_data(data,
                                                shapes=queue_shapes,
                                                seed=seed)
     else:
-      if num_threads > 1:
-        # TODO(jamieas): Add TensorBoard warning here once available.
-        logging.warning(
-            "enqueue_data was called with shuffle=False and num_threads > 1. "
-            "This will create multiple threads, all reading the "
-            "array/dataframe in order. If you want examples read in order, use"
-            " one thread; if you want multiple threads, enable shuffling.")
       min_after_dequeue = 0  # just for the summary text
       queue = data_flow_ops.FIFOQueue(capacity,
                                       dtypes=types,
@@ -183,7 +234,8 @@ def enqueue_data(data,
                                   data,
                                   enqueue_size,
                                   random_start=shuffle,
-                                  seed=seed_i))
+                                  seed=seed_i,
+                                  num_epochs=num_epochs))
 
     runner = fqr.FeedingQueueRunner(queue=queue,
                                     enqueue_ops=enqueue_ops,
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py
index ee4bdb1502..53e276adf1 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py
@@ -97,7 +97,8 @@ class BaseInMemorySource(transform.Transform):
                                            num_threads=self.num_threads,
                                            seed=self.seed,
                                            name=self.data_name,
-                                           enqueue_size=self.enqueue_size)
+                                           enqueue_size=self.enqueue_size,
+                                           num_epochs=kwargs.get("num_epochs"))
 
     dequeued = queue.dequeue_many(self.batch_size)
 
-- 
GitLab


From d37d20062aebab2d748cdac445683f29a82006c3 Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Tue, 23 Aug 2016 07:29:38 -0800
Subject: [PATCH 033/223] TensorShape and Queue API fixes

* Fix handling of TensorShape(TensorShape(None)); before it would loop forever
* Add shape merging for QueueBase.from_list
Change: 131058295
---
 tensorflow/python/framework/tensor_shape.py   |  2 +
 .../kernel_tests/padding_fifo_queue_test.py   | 50 +++++++++++++++++++
 tensorflow/python/ops/data_flow_ops.py        | 26 +++++++++-
 3 files changed, 76 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/framework/tensor_shape.py b/tensorflow/python/framework/tensor_shape.py
index bf24763e81..6d3fdb6d80 100644
--- a/tensorflow/python/framework/tensor_shape.py
+++ b/tensorflow/python/framework/tensor_shape.py
@@ -438,6 +438,8 @@ class TensorShape(object):
             # Protos store variable-size dimensions as -1
             as_dimension(dim.size if dim.size != -1 else None)
             for dim in dims.dim]
+    elif isinstance(dims, TensorShape):
+      self._dims = dims.dims
     else:
       try:
         dims_iter = iter(dims)
diff --git a/tensorflow/python/kernel_tests/padding_fifo_queue_test.py b/tensorflow/python/kernel_tests/padding_fifo_queue_test.py
index ad2a52cd43..15ebd4042c 100644
--- a/tensorflow/python/kernel_tests/padding_fifo_queue_test.py
+++ b/tensorflow/python/kernel_tests/padding_fifo_queue_test.py
@@ -1483,5 +1483,55 @@ class PaddingFIFOQueueTest(tf.test.TestCase):
       tf.PaddingFIFOQueue(32, [tf.float32], [tf.TensorShape(None)])
 
 
+class QueueFromListTest(tf.test.TestCase):
+
+  def testQueueFromListShapes(self):
+    which = tf.constant(1)
+    def _cmp(expected, *shapes):
+      qs = [
+          tf.PaddingFIFOQueue(10, [tf.float32], [tf.TensorShape(s)])
+          for s in shapes]
+      s_expected = tf.TensorShape(expected)
+      s = tf.QueueBase.from_list(which, qs).shapes[0]
+      if s_expected.ndims is None:
+        self.assertEqual(s_expected.ndims, s.ndims)
+      else:
+        self.assertEqual(s_expected.as_list(), s.as_list())
+
+    _cmp(None, [1, None], [None])
+    _cmp([None], [1], [2])
+    _cmp([1, None], [1, 1], [1, 2])
+    _cmp([1, None], [1, 1], [1, None])
+    _cmp([None, None], [None, 1], [1, None])
+    _cmp([1], [1], [1], [1])
+    _cmp([None], [1], [None], [1])
+    _cmp(None, [1, None], [1], [1])
+
+  def testQueueFromListShapesMultipleComponents(self):
+    q_u_u = tf.PaddingFIFOQueue(
+        10,
+        [tf.float32, tf.int32],
+        [tf.TensorShape([None]), tf.TensorShape([None])])
+    q_u_f = tf.PaddingFIFOQueue(
+        10, [tf.float32, tf.int32],
+        [tf.TensorShape([None]), tf.TensorShape([1, 2])])
+    q_f_f = tf.PaddingFIFOQueue(
+        10, [tf.float32, tf.int32],
+        [tf.TensorShape([3, 4]), tf.TensorShape([1, 2])])
+    which = tf.constant(1)
+
+    s_cmp_1 = tf.QueueBase.from_list(which, [q_u_u, q_u_u, q_u_u]).shapes
+    self.assertEqual([1, 1], [x.ndims for x in s_cmp_1])
+    self.assertEqual([None, None], [x.as_list()[0] for x in s_cmp_1])
+
+    s_cmp_2 = tf.QueueBase.from_list(which, [q_u_u, q_u_u, q_u_f]).shapes
+    self.assertEqual([1, None], [x.ndims for x in s_cmp_2])
+    self.assertEqual([None], s_cmp_2[0].as_list())
+
+    s_cmp_3 = tf.QueueBase.from_list(which, [q_f_f, q_f_f]).shapes
+    self.assertEqual([2, 2], [x.ndims for x in s_cmp_3])
+    self.assertEqual([[3, 4], [1, 2]], [x.as_list() for x in s_cmp_3])
+
+
 if __name__ == "__main__":
   tf.test.main()
diff --git a/tensorflow/python/ops/data_flow_ops.py b/tensorflow/python/ops/data_flow_ops.py
index 982807de8a..7262267d5e 100644
--- a/tensorflow/python/ops/data_flow_ops.py
+++ b/tensorflow/python/ops/data_flow_ops.py
@@ -22,6 +22,8 @@ from __future__ import print_function
 import collections
 import re
 
+import six
+
 from tensorflow.python.framework import common_shapes
 from tensorflow.python.framework import dtypes as _dtypes
 from tensorflow.python.framework import ops
@@ -88,6 +90,18 @@ def _as_name_list(names, dtypes):
   return list(names)
 
 
+def _shape_common(s1, s2):
+  """The greatest lower bound (ordered by specificity) TensorShape."""
+  s1 = tensor_shape.TensorShape(s1)
+  s2 = tensor_shape.TensorShape(s2)
+  if s1.ndims is None or s2.ndims is None or s1.ndims != s2.ndims:
+    return tensor_shape.unknown_shape()
+  d = [
+      d1 if d1 is not None and d1 == d2 else None
+      for (d1, d2) in zip(s1.as_list(), s2.as_list())]
+  return tensor_shape.TensorShape(d)
+
+
 # pylint: disable=protected-access
 class QueueBase(object):
   """Base class for queue implementations.
@@ -186,10 +200,13 @@ class QueueBase(object):
     if not all([names == q.names for q in queues[1:]]):
       raise TypeError("Queues do not have matching component names.")
 
+    queue_shapes = [q.shapes for q in queues]
+    reduced_shapes = [
+        six.moves.reduce(_shape_common, s) for s in zip(*queue_shapes)]
+
     queue_refs = [x.queue_ref for x in queues]
     selected_queue = control_flow_ops.ref_select(index, queue_refs)
-    # TODO(josh11b): Unify the shapes of the queues too?
-    return QueueBase(dtypes=dtypes, shapes=None, names=names,
+    return QueueBase(dtypes=dtypes, shapes=reduced_shapes, names=names,
                      queue_ref=selected_queue)
 
   @property
@@ -207,6 +224,11 @@ class QueueBase(object):
     """The list of dtypes for each component of a queue element."""
     return self._dtypes
 
+  @property
+  def shapes(self):
+    """The list of shapes for each component of a queue element."""
+    return self._shapes
+
   @property
   def names(self):
     """The list of names for each component of a queue element."""
-- 
GitLab


From 644b6fbb5eec0fb8fee23bb756342244f6188b18 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 07:33:57 -0800
Subject: [PATCH 034/223] Update generated Python Op docs. Change: 131058624

---
 .../g3doc/api_docs/python/contrib.learn.md    | 105 ++++++++++++++++++
 .../tf.contrib.learn.LinearRegressor.md       |   7 ++
 .../tf.contrib.learn.LinearClassifier.md      |   7 ++
 .../shard2/tf.contrib.learn.BaseEstimator.md  |   7 ++
 ...tf.contrib.learn.TensorFlowDNNRegressor.md |   7 ++
 .../shard3/tf.contrib.learn.Estimator.md      |   7 ++
 .../shard4/tf.contrib.learn.DNNClassifier.md  |   7 ++
 .../tf.contrib.learn.TensorFlowEstimator.md   |   7 ++
 ...ontrib.learn.TensorFlowLinearClassifier.md |   7 ++
 ...tf.contrib.learn.TensorFlowRNNRegressor.md |   7 ++
 ...f.contrib.learn.TensorFlowDNNClassifier.md |   7 ++
 .../shard6/tf.QueueBase.md                    |   7 ++
 .../tf.contrib.learn.TensorFlowClassifier.md  |   7 ++
 .../tf.contrib.learn.TensorFlowRegressor.md   |   7 ++
 .../shard9/tf.contrib.learn.DNNRegressor.md   |   7 ++
 ...contrib.learn.TensorFlowLinearRegressor.md |   7 ++
 ...f.contrib.learn.TensorFlowRNNClassifier.md |   7 ++
 tensorflow/g3doc/api_docs/python/io_ops.md    |   7 ++
 18 files changed, 224 insertions(+)

diff --git a/tensorflow/g3doc/api_docs/python/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md
index 668ca09b07..211a200659 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.learn.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md
@@ -37,6 +37,13 @@ Initializes a BaseEstimator instance.
 *  <b>`config`</b>: A RunConfig instance.
 
 
+- - -
+
+#### `tf.contrib.learn.BaseEstimator.config` {#BaseEstimator.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.BaseEstimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#BaseEstimator.evaluate}
@@ -295,6 +302,13 @@ Constructs an Estimator instance.
 *  <b>`ValueError`</b>: parameters of `model_fn` don't match `params`.
 
 
+- - -
+
+#### `tf.contrib.learn.Estimator.config` {#Estimator.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.Estimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#Estimator.evaluate}
@@ -533,6 +547,13 @@ The following standard keys are defined:
 
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.config` {#TensorFlowClassifier.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowClassifier.dnn_bias_` {#TensorFlowClassifier.dnn_bias_}
@@ -867,6 +888,13 @@ Initializes a DNNClassifier instance.
 
 
 
+- - -
+
+#### `tf.contrib.learn.DNNClassifier.config` {#DNNClassifier.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.DNNClassifier.dnn_bias_` {#DNNClassifier.dnn_bias_}
@@ -1231,6 +1259,13 @@ Initializes a `DNNRegressor` instance.
 
 
 
+- - -
+
+#### `tf.contrib.learn.DNNRegressor.config` {#DNNRegressor.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.DNNRegressor.dnn_bias_` {#DNNRegressor.dnn_bias_}
@@ -1492,6 +1527,13 @@ component of a nested object.
 
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.config` {#TensorFlowDNNClassifier.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowDNNClassifier.dnn_bias_` {#TensorFlowDNNClassifier.dnn_bias_}
@@ -1737,6 +1779,13 @@ component of a nested object.
 
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.config` {#TensorFlowDNNRegressor.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowDNNRegressor.dnn_bias_` {#TensorFlowDNNRegressor.dnn_bias_}
@@ -2013,6 +2062,13 @@ Initializes a TensorFlowEstimator instance.
     * 2: log device placement is printed.
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.config` {#TensorFlowEstimator.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowEstimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#TensorFlowEstimator.evaluate}
@@ -2409,6 +2465,13 @@ Construct a `LinearClassifier` estimator object.
 
 
 
+- - -
+
+#### `tf.contrib.learn.LinearClassifier.config` {#LinearClassifier.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.LinearClassifier.dnn_bias_` {#LinearClassifier.dnn_bias_}
@@ -2756,6 +2819,13 @@ Construct a `LinearRegressor` estimator object.
 
 
 
+- - -
+
+#### `tf.contrib.learn.LinearRegressor.config` {#LinearRegressor.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.LinearRegressor.dnn_bias_` {#LinearRegressor.dnn_bias_}
@@ -3017,6 +3087,13 @@ component of a nested object.
 
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.config` {#TensorFlowLinearClassifier.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowLinearClassifier.dnn_bias_` {#TensorFlowLinearClassifier.dnn_bias_}
@@ -3262,6 +3339,13 @@ component of a nested object.
 
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.config` {#TensorFlowLinearRegressor.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowLinearRegressor.dnn_bias_` {#TensorFlowLinearRegressor.dnn_bias_}
@@ -3552,6 +3636,13 @@ Initializes a TensorFlowRNNClassifier instance.
 Returns bias of the rnn layer.
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.config` {#TensorFlowRNNClassifier.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowRNNClassifier.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#TensorFlowRNNClassifier.evaluate}
@@ -3913,6 +4004,13 @@ Initializes a TensorFlowRNNRegressor instance.
 Returns bias of the rnn layer.
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.config` {#TensorFlowRNNRegressor.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowRNNRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#TensorFlowRNNRegressor.evaluate}
@@ -4228,6 +4326,13 @@ Returns weights of the rnn layer.
 
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.config` {#TensorFlowRegressor.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowRegressor.dnn_bias_` {#TensorFlowRegressor.dnn_bias_}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.LinearRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.LinearRegressor.md
index 84a4ee1c7e..879981f9a6 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.LinearRegressor.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.LinearRegressor.md
@@ -83,6 +83,13 @@ Construct a `LinearRegressor` estimator object.
 
 
 
+- - -
+
+#### `tf.contrib.learn.LinearRegressor.config` {#LinearRegressor.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.LinearRegressor.dnn_bias_` {#LinearRegressor.dnn_bias_}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.LinearClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.LinearClassifier.md
index 938e2dbc8e..241425383a 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.LinearClassifier.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.LinearClassifier.md
@@ -101,6 +101,13 @@ Construct a `LinearClassifier` estimator object.
 
 
 
+- - -
+
+#### `tf.contrib.learn.LinearClassifier.config` {#LinearClassifier.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.LinearClassifier.dnn_bias_` {#LinearClassifier.dnn_bias_}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md
index d28802d992..45bf50461a 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md
@@ -22,6 +22,13 @@ Initializes a BaseEstimator instance.
 *  <b>`config`</b>: A RunConfig instance.
 
 
+- - -
+
+#### `tf.contrib.learn.BaseEstimator.config` {#BaseEstimator.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.BaseEstimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#BaseEstimator.evaluate}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.TensorFlowDNNRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.TensorFlowDNNRegressor.md
index 3a3396aeaf..ffba46a17b 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.TensorFlowDNNRegressor.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.TensorFlowDNNRegressor.md
@@ -13,6 +13,13 @@
 
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNRegressor.config` {#TensorFlowDNNRegressor.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowDNNRegressor.dnn_bias_` {#TensorFlowDNNRegressor.dnn_bias_}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md
index 631156b06f..e98d0138fb 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md
@@ -45,6 +45,13 @@ Constructs an Estimator instance.
 *  <b>`ValueError`</b>: parameters of `model_fn` don't match `params`.
 
 
+- - -
+
+#### `tf.contrib.learn.Estimator.config` {#Estimator.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.Estimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#Estimator.evaluate}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md
index c6c2fb92ff..a16a419c5c 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md
@@ -102,6 +102,13 @@ Initializes a DNNClassifier instance.
 
 
 
+- - -
+
+#### `tf.contrib.learn.DNNClassifier.config` {#DNNClassifier.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.DNNClassifier.dnn_bias_` {#DNNClassifier.dnn_bias_}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowEstimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowEstimator.md
index 1d85fcdab6..10daed3f79 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowEstimator.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowEstimator.md
@@ -44,6 +44,13 @@ Initializes a TensorFlowEstimator instance.
     * 2: log device placement is printed.
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowEstimator.config` {#TensorFlowEstimator.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowEstimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#TensorFlowEstimator.evaluate}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowLinearClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowLinearClassifier.md
index ef6f13c7ab..d2cc47c993 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowLinearClassifier.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowLinearClassifier.md
@@ -13,6 +13,13 @@
 
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearClassifier.config` {#TensorFlowLinearClassifier.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowLinearClassifier.dnn_bias_` {#TensorFlowLinearClassifier.dnn_bias_}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowRNNRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowRNNRegressor.md
index 3dd086478b..a4f0affc3b 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowRNNRegressor.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowRNNRegressor.md
@@ -59,6 +59,13 @@ Initializes a TensorFlowRNNRegressor instance.
 Returns bias of the rnn layer.
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNRegressor.config` {#TensorFlowRNNRegressor.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowRNNRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#TensorFlowRNNRegressor.evaluate}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.TensorFlowDNNClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.TensorFlowDNNClassifier.md
index b8f451990e..16aead9ed8 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.TensorFlowDNNClassifier.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.TensorFlowDNNClassifier.md
@@ -13,6 +13,13 @@
 
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowDNNClassifier.config` {#TensorFlowDNNClassifier.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowDNNClassifier.dnn_bias_` {#TensorFlowDNNClassifier.dnn_bias_}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.QueueBase.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.QueueBase.md
index 9a8c841778..fe81a50549 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.QueueBase.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.QueueBase.md
@@ -302,3 +302,10 @@ The list of names for each component of a queue element.
 The underlying queue reference.
 
 
+- - -
+
+#### `tf.QueueBase.shapes` {#QueueBase.shapes}
+
+The list of shapes for each component of a queue element.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowClassifier.md
index fa099a4aad..39f9040f3b 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowClassifier.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowClassifier.md
@@ -13,6 +13,13 @@
 
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowClassifier.config` {#TensorFlowClassifier.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowClassifier.dnn_bias_` {#TensorFlowClassifier.dnn_bias_}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.TensorFlowRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.TensorFlowRegressor.md
index 69c99f3aa0..44b2458369 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.TensorFlowRegressor.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.TensorFlowRegressor.md
@@ -13,6 +13,13 @@
 
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowRegressor.config` {#TensorFlowRegressor.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowRegressor.dnn_bias_` {#TensorFlowRegressor.dnn_bias_}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md
index 88d52160d6..15f5f79a1d 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md
@@ -100,6 +100,13 @@ Initializes a `DNNRegressor` instance.
 
 
 
+- - -
+
+#### `tf.contrib.learn.DNNRegressor.config` {#DNNRegressor.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.DNNRegressor.dnn_bias_` {#DNNRegressor.dnn_bias_}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowLinearRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowLinearRegressor.md
index 4d994c7be6..1b378411b2 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowLinearRegressor.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowLinearRegressor.md
@@ -13,6 +13,13 @@
 
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowLinearRegressor.config` {#TensorFlowLinearRegressor.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowLinearRegressor.dnn_bias_` {#TensorFlowLinearRegressor.dnn_bias_}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRNNClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRNNClassifier.md
index 80803e9a7e..077d3336d8 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRNNClassifier.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRNNClassifier.md
@@ -58,6 +58,13 @@ Initializes a TensorFlowRNNClassifier instance.
 Returns bias of the rnn layer.
 
 
+- - -
+
+#### `tf.contrib.learn.TensorFlowRNNClassifier.config` {#TensorFlowRNNClassifier.config}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.TensorFlowRNNClassifier.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#TensorFlowRNNClassifier.evaluate}
diff --git a/tensorflow/g3doc/api_docs/python/io_ops.md b/tensorflow/g3doc/api_docs/python/io_ops.md
index 464ef6f6ae..d00ffcd51b 100644
--- a/tensorflow/g3doc/api_docs/python/io_ops.md
+++ b/tensorflow/g3doc/api_docs/python/io_ops.md
@@ -1934,6 +1934,13 @@ The list of names for each component of a queue element.
 The underlying queue reference.
 
 
+- - -
+
+#### `tf.QueueBase.shapes` {#QueueBase.shapes}
+
+The list of shapes for each component of a queue element.
+
+
 
 - - -
 
-- 
GitLab


From 459c2fed498530b794c4871892fd68d1e6834ac6 Mon Sep 17 00:00:00 2001
From: Pete Warden <pete@petewarden.com>
Date: Tue, 23 Aug 2016 08:49:14 -0700
Subject: [PATCH 035/223] Reduce memory usage and increase performance for
 convolution on iOS (#3778)

* Reduce memory usage and increase performance for convolution on iOS

* Switched to persistent memory allocation, added documentation, and make all the implementations functors

* Updated conv kernel based on review comments
---
 tensorflow/contrib/ios_examples/README.md     |   3 +
 .../benchmark.xcodeproj/project.pbxproj       |   4 +
 .../camera_example.xcodeproj/project.pbxproj  |   4 +
 .../project.pbxproj                           |   4 +
 tensorflow/contrib/makefile/Makefile          |  10 +
 tensorflow/contrib/makefile/tf_op_files.txt   |   1 +
 tensorflow/core/kernels/conv_ops.cc           |   4 +
 .../core/kernels/conv_ops_using_gemm.cc       | 623 ++++++++++++++++++
 8 files changed, 653 insertions(+)
 create mode 100644 tensorflow/core/kernels/conv_ops_using_gemm.cc

diff --git a/tensorflow/contrib/ios_examples/README.md b/tensorflow/contrib/ios_examples/README.md
index 1c29c74e51..83b1d7868a 100644
--- a/tensorflow/contrib/ios_examples/README.md
+++ b/tensorflow/contrib/ios_examples/README.md
@@ -71,6 +71,9 @@ rundown:
    inside the library are not stripped out. To the linker, they can appear
    unused because no other code references the variables, but in fact their
    constructors have the important side effect of registering the class.
+
+ - You'll need to include the Accelerate framework in the "Link Binary with
+   Libraries" build phase of your project.
  
  - C++11 support (or later) should be enabled by setting `C++ Language Dialect` to
    `GNU++11` (or `GNU++14`), and `C++ Standard Library` to `libc++`.
diff --git a/tensorflow/contrib/ios_examples/benchmark/benchmark.xcodeproj/project.pbxproj b/tensorflow/contrib/ios_examples/benchmark/benchmark.xcodeproj/project.pbxproj
index 934bafdeb9..a726698747 100644
--- a/tensorflow/contrib/ios_examples/benchmark/benchmark.xcodeproj/project.pbxproj
+++ b/tensorflow/contrib/ios_examples/benchmark/benchmark.xcodeproj/project.pbxproj
@@ -9,6 +9,7 @@
 /* Begin PBXBuildFile section */
 		590E7D881D02091F00DF5523 /* libprotobuf-lite.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 590E7D861D02091F00DF5523 /* libprotobuf-lite.a */; };
 		590E7D8A1D0209DD00DF5523 /* libprotobuf.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 590E7D871D02091F00DF5523 /* libprotobuf.a */; };
+		5993C7701D5D4E7F0048CE6A /* Accelerate.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 5993C76F1D5D4E7F0048CE6A /* Accelerate.framework */; };
 		59A3D0011CF4E68100C4259F /* AppDelegate.mm in Sources */ = {isa = PBXBuildFile; fileRef = 59A3CFF21CF4E68100C4259F /* AppDelegate.mm */; };
 		59A3D0031CF4E68100C4259F /* grace_hopper.jpg in Resources */ = {isa = PBXBuildFile; fileRef = 59A3CFF51CF4E68100C4259F /* grace_hopper.jpg */; };
 		59A3D0051CF4E68100C4259F /* imagenet_comp_graph_label_strings.txt in Resources */ = {isa = PBXBuildFile; fileRef = 59A3CFF71CF4E68100C4259F /* imagenet_comp_graph_label_strings.txt */; };
@@ -25,6 +26,7 @@
 		590E7D861D02091F00DF5523 /* libprotobuf-lite.a */ = {isa = PBXFileReference; lastKnownFileType = archive.ar; name = "libprotobuf-lite.a"; path = "../../makefile/gen/protobuf_ios/lib/libprotobuf-lite.a"; sourceTree = "<group>"; };
 		590E7D871D02091F00DF5523 /* libprotobuf.a */ = {isa = PBXFileReference; lastKnownFileType = archive.ar; name = libprotobuf.a; path = ../../makefile/gen/protobuf_ios/lib/libprotobuf.a; sourceTree = "<group>"; };
 		5911579B1CF4011C00C31E3A /* benchmark.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = benchmark.app; sourceTree = BUILT_PRODUCTS_DIR; };
+		5993C76F1D5D4E7F0048CE6A /* Accelerate.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Accelerate.framework; path = System/Library/Frameworks/Accelerate.framework; sourceTree = SDKROOT; };
 		59A3CFF11CF4E68100C4259F /* AppDelegate.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = AppDelegate.h; sourceTree = "<group>"; };
 		59A3CFF21CF4E68100C4259F /* AppDelegate.mm */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.objcpp; path = AppDelegate.mm; sourceTree = "<group>"; };
 		59A3CFF41CF4E68100C4259F /* cropped_panda.jpg */ = {isa = PBXFileReference; lastKnownFileType = image.jpeg; path = cropped_panda.jpg; sourceTree = "<group>"; };
@@ -50,6 +52,7 @@
 			isa = PBXFrameworksBuildPhase;
 			buildActionMask = 2147483647;
 			files = (
+				5993C7701D5D4E7F0048CE6A /* Accelerate.framework in Frameworks */,
 				590E7D8A1D0209DD00DF5523 /* libprotobuf.a in Frameworks */,
 				590E7D881D02091F00DF5523 /* libprotobuf-lite.a in Frameworks */,
 				59A3D0181CF4E86100C4259F /* UIKit.framework in Frameworks */,
@@ -63,6 +66,7 @@
 		591157921CF4011C00C31E3A = {
 			isa = PBXGroup;
 			children = (
+				5993C76F1D5D4E7F0048CE6A /* Accelerate.framework */,
 				590E7D861D02091F00DF5523 /* libprotobuf-lite.a */,
 				590E7D871D02091F00DF5523 /* libprotobuf.a */,
 				59A3D0171CF4E86100C4259F /* UIKit.framework */,
diff --git a/tensorflow/contrib/ios_examples/camera/camera_example.xcodeproj/project.pbxproj b/tensorflow/contrib/ios_examples/camera/camera_example.xcodeproj/project.pbxproj
index 14785cc708..451f536a7c 100644
--- a/tensorflow/contrib/ios_examples/camera/camera_example.xcodeproj/project.pbxproj
+++ b/tensorflow/contrib/ios_examples/camera/camera_example.xcodeproj/project.pbxproj
@@ -24,6 +24,7 @@
 		592FF90D18EDD0DA00C164F8 /* MainStoryboard_iPhone.storyboard in Resources */ = {isa = PBXBuildFile; fileRef = 592FF90A18EDD0DA00C164F8 /* MainStoryboard_iPhone.storyboard */; };
 		592FF92518EE240200C164F8 /* CameraExampleAppDelegate.m in Sources */ = {isa = PBXBuildFile; fileRef = 592FF92218EE240200C164F8 /* CameraExampleAppDelegate.m */; };
 		592FF92618EE240200C164F8 /* CameraExampleViewController.mm in Sources */ = {isa = PBXBuildFile; fileRef = 592FF92418EE240200C164F8 /* CameraExampleViewController.mm */; };
+		5993C7721D5D4E980048CE6A /* Accelerate.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 5993C7711D5D4E980048CE6A /* Accelerate.framework */; };
 /* End PBXBuildFile section */
 
 /* Begin PBXFileReference section */
@@ -52,6 +53,7 @@
 		592FF92218EE240200C164F8 /* CameraExampleAppDelegate.m */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.objc; path = CameraExampleAppDelegate.m; sourceTree = SOURCE_ROOT; };
 		592FF92318EE240200C164F8 /* CameraExampleViewController.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = CameraExampleViewController.h; sourceTree = SOURCE_ROOT; };
 		592FF92418EE240200C164F8 /* CameraExampleViewController.mm */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.objcpp; path = CameraExampleViewController.mm; sourceTree = SOURCE_ROOT; };
+		5993C7711D5D4E980048CE6A /* Accelerate.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Accelerate.framework; path = Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS9.3.sdk/System/Library/Frameworks/Accelerate.framework; sourceTree = DEVELOPER_DIR; };
 /* End PBXFileReference section */
 
 /* Begin PBXFrameworksBuildPhase section */
@@ -59,6 +61,7 @@
 			isa = PBXFrameworksBuildPhase;
 			buildActionMask = 2147483647;
 			files = (
+				5993C7721D5D4E980048CE6A /* Accelerate.framework in Frameworks */,
 				591D3EDF1CFFAD230059011C /* libprotobuf-lite.a in Frameworks */,
 				591D3EE01CFFAD230059011C /* libprotobuf.a in Frameworks */,
 				591D3ECF1CFF7FCE0059011C /* ImageIO.framework in Frameworks */,
@@ -103,6 +106,7 @@
 		592FF8B718ECBD7600C164F8 /* Frameworks */ = {
 			isa = PBXGroup;
 			children = (
+				5993C7711D5D4E980048CE6A /* Accelerate.framework */,
 				591D3EDD1CFFAD230059011C /* libprotobuf-lite.a */,
 				591D3EDE1CFFAD230059011C /* libprotobuf.a */,
 				591D3ECE1CFF7FCE0059011C /* ImageIO.framework */,
diff --git a/tensorflow/contrib/ios_examples/simple/tf_ios_makefile_example.xcodeproj/project.pbxproj b/tensorflow/contrib/ios_examples/simple/tf_ios_makefile_example.xcodeproj/project.pbxproj
index a81286a5b1..9e058be281 100644
--- a/tensorflow/contrib/ios_examples/simple/tf_ios_makefile_example.xcodeproj/project.pbxproj
+++ b/tensorflow/contrib/ios_examples/simple/tf_ios_makefile_example.xcodeproj/project.pbxproj
@@ -9,6 +9,7 @@
 /* Begin PBXBuildFile section */
 		590E7D881D02091F00DF5523 /* libprotobuf-lite.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 590E7D861D02091F00DF5523 /* libprotobuf-lite.a */; };
 		590E7D8A1D0209DD00DF5523 /* libprotobuf.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 590E7D871D02091F00DF5523 /* libprotobuf.a */; };
+		5993C7741D5D4EAF0048CE6A /* Accelerate.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 5993C7731D5D4EAF0048CE6A /* Accelerate.framework */; };
 		59A3D0011CF4E68100C4259F /* AppDelegate.mm in Sources */ = {isa = PBXBuildFile; fileRef = 59A3CFF21CF4E68100C4259F /* AppDelegate.mm */; };
 		59A3D0031CF4E68100C4259F /* grace_hopper.jpg in Resources */ = {isa = PBXBuildFile; fileRef = 59A3CFF51CF4E68100C4259F /* grace_hopper.jpg */; };
 		59A3D0051CF4E68100C4259F /* imagenet_comp_graph_label_strings.txt in Resources */ = {isa = PBXBuildFile; fileRef = 59A3CFF71CF4E68100C4259F /* imagenet_comp_graph_label_strings.txt */; };
@@ -25,6 +26,7 @@
 		590E7D861D02091F00DF5523 /* libprotobuf-lite.a */ = {isa = PBXFileReference; lastKnownFileType = archive.ar; name = "libprotobuf-lite.a"; path = "../../makefile/gen/protobuf_ios/lib/libprotobuf-lite.a"; sourceTree = "<group>"; };
 		590E7D871D02091F00DF5523 /* libprotobuf.a */ = {isa = PBXFileReference; lastKnownFileType = archive.ar; name = libprotobuf.a; path = ../../makefile/gen/protobuf_ios/lib/libprotobuf.a; sourceTree = "<group>"; };
 		5911579B1CF4011C00C31E3A /* tf_ios_makefile_example.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = tf_ios_makefile_example.app; sourceTree = BUILT_PRODUCTS_DIR; };
+		5993C7731D5D4EAF0048CE6A /* Accelerate.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Accelerate.framework; path = System/Library/Frameworks/Accelerate.framework; sourceTree = SDKROOT; };
 		59A3CFF11CF4E68100C4259F /* AppDelegate.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = AppDelegate.h; sourceTree = "<group>"; };
 		59A3CFF21CF4E68100C4259F /* AppDelegate.mm */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.objcpp; path = AppDelegate.mm; sourceTree = "<group>"; };
 		59A3CFF41CF4E68100C4259F /* cropped_panda.jpg */ = {isa = PBXFileReference; lastKnownFileType = image.jpeg; path = cropped_panda.jpg; sourceTree = "<group>"; };
@@ -50,6 +52,7 @@
 			isa = PBXFrameworksBuildPhase;
 			buildActionMask = 2147483647;
 			files = (
+				5993C7741D5D4EAF0048CE6A /* Accelerate.framework in Frameworks */,
 				590E7D8A1D0209DD00DF5523 /* libprotobuf.a in Frameworks */,
 				590E7D881D02091F00DF5523 /* libprotobuf-lite.a in Frameworks */,
 				59A3D0181CF4E86100C4259F /* UIKit.framework in Frameworks */,
@@ -63,6 +66,7 @@
 		591157921CF4011C00C31E3A = {
 			isa = PBXGroup;
 			children = (
+				5993C7731D5D4EAF0048CE6A /* Accelerate.framework */,
 				590E7D861D02091F00DF5523 /* libprotobuf-lite.a */,
 				590E7D871D02091F00DF5523 /* libprotobuf.a */,
 				59A3D0171CF4E86100C4259F /* UIKit.framework */,
diff --git a/tensorflow/contrib/makefile/Makefile b/tensorflow/contrib/makefile/Makefile
index e7c0488c2c..1e501f2255 100644
--- a/tensorflow/contrib/makefile/Makefile
+++ b/tensorflow/contrib/makefile/Makefile
@@ -284,6 +284,7 @@ ifeq ($(TARGET),IOS)
 		CXXFLAGS += -miphoneos-version-min=$(MIN_SDK_VERSION) \
 		-arch armv7 \
 		-D__thread= \
+		-DUSE_GEMM_FOR_CONV \
 		-Wno-c++11-narrowing \
 		-mno-thumb \
 		-DTF_LEAN_BINARY \
@@ -294,6 +295,7 @@ ifeq ($(TARGET),IOS)
 		${IPHONEOS_SYSROOT}
 		LDFLAGS := -arch armv7 \
 		-miphoneos-version-min=${MIN_SDK_VERSION} \
+		-framework Accelerate \
 		-Xlinker -S \
 		-Xlinker -x \
 		-Xlinker -dead_strip \
@@ -305,6 +307,7 @@ ifeq ($(TARGET),IOS)
 		CXXFLAGS += -miphoneos-version-min=$(MIN_SDK_VERSION) \
 		-arch armv7s \
 		-D__thread= \
+		-DUSE_GEMM_FOR_CONV \
 		-Wno-c++11-narrowing \
 		-mno-thumb \
 		-DTF_LEAN_BINARY \
@@ -315,6 +318,7 @@ ifeq ($(TARGET),IOS)
 		${IPHONEOS_SYSROOT}
 		LDFLAGS := -arch armv7s \
 		-miphoneos-version-min=${MIN_SDK_VERSION} \
+		-framework Accelerate \
 		-Xlinker -S \
 		-Xlinker -x \
 		-Xlinker -dead_strip \
@@ -326,6 +330,7 @@ ifeq ($(TARGET),IOS)
 		CXXFLAGS += -miphoneos-version-min=$(MIN_SDK_VERSION) \
 		-arch arm64 \
 		-D__thread= \
+		-DUSE_GEMM_FOR_CONV \
 		-Wno-c++11-narrowing \
 		-DTF_LEAN_BINARY \
 		-D__ANDROID_TYPES_SLIM__ \
@@ -335,6 +340,7 @@ ifeq ($(TARGET),IOS)
 		${IPHONEOS_SYSROOT}
 		LDFLAGS := -arch arm64 \
 		-miphoneos-version-min=${MIN_SDK_VERSION} \
+		-framework Accelerate \
 		-Xlinker -S \
 		-Xlinker -x \
 		-Xlinker -dead_strip \
@@ -346,6 +352,7 @@ ifeq ($(TARGET),IOS)
 		CXXFLAGS += -mios-simulator-version-min=$(MIN_SDK_VERSION) \
 		-arch i386 \
 		-D__thread= \
+		-DUSE_GEMM_FOR_CONV \
 		-Wno-c++11-narrowing \
 		-DTF_LEAN_BINARY \
 		-D__ANDROID_TYPES_SLIM__ \
@@ -355,6 +362,7 @@ ifeq ($(TARGET),IOS)
 		${IPHONESIMULATOR_SYSROOT}
 		LDFLAGS := -arch i386 \
 		-mios-simulator-version-min=${MIN_SDK_VERSION} \
+		-framework Accelerate \
 		-Xlinker -S \
 		-Xlinker -x \
 		-Xlinker -dead_strip \
@@ -366,6 +374,7 @@ ifeq ($(TARGET),IOS)
 		CXXFLAGS += -mios-simulator-version-min=$(MIN_SDK_VERSION) \
 		-arch x86_64 \
 		-D__thread= \
+		-DUSE_GEMM_FOR_CONV \
 		-Wno-c++11-narrowing \
 		-DTF_LEAN_BINARY \
 		-D__ANDROID_TYPES_SLIM__ \
@@ -375,6 +384,7 @@ ifeq ($(TARGET),IOS)
 		${IPHONESIMULATOR_SYSROOT}
 		LDFLAGS := -arch x86_64 \
 		-mios-simulator-version-min=${MIN_SDK_VERSION} \
+		-framework Accelerate \
 		-Xlinker -S \
 		-Xlinker -x \
 		-Xlinker -dead_strip \
diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt
index 4a92a2f000..d96f0e3af9 100644
--- a/tensorflow/contrib/makefile/tf_op_files.txt
+++ b/tensorflow/contrib/makefile/tf_op_files.txt
@@ -98,6 +98,7 @@ tensorflow/core/kernels/cwise_op_equal_to.cc
 tensorflow/core/kernels/cwise_op_div.cc
 tensorflow/core/kernels/cwise_op_add.cc
 tensorflow/core/kernels/ctc_decoder_ops.cc
+tensorflow/core/kernels/conv_ops_using_gemm.cc
 tensorflow/core/kernels/conv_ops.cc
 tensorflow/core/kernels/conv_grad_ops.cc
 tensorflow/core/kernels/control_flow_ops.cc
diff --git a/tensorflow/core/kernels/conv_ops.cc b/tensorflow/core/kernels/conv_ops.cc
index e2182d0ec0..86d498fe4a 100644
--- a/tensorflow/core/kernels/conv_ops.cc
+++ b/tensorflow/core/kernels/conv_ops.cc
@@ -242,8 +242,12 @@ class Conv2DOp : public BinaryOp<T> {
       Name("Conv2D").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
       Conv2DOp<CPUDevice, T>);
 
+// If we're using the alternative GEMM-based implementation of Conv2D for the
+// CPU implementation, don't register this EigenTensor-based version.
+#if !defined(USE_GEMM_FOR_CONV)
 TF_CALL_half(REGISTER_CPU);
 TF_CALL_float(REGISTER_CPU);
+#endif  // USE_GEMM_FOR_CONV
 
 // To be used inside depthwise_conv_op.cc.
 template class LaunchConv2DOp<CPUDevice, float>;
diff --git a/tensorflow/core/kernels/conv_ops_using_gemm.cc b/tensorflow/core/kernels/conv_ops_using_gemm.cc
new file mode 100644
index 0000000000..9210d772b3
--- /dev/null
+++ b/tensorflow/core/kernels/conv_ops_using_gemm.cc
@@ -0,0 +1,623 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// This file contains a set of different implementations of the two-dimensional
+// convolution operation. The standard TensorFlow Conv2d kernel uses EigenTensor
+// to implement the computation, but this module has a variety of different ways
+// of producing the same result. These methods are designed to be easier to
+// understand and connect to other libraries, so that we can take advantage of
+// platforms that have specialized implementations of GEMM for example.
+//
+// The basic interface is a Conv functor object that's templated by the types
+// of the data it will be operating on, and is passed in the arguments needed to
+// calculate the convolution. The simplest implementation of this functor is
+// ReferenceConvFunctor, which is a readable but slow reference version.
+//
+// A faster version uses the approach of packing image patches into a matrix
+// before calling a matrix multiply, the Im2ColConvFunctor. In turn, this can
+// use a variety of different methods to calculate the matrix multiplication,
+// or GEMM. The simplest but slowest is the ReferenceGemmFunctor, but the
+// FastGemmFunctor will use whatever optimized libraries are available. By
+// default it uses Eigen, but on Apple platforms it will take advantage of the
+// system's Accelerate BLAS library to get better performance than the standard
+// TensorFlow convolution kernel.
+//
+// The version actually used is defined at the bottom of this file using the
+// REGISTER_KERNEL_BUILDER() macro. To try out different implementations (for
+// example to switch to a reference one for easier debugging) you can swap out
+// the default functors in that call.
+//
+// The registration itself is guarded with the USE_GEMM_FOR_CONV macro. The iOS
+// makefile build defines this, but if you want to enable this implementation
+// and disable the standard EigenTensor one in other build setups, you'll need
+// to define it there too.
+
+#include <string.h>
+#include <map>
+#include <vector>
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/numeric_op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_slice.h"
+#include "tensorflow/core/kernels/bounds_check.h"
+#include "tensorflow/core/util/padding.h"
+#include "tensorflow/core/util/tensor_format.h"
+
+#if defined(__APPLE__)
+#include <Accelerate/Accelerate.h>
+#define USE_ACCELERATE_GEMM
+#endif  // __APPLE__
+
+namespace tensorflow {
+
+namespace {
+// This function implements the convolution operation in as simple a form as
+// possible. It won't give great performance, but it is very useful for
+// stepping through and instrumenting for debugging, creating minimal benchmarks
+// to prototype with, and sharing with teams that want to run this outside of
+// our environment.
+// With that in mind, I've avoided using anything except pretty standard C++
+// types. This is especially noticeable in the data access through raw array
+// indexing. It's deliberate in this case though, since it makes the underlying
+// memory order very explicit, which is important for both inspecting memory
+// contents during debugging and for specifying what we expect to others.
+// The memory layout of the data is, from biggest stride to smallest:
+// input_data = [input_batches, input_height, input_width, input_depth]
+// filter_data = [filter_height, filter_width, input_depth, filter_count]
+// output_data = [input_batches, output_height, output_width, filter_count]
+template <class T1, class T2, class T3>
+class ReferenceConvFunctor {
+ public:
+  void operator()(OpKernelContext* context, const T1* input_data,
+                  int input_batches, int input_height, int input_width,
+                  int input_depth, const T2* filter_data, int filter_height,
+                  int filter_width, int filter_count, int stride_rows,
+                  int stride_cols, Padding padding, T3* output_data,
+                  int output_height, int output_width) {
+    // The two different padding modes we support can be a bit confusing. SAME
+    // means we're trying to produce an output image that's the same size as the
+    // input. It's complicated by stride, which shrinks the output image by a
+    // a factor, but it means we end up sampling from outside the borders of the
+    // input. These out-of-bounds values are read as zeroes. VALID means only
+    // produce output values where the filters can read all their values from
+    // within the input image. It effectively removes the margins of the output
+    // image compared to the one produced by SAME. Stride complicates this
+    // definition though, because it can result in the right and bottom filter
+    // patches sampling from outside the borders if it's greater than 1.
+    // Most of the logic for sorting this all out is done before this function,
+    // when we calculate the output size, but the positioning of the origin of
+    // the filters is different between the two modes, since SAME positions the
+    // first filter off the edge of the input.
+    int filter_left_offset;
+    int filter_top_offset;
+    if (padding == VALID) {
+      filter_left_offset =
+          ((output_width - 1) * stride_cols + filter_width - input_width + 1) /
+          2;
+      filter_top_offset = ((output_height - 1) * stride_rows + filter_height -
+                           input_height + 1) /
+                          2;
+    } else {
+      filter_left_offset =
+          ((output_width - 1) * stride_cols + filter_width - input_width) / 2;
+      filter_top_offset =
+          ((output_height - 1) * stride_rows + filter_height - input_height) /
+          2;
+    }
+
+    // If we've got multiple images in our input, work through each of them.
+    for (int batch = 0; batch < input_batches; ++batch) {
+      // Walk through all the output image values, sliding the filter to
+      // different positions in the input.
+      for (int out_y = 0; out_y < output_height; ++out_y) {
+        for (int out_x = 0; out_x < output_width; ++out_x) {
+          // Each filter kernel produces one output channel.
+          for (int out_channel = 0; out_channel < filter_count; ++out_channel) {
+            // We're going to calculate a single output value, which means we
+            // need to multiply a three dimensional kernel of weights against
+            // the current location within the input image.
+            /*
+             *-------------------------------...
+             |\ ^
+             | \in_depth
+             |  \ v
+             |   *-------------------------------...
+             |   |            ^
+             |   |       in_y_origin
+             |   |            v   \
+             |   |<in_x_origin>*---*^
+             |   |            \|   |filter_height
+             .   |             *---*v
+             .   |             <--->
+             .         filter_width
+             .
+            */
+            const int in_x_origin = (out_x * stride_cols) - filter_left_offset;
+            const int in_y_origin = (out_y * stride_rows) - filter_top_offset;
+            T3 total(0);
+            for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
+              for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
+                for (int in_channel = 0; in_channel < input_depth;
+                     ++in_channel) {
+                  const int in_x = in_x_origin + filter_x;
+                  const int in_y = in_y_origin + filter_y;
+                  T1 input_value;
+                  // If the location is outside the bounds of the input image,
+                  // use zero as a default value.
+                  if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
+                      (in_y < input_height)) {
+                    input_value =
+                        input_data[(batch * input_height * input_width *
+                                    input_depth) +
+                                   (in_y * input_width * input_depth) +
+                                   (in_x * input_depth) + in_channel];
+                  } else {
+                    input_value = T1(0);
+                  }
+                  const T2 filter_value =
+                      filter_data[(filter_y * filter_width * input_depth *
+                                   filter_count) +
+                                  (filter_x * input_depth * filter_count) +
+                                  (in_channel * filter_count) + out_channel];
+                  total += (input_value * filter_value);
+                }
+              }
+            }
+            output_data[(batch * output_height * output_width * filter_count) +
+                        (out_y * output_width * filter_count) +
+                        (out_x * filter_count) + out_channel] = total;
+          }
+        }
+      }
+    }
+  }
+};
+
+// A readable but slow implementation of matrix multiplication, useful for
+// debugging and understanding the algorithm. Use instead of FastGemmFunctor in
+// the Im2ColConvFunctor template definition inside the op registration to
+// enable. Assumes row-major ordering of the values in memory.
+template <class T1, class T2, class T3>
+class ReferenceGemmFunctor {
+ public:
+  void operator()(size_t m, size_t n, size_t k, const T1* a, size_t lda,
+                  const T2* b, size_t ldb, T3* c, size_t ldc) {
+    const size_t a_i_stride = lda;
+    const size_t a_l_stride = 1;
+    const size_t b_j_stride = 1;
+    const size_t b_l_stride = ldb;
+    const size_t c_i_stride = ldc;
+    const size_t c_j_stride = 1;
+    size_t i, j, l;
+    for (j = 0; j < n; j++) {
+      for (i = 0; i < m; i++) {
+        T3 total(0);
+        for (l = 0; l < k; l++) {
+          const size_t a_index = ((i * a_i_stride) + (l * a_l_stride));
+          const T1 a_value = a[a_index];
+          const size_t b_index = ((j * b_j_stride) + (l * b_l_stride));
+          const T2 b_value = b[b_index];
+          total += (a_value * b_value);
+        }
+        const size_t c_index = ((i * c_i_stride) + (j * c_j_stride));
+        c[c_index] = total;
+      }
+    }
+  }
+};
+
+// Uses the optimized Eigen library to implement the matrix multiplication
+// required by the Im2ColConvFunctor class. We supply the two input and one
+// output types so that the accumulator can potentially be higher-precision than
+// the inputs, even though we don't currently take advantage of this.
+template <class T1, class T2, class T3>
+class FastGemmFunctor {
+ public:
+  // Convenience wrappers for the Eigen matrix types we'll be using.
+  typedef Eigen::Map<
+      const Eigen::Matrix<T1, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>
+      ConstMatrixT1;
+  typedef Eigen::Map<
+      const Eigen::Matrix<T2, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>
+      ConstMatrixT2;
+  typedef Eigen::Map<
+      Eigen::Matrix<T3, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>
+      MatrixT3;
+  void operator()(size_t m, size_t n, size_t k, const T1* a, size_t lda,
+                  const T2* b, size_t ldb, T3* c, size_t ldc) {
+    ConstMatrixT1 a_matrix(a, m, k);
+    ConstMatrixT2 b_matrix(b, k, n);
+    MatrixT3 c_matrix(c, m, n);
+    c_matrix.noalias() = a_matrix * b_matrix;
+  }
+};
+
+// If we have Apple's Accelerate framework, use their implementation of GEMM to
+// get a performance boost for float.
+#if defined(USE_ACCELERATE_GEMM)
+template <>
+class FastGemmFunctor<float, float, float> {
+ public:
+  void operator()(size_t m, size_t n, size_t k, const float* a, size_t lda,
+                  const float* b, size_t ldb, float* c, size_t ldc) {
+    cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, m, n, k, 1.0f, a,
+                lda, b, ldb, 0.0f, c, ldc);
+  }
+};
+#endif  // USE_ACCELERATE_GEMM
+
+// Used to keep track of persistent memory buffers used within the op.
+template <class T, size_t size>
+struct Im2ColBufferResource : public ResourceBase {
+  mutex mu;
+  T data[size];
+  string DebugString() { return "Im2ColBufferResource"; }
+};
+
+// Implements convolution as a two stage process, first packing the patches of
+// the input image into columns (im2col) and then running GEMM to produce the
+// final result.
+template <class T1, class T2, class T3, class TGemmFunctor>
+class Im2ColConvFunctor {
+ public:
+  void operator()(OpKernelContext* context, const T1* input_data,
+                  int input_batches, int input_height, int input_width,
+                  int input_depth, const T2* filter_data, int filter_height,
+                  int filter_width, int filter_count, int stride_rows,
+                  int stride_cols, Padding padding, T3* output_data,
+                  int output_height, int output_width) {
+    if ((input_batches <= 0) || (input_width <= 0) || (input_height <= 0) ||
+        (input_depth <= 0)) {
+      LOG(WARNING) << "Conv2D was called with bad input dimensions: "
+                   << input_batches << ", " << input_height << ", "
+                   << input_width << ", " << input_depth;
+      return;
+    }
+    if ((filter_width <= 0) || (filter_height <= 0) || (filter_count <= 0)) {
+      LOG(WARNING) << "Conv2D was called with bad filter dimensions: "
+                   << filter_width << ", " << filter_height << ", "
+                   << filter_count;
+      return;
+    }
+    if ((output_width <= 0) || (output_height <= 0)) {
+      LOG(WARNING) << "Conv2D was called with bad output width or height: "
+                   << output_width << ", " << output_height;
+      return;
+    }
+
+    // These calculations define how the patches will be positioned within the
+    // input image. The actual definitions are quite complex, and rely on the
+    // previously-calculated output size.
+    int filter_left_offset;
+    int filter_top_offset;
+    if (padding == VALID) {
+      filter_left_offset =
+          ((output_width - 1) * stride_cols + filter_width - input_width + 1) /
+          2;
+      filter_top_offset = ((output_height - 1) * stride_rows + filter_height -
+                           input_height + 1) /
+                          2;
+    } else {
+      filter_left_offset =
+          ((output_width - 1) * stride_cols + filter_width - input_width) / 2;
+      filter_top_offset =
+          ((output_height - 1) * stride_rows + filter_height - input_height) /
+          2;
+    }
+
+    // The im2col buffer has # of patches rows, and # of filters cols.
+    // It's laid out like this, in row major order in memory:
+    //        < filter value count >
+    //   ^   +---------------------+
+    // patch |                     |
+    // count |                     |
+    //   v   +---------------------+
+    // Each patch row contains a filter_width x filter_height patch of the
+    // input, with the depth channel as the most contiguous in memory, followed
+    // by the width, then the height. This is the standard memory order in the
+    // image world if it helps to visualize it.
+    const int filter_value_count = filter_width * filter_height * input_depth;
+    const int patch_count = input_batches * output_width * output_height;
+
+    // We don't want to allocate a buffer to hold all the patches if the size is
+    // going to be extremely large, so break it into chunks if it's bigger than
+    // a limit. Each chunk will be processed serially, so we can refill the
+    // buffer for the next chunk and reuse it, keeping maximum memory size down.
+    // In this case, we've picked 16 megabytes as a reasonable limit.
+    const size_t max_chunk_size = (16 * 1024 * 1024);
+    OP_REQUIRES(context, (filter_value_count * sizeof(T1)) <= max_chunk_size,
+                errors::InvalidArgument("Im2Col patch too large for buffer"));
+    const size_t patches_per_chunk =
+        max_chunk_size / (filter_value_count * sizeof(T1));
+    const size_t im2col_size = patches_per_chunk * filter_value_count;
+    // Because memory allocation is very expensive on mobile platforms, try to
+    // allocate a persistent buffer that will be kept around between calls. We
+    // use TensorFlow's resource management to ensure that the memory will be
+    // released when the session is over.
+    Im2ColBufferResource<T1, max_chunk_size>* im2col_buffer_resource;
+    std::function<Status(Im2ColBufferResource<T1, max_chunk_size>**)> creator =
+        [](Im2ColBufferResource<T1, max_chunk_size>** resource) {
+          *resource = new Im2ColBufferResource<T1, max_chunk_size>();
+          return Status::OK();
+        };
+    OP_REQUIRES_OK(context, context->resource_manager()->LookupOrCreate(
+                                "Conv2d", "im2col_buffer",
+                                &im2col_buffer_resource, creator));
+    // This means that multiple ops can't be run simultaneously on different
+    // threads, because we have a single shared resource. The platforms this is
+    // aimed at have intra-op parallelism as their focus though, so it shouldn't
+    // be an issue.
+    mutex_lock lock_buffer(im2col_buffer_resource->mu);
+    core::ScopedUnref unref_buffer(im2col_buffer_resource);
+    T1* im2col_buffer = im2col_buffer_resource->data;
+
+    for (int batch = 0; batch < input_batches; ++batch) {
+      const T1* input_batch_start =
+          input_data + (batch * input_height * input_width * input_depth);
+      for (int out_y = 0; out_y < output_height; ++out_y) {
+        const int in_y_origin = (out_y * stride_rows) - filter_top_offset;
+        for (int out_x = 0; out_x < output_width; ++out_x) {
+          const int in_x_origin = (out_x * stride_cols) - filter_left_offset;
+          const int patch_index = (batch * output_width * output_height) +
+                                  (out_y * output_width) + out_x;
+          const int patch_index_within_chunk = patch_index % patches_per_chunk;
+          T1* im2col_patch_start =
+              im2col_buffer + (patch_index_within_chunk * filter_value_count);
+          for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
+            const int in_y = in_y_origin + filter_y;
+            T1* im2col_row_start =
+                im2col_patch_start + (filter_y * filter_width * input_depth);
+            // If we're off the top or the bottom of the input, fill the whole
+            // row with zeroes.
+            if ((in_y < 0) || (in_y >= input_height)) {
+              T1* im2col_row_end =
+                  im2col_row_start + (filter_width * input_depth);
+              std::fill(im2col_row_start, im2col_row_end, T1(0));
+            } else {
+              // What we're doing here is trying to copy and fill the im2col
+              // buffer as efficiently as possible, using functions to set or
+              // duplicate values en masse. We know we don't have to worry about
+              // vertical edges because we dealt with that case above, so we
+              // just need to handle filters that overlap the left or right
+              // edges. Here's what that looks like:
+              //
+              // < left_zero_count > < center_copy_count > < right_zero_count >
+              // +------------------+---------------------+--------------------+
+              // |     (filter)     |       (image)       |      (filter)      |
+              // +------------------+---------------------+--------------------+
+              // in_x_origin        0                 input_width       in_x_end
+              //
+              // In reality it's unlikely that a filter patch will be wider
+              // than an input, but this shows all the edge cases.
+              // We use std::fill() to set the left and right sections to zeroes
+              // and std::copy() to copy over the input data for the center.
+              const int in_x_end = in_x_origin + filter_width;
+              const int left_zero_count = std::max(0, 0 - in_x_origin);
+              const int right_zero_count = std::max(0, in_x_end - input_width);
+              const int center_copy_count =
+                  filter_width - (left_zero_count + right_zero_count);
+              if (left_zero_count > 0) {
+                T1* im2col_left_start = im2col_row_start;
+                T1* im2col_left_end =
+                    im2col_left_start + (left_zero_count * input_depth);
+                std::fill(im2col_left_start, im2col_left_end, T1(0));
+              }
+              if (center_copy_count > 0) {
+                const T1* input_row_start =
+                    input_batch_start + (in_y * input_width * input_depth) +
+                    (std::max(0, in_x_origin) * input_depth);
+                const T1* input_row_end =
+                    input_row_start + (center_copy_count * input_depth);
+                T1* im2col_center_start =
+                    im2col_row_start + (left_zero_count * input_depth);
+                std::copy(input_row_start, input_row_end, im2col_center_start);
+              }
+              if (right_zero_count > 0) {
+                T1* im2col_right_start =
+                    im2col_row_start +
+                    ((left_zero_count + center_copy_count) * input_depth);
+                T1* im2col_right_end =
+                    im2col_right_start + (right_zero_count * input_depth);
+                std::fill(im2col_right_start, im2col_right_end, T1(0));
+              }
+            }
+          }
+          const bool is_last_in_chunk =
+              (patch_index_within_chunk == (patches_per_chunk - 1));
+          const bool is_last_overall =
+              ((batch == (input_batches - 1)) &&
+               (out_y == (output_height - 1)) && (out_x == (output_width - 1)));
+          if (is_last_in_chunk || is_last_overall) {
+            // Now we've assembled a set of image patches into a matrix, apply a
+            // GEMM matrix multiply of the patches as rows, times the filter
+            // weights in columns, to get partial results in the output matrix.
+            const int how_many_patches = patch_index_within_chunk + 1;
+            const int m = how_many_patches;
+            const int n = filter_count;
+            const int k = filter_value_count;
+            const int lda = filter_value_count;
+            const int ldb = filter_count;
+            const int ldc = filter_count;
+            const size_t start_patch_index =
+                patch_index - (how_many_patches - 1);
+            T3* chunk_output_data =
+                output_data + (start_patch_index * filter_count);
+            TGemmFunctor gemm_functor;
+            gemm_functor(m, n, k, im2col_buffer, lda, filter_data, ldb,
+                         chunk_output_data, ldc);
+          }
+        }
+      }
+    }
+  }
+};
+
+}  // namespace
+
+// This TensorFlow kernel class handles all of the IO and housekeeping for the
+// functors that actually implement the underlying algorithm. To swap in
+// different implementations of the main calculations, use a different
+// TConvFunctor parameter when instantiating the template.
+template <class T, class TConvFunctor>
+class Conv2DUsingGemmOp : public BinaryOp<T> {
+ public:
+  explicit Conv2DUsingGemmOp(OpKernelConstruction* context)
+      : BinaryOp<T>(context) {
+    OP_REQUIRES_OK(context, context->GetAttr("strides", &strides_));
+    string data_format;
+    OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format));
+    OP_REQUIRES(context, FormatFromString(data_format, &data_format_),
+                errors::InvalidArgument("Invalid data format"));
+    OP_REQUIRES(context, data_format_ == FORMAT_NHWC,
+                errors::InvalidArgument(
+                    "Data format not supported by this kernel", data_format));
+    OP_REQUIRES(context, strides_.size() == 4,
+                errors::InvalidArgument("Sliding window strides field must "
+                                        "specify 4 dimensions"));
+    const int64 stride_n = GetTensorDim(strides_, data_format_, 'N');
+    const int64 stride_c = GetTensorDim(strides_, data_format_, 'C');
+    OP_REQUIRES(
+        context, stride_n == 1 && stride_c == 1,
+        errors::InvalidArgument("Current implementation does not yet support "
+                                "strides in the batch and depth dimensions."));
+    OP_REQUIRES_OK(context, context->GetAttr("padding", &padding_));
+  }
+
+  void Compute(OpKernelContext* context) override {
+    // Input tensor is of the following dimensions:
+    // [ batch, in_rows, in_cols, in_depth ]
+    const Tensor& input = context->input(0);
+
+    // Input filter is of the following dimensions:
+    // [ filter_rows, filter_cols, in_depth, out_depth]
+    const Tensor& filter = context->input(1);
+
+    // For 2D convolution, there should be 4 dimensions.
+    OP_REQUIRES(context, input.dims() == 4,
+                errors::InvalidArgument("input must be 4-dimensional",
+                                        input.shape().DebugString()));
+    OP_REQUIRES(context, filter.dims() == 4,
+                errors::InvalidArgument("filter must be 4-dimensional: ",
+                                        filter.shape().DebugString()));
+
+    for (int i = 0; i < 3; i++) {
+      OP_REQUIRES(context, FastBoundsCheck(filter.dim_size(i),
+                                           std::numeric_limits<int>::max()),
+                  errors::InvalidArgument("filter too large"));
+    }
+
+    // The last dimension for input is in_depth. It must be the same as the
+    // filter's in_depth.
+    const int64 in_depth = GetTensorDim(input, data_format_, 'C');
+    OP_REQUIRES(
+        context, in_depth == filter.dim_size(2),
+        errors::InvalidArgument("input and filter must have the same depth: ",
+                                in_depth, " vs ", filter.dim_size(2)));
+
+    // The last dimension for filter is out_depth.
+    const int out_depth = static_cast<int>(filter.dim_size(3));
+
+    // The second dimension for input is rows/height.
+    // The first dimension for filter is rows/height.
+    const int64 input_rows_raw = GetTensorDim(input, data_format_, 'H');
+    OP_REQUIRES(context, FastBoundsCheck(input_rows_raw,
+                                         std::numeric_limits<int>::max()),
+                errors::InvalidArgument("Input rows too large"));
+    const int input_rows = static_cast<int>(input_rows_raw);
+    const int filter_rows = static_cast<int>(filter.dim_size(0));
+
+    // The third dimension for input is columns/width.
+    // The second dimension for filter is columns/width.
+    const int64 input_cols_raw = GetTensorDim(input, data_format_, 'W');
+    OP_REQUIRES(context, FastBoundsCheck(input_cols_raw,
+                                         std::numeric_limits<int>::max()),
+                errors::InvalidArgument("Input cols too large"));
+    const int input_cols = static_cast<int>(input_cols_raw);
+    const int filter_cols = static_cast<int>(filter.dim_size(1));
+
+    // The first dimension for input is batch.
+    const int64 batch_raw = GetTensorDim(input, data_format_, 'N');
+    OP_REQUIRES(context,
+                FastBoundsCheck(batch_raw, std::numeric_limits<int>::max()),
+                errors::InvalidArgument("batch is too large"));
+    const int batch = static_cast<int>(batch_raw);
+
+    // For now we take the stride from the second and third dimensions only (we
+    // do not support striding on the batch or depth dimension).
+    const int stride_rows = GetTensorDim(strides_, data_format_, 'H');
+    const int stride_cols = GetTensorDim(strides_, data_format_, 'W');
+
+    int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0;
+    OP_REQUIRES_OK(context,
+                   GetWindowedOutputSize(input_rows, filter_rows, stride_rows,
+                                         padding_, &out_rows, &pad_rows));
+    OP_REQUIRES_OK(context,
+                   GetWindowedOutputSize(input_cols, filter_cols, stride_cols,
+                                         padding_, &out_cols, &pad_cols));
+    TensorShape out_shape =
+        ShapeFromFormat(data_format_, batch, out_rows, out_cols, out_depth);
+
+    // Output tensor is of the following dimensions:
+    // [ in_batch, out_rows, out_cols, out_depth ]
+    Tensor* output = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &output));
+
+    VLOG(2) << "Conv2D: in_depth = " << in_depth
+            << ", input_cols = " << input_cols
+            << ", filter_cols = " << filter_cols
+            << ", input_rows = " << input_rows
+            << ", filter_rows = " << filter_rows
+            << ", stride_rows = " << stride_rows
+            << ", stride_cols = " << stride_cols
+            << ", out_depth = " << out_depth;
+
+    // If there is nothing to compute, return.
+    if (out_shape.num_elements() == 0) {
+      return;
+    }
+    TConvFunctor conv_functor;
+    conv_functor(context, input.flat<T>().data(), batch, input_rows, input_cols,
+                 in_depth, filter.flat<T>().data(), filter_rows, filter_cols,
+                 out_depth, stride_rows, stride_cols, padding_,
+                 output->flat<T>().data(), out_rows, out_cols);
+  }
+
+ private:
+  std::vector<int32> strides_;
+  Padding padding_;
+  TensorFormat data_format_;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(Conv2DUsingGemmOp);
+};
+
+#define REGISTER_CPU(T)                                         \
+  REGISTER_KERNEL_BUILDER(                                      \
+      Name("Conv2D").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      Conv2DUsingGemmOp<                                        \
+          T, Im2ColConvFunctor<T, T, T, FastGemmFunctor<T, T, T>>>);
+
+// Only register this GEMM-based implementation of Conv2d if the compiler flags
+// request the implementation explicitly, since otherwise it will clash with the
+// default EigenTensor-based kernel.
+#if defined(USE_GEMM_FOR_CONV)
+TF_CALL_half(REGISTER_CPU);
+TF_CALL_float(REGISTER_CPU);
+#endif  // USE_GEMM_FOR_CONV
+
+}  // namespace tensorflow
-- 
GitLab


From 6dcb7d09201fafdb967a8b75ab3ed31705780e93 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 07:47:44 -0800
Subject: [PATCH 036/223] Improve error reporting for invalid schedule values.
 Change: 131059518

---
 .../contrib/learn/python/learn/learn_runner.py  | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/learn_runner.py b/tensorflow/contrib/learn/python/learn/learn_runner.py
index 331ca8df93..65390af4c7 100644
--- a/tensorflow/contrib/learn/python/learn/learn_runner.py
+++ b/tensorflow/contrib/learn/python/learn/learn_runner.py
@@ -74,19 +74,20 @@ def run(experiment_fn, output_dir, schedule):
   # Execute the schedule
   if not hasattr(experiment, schedule):
     logging.error('Schedule references non-existent task %s', schedule)
-    valid_tasks = [x for x in experiment.__dict__
-                   if callable(getattr(experiment, x))]
+    valid_tasks = _valid_tasks(experiment)
     logging.error('Allowed values for this experiment are: %s', valid_tasks)
-    raise ValueError('Schedule references non-existent task %s', schedule)
+    raise ValueError('Schedule references non-existent task %s' % schedule)
 
   task = getattr(experiment, schedule)
   if not callable(task):
     logging.error('Schedule references non-callable member %s', schedule)
-    valid_tasks = [
-        x for x in experiment.__dict__
-        if callable(getattr(experiment, x)) and not x.startswith('_')
-    ]
+    valid_tasks = _valid_tasks(experiment)
     logging.error('Allowed values for this experiment are: %s', valid_tasks)
-    raise TypeError('Schedule references non-callable member %s', schedule)
+    raise TypeError('Schedule references non-callable member %s' % schedule)
 
   return task()
+
+
+def _valid_tasks(experiment):
+  return [x for x in dir(experiment)
+          if callable(getattr(experiment, x)) and not x.startswith('_')]
-- 
GitLab


From 08dbd40c9f278bca8b07b8733c019aba813e9651 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 08:16:25 -0800
Subject: [PATCH 037/223] Automated rollback of change 131059518 Change:
 131061856

---
 .../contrib/learn/python/learn/learn_runner.py  | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/learn_runner.py b/tensorflow/contrib/learn/python/learn/learn_runner.py
index 65390af4c7..331ca8df93 100644
--- a/tensorflow/contrib/learn/python/learn/learn_runner.py
+++ b/tensorflow/contrib/learn/python/learn/learn_runner.py
@@ -74,20 +74,19 @@ def run(experiment_fn, output_dir, schedule):
   # Execute the schedule
   if not hasattr(experiment, schedule):
     logging.error('Schedule references non-existent task %s', schedule)
-    valid_tasks = _valid_tasks(experiment)
+    valid_tasks = [x for x in experiment.__dict__
+                   if callable(getattr(experiment, x))]
     logging.error('Allowed values for this experiment are: %s', valid_tasks)
-    raise ValueError('Schedule references non-existent task %s' % schedule)
+    raise ValueError('Schedule references non-existent task %s', schedule)
 
   task = getattr(experiment, schedule)
   if not callable(task):
     logging.error('Schedule references non-callable member %s', schedule)
-    valid_tasks = _valid_tasks(experiment)
+    valid_tasks = [
+        x for x in experiment.__dict__
+        if callable(getattr(experiment, x)) and not x.startswith('_')
+    ]
     logging.error('Allowed values for this experiment are: %s', valid_tasks)
-    raise TypeError('Schedule references non-callable member %s' % schedule)
+    raise TypeError('Schedule references non-callable member %s', schedule)
 
   return task()
-
-
-def _valid_tasks(experiment):
-  return [x for x in dir(experiment)
-          if callable(getattr(experiment, x)) and not x.startswith('_')]
-- 
GitLab


From 096069687c52e16eaa18c1db6e7bbf2737639257 Mon Sep 17 00:00:00 2001
From: Yuan Yu <yuanbyu@google.com>
Date: Tue, 23 Aug 2016 08:20:28 -0800
Subject: [PATCH 038/223] Fix a bug introduced in cl/130951050. Second attempt
 to make sure that no control edges by an outer control dependency context are
 added to nodes inside cond/while. Change: 131062259

---
 .../kernel_tests/control_flow_ops_py_test.py  | 24 ++++++++++++++++---
 tensorflow/python/ops/control_flow_ops.py     | 14 +++++++----
 2 files changed, 31 insertions(+), 7 deletions(-)

diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
index f221387a1b..c4a99d5d9a 100644
--- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
+++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
@@ -685,7 +685,7 @@ class ControlFlowTest(tf.test.TestCase):
     self._testNestedWhile_1(use_gpu=True)
 
   def _testNestedWhile_2(self, use_gpu):
-    """ Test the cases that A -> Enter and Exit -> A are partitioned."""
+    # Test the cases that A -> Enter and Exit -> A are partitioned.
     with self.test_session(use_gpu=use_gpu):
       s0 = tf.constant(2.0)
       def inner_loop(s):
@@ -722,8 +722,7 @@ class ControlFlowTest(tf.test.TestCase):
           r_ = tf.constant(12)
         return [n_, r_]
 
-      res = tf.while_loop(condition, body, [n, r],
-                          parallel_iterations=1)
+      res = tf.while_loop(condition, body, [n, r], parallel_iterations=1)
       self.assertAllEqual(12, res[1].eval())
 
   def testWhileWithControl_2(self):
@@ -770,6 +769,25 @@ class ControlFlowTest(tf.test.TestCase):
         loop = tf.while_loop(cond, body, (tf.constant(5),))
       self.assertEqual(0, sess.run(loop))
 
+  def testWhileCondExitControl(self):
+    with self.test_session():
+      v = tf.Variable(1)
+      def false_branch():
+        cond = lambda i: i < 100
+        def body(i):
+          x = tf.assign(v, i)
+          return x + 1
+        loop = tf.while_loop(cond, body, [0])
+        # Make sure to handle correctly control edge from Exit to a node.
+        with tf.control_dependencies([loop]):
+          return tf.constant(6.0)
+      r = tf.cond(tf.constant(False),
+                  lambda: tf.constant(1.0),
+                  false_branch)
+      tf.initialize_all_variables().run()
+      self.assertEqual(6.0, r.eval())
+      self.assertEqual(99, v.eval())
+
   def testCondWhile_1(self):
     with self.test_session():
       n = tf.convert_to_tensor(0, name="n")
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 1bd6481852..15a780d767 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -432,6 +432,14 @@ def _IsLoopExit(op):
   return op.type == "Exit" or op.type == "RefExit"
 
 
+def _GetOutputContext(op):
+  """Return the control flow context for the output of an op."""
+  ctxt = op._get_control_flow_context()
+  if _IsLoopExit(op):
+    ctxt = ctxt.outer_context
+  return ctxt
+
+
 def _ShapeIntersection(shape1, shape2):
   if shape1.dims is None or shape1.ndims != shape2.ndims:
     return tensor_shape.unknown_shape()
@@ -665,9 +673,7 @@ class GradLoopState(object):
 
       # Add the stack_push op in the context of value.op.
       swap_enabled = self.forward_context.swap_memory
-      value_ctxt = value.op._get_control_flow_context()
-      if _IsLoopExit(value.op):
-        value_ctxt = value_ctxt.outer_context
+      value_ctxt = _GetOutputContext(value.op)
       if value_ctxt == self.forward_context:
         # value is not nested in the forward context.
         self.forward_context.Enter()
@@ -1153,7 +1159,7 @@ class ControlFlowContext(object):
   def _MaybeRemoveExternalControlEdges(self, op):
     """Remove any external control dependency on this op."""
     internal_control_inputs = [x for x in op.control_inputs
-                               if x._get_control_flow_context() == self]
+                               if _GetOutputContext(x) == self]
     if len(internal_control_inputs) != len(op.control_inputs):
       del op.control_inputs[:]
       op._add_control_inputs(internal_control_inputs)
-- 
GitLab


From 783c52edeb3c676937dbb97ed0d40958015050d6 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Tue, 23 Aug 2016 09:01:25 -0800
Subject: [PATCH 039/223] Initial version of the Go API. The API is subject to
 change.

Remaining work to do:
- Generated ops.
- Generated protocol buffers.
- A few calls requiring protocol buffers aren't in this change.
Change: 131066649
---
 tensorflow/BUILD                      |   3 +
 tensorflow/core/framework/types.proto |   2 +
 tensorflow/go/BUILD                   |  22 +++
 tensorflow/go/doc.go                  |  18 ++
 tensorflow/go/graph.go                |  38 ++++
 tensorflow/go/lib.go                  |  19 ++
 tensorflow/go/operation.go            |  82 ++++++++
 tensorflow/go/session.go              | 187 +++++++++++++++++++
 tensorflow/go/session_test.go         | 114 ++++++++++++
 tensorflow/go/status.go               |  65 +++++++
 tensorflow/go/tensor.go               | 259 ++++++++++++++++++++++++++
 tensorflow/go/tensor_test.go          |  97 ++++++++++
 12 files changed, 906 insertions(+)
 create mode 100644 tensorflow/go/BUILD
 create mode 100644 tensorflow/go/doc.go
 create mode 100644 tensorflow/go/graph.go
 create mode 100644 tensorflow/go/lib.go
 create mode 100644 tensorflow/go/operation.go
 create mode 100644 tensorflow/go/session.go
 create mode 100644 tensorflow/go/session_test.go
 create mode 100644 tensorflow/go/status.go
 create mode 100644 tensorflow/go/tensor.go
 create mode 100644 tensorflow/go/tensor_test.go

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 2ab4f5a3a9..5fcdb7aa56 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -130,6 +130,7 @@ filegroup(
         "//tensorflow/examples/tutorials/word2vec:all_files",
         "//tensorflow/g3doc/how_tos/adding_an_op:all_files",
         "//tensorflow/g3doc/tutorials:all_files",
+        "//tensorflow/go:all_files",
         "//tensorflow/models/embedding:all_files",
         "//tensorflow/models/image/alexnet:all_files",
         "//tensorflow/models/image/cifar10:all_files",
@@ -167,6 +168,7 @@ cc_binary(
     name = "libtensorflow.so",
     linkshared = 1,
     deps = [
+        "//tensorflow/c:c_api",
         "//tensorflow/core:tensorflow",
     ],
 )
@@ -175,6 +177,7 @@ cc_binary(
     name = "libtensorflow_cc.so",
     linkshared = 1,
     deps = [
+        "//tensorflow/c:c_api",
         "//tensorflow/cc:cc_ops",
         "//tensorflow/core:tensorflow",
     ],
diff --git a/tensorflow/core/framework/types.proto b/tensorflow/core/framework/types.proto
index 051361bbed..c744594a49 100644
--- a/tensorflow/core/framework/types.proto
+++ b/tensorflow/core/framework/types.proto
@@ -6,6 +6,7 @@ option java_outer_classname = "TypesProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
 
+// LINT.IfChange
 enum DataType {
   // Not a legal value for DataType.  Used to indicate a DataType field
   // has not been set.
@@ -58,3 +59,4 @@ enum DataType {
   DT_COMPLEX128_REF = 118;
   DT_HALF_REF = 119;
 }
+// LINT.ThenChange(https://www.tensorflow.org/code/tensorflow/c/c_api.h,https://www.tensorflow.org/code/tensorflow/go/tensor.go)
diff --git a/tensorflow/go/BUILD b/tensorflow/go/BUILD
new file mode 100644
index 0000000000..d69233f4fe
--- /dev/null
+++ b/tensorflow/go/BUILD
@@ -0,0 +1,22 @@
+# Description:
+# Go API for TensorFlow.
+
+package(
+    default_visibility = ["//visibility:private"],
+)
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
diff --git a/tensorflow/go/doc.go b/tensorflow/go/doc.go
new file mode 100644
index 0000000000..4494d49556
--- /dev/null
+++ b/tensorflow/go/doc.go
@@ -0,0 +1,18 @@
+// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// Package tensorflow is a Go binding to TensorFlow.
+//
+// The API is subject to change and may break at any time.
+package tensorflow
diff --git a/tensorflow/go/graph.go b/tensorflow/go/graph.go
new file mode 100644
index 0000000000..3e43a39817
--- /dev/null
+++ b/tensorflow/go/graph.go
@@ -0,0 +1,38 @@
+// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+package tensorflow
+
+// #include "tensorflow/c/c_api.h"
+import "C"
+
+import (
+	"runtime"
+)
+
+// Graph represents a computation graph. Graphs may be shared between sessions.
+type Graph struct {
+	c *C.TF_Graph
+}
+
+// NewGraph returns a new Graph.
+func NewGraph() *Graph {
+	g := &Graph{C.TF_NewGraph()}
+	runtime.SetFinalizer(g, (*Graph).finalizer)
+	return g
+}
+
+func (g *Graph) finalizer() {
+	C.TF_DeleteGraph(g.c)
+}
diff --git a/tensorflow/go/lib.go b/tensorflow/go/lib.go
new file mode 100644
index 0000000000..dcab7a90f8
--- /dev/null
+++ b/tensorflow/go/lib.go
@@ -0,0 +1,19 @@
+// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+package tensorflow
+
+// #cgo LDFLAGS: -ltensorflow
+// #cgo CFLAGS: -I${SRCDIR}/../../
+import "C"
diff --git a/tensorflow/go/operation.go b/tensorflow/go/operation.go
new file mode 100644
index 0000000000..19eb1a5816
--- /dev/null
+++ b/tensorflow/go/operation.go
@@ -0,0 +1,82 @@
+// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+package tensorflow
+
+// #include <stdlib.h>
+// #include "tensorflow/c/c_api.h"
+import "C"
+import "unsafe"
+
+// Operation that has been added to the graph.
+type Operation struct {
+	c *C.TF_Operation
+}
+
+// Port represents a specific input or output of an operation, e.g. to specify
+// the specific output to pass as an input to a new op.
+//
+// Note the difference in naming convention: Port corresponds to Tensor/Output
+// in the Python API.
+type Port struct {
+	Op    *Operation
+	Index int
+}
+
+func (p *Port) c() C.TF_Port {
+	return C.TF_Port{oper: p.Op.c, index: C.int(p.Index)}
+}
+
+// opBuilder is for use by the generated op code to create new Operations.
+// Build() must be called for any in-progress Operation, or else we leak.
+type opBuilder struct {
+	c *C.TF_OperationDescription
+}
+
+func newOpBuilder(g *Graph, typ string, name string) *opBuilder {
+	opType := C.CString(typ)
+	opName := C.CString(name)
+	b := &opBuilder{c: C.TF_NewOperation(g.c, opType, opName)}
+	C.free(unsafe.Pointer(opType))
+	C.free(unsafe.Pointer(opName))
+	return b
+}
+
+func (b *opBuilder) SetAttrTensor(name string, t *Tensor) error {
+	status := newStatus()
+	attrName := C.CString(name)
+	C.TF_SetAttrTensor(b.c, attrName, t.c(), status.c)
+	C.free(unsafe.Pointer(attrName))
+	return status.Err()
+}
+
+func (b *opBuilder) SetAttrType(name string, typ DataType) {
+	attrName := C.CString(name)
+	C.TF_SetAttrType(b.c, attrName, C.TF_DataType(typ))
+	C.free(unsafe.Pointer(attrName))
+}
+
+func (b *opBuilder) AddInput(port Port) {
+	C.TF_AddInput(b.c, port.c())
+}
+
+func (b *opBuilder) Build() (*Operation, error) {
+	status := newStatus()
+	op := &Operation{c: C.TF_FinishOperation(b.c, status.c)}
+	if err := status.Err(); err != nil {
+		return nil, err
+	}
+	b.c = nil
+	return op, nil
+}
diff --git a/tensorflow/go/session.go b/tensorflow/go/session.go
new file mode 100644
index 0000000000..98a87602d1
--- /dev/null
+++ b/tensorflow/go/session.go
@@ -0,0 +1,187 @@
+// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+package tensorflow
+
+// #include <stdlib.h>
+// #include "tensorflow/c/c_api.h"
+import "C"
+
+import (
+	"errors"
+	"runtime"
+	"sync"
+	"unsafe"
+)
+
+// Session drives a TensorFlow graph computation.
+//
+// When a Session is created with a given target, a new Session object is bound
+// to the universe of resources specified by that target. Those resources are
+// available to this session to perform computation described in the GraphDef.
+// After creating the session with a graph, the caller uses the Run() API to
+// perform the computation and potentially fetch outputs as Tensors.
+// A Session allows concurrent calls to Run().
+type Session struct {
+	c *C.TF_SessionWithGraph
+
+	// For ensuring that:
+	// - Close() blocks on all Run() calls to complete.
+	// - Close() can be called multiple times.
+	wg sync.WaitGroup
+	mu sync.Mutex
+}
+
+// NewSession creates a new execution session with the associated graph.
+// options may be nil to use the default options.
+func NewSession(graph *Graph, options *SessionOptions) (*Session, error) {
+	status := newStatus()
+	cOpt := options.c()
+	cSess := C.TF_NewSessionWithGraph(graph.c, cOpt, status.c)
+	C.TF_DeleteSessionOptions(cOpt)
+	if err := status.Err(); err != nil {
+		return nil, err
+	}
+
+	s := &Session{c: cSess}
+	runtime.SetFinalizer(s, func(s *Session) { s.Close() })
+	return s, nil
+}
+
+// Run the graph with the associated session starting with the supplied inputs.
+// inputs and outputs may be set to nil. Runs, but does not return Tensors
+// for operations specified in targets.
+//
+// On success, returns the Tensor outputs in the same order as supplied in
+// the outputs argument. If outputs is set to nil, the returned Tensor outputs
+// is empty.
+func (s *Session) Run(inputs map[Port]*Tensor, outputs []Port, targets []*Operation) ([]*Tensor, error) {
+	s.mu.Lock()
+	if s.c == nil {
+		s.mu.Unlock()
+		return nil, errors.New("session is closed")
+	}
+	s.wg.Add(1)
+	s.mu.Unlock()
+	defer s.wg.Done()
+
+	var inputPorts []C.TF_Port
+	var inputValues []*C.TF_Tensor
+	if inputs != nil {
+		for port, tensor := range inputs {
+			inputPorts = append(inputPorts, port.c())
+			inputValues = append(inputValues, tensor.c())
+		}
+	}
+
+	var outputPorts []C.TF_Port
+	for _, port := range outputs {
+		outputPorts = append(outputPorts, port.c())
+	}
+	outputValues := make([]*C.TF_Tensor, len(outputs))
+	var cTargets []*C.TF_Operation
+	for _, target := range targets {
+		cTargets = append(cTargets, target.c)
+	}
+
+	status := newStatus()
+	var inputPortsPtr *C.TF_Port
+	var inputValuesPtr **C.TF_Tensor
+	if len(inputPorts) > 0 {
+		inputPortsPtr = &inputPorts[0]
+		inputValuesPtr = &inputValues[0]
+	}
+
+	var outputPortsPtr *C.TF_Port
+	var outputValuesPtr **C.TF_Tensor
+	if len(outputPorts) > 0 {
+		outputPortsPtr = &outputPorts[0]
+		outputValuesPtr = &outputValues[0]
+	}
+
+	var cTargetsPtr **C.TF_Operation
+	if len(cTargets) > 0 {
+		cTargetsPtr = &cTargets[0]
+	}
+
+	C.TF_SessionRun(s.c, nil, inputPortsPtr, inputValuesPtr, C.int(len(inputPorts)), outputPortsPtr, outputValuesPtr, C.int(len(outputPorts)), cTargetsPtr, C.int(len(cTargets)), nil, status.c)
+	if err := status.Err(); err != nil {
+		return nil, err
+	}
+
+	var tensors []*Tensor
+	for _, val := range outputValues {
+		tensors = append(tensors, newTensorFromC(val))
+		C.TF_DeleteTensor(val)
+	}
+
+	return tensors, nil
+}
+
+// Close a session. This contacts any other processes associated with this
+// session, if applicable. Blocks until all previous calls to Run have returned.
+func (s *Session) Close() error {
+	s.mu.Lock()
+	defer s.mu.Unlock()
+	s.wg.Wait()
+	if s.c == nil {
+		return nil
+	}
+	status := newStatus()
+	C.TF_CloseSessionWithGraph(s.c, status.c)
+	if err := status.Err(); err != nil {
+		return err
+	}
+	C.TF_DeleteSessionWithGraph(s.c, status.c)
+	s.c = nil
+	return status.Err()
+}
+
+// SessionOptions contains configuration information for a session.
+type SessionOptions struct {
+	// Target indicates the TensorFlow runtime to connect to.
+	//
+	// If 'target' is empty or unspecified, the local TensorFlow runtime
+	// implementation will be used.  Otherwise, the TensorFlow engine
+	// defined by 'target' will be used to perform all computations.
+	//
+	// "target" can be either a single entry or a comma separated list
+	// of entries. Each entry is a resolvable address of one of the
+	// following formats:
+	//   local
+	//   ip:port
+	//   host:port
+	//   ... other system-specific formats to identify tasks and jobs ...
+	//
+	// NOTE: at the moment 'local' maps to an in-process service-based
+	// runtime.
+	//
+	// Upon creation, a single session affines itself to one of the
+	// remote processes, with possible load balancing choices when the
+	// "target" resolves to a list of possible processes.
+	//
+	// If the session disconnects from the remote process during its
+	// lifetime, session calls may fail immediately.
+	Target string
+}
+
+// c converts the SessionOptions to the C API's TF_SessionOptions. Callers must
+// deallocate by calling C.TF_DeleteSessionOptions().
+func (o *SessionOptions) c() *C.TF_SessionOptions {
+	opt := C.TF_NewSessionOptions()
+	t := C.CString(o.Target)
+	C.TF_SetTarget(opt, t)
+	C.free(unsafe.Pointer(t))
+	return opt
+}
diff --git a/tensorflow/go/session_test.go b/tensorflow/go/session_test.go
new file mode 100644
index 0000000000..78f6bccfd6
--- /dev/null
+++ b/tensorflow/go/session_test.go
@@ -0,0 +1,114 @@
+// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+package tensorflow
+
+import (
+	"reflect"
+	"testing"
+)
+
+func Placeholder(g *Graph, name string, dt DataType) (Port, error) {
+	b := newOpBuilder(g, "Placeholder", name)
+	b.SetAttrType("dtype", dt)
+	op, err := b.Build()
+	if err != nil {
+		return Port{}, err
+	}
+	return Port{op, 0}, nil
+}
+
+func Neg(g *Graph, name string, port Port) (Port, error) {
+	b := newOpBuilder(g, "Neg", name)
+	b.AddInput(port)
+	op, err := b.Build()
+	if err != nil {
+		return Port{}, err
+	}
+	return Port{op, 0}, nil
+}
+
+func createTestGraph(t *testing.T, dt DataType) (*Graph, Port, Port) {
+	g := NewGraph()
+	inp, err := Placeholder(g, "p1", dt)
+	if err != nil {
+		t.Fatalf("Placeholder() for %v: %v", dt, err)
+	}
+	out, err := Neg(g, "neg1", inp)
+	if err != nil {
+		t.Fatalf("Neg() for %v: %v", dt, err)
+	}
+	return g, inp, out
+}
+
+func TestSessionRunNeg(t *testing.T) {
+	var tests = []struct {
+		input    interface{}
+		expected interface{}
+	}{
+		{int64(1), int64(-1)},
+		{[]float64{-1, -2, 3}, []float64{1, 2, -3}},
+		{[][]float32{{1, -2}, {-3, 4}}, [][]float32{{-1, 2}, {3, -4}}},
+	}
+
+	for _, test := range tests {
+		t1, err := NewTensor(test.input)
+		if err != nil {
+			t.Fatalf("NewTensor(%v): %v", test.input, err)
+		}
+		graph, inp, out := createTestGraph(t, t1.DataType())
+		s, err := NewSession(graph, &SessionOptions{})
+		if err != nil {
+			t.Fatalf("NewSession() for %v: %v", test.input, err)
+		}
+		output, err := s.Run(map[Port]*Tensor{inp: t1}, []Port{out}, []*Operation{out.Op})
+		if err != nil {
+			t.Fatalf("Run() for %v: %v", test.input, err)
+		}
+		if len(output) != 1 {
+			t.Errorf("%v: got %d outputs, want 1", test.input, len(output))
+			continue
+		}
+		val := output[0].Value()
+		if !reflect.DeepEqual(test.expected, val) {
+			t.Errorf("got %v, want %v", val, test.expected)
+		}
+		if err := s.Close(); err != nil {
+			t.Errorf("Close(): %v", err)
+		}
+	}
+}
+
+func TestConcurrency(t *testing.T) {
+	tensor, err := NewTensor(int64(1))
+	if err != nil {
+		t.Fatalf("NewTensor(): %v", err)
+	}
+
+	graph, inp, out := createTestGraph(t, tensor.DataType())
+	s, err := NewSession(graph, &SessionOptions{})
+	if err != nil {
+		t.Fatalf("NewSession(): %v", err)
+	}
+	for i := 0; i < 100; i++ {
+		// Session may close before Run() starts, so we don't check the error.
+		go s.Run(map[Port]*Tensor{inp: tensor}, []Port{out}, []*Operation{out.Op})
+	}
+	if err = s.Close(); err != nil {
+		t.Errorf("Close() 1: %v", err)
+	}
+	if err = s.Close(); err != nil {
+		t.Errorf("Close() 2: %v", err)
+	}
+}
diff --git a/tensorflow/go/status.go b/tensorflow/go/status.go
new file mode 100644
index 0000000000..a1f7ed5481
--- /dev/null
+++ b/tensorflow/go/status.go
@@ -0,0 +1,65 @@
+// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+package tensorflow
+
+// #include "tensorflow/c/c_api.h"
+import "C"
+
+import "runtime"
+
+type code C.TF_Code
+
+// status holds error information returned by TensorFlow. We convert all
+// TF statuses to Go errors.
+type status struct {
+	c *C.TF_Status
+}
+
+func newStatus() *status {
+	s := &status{C.TF_NewStatus()}
+	runtime.SetFinalizer(s, (*status).finalizer)
+	return s
+}
+
+func (s *status) finalizer() {
+	C.TF_DeleteStatus(s.c)
+}
+
+func (s *status) Code() code {
+	return code(C.TF_GetCode(s.c))
+}
+
+func (s *status) String() string {
+	return C.GoString(C.TF_Message(s.c))
+}
+
+// Err converts the status to a Go error and returns nil if the status is OK.
+func (s *status) Err() error {
+	if s == nil || s.Code() == C.TF_OK {
+		return nil
+	}
+	return (*statusError)(s)
+}
+
+// statusError is distinct from status because it fulfills the error interface.
+// status itself may have a TF_OK code and is not always considered an error.
+//
+// TODO(jhseu): Make public, rename to Error, and provide a way for users to
+// check status codes.
+type statusError status
+
+func (s *statusError) Error() string {
+	return (*status)(s).String()
+}
diff --git a/tensorflow/go/tensor.go b/tensorflow/go/tensor.go
new file mode 100644
index 0000000000..e364e80f86
--- /dev/null
+++ b/tensorflow/go/tensor.go
@@ -0,0 +1,259 @@
+// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+package tensorflow
+
+// #include <string.h>
+// #include "tensorflow/c/c_api.h"
+import "C"
+
+import (
+	"bytes"
+	"encoding/binary"
+	"fmt"
+	"reflect"
+	"unsafe"
+)
+
+// DataType holds the type for a scalar value.  E.g., one slot in a tensor.
+// The values here are identical to corresponding values in types.proto.
+type DataType C.TF_DataType
+
+// Tensor holds a multi-dimensional array of elements of a single data type.
+type Tensor struct {
+	// We create TF_Tensor on demand rather than keep a handle to C.TF_Tensor
+	// because many functions, such as Session.Run() and Operations take
+	// ownership of the C.TF_Tensor. Translating on-demand provides for a safe
+	// API.
+	//
+	// A memcpy is required because cgo rules prohibit us from maintaining
+	// a pointer to Go memory.
+	// call: https://golang.org/cmd/cgo/
+	buf   *bytes.Buffer
+	dt    DataType
+	shape []int64
+}
+
+// NewTensor converts from a Go value to a Tensor. Valid values are scalars,
+// slices, and arrays. Every element of a slice must have the same length so
+// that the resulting Tensor has a valid shape.
+func NewTensor(value interface{}) (*Tensor, error) {
+	val := reflect.ValueOf(value)
+	rank, dataType, err := rankAndDataTypeOf(val.Type())
+	if err != nil {
+		return nil, err
+	}
+	t := &Tensor{buf: bytes.NewBuffer(nil), dt: dataType, shape: make([]int64, rank)}
+	if err = encodeTensor(t.buf, t.shape, val); err != nil {
+		return nil, err
+	}
+	return t, nil
+}
+
+// newTensorFromC converts from a C.TF_Tensor to a Tensor.
+func newTensorFromC(ct *C.TF_Tensor) *Tensor {
+	t := &Tensor{dt: DataType(C.TF_TensorType(ct))}
+	numDims := int(C.TF_NumDims(ct))
+	for i := 0; i < numDims; i++ {
+		t.shape = append(t.shape, int64(C.TF_Dim(ct, C.int(i))))
+	}
+	b := make([]byte, int(C.TF_TensorByteSize(ct)))
+	if len(b) > 0 {
+		C.memcpy(unsafe.Pointer(&b[0]), C.TF_TensorData(ct), C.size_t(len(b)))
+	}
+	t.buf = bytes.NewBuffer(b)
+	return t
+}
+
+// DataType returns the scalar datatype of the Tensor.
+func (t *Tensor) DataType() DataType {
+	return t.dt
+}
+
+// Shape returns the shape of the Tensor.
+func (t *Tensor) Shape() []int64 {
+	return t.shape
+}
+
+// Value converts the Tensor to a Go value. For now, not all Tensor types are
+// supported, and this function may panic if it encounters an unsupported
+// DataType.
+//
+// The type of the output depends on the Tensor type and rank. For example:
+// Tensor(int64, 0): int64
+// Tensor(float64, 3): [][][]float64
+func (t *Tensor) Value() interface{} {
+	typ, err := typeOf(t.DataType(), t.Shape())
+	if err != nil {
+		panic(err)
+	}
+	val := reflect.New(typ)
+	if err := decodeTensor(t.buf, t.Shape(), typ, val); err != nil {
+		panic(err)
+	}
+	return reflect.Indirect(val).Interface()
+}
+
+// c converts the Tensor to a *C.TF_Tensor. Callers must take ownership of
+// the *C.TF_Tensor, either by passing ownership to the C API or explicitly
+// calling C.TF_DeleteTensor() on it.
+func (t *Tensor) c() *C.TF_Tensor {
+	var shapePtr *C.int64_t
+	if len(t.shape) > 0 {
+		shapePtr = (*C.int64_t)(unsafe.Pointer(&t.shape[0]))
+	}
+	tensor := C.TF_AllocateTensor(C.TF_DataType(t.dt), shapePtr, C.int(len(t.shape)), C.size_t(t.buf.Len()))
+	if t.buf.Len() > 0 {
+		slice := t.buf.Bytes() // https://github.com/golang/go/issues/14210
+		C.memcpy(C.TF_TensorData(tensor), unsafe.Pointer(&slice[0]), C.size_t(t.buf.Len()))
+	}
+	return tensor
+}
+
+// deleteCTensor only exists to delete C.TF_Tensors in tests. go test doesn't
+// support cgo.
+func deleteCTensor(ct *C.TF_Tensor) {
+	C.TF_DeleteTensor(ct)
+}
+
+var types = []struct {
+	typ      reflect.Type
+	dataType C.TF_DataType
+}{
+	{reflect.TypeOf(float32(0)), C.TF_FLOAT},
+	{reflect.TypeOf(float64(0)), C.TF_DOUBLE},
+	{reflect.TypeOf(int32(0)), C.TF_INT32},
+	{reflect.TypeOf(uint8(0)), C.TF_UINT8},
+	{reflect.TypeOf(int16(0)), C.TF_INT16},
+	{reflect.TypeOf(int8(0)), C.TF_INT8},
+	{reflect.TypeOf(""), C.TF_STRING},
+	{reflect.TypeOf(complex(float32(0), float32(0))), C.TF_COMPLEX64},
+	{reflect.TypeOf(int64(0)), C.TF_INT64},
+	{reflect.TypeOf(false), C.TF_BOOL},
+	{reflect.TypeOf(uint16(0)), C.TF_UINT16},
+	{reflect.TypeOf(complex(float64(0), float64(0))), C.TF_COMPLEX128},
+}
+
+// rankAndDataTypeOf returns the data type and rank of a Go type for use when
+// encoding. We fetch them separately from encoding to support 0-sized
+// dimensions.
+func rankAndDataTypeOf(typ reflect.Type) (int, DataType, error) {
+	rank := 0
+	elem := typ
+	for ; elem.Kind() == reflect.Array || elem.Kind() == reflect.Slice; elem = elem.Elem() {
+		rank++
+	}
+	for _, t := range types {
+		if elem.Kind() == t.typ.Kind() {
+			return rank, DataType(t.dataType), nil
+		}
+	}
+	return 0, DataType(0), fmt.Errorf("unsupported type %v", typ)
+}
+
+// typeOf converts from a DataType and Shape to the equivalent Go type.
+func typeOf(dt DataType, shape []int64) (reflect.Type, error) {
+	var ret reflect.Type
+	for _, t := range types {
+		if dt == DataType(t.dataType) {
+			ret = t.typ
+			break
+		}
+	}
+	if ret == nil {
+		return nil, fmt.Errorf("DataType %v unsupported", dt)
+	}
+	for _ = range shape {
+		ret = reflect.SliceOf(ret)
+	}
+	return ret, nil
+}
+
+// encodeTensor writes v to the specified buffer using the format specified in
+// c_api.h
+func encodeTensor(buf *bytes.Buffer, shape []int64, v reflect.Value) error {
+	switch v.Kind() {
+	case reflect.Int8, reflect.Int16, reflect.Int32, reflect.Int64, reflect.Uint8, reflect.Uint16, reflect.Float32, reflect.Float64, reflect.Complex64, reflect.Complex128:
+		if err := binary.Write(buf, nativeEndian, v.Interface()); err != nil {
+			return err
+		}
+
+	case reflect.Array, reflect.Slice:
+		// If slice elements are slices, verify that all of them have the same size.
+		// Go's type system makes that guarantee for arrays.
+		if v.Len() > 0 && v.Type().Elem().Kind() == reflect.Slice {
+			expected := v.Index(0).Len()
+			for i := 1; i < v.Len(); i++ {
+				if v.Index(i).Len() != expected {
+					return fmt.Errorf("mismatched slice lengths: %d and %d", v.Index(i).Len(), expected)
+				}
+			}
+		}
+
+		shape[0] = int64(v.Len())
+		for i := 0; i < v.Len(); i++ {
+			err := encodeTensor(buf, shape[1:], v.Index(i))
+			if err != nil {
+				return err
+			}
+		}
+
+	default:
+		return fmt.Errorf("unsupported type %v", v.Type())
+	}
+	return nil
+}
+
+// decodeTensor decodes the Tensor from the buffer to ptr using the format
+// specified in c_api.h
+func decodeTensor(buf *bytes.Buffer, shape []int64, typ reflect.Type, ptr reflect.Value) error {
+	switch typ.Kind() {
+	case reflect.Int8, reflect.Int16, reflect.Int32, reflect.Int64, reflect.Uint8, reflect.Uint16, reflect.Float32, reflect.Float64, reflect.Complex64, reflect.Complex128:
+		if err := binary.Read(buf, nativeEndian, ptr.Interface()); err != nil {
+			return err
+		}
+
+	case reflect.Slice:
+		val := reflect.Indirect(ptr)
+		val.Set(reflect.MakeSlice(typ, int(shape[0]), int(shape[0])))
+		for i := 0; i < val.Len(); i++ {
+			if err := decodeTensor(buf, shape[1:], typ.Elem(), val.Index(i).Addr()); err != nil {
+				return err
+			}
+		}
+
+	default:
+		return fmt.Errorf("unsupported type %v", typ)
+	}
+	return nil
+}
+
+// nativeEndian is the byte order for the local platform. Used to send back and
+// forth Tensors with the C API. We test for endianness at runtime because
+// some architectures can be booted into different endian modes.
+var nativeEndian binary.ByteOrder
+
+func init() {
+	buf := [2]byte{}
+	*(*uint16)(unsafe.Pointer(&buf[0])) = uint16(0xABCD)
+
+	switch buf {
+	case [2]byte{0xCD, 0xAB}:
+		nativeEndian = binary.LittleEndian
+	case [2]byte{0xAB, 0xCD}:
+		nativeEndian = binary.BigEndian
+	default:
+		panic("Could not determine native endianness.")
+	}
+}
diff --git a/tensorflow/go/tensor_test.go b/tensorflow/go/tensor_test.go
new file mode 100644
index 0000000000..630d613729
--- /dev/null
+++ b/tensorflow/go/tensor_test.go
@@ -0,0 +1,97 @@
+// Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+package tensorflow
+
+import (
+	"reflect"
+	"testing"
+)
+
+func TestNewTensor(t *testing.T) {
+	var tests = []struct {
+		shape []int64
+		value interface{}
+	}{
+		{[]int64{}, int8(5)},
+		{[]int64{}, int16(5)},
+		{[]int64{}, int32(5)},
+		{[]int64{}, int64(5)},
+		{[]int64{}, int64(5)},
+		{[]int64{}, uint8(5)},
+		{[]int64{}, uint16(5)},
+		{[]int64{}, float32(5)},
+		{[]int64{}, float64(5)},
+		{[]int64{}, complex(float32(5), float32(6))},
+		{[]int64{}, complex(float64(5), float64(6))},
+		{[]int64{1}, []float64{1}},
+		{[]int64{1}, [1]float64{1}},
+		{[]int64{3, 2}, [][]float64{{1, 2}, {3, 4}, {5, 6}}},
+		{[]int64{2, 3}, [2][3]float64{{1, 2, 3}, {3, 4, 6}}},
+		{[]int64{4, 3, 2}, [][][]float64{
+			{{1, 2}, {3, 4}, {5, 6}},
+			{{7, 8}, {9, 10}, {11, 12}},
+			{{0, -1}, {-2, -3}, {-4, -5}},
+			{{-6, -7}, {-8, -9}, {-10, -11}},
+		}},
+		{[]int64{2, 0}, [][]int64{{}, {}}},
+	}
+
+	var errorTests = []interface{}{
+		struct{ a int }{5},
+		new(int32),
+		new([]int32),
+		// native ints not supported
+		int(5),
+		[]int{5},
+		// uint32 and uint64 are not supported in TensorFlow
+		uint32(5),
+		[]uint32{5},
+		uint64(5),
+		[]uint64{5},
+	}
+
+	for _, test := range tests {
+		tensor, err := NewTensor(test.value)
+		if err != nil {
+			t.Errorf("NewTensor(%v): %v", test.value, err)
+			continue
+		}
+		if !reflect.DeepEqual(test.shape, tensor.Shape()) {
+			t.Errorf("Tensor.Shape(): got %v, want %v", tensor.Shape(), test.shape)
+		}
+
+		// Test that encode and decode gives the same value. We skip arrays because
+		// they're returned as slices.
+		if reflect.TypeOf(test.value).Kind() != reflect.Array {
+			cTensor := tensor.c()
+			gotTensor := newTensorFromC(cTensor)
+			deleteCTensor(cTensor)
+			got := gotTensor.Value()
+			if !reflect.DeepEqual(test.value, got) {
+				t.Errorf("encode/decode: got %v, want %v", got, test.value)
+			}
+		}
+	}
+
+	for _, test := range errorTests {
+		tensor, err := NewTensor(test)
+		if err == nil {
+			t.Errorf("NewTensor(%v): %v", test, err)
+		}
+		if tensor != nil {
+			t.Errorf("NewTensor(%v) = %v, want nil", test, tensor)
+		}
+	}
+}
-- 
GitLab


From 5b6b8905e2937b167285cf6d8f32c494a343b369 Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@google.com>
Date: Tue, 23 Aug 2016 09:19:47 -0800
Subject: [PATCH 040/223] Automated rollback of change 130904334 Change:
 131069022

---
 tensorflow/python/framework/gen_docs_combined.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tensorflow/python/framework/gen_docs_combined.py b/tensorflow/python/framework/gen_docs_combined.py
index a5c255c25b..172441e2e9 100644
--- a/tensorflow/python/framework/gen_docs_combined.py
+++ b/tensorflow/python/framework/gen_docs_combined.py
@@ -70,7 +70,6 @@ def module_names():
       "tf.contrib.metrics",
       "tf.contrib.training",
       "tf.contrib.util",
-      "tf.contrib.slim",
   ]
 
 
-- 
GitLab


From 308e34fbd1277ad38dd4c7d10d3aef2712b3d4b8 Mon Sep 17 00:00:00 2001
From: Olivia Nordquist <nolivia@google.com>
Date: Tue, 23 Aug 2016 09:27:27 -0800
Subject: [PATCH 041/223] fixes #3933 virtualenv using python 2.6 site Change:
 131069999

---
 util/python/python_config.sh | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/util/python/python_config.sh b/util/python/python_config.sh
index 76ad559f02..00619c6f82 100755
--- a/util/python/python_config.sh
+++ b/util/python/python_config.sh
@@ -59,8 +59,12 @@ except NameError:
 python_paths = []
 if os.getenv('PYTHONPATH') is not None:
   python_paths = os.getenv('PYTHONPATH').split(':')
-
-all_paths = set(python_paths + site.getsitepackages())
+try:
+  library_paths =  site.getsitepackages()
+except AttributeError:
+ from distutils.sysconfig import get_python_lib
+ library_paths = get_python_lib()
+all_paths = set(python_paths + library_paths)
 
 paths = []
 for path in all_paths:
-- 
GitLab


From 09999037cdbbea183ec4e85825f5b06b54745783 Mon Sep 17 00:00:00 2001
From: Ziming Dong <dzm1016397507@gmail.com>
Date: Tue, 23 Aug 2016 13:46:11 -0400
Subject: [PATCH 042/223] add int32 support for param seq_length of op
 reverse_sequence (#3774)

* add int32 support for seq_length in reverse_sequence

* modify gpu version of reverse_sequence

* fix bugs

* fix backwards_compatibility
---
 .../core/kernels/reverse_sequence_op.cc       | 88 ++++++++++++-------
 tensorflow/core/kernels/reverse_sequence_op.h | 15 ++--
 .../kernels/reverse_sequence_op_gpu.cu.cc     | 18 ++--
 tensorflow/core/ops/array_ops.cc              |  3 +-
 .../kernel_tests/reverse_sequence_op_test.py  |  7 +-
 5 files changed, 83 insertions(+), 48 deletions(-)

diff --git a/tensorflow/core/kernels/reverse_sequence_op.cc b/tensorflow/core/kernels/reverse_sequence_op.cc
index 5825b1cd4d..505c512cc4 100644
--- a/tensorflow/core/kernels/reverse_sequence_op.cc
+++ b/tensorflow/core/kernels/reverse_sequence_op.cc
@@ -40,19 +40,19 @@ namespace tensorflow {
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
 
-template <typename Device>
+template <typename Device, typename Tlen>
 void CheckErrors(OpKernelContext* context, int batch_dim, int seq_dim) {
   const Tensor& input = context->input(0);
   const Tensor& seq_lens = context->input(1);
 
-  auto seq_lens_t = seq_lens.vec<int64>();
+  auto seq_lens_t = seq_lens.vec<Tlen>();
 
-  std::vector<int64> seq_lens_vec(seq_lens_t.size());
+  std::vector<Tlen> seq_lens_vec(seq_lens_t.size());
 
   // Copy seq_len info down for validity checks
   context->eigen_device<Device>().memcpyDeviceToHost(
       seq_lens_vec.data(), seq_lens_t.data(),
-      sizeof(int64) * seq_lens_t.size());
+      sizeof(Tlen) * seq_lens_t.size());
 
   OP_REQUIRES(context, batch_dim != seq_dim,
               errors::InvalidArgument("batch_dim == seq_dim == ", seq_dim));
@@ -76,8 +76,7 @@ void CheckErrors(OpKernelContext* context, int batch_dim, int seq_dim) {
   }
 }
 
-template <>
-void CheckErrors<GPUDevice>(OpKernelContext* context, int batch_dim,
+void CheckErrorsGPU(OpKernelContext* context, int batch_dim,
                             int seq_dim) {
   const Tensor& input = context->input(0);
   const Tensor& seq_lens = context->input(1);
@@ -97,7 +96,19 @@ void CheckErrors<GPUDevice>(OpKernelContext* context, int batch_dim,
                                       " vs. ", input.dim_size(batch_dim)));
 }
 
-template <typename Device, typename T>
+template <>
+void CheckErrors<GPUDevice, int32>(OpKernelContext* context, int batch_dim,
+                            int seq_dim) {
+  CheckErrorsGPU(context, batch_dim, seq_dim);
+}
+
+template <>
+void CheckErrors<GPUDevice, int64>(OpKernelContext* context, int batch_dim,
+                            int seq_dim) {
+  CheckErrorsGPU(context, batch_dim, seq_dim);
+}
+
+template <typename Device, typename T, typename Tlen>
 class ReverseSequenceOp : public OpKernel {
  public:
   explicit ReverseSequenceOp(OpKernelConstruction* context)
@@ -115,9 +126,9 @@ class ReverseSequenceOp : public OpKernel {
                 errors::InvalidArgument("seq_lens input must be 1-dim, not ",
                                         seq_lens.dims()));
 
-    auto seq_lens_t = seq_lens.vec<int64>();
+    auto seq_lens_t = seq_lens.vec<Tlen>();
 
-    CheckErrors<Device>(context, batch_dim_, seq_dim_);
+    CheckErrors<Device, Tlen>(context, batch_dim_, seq_dim_);
 
     const int input_dims = input.dims();
 
@@ -127,7 +138,7 @@ class ReverseSequenceOp : public OpKernel {
 
 #define HANDLE_DIM(NDIM)                                                      \
   case NDIM:                                                                  \
-    functor::ReverseSequence<Device, T, NDIM>::Compute(                       \
+    functor::ReverseSequence<Device, T, Tlen, NDIM>::Compute(                 \
         context->eigen_device<Device>(), input.tensor<T, NDIM>(), batch_dim_, \
         seq_dim_, seq_lens_t, output->tensor<T, NDIM>());                     \
     break;
@@ -153,42 +164,57 @@ class ReverseSequenceOp : public OpKernel {
   TF_DISALLOW_COPY_AND_ASSIGN(ReverseSequenceOp);
 };
 
-#define REGISTER_REVERSE_SEQUENCE(type)                                     \
+#define REGISTER_REVERSE_SEQUENCE(type, len_type)                           \
   REGISTER_KERNEL_BUILDER(                                                  \
-      Name("ReverseSequence").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
-      ReverseSequenceOp<CPUDevice, type>);
+      Name("ReverseSequence").Device(DEVICE_CPU).TypeConstraint<type>("T"). \
+      TypeConstraint<len_type>("Tlen"),                                     \
+      ReverseSequenceOp<CPUDevice, type, len_type>);
 
-TF_CALL_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE);
+#define REGISTER_REVERSE_SEQUENCE_LEN(type)    \
+  REGISTER_REVERSE_SEQUENCE(type, int32);      \
+  REGISTER_REVERSE_SEQUENCE(type, int64);
+
+TF_CALL_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE_LEN);
 
 #if GOOGLE_CUDA
 
 // Forward declarations of the functor specializations for GPU.
 namespace functor {
-#define DECLARE_GPU_SPEC(T, Dims)                                       \
-  template <>                                                           \
-  void ReverseSequence<GPUDevice, T, Dims>::Compute(                    \
-      const GPUDevice& d, typename TTypes<T, Dims>::ConstTensor input,  \
-      int32 batch_dim, int32 seq_dim, TTypes<int64>::ConstVec seq_lens, \
-      typename TTypes<T, Dims>::Tensor output);                         \
-  extern template struct ReverseSequence<GPUDevice, T, Dims>;
-
-#define DECLARE_GPU_SPECS(T) \
-  DECLARE_GPU_SPEC(T, 2);    \
-  DECLARE_GPU_SPEC(T, 3);    \
-  DECLARE_GPU_SPEC(T, 4);    \
-  DECLARE_GPU_SPEC(T, 5);
+#define DECLARE_GPU_SPEC(T, Tlen, Dims)                                     \
+  template <>                                                               \
+  void ReverseSequence<GPUDevice, T, Tlen, Dims>::Compute(                  \
+      const GPUDevice& d, typename TTypes<T, Dims>::ConstTensor input,      \
+      int32 batch_dim, int32 seq_dim,                                       \
+      typename TTypes<Tlen>::ConstVec seq_lens,                             \
+      typename TTypes<T, Dims>::Tensor output);                             \
+  extern template struct ReverseSequence<GPUDevice, T, Tlen, Dims>;
+
+#define DECLARE_GPU_SPEC_LEN(T, Dims)    \
+  DECLARE_GPU_SPEC(T, int32, Dims);      \
+  DECLARE_GPU_SPEC(T, int64, Dims);
+
+#define DECLARE_GPU_SPECS(T)     \
+  DECLARE_GPU_SPEC_LEN(T, 2);    \
+  DECLARE_GPU_SPEC_LEN(T, 3);    \
+  DECLARE_GPU_SPEC_LEN(T, 4);    \
+  DECLARE_GPU_SPEC_LEN(T, 5);
 
 TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS);
 
 }  // namespace functor
 
 // Registration of the GPU implementations.
-#define REGISTER_REVERSE_SEQUENCE_GPU(type)                                 \
+#define REGISTER_REVERSE_SEQUENCE_GPU(type, len_type)                       \
   REGISTER_KERNEL_BUILDER(                                                  \
-      Name("ReverseSequence").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
-      ReverseSequenceOp<GPUDevice, type>);
+      Name("ReverseSequence").Device(DEVICE_GPU).TypeConstraint<type>("T"). \
+      TypeConstraint<len_type>("Tlen"),                                     \
+      ReverseSequenceOp<GPUDevice, type, len_type>);
+
+#define REGISTER_REVERSE_SEQUENCE_GPU_LEN(type)   \
+  REGISTER_REVERSE_SEQUENCE_GPU(type, int32);     \
+  REGISTER_REVERSE_SEQUENCE_GPU(type, int64);
 
-TF_CALL_GPU_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE_GPU);
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE_GPU_LEN);
 
 #undef REGISTER_REVERSE_SEQUENCE_GPU
 
diff --git a/tensorflow/core/kernels/reverse_sequence_op.h b/tensorflow/core/kernels/reverse_sequence_op.h
index 72c59d59aa..8ccd32ea16 100644
--- a/tensorflow/core/kernels/reverse_sequence_op.h
+++ b/tensorflow/core/kernels/reverse_sequence_op.h
@@ -25,12 +25,12 @@ namespace tensorflow {
 
 namespace generator {
 
-template <typename T, size_t Dims>
+template <typename T, typename Tlen, size_t Dims>
 class ReverseGenerator {
  public:
   EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
   ReverseGenerator(typename TTypes<T, Dims>::ConstTensor input, int32 batch_dim,
-                   int32 seq_dim, TTypes<int64>::ConstVec seq_lengths)
+                   int32 seq_dim, typename TTypes<Tlen>::ConstVec seq_lengths)
       : input_(input),
         batch_dim_(batch_dim),
         seq_dim_(seq_dim),
@@ -51,21 +51,22 @@ class ReverseGenerator {
   typename TTypes<T, Dims>::ConstTensor input_;
   int32 batch_dim_;
   int32 seq_dim_;
-  TTypes<int64>::ConstVec seq_lengths_;
+  typename TTypes<Tlen>::ConstVec seq_lengths_;
 };
 
 }  // namespace generator
 
 namespace functor {
 
-template <typename Device, typename T, size_t Dims>
+template <typename Device, typename T, typename Tlen, size_t Dims>
 struct ReverseSequence {
   EIGEN_ALWAYS_INLINE static void Compute(
       const Device& d, typename TTypes<T, Dims>::ConstTensor input,
-      int32 batch_dim, int32 seq_dim, TTypes<int64>::ConstVec seq_lengths,
+      int32 batch_dim, int32 seq_dim,
+      typename TTypes<Tlen>::ConstVec seq_lengths,
       typename TTypes<T, Dims>::Tensor output) {
-    generator::ReverseGenerator<T, Dims> generator(input, batch_dim, seq_dim,
-                                                   seq_lengths);
+    generator::ReverseGenerator<T, Tlen, Dims> generator(input, batch_dim,
+                                                         seq_dim, seq_lengths);
     output.device(d) = input.generate(generator);
   }
 };
diff --git a/tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc b/tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc
index bcc265c624..373fd60687 100644
--- a/tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc
@@ -24,15 +24,19 @@ namespace tensorflow {
 
 typedef Eigen::GpuDevice GPUDevice;
 
-#define DEFINE_GPU_SPEC(T, dims)                       \
-  template class generator::ReverseGenerator<T, dims>; \
-  template struct functor::ReverseSequence<GPUDevice, T, dims>;
+#define DEFINE_GPU_SPEC(T, Tlen, dims)                       \
+  template class generator::ReverseGenerator<T, Tlen, dims>; \
+  template struct functor::ReverseSequence<GPUDevice, T, Tlen, dims>;
+
+#define DEFINE_GPU_SPEC_LEN(T, dims)  \
+  DEFINE_GPU_SPEC(T, int32, dims);    \
+  DEFINE_GPU_SPEC(T, int64, dims);
 
 #define DEFINE_GPU_SPECS(T) \
-  DEFINE_GPU_SPEC(T, 2);    \
-  DEFINE_GPU_SPEC(T, 3);    \
-  DEFINE_GPU_SPEC(T, 4);    \
-  DEFINE_GPU_SPEC(T, 5);
+  DEFINE_GPU_SPEC_LEN(T, 2);    \
+  DEFINE_GPU_SPEC_LEN(T, 3);    \
+  DEFINE_GPU_SPEC_LEN(T, 4);    \
+  DEFINE_GPU_SPEC_LEN(T, 5);
 
 TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS);
 
diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index 528d6407d4..8f1a35bd2f 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -1471,11 +1471,12 @@ This operation returns N 1-D integer tensors representing shape of `input[i]s`.
 // --------------------------------------------------------------------------
 REGISTER_OP("ReverseSequence")
     .Input("input: T")
-    .Input("seq_lengths: int64")
+    .Input("seq_lengths: Tlen")
     .Output("output: T")
     .Attr("seq_dim: int")
     .Attr("batch_dim: int = 0")
     .Attr("T: type")
+    .Attr("Tlen: {int32, int64} = DT_INT64")
     .SetShapeFn([](InferenceContext* c) {
       const Shape* input = c->input(0);
       const Shape* seq_lens_shape;
diff --git a/tensorflow/python/kernel_tests/reverse_sequence_op_test.py b/tensorflow/python/kernel_tests/reverse_sequence_op_test.py
index 48fbccd686..cea9711d32 100644
--- a/tensorflow/python/kernel_tests/reverse_sequence_op_test.py
+++ b/tensorflow/python/kernel_tests/reverse_sequence_op_test.py
@@ -47,7 +47,7 @@ class ReverseSequenceTest(tf.test.TestCase):
     self._testReverseSequence(x, batch_dim, seq_dim, seq_lengths,
                               truth, False, expected_err_re)
 
-  def _testBasic(self, dtype):
+  def _testBasic(self, dtype, len_dtype=np.int64):
     x = np.asarray([
         [[1, 2, 3, 4], [5, 6, 7, 8]],
         [[9, 10, 11, 12], [13, 14, 15, 16]],
@@ -56,7 +56,7 @@ class ReverseSequenceTest(tf.test.TestCase):
     x = x.transpose([2, 1, 0, 3, 4])  # permute axes 0 <=> 2
 
     # reverse dim 2 up to (0:3, none, 0:4) along dim=0
-    seq_lengths = np.asarray([3, 0, 4], dtype=np.int64)
+    seq_lengths = np.asarray([3, 0, 4], dtype=len_dtype)
 
     truth_orig = np.asarray(
         [[[3, 2, 1, 4], [7, 6, 5, 8]],  # reverse 0:3
@@ -70,6 +70,9 @@ class ReverseSequenceTest(tf.test.TestCase):
     batch_dim = 2
     self._testBothReverseSequence(x, batch_dim, seq_dim, seq_lengths, truth)
 
+  def testSeqLenghtInt32(self):
+    self._testBasic(np.float32, np.int32)
+
   def testFloatBasic(self):
     self._testBasic(np.float32)
 
-- 
GitLab


From 28c48e68a9ea5ff67cfcb51065bfcb0d5628f1ee Mon Sep 17 00:00:00 2001
From: Nathan Silberman <nsilberman@google.com>
Date: Tue, 23 Aug 2016 09:51:46 -0800
Subject: [PATCH 043/223] Setting image dimensions after decoding, adding
 tfexample_decoder.BoundingBox. Change: 131073088

---
 .../python/slim/data/tfexample_decoder.py     |  45 ++++++++
 .../slim/data/tfexample_decoder_test.py       | 101 ++++++++++++++----
 2 files changed, 124 insertions(+), 22 deletions(-)

diff --git a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py
index d768722cd8..969c2d1e7e 100644
--- a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py
+++ b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py
@@ -96,6 +96,50 @@ class ItemHandlerCallback(ItemHandler):
     return self._func(keys_to_tensors)
 
 
+class BoundingBox(ItemHandler):
+  """An ItemHandler that concatenates a set of parsed Tensors to Bounding Boxes.
+  """
+
+  def __init__(self, keys=None, prefix=None):
+    """Initialize the bounding box handler.
+
+    Args:
+      keys: A list of four key names representing the ymin, xmin, ymax, mmax
+      prefix: An optional prefix for each of the bounding box keys.
+        If provided, `prefix` is appended to each key in `keys`.
+
+    Raises:
+      ValueError: if keys is not `None` and also not a list of exactly 4 keys
+    """
+    if keys is None:
+      keys = ['ymin', 'xmin', 'ymax', 'xmax']
+    elif len(keys) != 4:
+      raise ValueError('BoundingBox expects 4 keys but got {}'.format(
+          len(keys)))
+    self._prefix = prefix
+    self._keys = keys
+    self._full_keys = [prefix + k for k in keys]
+    super(BoundingBox, self).__init__(self._full_keys)
+
+  def tensors_to_item(self, keys_to_tensors):
+    """Maps the given dictionary of tensors to a contatenated list of bboxes.
+
+    Args:
+      keys_to_tensors: a mapping of TF-Example keys to parsed tensors.
+
+    Returns:
+      [num_boxes, 4] tensor of bounding box coordinates,
+        i.e. 1 bounding box per row, in order [y_min, x_min, y_max, x_max].
+    """
+    sides = []
+    for key in self._full_keys:
+      side = array_ops.expand_dims(keys_to_tensors[key].values, 0)
+      sides.append(side)
+
+    bounding_box = array_ops.concat(0, sides)
+    return array_ops.transpose(bounding_box)
+
+
 class Tensor(ItemHandler):
   """An ItemHandler that returns a parsed Tensor."""
 
@@ -273,6 +317,7 @@ class Image(ItemHandler):
         math_ops.equal(image_format, 'png'): decode_png,
     }, default=decode_jpg, exclusive=True)
 
+    image.set_shape([None, None, self._channels])
     if self._shape is not None:
       image = array_ops.reshape(image, self._shape)
     return image
diff --git a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder_test.py b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder_test.py
index 7f0dd30ed9..12c22ff967 100644
--- a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder_test.py
+++ b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder_test.py
@@ -1,4 +1,4 @@
-# Copyright 2016 Google Inc. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -76,7 +76,9 @@ class TFExampleDecoderTest(tf.test.TestCase):
         serialized image and a feature key 'image/format' set to the image
         encoding format ['jpeg', 'png'].
     """
-    image = np.linspace(0, 17, num=18).reshape(image_shape).astype(np.uint8)
+    num_pixels = image_shape[0] * image_shape[1] * image_shape[2]
+    image = np.linspace(0, num_pixels-1, num=num_pixels).reshape(
+        image_shape).astype(np.uint8)
     tf_encoded = self._Encoder(image, image_format)
     example = tf.train.Example(features=tf.train.Features(feature={
         'image/encoded': self._EncodedBytesFeature(tf_encoded),
@@ -85,35 +87,39 @@ class TFExampleDecoderTest(tf.test.TestCase):
 
     return image, example.SerializeToString()
 
-  def DecodeExample(self, serialized_example, item_handler, image_shape,
-                    image_format):
+  def DecodeExample(self, serialized_example, item_handler, image_format):
     """Decodes the given serialized example with the specified item handler.
 
     Args:
       serialized_example: a serialized TF example string.
       item_handler: the item handler used to decode the image.
-      image_shape: the shape of the image being decoded.
       image_format: the image format being decoded.
 
     Returns:
       the decoded image found in the serialized Example.
     """
+    serialized_example = tf.reshape(serialized_example, shape=[])
+    decoder = slim.tfexample_decoder.TFExampleDecoder(
+        keys_to_features={
+            'image/encoded': tf.FixedLenFeature(
+                (), tf.string, default_value=''),
+            'image/format': tf.FixedLenFeature(
+                (), tf.string, default_value=image_format),
+        },
+        items_to_handlers={'image': item_handler}
+    )
+    [tf_image] = decoder.decode(serialized_example, ['image'])
+    return tf_image
+
+  def RunDecodeExample(self, serialized_example, item_handler, image_format):
+    tf_image = self.DecodeExample(serialized_example, item_handler,
+                                  image_format)
+
     with self.test_session():
-      serialized_example = tf.reshape(serialized_example, shape=[])
-      decoder = slim.tfexample_decoder.TFExampleDecoder(
-          keys_to_features={
-              'image/encoded': tf.FixedLenFeature(
-                  (), tf.string, default_value=''),
-              'image/format': tf.FixedLenFeature(
-                  (), tf.string, default_value=image_format),
-          },
-          items_to_handlers={'image': item_handler}
-      )
-      [tf_image] = decoder.decode(serialized_example, ['image'])
       decoded_image = tf_image.eval()
 
-    # We need to recast them here to avoid some issues with uint8.
-    return decoded_image.astype(np.float32)
+      # We need to recast them here to avoid some issues with uint8.
+      return decoded_image.astype(np.float32)
 
   def testDecodeExampleWithJpegEncoding(self):
     image_shape = (2, 3, 3)
@@ -121,25 +127,37 @@ class TFExampleDecoderTest(tf.test.TestCase):
         image_format='jpeg',
         image_shape=image_shape)
 
-    decoded_image = self.DecodeExample(
+    decoded_image = self.RunDecodeExample(
         serialized_example,
         slim.tfexample_decoder.Image(),
-        image_shape=image_shape,
         image_format='jpeg')
 
     # Need to use a tolerance of 1 because of noise in the jpeg encode/decode
     self.assertAllClose(image, decoded_image, atol=1.001)
 
+  def testDecodeExampleWithNoShapeInfo(self):
+    test_image_channels = [1, 3]
+    for channels in test_image_channels:
+      image_shape = (2, 3, channels)
+      _, serialized_example = self.GenerateImage(
+          image_format='jpeg',
+          image_shape=image_shape)
+
+      tf_decoded_image = self.DecodeExample(
+          serialized_example,
+          slim.tfexample_decoder.Image(shape=None, channels=channels),
+          image_format='jpeg')
+      self.assertEqual(tf_decoded_image.get_shape().ndims, 3)
+
   def testDecodeExampleWithPngEncoding(self):
     image_shape = (2, 3, 3)
     image, serialized_example = self.GenerateImage(
         image_format='png',
         image_shape=image_shape)
 
-    decoded_image = self.DecodeExample(
+    decoded_image = self.RunDecodeExample(
         serialized_example,
         slim.tfexample_decoder.Image(),
-        image_shape=image_shape,
         image_format='png')
 
     self.assertAllClose(image, decoded_image, atol=0)
@@ -594,5 +612,44 @@ class TFExampleDecoderTest(tf.test.TestCase):
         else:
           self.assertAllClose(image, decoded_image, atol=0)
 
+  def testDecodeExampleWithBoundingBox(self):
+    num_bboxes = 10
+    np_ymin = np.random.rand(num_bboxes, 1)
+    np_xmin = np.random.rand(num_bboxes, 1)
+    np_ymax = np.random.rand(num_bboxes, 1)
+    np_xmax = np.random.rand(num_bboxes, 1)
+    np_bboxes = np.hstack([np_ymin, np_xmin, np_ymax, np_xmax])
+
+    example = tf.train.Example(features=tf.train.Features(feature={
+        'image/object/bbox/ymin': self._EncodedFloatFeature(np_ymin),
+        'image/object/bbox/xmin': self._EncodedFloatFeature(np_xmin),
+        'image/object/bbox/ymax': self._EncodedFloatFeature(np_ymax),
+        'image/object/bbox/xmax': self._EncodedFloatFeature(np_xmax),
+    }))
+    serialized_example = example.SerializeToString()
+
+    with self.test_session():
+      serialized_example = tf.reshape(serialized_example, shape=[])
+
+      keys_to_features = {
+          'image/object/bbox/ymin': tf.VarLenFeature(tf.float32),
+          'image/object/bbox/xmin': tf.VarLenFeature(tf.float32),
+          'image/object/bbox/ymax': tf.VarLenFeature(tf.float32),
+          'image/object/bbox/xmax': tf.VarLenFeature(tf.float32),
+      }
+
+      items_to_handlers = {
+          'object/bbox': slim.tfexample_decoder.BoundingBox(
+              ['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/'),
+      }
+
+      decoder = slim.tfexample_decoder.TFExampleDecoder(
+          keys_to_features,
+          items_to_handlers)
+      [tf_bboxes] = decoder.decode(serialized_example, ['object/bbox'])
+      bboxes = tf_bboxes.eval()
+
+    self.assertAllClose(np_bboxes, bboxes)
+
 if __name__ == '__main__':
   tf.test.main()
-- 
GitLab


From 7d310ef96e81e02731fb87df10049d7a3c6d8b57 Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Tue, 23 Aug 2016 10:32:32 -0800
Subject: [PATCH 044/223] Make map_util and stl_util libraries internal, fix up
 some dependencies that moving this internal caused. Change: 131078616

---
 tensorflow/cc/BUILD                           | 2 ++
 tensorflow/core/BUILD                         | 5 +++--
 tensorflow/core/distributed_runtime/BUILD     | 1 +
 tensorflow/core/distributed_runtime/rpc/BUILD | 4 +---
 4 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/tensorflow/cc/BUILD b/tensorflow/cc/BUILD
index ef1423dfed..8f9ac46676 100644
--- a/tensorflow/cc/BUILD
+++ b/tensorflow/cc/BUILD
@@ -27,6 +27,7 @@ cc_library(
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
     ],
 )
 
@@ -239,6 +240,7 @@ cc_library(
     deps = [
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
         "//tensorflow/core:proto_text",
         "//tensorflow/core:protos_all_cc",
     ],
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index c60ad1ef7e..e0a4cc7617 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -143,9 +143,7 @@ cc_library(
         "lib/core/threadpool.h",
         "lib/gtl/array_slice.h",
         "lib/gtl/inlined_vector.h",
-        "lib/gtl/map_util.h",  # TODO(josh11b): make internal
         "lib/gtl/priority_queue_util.h",
-        "lib/gtl/stl_util.h",  # TODO(josh11b): make internal
         "lib/hash/crc32c.h",  # TODO(josh11b): make internal
         "lib/histogram/histogram.h",
         "lib/io/inputbuffer.h",  # TODO(josh11b): make internal
@@ -875,6 +873,8 @@ cc_library(
         "lib/gtl/int_type.h",
         "lib/gtl/iterator_range.h",
         "lib/gtl/manual_constructor.h",
+        "lib/gtl/map_util.h",
+        "lib/gtl/stl_util.h",
         "lib/gtl/top_n.h",
         "lib/hash/hash.h",
         "lib/io/inputstream_interface.h",
@@ -1264,6 +1264,7 @@ cc_library(
     deps = [
         ":framework",
         ":lib",
+        ":lib_internal",
         ":test",
     ],
 )
diff --git a/tensorflow/core/distributed_runtime/BUILD b/tensorflow/core/distributed_runtime/BUILD
index 601a4f34b4..3223d66b3d 100644
--- a/tensorflow/core/distributed_runtime/BUILD
+++ b/tensorflow/core/distributed_runtime/BUILD
@@ -145,6 +145,7 @@ cc_library(
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
         "//tensorflow/core:master_proto_cc",
         "//tensorflow/core:worker_proto_cc",
     ],
diff --git a/tensorflow/core/distributed_runtime/rpc/BUILD b/tensorflow/core/distributed_runtime/rpc/BUILD
index 527e5281e0..5c13c0e654 100644
--- a/tensorflow/core/distributed_runtime/rpc/BUILD
+++ b/tensorflow/core/distributed_runtime/rpc/BUILD
@@ -92,6 +92,7 @@ cc_library(
     deps = [
         ":grpc_util",
         "//tensorflow/core:lib",
+        "//tensorflow/core:lib_internal",
         "@grpc//:grpc++_unsecure",
     ],
 )
@@ -101,7 +102,6 @@ cc_library(
     srcs = ["grpc_tensor_coding.cc"],
     hdrs = ["grpc_tensor_coding.h"],
     deps = [
-        ":grpc_util",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",
         "//tensorflow/core:framework_internal",
@@ -160,12 +160,10 @@ cc_library(
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:worker_proto_cc",
-        "//tensorflow/core/distributed_runtime:call_options",
         "//tensorflow/core/distributed_runtime:graph_mgr",
         "//tensorflow/core/distributed_runtime:rendezvous_mgr_interface",
         "//tensorflow/core/distributed_runtime:worker_cache",
         "//tensorflow/core/distributed_runtime:worker_env",
-        "//tensorflow/core/distributed_runtime:worker_interface",
         "@grpc//:grpc++_unsecure",
     ],
 )
-- 
GitLab


From add528cd2f252d4c2f902df6f8eb368747af40ae Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 11:04:15 -0800
Subject: [PATCH 045/223] Log step with loss in default monitor. Change:
 131082486

---
 tensorflow/contrib/learn/python/learn/graph_actions.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/graph_actions.py b/tensorflow/contrib/learn/python/learn/graph_actions.py
index 05edc6dfae..4665caa2d4 100644
--- a/tensorflow/contrib/learn/python/learn/graph_actions.py
+++ b/tensorflow/contrib/learn/python/learn/graph_actions.py
@@ -234,8 +234,10 @@ def _monitored_train(graph,
     all_hooks.extend([
         basic_session_run_hooks.NanTensorHook(
             loss_op, fail_on_nan_loss=fail_on_nan_loss),
-        basic_session_run_hooks.LoggingTensorHook(
-            {'loss': loss_op.name}, every_n_iter=log_every_steps),
+        basic_session_run_hooks.LoggingTensorHook({
+            'loss': loss_op.name,
+            'step': global_step_tensor.name
+        }, every_n_iter=log_every_steps),
     ])
 
     scaffold = monitored_session.Scaffold(
-- 
GitLab


From e00866a19bd2c46ecd51f51e8da7f025246be775 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 11:06:36 -0800
Subject: [PATCH 046/223] Move `stratified_sample` from contrib/framework to
 contrib/training. Change: 131082787

---
 tensorflow/contrib/framework/BUILD            | 18 -------
 .../contrib/framework/python/ops/__init__.py  |  1 -
 tensorflow/contrib/training/BUILD             | 26 ++++++++++
 tensorflow/contrib/training/__init__.py       | 14 ++++++
 .../python/training}/sampling_ops.py          |  0
 .../python/training}/sampling_ops_test.py     | 47 +++++++++----------
 .../training}/sampling_ops_threading_test.py  |  5 +-
 7 files changed, 66 insertions(+), 45 deletions(-)
 rename tensorflow/contrib/{framework/python/ops => training/python/training}/sampling_ops.py (100%)
 rename tensorflow/contrib/{framework/python/ops => training/python/training}/sampling_ops_test.py (89%)
 rename tensorflow/contrib/{framework/python/ops => training/python/training}/sampling_ops_threading_test.py (88%)

diff --git a/tensorflow/contrib/framework/BUILD b/tensorflow/contrib/framework/BUILD
index b77fe259f8..133339d978 100644
--- a/tensorflow/contrib/framework/BUILD
+++ b/tensorflow/contrib/framework/BUILD
@@ -22,7 +22,6 @@ py_library(
         "python/ops/embedding_ops.py",
         "python/ops/ops.py",
         "python/ops/prettyprint_ops.py",
-        "python/ops/sampling_ops.py",
         "python/ops/variables.py",
     ],
     srcs_version = "PY2AND3",
@@ -83,23 +82,6 @@ py_test(
     deps = ["//tensorflow:tensorflow_py"],
 )
 
-py_test(
-    name = "sampling_ops_test",
-    size = "small",
-    srcs = ["python/ops/sampling_ops_test.py"],
-    srcs_version = "PY2AND3",
-    deps = ["//tensorflow:tensorflow_py"],
-)
-
-py_test(
-    name = "sampling_ops_threading_test",
-    size = "small",
-    srcs = ["python/ops/sampling_ops_threading_test.py"],
-    srcs_version = "PY2AND3",
-    tags = ["notsan"],
-    deps = ["//tensorflow:tensorflow_py"],
-)
-
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/framework/python/ops/__init__.py b/tensorflow/contrib/framework/python/ops/__init__.py
index e868989a73..b0b1935595 100644
--- a/tensorflow/contrib/framework/python/ops/__init__.py
+++ b/tensorflow/contrib/framework/python/ops/__init__.py
@@ -24,6 +24,5 @@ from tensorflow.contrib.framework.python.ops.arg_scope import *
 from tensorflow.contrib.framework.python.ops.embedding_ops import *
 from tensorflow.contrib.framework.python.ops.ops import *
 from tensorflow.contrib.framework.python.ops.prettyprint_ops import *
-from tensorflow.contrib.framework.python.ops.sampling_ops import *
 from tensorflow.contrib.framework.python.ops.variables import *
 # pylint: enable=wildcard-import
diff --git a/tensorflow/contrib/training/BUILD b/tensorflow/contrib/training/BUILD
index 5b7046919e..a44143ba40 100644
--- a/tensorflow/contrib/training/BUILD
+++ b/tensorflow/contrib/training/BUILD
@@ -11,6 +11,7 @@ py_library(
     name = "training_py",
     srcs = [
         "__init__.py",
+        "python/training/sampling_ops.py",
         "python/training/sequence_queueing_state_saver.py",
     ],
     srcs_version = "PY2AND3",
@@ -41,6 +42,31 @@ py_test(
     ],
 )
 
+py_test(
+    name = "sampling_ops_test",
+    size = "small",
+    srcs = ["python/training/sampling_ops_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":training_py",
+        "//tensorflow:tensorflow_py",
+        "//tensorflow/python:framework_test_lib",
+    ],
+)
+
+py_test(
+    name = "sampling_ops_threading_test",
+    size = "small",
+    srcs = ["python/training/sampling_ops_threading_test.py"],
+    srcs_version = "PY2AND3",
+    tags = ["notsan"],
+    deps = [
+        ":training_py",
+        "//tensorflow:tensorflow_py",
+        "//tensorflow/python:framework_test_lib",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/training/__init__.py b/tensorflow/contrib/training/__init__.py
index fcd4f4b6cc..d8cd905800 100644
--- a/tensorflow/contrib/training/__init__.py
+++ b/tensorflow/contrib/training/__init__.py
@@ -25,6 +25,19 @@ like to store state in the forward direction across segments of an example.
 @@batch_sequences_with_states
 @@NextQueuedSequenceBatch
 @@SequenceQueueingStateSaver
+
+
+## Online data resampling
+
+Use ['stratified_sample'](#stratified_sample) or
+['stratified_sample_unknown_dist'](#stratified_sample_unknown_dist) to resample
+from the data and change the class proportions that the Tensorflow graph sees.
+For instance, if you have a binary classification dataset that is 99.9% class
+1, a common approach is to resample from the data so that the data is more
+balanced.
+
+@@stratified_sample
+@@stratified_sample_unknown_dist
 """
 
 from __future__ import absolute_import
@@ -32,6 +45,7 @@ from __future__ import division
 from __future__ import print_function
 
 # pylint: disable=unused-import,wildcard-import
+from tensorflow.contrib.training.python.training.sampling_ops import *
 from tensorflow.contrib.training.python.training.sequence_queueing_state_saver import *
 from tensorflow.python.util.all_util import make_all
 
diff --git a/tensorflow/contrib/framework/python/ops/sampling_ops.py b/tensorflow/contrib/training/python/training/sampling_ops.py
similarity index 100%
rename from tensorflow/contrib/framework/python/ops/sampling_ops.py
rename to tensorflow/contrib/training/python/training/sampling_ops.py
diff --git a/tensorflow/contrib/framework/python/ops/sampling_ops_test.py b/tensorflow/contrib/training/python/training/sampling_ops_test.py
similarity index 89%
rename from tensorflow/contrib/framework/python/ops/sampling_ops_test.py
rename to tensorflow/contrib/training/python/training/sampling_ops_test.py
index 5b974f5848..6f7c2eda0d 100644
--- a/tensorflow/contrib/framework/python/ops/sampling_ops_test.py
+++ b/tensorflow/contrib/training/python/training/sampling_ops_test.py
@@ -20,6 +20,7 @@ from __future__ import print_function
 
 import numpy as np
 import tensorflow as tf
+from tensorflow.contrib.training.python.training import sampling_ops
 from tensorflow.python.platform import tf_logging as logging
 
 
@@ -35,7 +36,7 @@ class SamplingOpsTest(tf.test.TestCase):
     # Curry the rejection sampler so we can easily run the same tests on both
     # stratified_sample and stratified_sample_unknown_dist.
     def curried_sampler(tensors, labels, probs, batch_size, enqueue_many=True):
-      return tf.contrib.framework.sampling_ops.stratified_sample(
+      return tf.contrib.training.stratified_sample(
           tensors=tensors,
           labels=labels,
           target_probs=probs,
@@ -44,7 +45,7 @@ class SamplingOpsTest(tf.test.TestCase):
           enqueue_many=enqueue_many)
 
     samplers = [
-        tf.contrib.framework.sampling_ops.stratified_sample_unknown_dist,
+        tf.contrib.training.stratified_sample_unknown_dist,
         curried_sampler,
     ]
 
@@ -85,18 +86,18 @@ class SamplingOpsTest(tf.test.TestCase):
     # In the rejection sampling case, make sure that probability lengths are
     # the same.
     with self.assertRaises(ValueError):
-      tf.contrib.framework.sampling_ops.stratified_sample(
+      tf.contrib.training.stratified_sample(
           val, label, [.1] * 10, batch_size, init_probs=[.2] * 5)
 
     # In the rejection sampling case, make sure that zero initial probability
     # classes also have zero target probability.
     with self.assertRaises(ValueError):
-      tf.contrib.framework.sampling_ops.stratified_sample(
+      tf.contrib.training.stratified_sample(
           val, label, [.2, .4, .4], batch_size, init_probs=[0, .5, .5])
 
     # Probabilities must be 1D.
     with self.assertRaises(ValueError):
-      tf.contrib.framework.sampling_ops.stratified_sample_unknown_dist(
+      tf.contrib.training.stratified_sample_unknown_dist(
           val, label, np.array([[.25, .25], [.25, .25]]), batch_size)
 
   def testRuntimeAssertionFailures(self):
@@ -118,7 +119,7 @@ class SamplingOpsTest(tf.test.TestCase):
     # Set up graph with illegal label vector.
     label_ph = tf.placeholder(tf.int32, shape=[None])
     probs_ph = tf.placeholder(tf.float32, shape=[5])  # shape must be defined
-    val_tf, lbl_tf, prob_tf = tf.contrib.framework.sampling_ops._verify_input(
+    val_tf, lbl_tf, prob_tf = sampling_ops._verify_input(  # pylint: disable=protected-access
         vals, label_ph, [probs_ph])
 
     for illegal_label in illegal_labels:
@@ -162,13 +163,13 @@ class SamplingOpsTest(tf.test.TestCase):
     val_input_batch = [tf.zeros([2, 3, 4])]
     lbl_input_batch = tf.ones([], dtype=tf.int32)
     probs = np.array([0, 1, 0, 0, 0])
-    batches = tf.contrib.framework.sampling_ops.stratified_sample(
+    batches = tf.contrib.training.stratified_sample(
         val_input_batch, lbl_input_batch, probs, batch_size, init_probs=probs)
-    batches += tf.contrib.framework.sampling_ops.stratified_sample(
+    batches += tf.contrib.training.stratified_sample(
         val_input_batch, lbl_input_batch, probs, batch_size, init_probs=probs)
-    batches += tf.contrib.framework.sampling_ops.stratified_sample_unknown_dist(
+    batches += tf.contrib.training.stratified_sample_unknown_dist(
         val_input_batch, lbl_input_batch, probs, batch_size)
-    batches += tf.contrib.framework.sampling_ops.stratified_sample_unknown_dist(
+    batches += tf.contrib.training.stratified_sample_unknown_dist(
         val_input_batch, lbl_input_batch, probs, batch_size)
     summary_op = tf.merge_summary(tf.get_collection(tf.GraphKeys.SUMMARIES))
 
@@ -183,13 +184,13 @@ class SamplingOpsTest(tf.test.TestCase):
 
   def testBatchingBehavior(self):
     self.batchingBehaviorHelper(
-        tf.contrib.framework.sampling_ops.stratified_sample_unknown_dist)
+        tf.contrib.training.stratified_sample_unknown_dist)
 
   def testRejectionBatchingBehavior(self):
     initial_p = [0, .3, 0, .7, 0]
 
     def curried_sampler(val, lbls, probs, batch, enqueue_many=True):
-      return tf.contrib.framework.sampling_ops.stratified_sample(
+      return tf.contrib.training.stratified_sample(
           val,
           lbls,
           probs,
@@ -212,7 +213,7 @@ class SamplingOpsTest(tf.test.TestCase):
     probs = tf.placeholder(tf.float32, shape=[5])
     batch_size = 2
 
-    data_batch, labels = tf.contrib.framework.sampling_ops.stratified_sample_unknown_dist(  # pylint: disable=line-too-long
+    data_batch, labels = tf.contrib.training.stratified_sample_unknown_dist(
         val, label, probs, batch_size)
 
     with self.test_session() as sess:
@@ -252,7 +253,7 @@ class SamplingOpsTest(tf.test.TestCase):
     # Set up graph with placeholders.
     vals_ph = tf.placeholder(tf.float32)  # completely undefined shape
     labels_ph = tf.placeholder(tf.int32)  # completely undefined shape
-    val_tf, labels_tf, _ = tf.contrib.framework.sampling_ops._verify_input(
+    val_tf, labels_tf, _ = sampling_ops._verify_input(  # pylint: disable=protected-access
         [vals_ph], labels_ph, [probs])
 
     # Run graph to make sure there are no shape-related runtime errors.
@@ -289,13 +290,13 @@ class SamplingOpsTest(tf.test.TestCase):
 
   def testDataListInput(self):
     self.dataListHelper(
-        tf.contrib.framework.sampling_ops.stratified_sample_unknown_dist)
+        tf.contrib.training.stratified_sample_unknown_dist)
 
   def testRejectionDataListInput(self):
     initial_p = [0, 1, 0, 0, 0]
 
     def curried_sampler(val, lbls, probs, batch, enqueue_many=False):
-      return tf.contrib.framework.sampling_ops.stratified_sample(
+      return tf.contrib.training.stratified_sample(
           val,
           lbls,
           probs,
@@ -358,13 +359,13 @@ class SamplingOpsTest(tf.test.TestCase):
 
   def testNormalBehavior(self):
     self.normalBehaviorHelper(
-        tf.contrib.framework.sampling_ops.stratified_sample_unknown_dist)
+        tf.contrib.training.stratified_sample_unknown_dist)
 
   def testRejectionNormalBehavior(self):
     initial_p = [.7, 0, 0, .3, 0]
 
     def curried_sampler(val, lbls, probs, batch, enqueue_many=False):
-      return tf.contrib.framework.sampling_ops.stratified_sample(
+      return tf.contrib.training.stratified_sample(
           val,
           lbls,
           probs,
@@ -377,7 +378,7 @@ class SamplingOpsTest(tf.test.TestCase):
   def testRejectionNormalBehaviorWithOnlineInitPEstimate(self):
 
     def curried_sampler(val, lbls, probs, batch, enqueue_many=False):
-      return tf.contrib.framework.sampling_ops.stratified_sample(
+      return tf.contrib.training.stratified_sample(
           val, lbls, probs, batch, init_probs=None, enqueue_many=enqueue_many)
 
     self.normalBehaviorHelper(curried_sampler)
@@ -392,8 +393,7 @@ class SamplingOpsTest(tf.test.TestCase):
     batch_size = 4
 
     # Set up the test graph.
-    [batch] = tf.contrib.framework.sampling_ops._conditional_batch(
-        [tensor], accept_prob, batch_size)
+    [batch] = sampling_ops._conditional_batch([tensor], accept_prob, batch_size)  # pylint: disable=protected-access
 
     # Check conditional operation.
     with self.test_session():
@@ -415,9 +415,8 @@ class SamplingOpsTest(tf.test.TestCase):
     batch_size = 4
 
     # Check that output types are the same for 1 and 2-length input lists.
-    output1 = tf.contrib.framework.sampling_ops._conditional_batch(
-        [tensor], accept_prob, batch_size)
-    output2 = tf.contrib.framework.sampling_ops._conditional_batch(
+    output1 = sampling_ops._conditional_batch([tensor], accept_prob, batch_size)  # pylint: disable=protected-access
+    output2 = sampling_ops._conditional_batch(  # pylint: disable=protected-access
         [tensor, tensor], accept_prob, batch_size)
     self.assertEqual(type(output1), type(output2))
 
diff --git a/tensorflow/contrib/framework/python/ops/sampling_ops_threading_test.py b/tensorflow/contrib/training/python/training/sampling_ops_threading_test.py
similarity index 88%
rename from tensorflow/contrib/framework/python/ops/sampling_ops_threading_test.py
rename to tensorflow/contrib/training/python/training/sampling_ops_threading_test.py
index 3812c3348c..b4dff881c3 100644
--- a/tensorflow/contrib/framework/python/ops/sampling_ops_threading_test.py
+++ b/tensorflow/contrib/training/python/training/sampling_ops_threading_test.py
@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 import tensorflow as tf
+from tensorflow.contrib.training.python.training import sampling_ops
 
 
 class SamplingOpsThreadingTest(tf.test.TestCase):
@@ -30,10 +31,10 @@ class SamplingOpsThreadingTest(tf.test.TestCase):
     tf.set_random_seed(1234)
     label = tf.cast(tf.round(tf.random_uniform([1]) * num_classes), tf.int32)
 
-    prob_estimate = tf.contrib.framework.sampling_ops._estimate_data_distribution(  # pylint: disable=line-too-long
+    prob_estimate = sampling_ops._estimate_data_distribution(  # pylint: disable=protected-access
         label, num_classes)
     # Check that prob_estimate is well-behaved in a multithreaded context.
-    _, _, [prob_estimate] = tf.contrib.framework.sampling_ops._verify_input(
+    _, _, [prob_estimate] = sampling_ops._verify_input(  # pylint: disable=protected-access
         [], label, [prob_estimate])
 
     # Use queues to run multiple threads over the graph, each of which
-- 
GitLab


From 8b378676d3993998a730dd0e6379c21f208b0813 Mon Sep 17 00:00:00 2001
From: Nathan Silberman <nsilberman@google.com>
Date: Tue, 23 Aug 2016 11:07:30 -0800
Subject: [PATCH 047/223] Adding Resnet default image sizes. Change: 131082863

---
 tensorflow/contrib/slim/python/slim/nets/resnet_v1.py | 1 +
 tensorflow/contrib/slim/python/slim/nets/resnet_v2.py | 1 +
 2 files changed, 2 insertions(+)

diff --git a/tensorflow/contrib/slim/python/slim/nets/resnet_v1.py b/tensorflow/contrib/slim/python/slim/nets/resnet_v1.py
index 072033edbf..221185620b 100644
--- a/tensorflow/contrib/slim/python/slim/nets/resnet_v1.py
+++ b/tensorflow/contrib/slim/python/slim/nets/resnet_v1.py
@@ -196,6 +196,7 @@ def resnet_v1(inputs,
       if num_classes is not None:
         end_points['predictions'] = slim.softmax(net, scope='predictions')
       return net, end_points
+resnet_v1.default_image_size = 224
 
 
 def resnet_v1_50(inputs,
diff --git a/tensorflow/contrib/slim/python/slim/nets/resnet_v2.py b/tensorflow/contrib/slim/python/slim/nets/resnet_v2.py
index d9ece105dc..3712692f47 100644
--- a/tensorflow/contrib/slim/python/slim/nets/resnet_v2.py
+++ b/tensorflow/contrib/slim/python/slim/nets/resnet_v2.py
@@ -205,6 +205,7 @@ def resnet_v2(inputs,
       if num_classes is not None:
         end_points['predictions'] = slim.softmax(net, scope='predictions')
       return net, end_points
+resnet_v2.default_image_size = 224
 
 
 def resnet_v2_50(inputs,
-- 
GitLab


From 0f441168e11ede33069c9c5c5a21418e0637e1e6 Mon Sep 17 00:00:00 2001
From: Nathan Silberman <nsilberman@google.com>
Date: Tue, 23 Aug 2016 11:12:51 -0800
Subject: [PATCH 048/223] Adding single_evaluation function to TF-Slim. Change:
 131083386

---
 .../contrib/slim/python/slim/evaluation.py    | 91 ++++++++++++++++++-
 .../slim/python/slim/evaluation_test.py       | 67 ++++++++++++--
 2 files changed, 148 insertions(+), 10 deletions(-)

diff --git a/tensorflow/contrib/slim/python/slim/evaluation.py b/tensorflow/contrib/slim/python/slim/evaluation.py
index 6c40f7743b..c69365a150 100644
--- a/tensorflow/contrib/slim/python/slim/evaluation.py
+++ b/tensorflow/contrib/slim/python/slim/evaluation.py
@@ -1,4 +1,4 @@
-# Copyright 2016 Google Inc. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -136,7 +136,12 @@ from tensorflow.python.training import summary_io
 from tensorflow.python.training import supervisor
 from tensorflow.python.training import training_util
 
-__all__ = ['evaluation', 'evaluation_loop', 'wait_for_new_checkpoint']
+__all__ = [
+    'evaluate_once',
+    'evaluation',
+    'evaluation_loop',
+    'wait_for_new_checkpoint'
+]
 
 
 def wait_for_new_checkpoint(checkpoint_dir,
@@ -241,6 +246,88 @@ def evaluation(sess,
 _USE_DEFAULT = 0
 
 
+def evaluate_once(checkpoint_path,
+                  logdir,
+                  master='',
+                  num_evals=1,
+                  eval_op=None,
+                  eval_op_feed_dict=None,
+                  final_op=None,
+                  final_op_feed_dict=None,
+                  summary_op=_USE_DEFAULT,
+                  summary_op_feed_dict=None,
+                  variables_to_restore=None,
+                  session_config=None):
+  """Evaluates the model at the given checkpoint path.
+
+  Args:
+    checkpoint_path: The path to a checkpoint to use for evaluation.
+    logdir: The directory where the TensorFlow summaries are written to.
+    master: The BNS address of the TensorFlow master.
+    num_evals: The number of times to run `eval_op`.
+    eval_op: A operation run `num_evals` times.
+    eval_op_feed_dict: The feed dictionary to use when executing the `eval_op`.
+    final_op: An operation to execute after all of the `eval_op` executions. The
+      value of `final_op` is returned.
+    final_op_feed_dict: A feed dictionary to use when executing `final_op`.
+    summary_op: The summary_op to evaluate after running TF-Slims metric ops. By
+      default the summary_op is set to tf.merge_all_summaries().
+    summary_op_feed_dict: An optional feed dictionary to use when running the
+      `summary_op`.
+    variables_to_restore: A list of TensorFlow variables to restore during
+      evaluation. If the argument is left as `None` then
+      slim.variables.GetVariablesToRestore() is used.
+    session_config: An instance of `tf.ConfigProto` that will be used to
+      configure the `Session`. If left as `None`, the default will be used.
+
+  Returns:
+    The value of `final_op` or `None` if `final_op` is `None`.
+  """
+  if summary_op == _USE_DEFAULT:
+    summary_op = logging_ops.merge_all_summaries()
+
+  global_step = variables.get_or_create_global_step()
+
+  init_op = control_flow_ops.group(tf_variables.initialize_all_variables(),
+                                   tf_variables.initialize_local_variables(),
+                                   data_flow_ops.initialize_all_tables())
+
+  saver = tf_saver.Saver(variables_to_restore or
+                         variables.get_variables_to_restore())
+
+  summary_writer = summary_io.SummaryWriter(logdir)
+
+  sv = supervisor.Supervisor(graph=ops.get_default_graph(),
+                             logdir=logdir,
+                             init_op=init_op,
+                             summary_op=None,
+                             summary_writer=None,
+                             global_step=None,
+                             saver=None)
+
+  logging.info('Starting evaluation at ' + time.strftime('%Y-%m-%d-%H:%M:%S',
+                                                         time.gmtime()))
+  with sv.managed_session(
+      master, start_standard_services=False, config=session_config) as sess:
+    saver.restore(sess, checkpoint_path)
+    sv.start_queue_runners(sess)
+    final_op_value = evaluation(sess,
+                                num_evals=num_evals,
+                                eval_op=eval_op,
+                                eval_op_feed_dict=eval_op_feed_dict,
+                                final_op=final_op,
+                                final_op_feed_dict=final_op_feed_dict,
+                                summary_op=summary_op,
+                                summary_op_feed_dict=summary_op_feed_dict,
+                                summary_writer=summary_writer,
+                                global_step=global_step)
+
+  logging.info('Finished evaluation at ' + time.strftime('%Y-%m-%d-%H:%M:%S',
+                                                         time.gmtime()))
+
+  return final_op_value
+
+
 def evaluation_loop(master,
                     checkpoint_dir,
                     logdir,
diff --git a/tensorflow/contrib/slim/python/slim/evaluation_test.py b/tensorflow/contrib/slim/python/slim/evaluation_test.py
index 4e59f3efe2..7cdd79a46b 100644
--- a/tensorflow/contrib/slim/python/slim/evaluation_test.py
+++ b/tensorflow/contrib/slim/python/slim/evaluation_test.py
@@ -1,4 +1,4 @@
-# Copyright 2016 Google Inc. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -95,7 +95,7 @@ class EvaluationTest(tf.test.TestCase):
       self.assertAlmostEqual(accuracy_value, self._expected_accuracy)
 
   def testFinalOpsOnEvaluationLoop(self):
-    _, update_op = slim.metrics.streaming_accuracy(
+    value_op, update_op = slim.metrics.streaming_accuracy(
         self._predictions, self._labels)
     init_op = tf.group(tf.initialize_all_variables(),
                        tf.initialize_local_variables())
@@ -104,15 +104,18 @@ class EvaluationTest(tf.test.TestCase):
     gfile.MakeDirs(chkpt_dir)
     logdir = os.path.join(self.get_temp_dir(), 'tmp_logs2/')
     gfile.MakeDirs(logdir)
-    saver = tf.train.Saver(tf.all_variables())
+
+    # Save initialized variables to checkpoint directory
+    saver = tf.train.Saver()
     with self.test_session() as sess:
       init_op.run()
-      # Save initialized variables to checkpoint directory
       saver.save(sess, os.path.join(chkpt_dir, 'chkpt'))
-      accuracy_value = slim.evaluation.evaluation_loop(
-          '', chkpt_dir, logdir, final_op=update_op,
-          max_number_of_evaluations=1)
-      self.assertAlmostEqual(accuracy_value, self._expected_accuracy)
+
+    # Now, run the evaluation loop:
+    accuracy_value = slim.evaluation.evaluation_loop(
+        '', chkpt_dir, logdir, eval_op=update_op, final_op=value_op,
+        max_number_of_evaluations=1)
+    self.assertAlmostEqual(accuracy_value, self._expected_accuracy)
 
   def _create_names_to_metrics(self, predictions, labels):
     accuracy0, update_op0 = tf.contrib.metrics.streaming_accuracy(
@@ -247,5 +250,53 @@ class EvaluationTest(tf.test.TestCase):
     # The timeout kicked in.
     self.assertLess(end, start + 1.1)
 
+
+class SingleEvaluationTest(tf.test.TestCase):
+
+  def setUp(self):
+    super(SingleEvaluationTest, self).setUp()
+
+    num_classes = 8
+    batch_size = 16
+    inputs, labels = GenerateTestData(num_classes, batch_size)
+    self._expected_accuracy = GroundTruthAccuracy(inputs, labels, batch_size)
+
+    self._global_step = slim.get_or_create_global_step()
+    self._inputs = tf.constant(inputs, dtype=tf.float32)
+    self._labels = tf.constant(labels, dtype=tf.int64)
+    self._predictions, self._scale = TestModel(self._inputs)
+
+  def testErrorRaisedIfCheckpointDoesntExist(self):
+    checkpoint_path = os.path.join(self.get_temp_dir(),
+                                   'this_file_doesnt_exist')
+    log_dir = os.path.join(self.get_temp_dir(), 'error_raised')
+    with self.assertRaises(ValueError):
+      slim.evaluation.evaluate_once(checkpoint_path, log_dir)
+
+  def testRestoredModelPerformance(self):
+    checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt')
+    log_dir = os.path.join(self.get_temp_dir(), 'log_dir1/')
+
+    # First, save out the current model to a checkpoint:
+    init_op = tf.group(tf.initialize_all_variables(),
+                       tf.initialize_local_variables())
+    saver = tf.train.Saver()
+    with self.test_session() as sess:
+      sess.run(init_op)
+      saver.save(sess, checkpoint_path)
+
+    # Next, determine the metric to evaluate:
+    value_op, update_op = slim.metrics.streaming_accuracy(
+        self._predictions, self._labels)
+
+    # Run the evaluation and verify the results:
+    accuracy_value = slim.evaluation.evaluate_once(
+        checkpoint_path,
+        log_dir,
+        eval_op=update_op,
+        final_op=value_op)
+    self.assertAlmostEqual(accuracy_value, self._expected_accuracy)
+
+
 if __name__ == '__main__':
   tf.test.main()
-- 
GitLab


From 6b4b5b01ed22f9a1aff86272dd99da56ce9e1b7c Mon Sep 17 00:00:00 2001
From: Simon DENEL <simondenel1@gmail.com>
Date: Tue, 23 Aug 2016 21:22:06 +0200
Subject: [PATCH 049/223] Remove some signed/unsigned integer comparison
 warnings (#3643)

* Remove some signed/unsigned integer comparison warnings

Update executor.cc

Leaving this modification apart, as requested by @rmlarsen #3643

* Update array_ops.cc

Patching a messy rebase
---
 tensorflow/cc/framework/cc_op_gen.cc                 |  2 +-
 tensorflow/core/debug/debug_graph_utils.cc           |  2 +-
 tensorflow/core/framework/log_memory.cc              |  2 +-
 tensorflow/core/framework/op.cc                      |  4 ++--
 tensorflow/core/framework/tensor.cc                  |  2 +-
 tensorflow/core/kernels/reduce_join_op.cc            |  2 +-
 .../core/kernels/sparse_dense_binary_op_shared.cc    |  2 +-
 tensorflow/core/util/example_proto_helper.cc         | 12 ++++++------
 tensorflow/core/util/sparse/sparse_tensor.h          |  2 +-
 9 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/tensorflow/cc/framework/cc_op_gen.cc b/tensorflow/cc/framework/cc_op_gen.cc
index 659fdfb153..8ed53404f1 100644
--- a/tensorflow/cc/framework/cc_op_gen.cc
+++ b/tensorflow/cc/framework/cc_op_gen.cc
@@ -232,7 +232,7 @@ string PrintAttrValue(string op, const AttrValue& attr_value) {
 string ToCamelCase(const string& str) {
   string result;
   const char joiner = '_';
-  int i = 0;
+  size_t i = 0;
   bool cap = true;
   while (i < str.size()) {
     const char c = str[i++];
diff --git a/tensorflow/core/debug/debug_graph_utils.cc b/tensorflow/core/debug/debug_graph_utils.cc
index 118847686d..c2e50f4d1b 100644
--- a/tensorflow/core/debug/debug_graph_utils.cc
+++ b/tensorflow/core/debug/debug_graph_utils.cc
@@ -154,7 +154,7 @@ Status DebugNodeInserter::InsertNodes(
 
       // Create all requested debug nodes and their edges to the Copy node.
       std::vector<Node*> node_added_debug_nodes;
-      for (int i = 0; i < tensor_watches[tensor_name].size(); ++i) {
+      for (size_t i = 0; i < tensor_watches[tensor_name].size(); ++i) {
         const string& debug_op_name = tensor_watches[tensor_name][i];
 
         Node* debug_node;
diff --git a/tensorflow/core/framework/log_memory.cc b/tensorflow/core/framework/log_memory.cc
index ba221179ce..dc82504d43 100644
--- a/tensorflow/core/framework/log_memory.cc
+++ b/tensorflow/core/framework/log_memory.cc
@@ -30,7 +30,7 @@ namespace {
 template <typename T>
 void OutputToLog(const T& proto) {
   string type_name = proto.GetTypeName();
-  const int index = type_name.find_last_of(".");
+  const size_t index = type_name.find_last_of(".");
   if (index != string::npos) type_name = type_name.substr(index + 1);
   LOG(INFO) << LogMemory::kLogMemoryLabel << " " << type_name << " { "
             << ProtoShortDebugString(proto) << " }";
diff --git a/tensorflow/core/framework/op.cc b/tensorflow/core/framework/op.cc
index 65bfd47433..6bff192b1e 100644
--- a/tensorflow/core/framework/op.cc
+++ b/tensorflow/core/framework/op.cc
@@ -156,7 +156,7 @@ string OpRegistry::DebugString(bool include_internal) const {
 bool OpRegistry::MustCallDeferred() const {
   if (initialized_) return false;
   initialized_ = true;
-  for (int i = 0; i < deferred_.size(); ++i) {
+  for (size_t i = 0; i < deferred_.size(); ++i) {
     TF_QCHECK_OK(RegisterAlreadyLocked(deferred_[i]));
   }
   deferred_.clear();
@@ -166,7 +166,7 @@ bool OpRegistry::MustCallDeferred() const {
 Status OpRegistry::CallDeferred() const {
   if (initialized_) return Status::OK();
   initialized_ = true;
-  for (int i = 0; i < deferred_.size(); ++i) {
+  for (size_t i = 0; i < deferred_.size(); ++i) {
     Status s = RegisterAlreadyLocked(deferred_[i]);
     if (!s.ok()) {
       return s;
diff --git a/tensorflow/core/framework/tensor.cc b/tensorflow/core/framework/tensor.cc
index 6361791cb0..c6c5aab476 100644
--- a/tensorflow/core/framework/tensor.cc
+++ b/tensorflow/core/framework/tensor.cc
@@ -740,7 +740,7 @@ string Tensor::SummarizeValue(int64 max_entries) const {
       string ret;
       // TODO(irving): Don't call flat every time around this
       // loop.
-      for (int64 i = 0; i < limit; ++i) {
+      for (size_t i = 0; i < limit; ++i) {
         if (i > 0) strings::StrAppend(&ret, " ");
         switch (dtype()) {
           case DT_STRING:
diff --git a/tensorflow/core/kernels/reduce_join_op.cc b/tensorflow/core/kernels/reduce_join_op.cc
index 1bd415c0d1..b72bfeb15f 100644
--- a/tensorflow/core/kernels/reduce_join_op.cc
+++ b/tensorflow/core/kernels/reduce_join_op.cc
@@ -105,7 +105,7 @@ void MakeUnreducedIndices(gtl::InlinedVector<bool, 8> index_is_reduced,
 TensorShape GetOutputShape(gtl::InlinedVector<bool, 8> index_is_reduced,
                            const TensorShape& input_shape, bool keep_dims) {
   TensorShape output_shape;
-  for (int32 index = 0; index < index_is_reduced.size(); ++index) {
+  for (size_t index = 0; index < index_is_reduced.size(); ++index) {
     if (index_is_reduced[index]) {
       if (keep_dims) output_shape.AddDim(1);
     } else {
diff --git a/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc b/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc
index dbd1d9466c..2d1539fb9d 100644
--- a/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc
+++ b/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc
@@ -91,7 +91,7 @@ class SparseDenseBinaryOpShared : public OpKernel {
     auto VecGreaterEq = [](ArraySlice<int64> lhs, ArraySlice<int64> rhs) {
       if (lhs.size() > rhs.size()) return true;
       if (lhs.size() < rhs.size()) return false;
-      for (int i = 0; i < lhs.size(); ++i) {
+      for (size_t i = 0; i < lhs.size(); ++i) {
         if (lhs[i] < rhs[i]) return false;
       }
       return true;
diff --git a/tensorflow/core/util/example_proto_helper.cc b/tensorflow/core/util/example_proto_helper.cc
index acab74732e..658b3588e6 100644
--- a/tensorflow/core/util/example_proto_helper.cc
+++ b/tensorflow/core/util/example_proto_helper.cc
@@ -222,7 +222,7 @@ Status SingleExampleProtoToTensors(
   const auto& feature_dict = features.feature();
 
   // Handle dense features.
-  for (int d = 0; d < fixed_len_features.size(); ++d) {
+  for (size_t d = 0; d < fixed_len_features.size(); ++d) {
     const FixedLenFeature& feature_config = fixed_len_features[d];
     const string& key = feature_config.key;
     const DataType& dtype = feature_config.dtype;
@@ -263,7 +263,7 @@ Status SingleExampleProtoToTensors(
   }
 
   // Handle sparse features.
-  for (int d = 0; d < var_len_features.size(); ++d) {
+  for (size_t d = 0; d < var_len_features.size(); ++d) {
     const VarLenFeature& feature_config = var_len_features[d];
     const string& key = feature_config.key;
     const DataType& dtype = feature_config.dtype;
@@ -338,7 +338,7 @@ Status BatchExampleProtoToTensors(
       fixed_len_features.size());
 
   // Preallocate dense_values, since we know their sizes.
-  for (int d = 0; d < fixed_len_features.size(); ++d) {
+  for (size_t d = 0; d < fixed_len_features.size(); ++d) {
     const FixedLenFeature& config = fixed_len_features[d];
     TensorShape out_shape;
     out_shape.AddDim(batch_size);
@@ -352,11 +352,11 @@ Status BatchExampleProtoToTensors(
   // Temporary vector to hold sparse values.
   std::vector<std::vector<Tensor>> sparse_values_tmp(var_len_features.size());
 
-  for (int d = 0; d < var_len_features.size(); ++d) {
+  for (size_t d = 0; d < var_len_features.size(); ++d) {
     sparse_values_tmp[d] = std::vector<Tensor>(batch_size);
   }
 
-  for (int b = 0; b < examples.size(); ++b) {
+  for (size_t b = 0; b < examples.size(); ++b) {
     const Example& ex = *(examples[b]);
     const string& example_name = (has_names) ? names[b] : "<unknown>";
     SingleExampleProtoToTensors(
@@ -364,7 +364,7 @@ Status BatchExampleProtoToTensors(
         &output_dense_values_tensor_ptrs, &sparse_values_tmp);
   }
 
-  for (int d = 0; d < var_len_features.size(); ++d) {
+  for (size_t d = 0; d < var_len_features.size(); ++d) {
     const VarLenFeature& feature_config = var_len_features[d];
     const DataType& dtype = feature_config.dtype;
     const std::vector<Tensor>& sparse_values_tensor = sparse_values_tmp[d];
diff --git a/tensorflow/core/util/sparse/sparse_tensor.h b/tensorflow/core/util/sparse/sparse_tensor.h
index 79064e9988..1665446aee 100644
--- a/tensorflow/core/util/sparse/sparse_tensor.h
+++ b/tensorflow/core/util/sparse/sparse_tensor.h
@@ -283,7 +283,7 @@ void SparseTensor::Reorder(const VarDimArray& order) {
   // permutation (the inverse).  This can be calculated with O(1)
   // additional
   // and O(n) time (INVPERM) but we just do the simple thing here.
-  std::vector<int64> permutation(reorder.size());
+  std::vector<size_t> permutation(reorder.size());
   for (std::size_t n = 0; n < reorder.size(); ++n) {
     permutation[reorder[n]] = n;
   }
-- 
GitLab


From cc3153a7a0a23533d14ead34db37e4ccd7892079 Mon Sep 17 00:00:00 2001
From: Egor-Krivov <egor.krivov@frtk.ru>
Date: Tue, 23 Aug 2016 23:22:50 +0400
Subject: [PATCH 050/223] Fixed inconsistency of outputs collection (#3635)

* Fixed inconsistency of outputs collection

Because naming logic for utils.collect_named_outputs in max_pool2d and convolution2d is different, collection is getting filled with inconsistent names.
code to reproduce:
```
graph = tf.Graph()

node_collection = 'nodes'
with graph.as_default():
    x = tf.placeholder(tf.float32, (1, 20, 20, 3))

    with tf.name_scope('name_scope'):
        conv = tf.contrib.layers.conv2d(
            x, num_outputs=8, kernel_size=4,
            scope='conv', outputs_collections=node_collection)
        max_pool = tf.contrib.layers.max_pool2d(
                conv, kernel_size=2, stride=2, scope='max_pool',
                outputs_collections=node_collection)

print([nt.name for nt in graph.get_collection(node_collection)])
=============================================
['conv', 'name_scope/max_pool']
```
Expected output is ['name_scope/conv', 'name_scope/max_pool'].

It is also important for those who use tf.name_scope to distinguish processing of different inputs with the same layer, like in siamese neural network. Currently, they will get identical names.
Code to reproduce:
```
graph = tf.Graph()

node_collection = 'nodes'
with graph.as_default():
  input_a = tf.placeholder(tf.float32, (1, 20, 20, 3))
  input_b = tf.placeholder(tf.float32, (1, 20, 20, 3))
  with tf.variable_scope('feature_extractor') as vs:
    for name_scope, inputs in [('a', input_a), ('b', input_b)]:
      with tf.name_scope(name_scope):
        conv = tf.contrib.layers.conv2d(
          inputs, num_outputs=8, kernel_size=4,
          scope='conv', outputs_collections=node_collection)
        max_pool = tf.contrib.layers.max_pool2d(
          conv, kernel_size=2, stride=2, scope='max_pool',
          outputs_collections=node_collection)
      vs.reuse_variables()

print([nt.name for nt in graph.get_collection(node_collection)])
=============================================
['feature_extractor/conv', 'feature_extractor/a/max_pool', 'feature_extractor/conv', 'feature_extractor/b/max_pool']
```

* Tests for collection naming

Tests to check if collection naming is correct for fully connected and convolution2d contrib layers.
---
 .../contrib/layers/python/layers/layers.py    | 21 ++++++++++++-------
 .../layers/python/layers/layers_test.py       | 20 ++++++++++++++++++
 2 files changed, 34 insertions(+), 7 deletions(-)

diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index cf1e13b97e..2a91711868 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -261,7 +261,8 @@ def batch_norm(inputs,
     outputs.set_shape(inputs_shape)
     if activation_fn:
       outputs = activation_fn(outputs)
-    return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope, outputs)
 
 
 @add_arg_scope
@@ -313,7 +314,8 @@ def bias_add(inputs,
     outputs = nn.bias_add(inputs, biases)
     if activation_fn:
       outputs = activation_fn(outputs)
-    return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope, outputs)
 
 
 @add_arg_scope
@@ -426,7 +428,8 @@ def convolution2d(inputs,
         outputs = nn.bias_add(outputs, biases)
     if activation_fn:
       outputs = activation_fn(outputs)
-    return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope, outputs)
 
 
 @add_arg_scope
@@ -526,7 +529,8 @@ def convolution2d_in_plane(
 
     if activation_fn:
       outputs = activation_fn(outputs)
-    return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope, outputs)
 
 
 @add_arg_scope
@@ -653,7 +657,8 @@ def convolution2d_transpose(
 
     if activation_fn:
       outputs = activation_fn(outputs)
-    return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope, outputs)
 
 
 @add_arg_scope
@@ -830,7 +835,8 @@ def fully_connected(inputs,
       # Reshape back outputs
       outputs = array_ops.reshape(outputs, array_ops.pack(out_shape))
       outputs.set_shape(static_shape)
-    return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope, outputs)
 
 
 @add_arg_scope
@@ -1079,7 +1085,8 @@ def separable_convolution2d(
 
     if activation_fn:
       outputs = activation_fn(outputs)
-    return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope, outputs)
 
 
 @add_arg_scope
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index 4d84989405..02eb517476 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -202,6 +202,16 @@ class Convolution2dTest(tf.test.TestCase):
       output = tf.contrib.layers.convolution2d(images, 32, [3, 3],
                                                scope='conv1')
       self.assertEquals(output.op.name, 'conv1/Relu')
+  
+  def testCreateConvWithCollection(self):
+    height, width = 3, 3
+    images = tf.random_uniform((5, height, width, 3), seed=1)
+    with tf.name_scope('fe'):
+      conv = tf.contrib.layers.convolution2d(
+        images, 32, [3, 3], outputs_collections='outputs',
+        scope='Conv')
+    namedOutputs = tf.get_collection('outputs')[0]
+    self.assertEquals(namedOutputs.name, 'fe/Conv')
 
   def testCreateConvWithoutActivation(self):
     height, width = 3, 3
@@ -988,6 +998,16 @@ class FCTest(tf.test.TestCase):
       inputs = tf.random_uniform((5, height * width * 3), seed=1)
       output = tf.contrib.layers.fully_connected(inputs, 32, scope='fc1')
       self.assertEquals(output.op.name, 'fc1/Relu')
+      
+  def testCreateFCWithCollection(self):
+    height, width = 3, 3
+    inputs = tf.random_uniform((5, height * width * 3), seed=1)
+    with tf.name_scope('fe'):
+      fc = tf.contrib.layers.fully_connected(
+        inputs, 7, outputs_collections='outputs',
+        scope='fc')
+    namedOutputs = tf.get_collection('outputs')[0]
+    self.assertEquals(namedOutputs.name, 'fe/fc')
 
   def testCreateFcCreatesWeightsAndBiasesVars(self):
     height, width = 3, 3
-- 
GitLab


From f36494c26ffbe5771bb48db95d98d9fbb83653b4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 11:19:50 -0800
Subject: [PATCH 051/223] Update generated Python Op docs. Change: 131084043

---
 .../g3doc/api_docs/python/contrib.training.md | 135 ++++++++++++++++++
 ...training.stratified_sample_unknown_dist.md |  58 ++++++++
 .../tf.contrib.training.stratified_sample.md  |  60 ++++++++
 tensorflow/g3doc/api_docs/python/index.md     |   2 +
 4 files changed, 255 insertions(+)
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.training.stratified_sample_unknown_dist.md
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.training.stratified_sample.md

diff --git a/tensorflow/g3doc/api_docs/python/contrib.training.md b/tensorflow/g3doc/api_docs/python/contrib.training.md
index e5a93fd944..dc71191409 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.training.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.training.md
@@ -718,3 +718,138 @@ It should be run in a separate thread via e.g. a `QueueRunner`.
 
 
 
+
+
+## Online data resampling
+
+Use ['stratified_sample'](#stratified_sample) or
+['stratified_sample_unknown_dist'](#stratified_sample_unknown_dist) to resample
+from the data and change the class proportions that the Tensorflow graph sees.
+For instance, if you have a binary classification dataset that is 99.9% class
+1, a common approach is to resample from the data so that the data is more
+balanced.
+
+- - -
+
+### `tf.contrib.training.stratified_sample(tensors, labels, target_probs, batch_size, init_probs=None, enqueue_many=False, queue_capacity=16, threads_per_queue=1, name=None)` {#stratified_sample}
+
+Stochastically creates batches based on per-class probabilities.
+
+This method discards examples. Internally, it creates one queue to amortize
+the cost of disk reads, and one queue to hold the properly-proportioned
+batch. See `stratified_sample_unknown_dist` for a function that performs
+stratified sampling with one queue per class and doesn't require knowing the
+class data-distribution ahead of time.
+
+##### Args:
+
+
+*  <b>`tensors`</b>: List of tensors for data. All tensors are either one item or a
+      batch, according to enqueue_many.
+*  <b>`labels`</b>: Tensor for label of data. Label is a single integer or a batch,
+      depending on enqueue_many. It is not a one-hot vector.
+*  <b>`target_probs`</b>: Target class proportions in batch. An object whose type has a
+      registered Tensor conversion function.
+*  <b>`batch_size`</b>: Size of batch to be returned.
+*  <b>`init_probs`</b>: Class proportions in the data. An object whose type has a
+      registered Tensor conversion function, or `None` for estimating the
+      initial distribution.
+*  <b>`enqueue_many`</b>: Bool. If true, interpret input tensors as having a batch
+      dimension.
+*  <b>`queue_capacity`</b>: Capacity of the large queue that holds input examples.
+*  <b>`threads_per_queue`</b>: Number of threads for the large queue that holds input
+      examples and for the final queue with the proper class proportions.
+*  <b>`name`</b>: Optional prefix for ops created by this function.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: enqueue_many is True and labels doesn't have a batch
+      dimension, or if enqueue_many is False and labels isn't a scalar.
+*  <b>`ValueError`</b>: enqueue_many is True, and batch dimension on data and labels
+      don't match.
+*  <b>`ValueError`</b>: if probs don't sum to one.
+*  <b>`ValueError`</b>: if a zero initial probability class has a nonzero target
+      probability.
+*  <b>`TFAssertion`</b>: if labels aren't integers in [0, num classes).
+
+##### Returns:
+
+  (data_batch, label_batch), where data_batch is a list of tensors of the same
+      length as `tensors`
+
+##### Example:
+
+  # Get tensor for a single data and label example.
+  data, label = data_provider.Get(['data', 'label'])
+
+  # Get stratified batch according to per-class probabilities.
+  target_probs = [...distribution you want...]
+  [data_batch], labels = tf.contrib.framework.sampling_ops.stratified_sample(
+      [data], label, target_probs)
+
+  # Run batch through network.
+  ...
+
+
+- - -
+
+### `tf.contrib.training.stratified_sample_unknown_dist(tensors, labels, probs, batch_size, enqueue_many=False, queue_capacity=16, threads_per_queue=1, name=None)` {#stratified_sample_unknown_dist}
+
+Stochastically creates batches based on per-class probabilities.
+
+**NOTICE** This sampler can be significantly slower than `stratified_sample`
+due to each thread discarding all examples not in its assigned class.
+
+This uses a number of threads proportional to the number of classes. See
+`stratified_sample` for an implementation that discards fewer examples and
+uses a fixed number of threads. This function's only advantage over
+`stratified_sample` is that the class data-distribution doesn't need to be
+known ahead of time.
+
+##### Args:
+
+
+*  <b>`tensors`</b>: List of tensors for data. All tensors are either one item or a
+      batch, according to enqueue_many.
+*  <b>`labels`</b>: Tensor for label of data. Label is a single integer or a batch,
+      depending on enqueue_many. It is not a one-hot vector.
+*  <b>`probs`</b>: Target class probabilities. An object whose type has a registered
+      Tensor conversion function.
+*  <b>`batch_size`</b>: Size of batch to be returned.
+*  <b>`enqueue_many`</b>: Bool. If true, interpret input tensors as having a batch
+      dimension.
+*  <b>`queue_capacity`</b>: Capacity of each per-class queue.
+*  <b>`threads_per_queue`</b>: Number of threads for each per-class queue.
+*  <b>`name`</b>: Optional prefix for ops created by this function.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: enqueue_many is True and labels doesn't have a batch
+      dimension, or if enqueue_many is False and labels isn't a scalar.
+*  <b>`ValueError`</b>: enqueue_many is True, and batch dimension of data and labels
+      don't match.
+*  <b>`ValueError`</b>: if probs don't sum to one.
+*  <b>`TFAssertion`</b>: if labels aren't integers in [0, num classes).
+
+##### Returns:
+
+  (data_batch, label_batch), where data_batch is a list of tensors of the same
+      length as `tensors`
+
+##### Example:
+
+  # Get tensor for a single data and label example.
+  data, label = data_provider.Get(['data', 'label'])
+
+  # Get stratified batch according to per-class probabilities.
+  init_probs = [1.0/NUM_CLASSES for _ in range(NUM_CLASSES)]
+  [data_batch], labels = (
+      tf.contrib.framework.sampling_ops.stratified_sample_unknown_dist(
+          [data], label, init_probs, 16))
+
+  # Run batch through network.
+  ...
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.training.stratified_sample_unknown_dist.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.training.stratified_sample_unknown_dist.md
new file mode 100644
index 0000000000..e4932aa73b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.training.stratified_sample_unknown_dist.md
@@ -0,0 +1,58 @@
+### `tf.contrib.training.stratified_sample_unknown_dist(tensors, labels, probs, batch_size, enqueue_many=False, queue_capacity=16, threads_per_queue=1, name=None)` {#stratified_sample_unknown_dist}
+
+Stochastically creates batches based on per-class probabilities.
+
+**NOTICE** This sampler can be significantly slower than `stratified_sample`
+due to each thread discarding all examples not in its assigned class.
+
+This uses a number of threads proportional to the number of classes. See
+`stratified_sample` for an implementation that discards fewer examples and
+uses a fixed number of threads. This function's only advantage over
+`stratified_sample` is that the class data-distribution doesn't need to be
+known ahead of time.
+
+##### Args:
+
+
+*  <b>`tensors`</b>: List of tensors for data. All tensors are either one item or a
+      batch, according to enqueue_many.
+*  <b>`labels`</b>: Tensor for label of data. Label is a single integer or a batch,
+      depending on enqueue_many. It is not a one-hot vector.
+*  <b>`probs`</b>: Target class probabilities. An object whose type has a registered
+      Tensor conversion function.
+*  <b>`batch_size`</b>: Size of batch to be returned.
+*  <b>`enqueue_many`</b>: Bool. If true, interpret input tensors as having a batch
+      dimension.
+*  <b>`queue_capacity`</b>: Capacity of each per-class queue.
+*  <b>`threads_per_queue`</b>: Number of threads for each per-class queue.
+*  <b>`name`</b>: Optional prefix for ops created by this function.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: enqueue_many is True and labels doesn't have a batch
+      dimension, or if enqueue_many is False and labels isn't a scalar.
+*  <b>`ValueError`</b>: enqueue_many is True, and batch dimension of data and labels
+      don't match.
+*  <b>`ValueError`</b>: if probs don't sum to one.
+*  <b>`TFAssertion`</b>: if labels aren't integers in [0, num classes).
+
+##### Returns:
+
+  (data_batch, label_batch), where data_batch is a list of tensors of the same
+      length as `tensors`
+
+##### Example:
+
+  # Get tensor for a single data and label example.
+  data, label = data_provider.Get(['data', 'label'])
+
+  # Get stratified batch according to per-class probabilities.
+  init_probs = [1.0/NUM_CLASSES for _ in range(NUM_CLASSES)]
+  [data_batch], labels = (
+      tf.contrib.framework.sampling_ops.stratified_sample_unknown_dist(
+          [data], label, init_probs, 16))
+
+  # Run batch through network.
+  ...
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.training.stratified_sample.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.training.stratified_sample.md
new file mode 100644
index 0000000000..95f2a69b1a
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.training.stratified_sample.md
@@ -0,0 +1,60 @@
+### `tf.contrib.training.stratified_sample(tensors, labels, target_probs, batch_size, init_probs=None, enqueue_many=False, queue_capacity=16, threads_per_queue=1, name=None)` {#stratified_sample}
+
+Stochastically creates batches based on per-class probabilities.
+
+This method discards examples. Internally, it creates one queue to amortize
+the cost of disk reads, and one queue to hold the properly-proportioned
+batch. See `stratified_sample_unknown_dist` for a function that performs
+stratified sampling with one queue per class and doesn't require knowing the
+class data-distribution ahead of time.
+
+##### Args:
+
+
+*  <b>`tensors`</b>: List of tensors for data. All tensors are either one item or a
+      batch, according to enqueue_many.
+*  <b>`labels`</b>: Tensor for label of data. Label is a single integer or a batch,
+      depending on enqueue_many. It is not a one-hot vector.
+*  <b>`target_probs`</b>: Target class proportions in batch. An object whose type has a
+      registered Tensor conversion function.
+*  <b>`batch_size`</b>: Size of batch to be returned.
+*  <b>`init_probs`</b>: Class proportions in the data. An object whose type has a
+      registered Tensor conversion function, or `None` for estimating the
+      initial distribution.
+*  <b>`enqueue_many`</b>: Bool. If true, interpret input tensors as having a batch
+      dimension.
+*  <b>`queue_capacity`</b>: Capacity of the large queue that holds input examples.
+*  <b>`threads_per_queue`</b>: Number of threads for the large queue that holds input
+      examples and for the final queue with the proper class proportions.
+*  <b>`name`</b>: Optional prefix for ops created by this function.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: enqueue_many is True and labels doesn't have a batch
+      dimension, or if enqueue_many is False and labels isn't a scalar.
+*  <b>`ValueError`</b>: enqueue_many is True, and batch dimension on data and labels
+      don't match.
+*  <b>`ValueError`</b>: if probs don't sum to one.
+*  <b>`ValueError`</b>: if a zero initial probability class has a nonzero target
+      probability.
+*  <b>`TFAssertion`</b>: if labels aren't integers in [0, num classes).
+
+##### Returns:
+
+  (data_batch, label_batch), where data_batch is a list of tensors of the same
+      length as `tensors`
+
+##### Example:
+
+  # Get tensor for a single data and label example.
+  data, label = data_provider.Get(['data', 'label'])
+
+  # Get stratified batch according to per-class probabilities.
+  target_probs = [...distribution you want...]
+  [data_batch], labels = tf.contrib.framework.sampling_ops.stratified_sample(
+      [data], label, target_probs)
+
+  # Run batch through network.
+  ...
+
diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md
index 8ab445b732..8a9c8b95c5 100644
--- a/tensorflow/g3doc/api_docs/python/index.md
+++ b/tensorflow/g3doc/api_docs/python/index.md
@@ -885,6 +885,8 @@
   * [`batch_sequences_with_states`](../../api_docs/python/contrib.training.md#batch_sequences_with_states)
   * [`NextQueuedSequenceBatch`](../../api_docs/python/contrib.training.md#NextQueuedSequenceBatch)
   * [`SequenceQueueingStateSaver`](../../api_docs/python/contrib.training.md#SequenceQueueingStateSaver)
+  * [`stratified_sample`](../../api_docs/python/contrib.training.md#stratified_sample)
+  * [`stratified_sample_unknown_dist`](../../api_docs/python/contrib.training.md#stratified_sample_unknown_dist)
 
 * **[Utilities (contrib)](../../api_docs/python/contrib.util.md)**:
   * [`constant_value`](../../api_docs/python/contrib.util.md#constant_value)
-- 
GitLab


From ed906d9df15fa16df7aa9fae8250b703b2e864f2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 11:42:27 -0800
Subject: [PATCH 052/223] Fix typo. Change: 131085840

---
 tensorflow/contrib/learn/python/learn/evaluable.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/learn/python/learn/evaluable.py b/tensorflow/contrib/learn/python/learn/evaluable.py
index 6a02d57a35..db76e3578d 100644
--- a/tensorflow/contrib/learn/python/learn/evaluable.py
+++ b/tensorflow/contrib/learn/python/learn/evaluable.py
@@ -39,7 +39,7 @@ class Evaluable(object):
     processed.
     - If `input_fn` is provided, and it raises an end-of-input
     exception (`OutOfRangeError` or `StopIteration`).
-    - If `x` is provided, and all items in `x` have ben processed.
+    - If `x` is provided, and all items in `x` have been processed.
 
     The return value is a dict containing the metrics specified in `metrics`, as
     well as an entry `global_step` which contains the value of the global step
-- 
GitLab


From b73bad5a5aaae94bfc112bfec3adc5fea8c006df Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 12:17:58 -0800
Subject: [PATCH 053/223] Fix build breakage on Android Makefile build Change:
 131088365

---
 tensorflow/contrib/makefile/Makefile                   | 2 +-
 tensorflow/contrib/makefile/proto_text_pb_cc_files.txt | 1 +
 tensorflow/contrib/makefile/proto_text_pb_h_files.txt  | 1 +
 tensorflow/contrib/makefile/tf_op_files.txt            | 1 +
 tensorflow/contrib/makefile/tf_pb_text_files.txt       | 1 +
 tensorflow/contrib/makefile/tf_proto_files.txt         | 1 +
 6 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/makefile/Makefile b/tensorflow/contrib/makefile/Makefile
index e7c0488c2c..6530d32fee 100644
--- a/tensorflow/contrib/makefile/Makefile
+++ b/tensorflow/contrib/makefile/Makefile
@@ -224,7 +224,7 @@ ifeq ($(TARGET),ANDROID)
 	CXX := $(CC_PREFIX) $(NDK_ROOT)/toolchains/arm-linux-androideabi-4.9/prebuilt/$(OS_PATH)-x86_64/bin/arm-linux-androideabi-g++
 	CXXFLAGS +=\
 --sysroot $(NDK_ROOT)/platforms/android-21/arch-arm \
--Wno-c++11-narrowing \
+-Wno-narrowing \
 -march=armv7-a \
 -fPIE
 
diff --git a/tensorflow/contrib/makefile/proto_text_pb_cc_files.txt b/tensorflow/contrib/makefile/proto_text_pb_cc_files.txt
index c680c0d04c..2b2513e80f 100644
--- a/tensorflow/contrib/makefile/proto_text_pb_cc_files.txt
+++ b/tensorflow/contrib/makefile/proto_text_pb_cc_files.txt
@@ -19,6 +19,7 @@ tensorflow/core/framework/tensor.pb.cc
 tensorflow/core/framework/summary.pb.cc
 tensorflow/core/framework/step_stats.pb.cc
 tensorflow/core/framework/op_def.pb.cc
+tensorflow/core/framework/node_def.pb.cc
 tensorflow/core/framework/log_memory.pb.cc
 tensorflow/core/framework/kernel_def.pb.cc
 tensorflow/core/framework/graph.pb.cc
diff --git a/tensorflow/contrib/makefile/proto_text_pb_h_files.txt b/tensorflow/contrib/makefile/proto_text_pb_h_files.txt
index 29aa8a8ef2..b602873439 100644
--- a/tensorflow/contrib/makefile/proto_text_pb_h_files.txt
+++ b/tensorflow/contrib/makefile/proto_text_pb_h_files.txt
@@ -19,6 +19,7 @@ tensorflow/core/framework/tensor.pb.h
 tensorflow/core/framework/summary.pb.h
 tensorflow/core/framework/step_stats.pb.h
 tensorflow/core/framework/op_def.pb.h
+tensorflow/core/framework/node_def.pb.h
 tensorflow/core/framework/log_memory.pb.h
 tensorflow/core/framework/kernel_def.pb.h
 tensorflow/core/framework/graph.pb.h
diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt
index 4a92a2f000..5c0128db78 100644
--- a/tensorflow/contrib/makefile/tf_op_files.txt
+++ b/tensorflow/contrib/makefile/tf_op_files.txt
@@ -12,6 +12,7 @@ tensorflow/core/kernels/tile_ops_cpu_impl_2.cc
 tensorflow/core/kernels/tile_ops_cpu_impl_3.cc
 tensorflow/core/kernels/tile_ops_cpu_impl_4.cc
 tensorflow/core/kernels/tile_ops_cpu_impl_5.cc
+tensorflow/core/kernels/tile_ops_cpu_impl_6.cc
 tensorflow/core/kernels/strided_slice_op_inst_6.cc
 tensorflow/core/kernels/strided_slice_op_inst_5.cc
 tensorflow/core/kernels/strided_slice_op_inst_4.cc
diff --git a/tensorflow/contrib/makefile/tf_pb_text_files.txt b/tensorflow/contrib/makefile/tf_pb_text_files.txt
index ff400d16fa..9db31e5d23 100644
--- a/tensorflow/contrib/makefile/tf_pb_text_files.txt
+++ b/tensorflow/contrib/makefile/tf_pb_text_files.txt
@@ -12,6 +12,7 @@ tensorflow/core/framework/tensor.pb_text.cc
 tensorflow/core/framework/summary.pb_text.cc
 tensorflow/core/framework/step_stats.pb_text.cc
 tensorflow/core/framework/op_def.pb_text.cc
+tensorflow/core/framework/node_def.pb_text.cc
 tensorflow/core/framework/log_memory.pb_text.cc
 tensorflow/core/framework/kernel_def.pb_text.cc
 tensorflow/core/framework/graph.pb_text.cc
diff --git a/tensorflow/contrib/makefile/tf_proto_files.txt b/tensorflow/contrib/makefile/tf_proto_files.txt
index a40bb046b9..1e048bc637 100644
--- a/tensorflow/contrib/makefile/tf_proto_files.txt
+++ b/tensorflow/contrib/makefile/tf_proto_files.txt
@@ -19,6 +19,7 @@ tensorflow/core/framework/tensor.proto
 tensorflow/core/framework/summary.proto
 tensorflow/core/framework/step_stats.proto
 tensorflow/core/framework/op_def.proto
+tensorflow/core/framework/node_def.proto
 tensorflow/core/framework/log_memory.proto
 tensorflow/core/framework/kernel_def.proto
 tensorflow/core/framework/graph.proto
-- 
GitLab


From 31e0b3bef5fa5e2108f8923633022bbd29b3d287 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Tue, 23 Aug 2016 12:29:10 -0800
Subject: [PATCH 054/223] Rename Port to Output in the Go API. Change:
 131089292

---
 tensorflow/go/operation.go    | 20 +++++++++++---------
 tensorflow/go/session.go      |  2 +-
 tensorflow/go/session_test.go | 18 +++++++++---------
 3 files changed, 21 insertions(+), 19 deletions(-)

diff --git a/tensorflow/go/operation.go b/tensorflow/go/operation.go
index 19eb1a5816..7d9a9655dc 100644
--- a/tensorflow/go/operation.go
+++ b/tensorflow/go/operation.go
@@ -24,17 +24,19 @@ type Operation struct {
 	c *C.TF_Operation
 }
 
-// Port represents a specific input or output of an operation, e.g. to specify
-// the specific output to pass as an input to a new op.
-//
-// Note the difference in naming convention: Port corresponds to Tensor/Output
-// in the Python API.
-type Port struct {
-	Op    *Operation
+// Output represents one of the outputs of an operation in the graph. Has a
+// DataType (and eventually a Shape).  May be passed as an input argument to a
+// function for adding operations to a graph, or to a Session's Run() method to
+// fetch that output as a tensor.
+type Output struct {
+	// Op is the Operation that produces this Output.
+	Op *Operation
+
+	// Index specifies the index of the output within the Operation.
 	Index int
 }
 
-func (p *Port) c() C.TF_Port {
+func (p *Output) c() C.TF_Port {
 	return C.TF_Port{oper: p.Op.c, index: C.int(p.Index)}
 }
 
@@ -67,7 +69,7 @@ func (b *opBuilder) SetAttrType(name string, typ DataType) {
 	C.free(unsafe.Pointer(attrName))
 }
 
-func (b *opBuilder) AddInput(port Port) {
+func (b *opBuilder) AddInput(port Output) {
 	C.TF_AddInput(b.c, port.c())
 }
 
diff --git a/tensorflow/go/session.go b/tensorflow/go/session.go
index 98a87602d1..41b6ad4683 100644
--- a/tensorflow/go/session.go
+++ b/tensorflow/go/session.go
@@ -66,7 +66,7 @@ func NewSession(graph *Graph, options *SessionOptions) (*Session, error) {
 // On success, returns the Tensor outputs in the same order as supplied in
 // the outputs argument. If outputs is set to nil, the returned Tensor outputs
 // is empty.
-func (s *Session) Run(inputs map[Port]*Tensor, outputs []Port, targets []*Operation) ([]*Tensor, error) {
+func (s *Session) Run(inputs map[Output]*Tensor, outputs []Output, targets []*Operation) ([]*Tensor, error) {
 	s.mu.Lock()
 	if s.c == nil {
 		s.mu.Unlock()
diff --git a/tensorflow/go/session_test.go b/tensorflow/go/session_test.go
index 78f6bccfd6..bebe2ac4de 100644
--- a/tensorflow/go/session_test.go
+++ b/tensorflow/go/session_test.go
@@ -19,27 +19,27 @@ import (
 	"testing"
 )
 
-func Placeholder(g *Graph, name string, dt DataType) (Port, error) {
+func Placeholder(g *Graph, name string, dt DataType) (Output, error) {
 	b := newOpBuilder(g, "Placeholder", name)
 	b.SetAttrType("dtype", dt)
 	op, err := b.Build()
 	if err != nil {
-		return Port{}, err
+		return Output{}, err
 	}
-	return Port{op, 0}, nil
+	return Output{op, 0}, nil
 }
 
-func Neg(g *Graph, name string, port Port) (Port, error) {
+func Neg(g *Graph, name string, port Output) (Output, error) {
 	b := newOpBuilder(g, "Neg", name)
 	b.AddInput(port)
 	op, err := b.Build()
 	if err != nil {
-		return Port{}, err
+		return Output{}, err
 	}
-	return Port{op, 0}, nil
+	return Output{op, 0}, nil
 }
 
-func createTestGraph(t *testing.T, dt DataType) (*Graph, Port, Port) {
+func createTestGraph(t *testing.T, dt DataType) (*Graph, Output, Output) {
 	g := NewGraph()
 	inp, err := Placeholder(g, "p1", dt)
 	if err != nil {
@@ -72,7 +72,7 @@ func TestSessionRunNeg(t *testing.T) {
 		if err != nil {
 			t.Fatalf("NewSession() for %v: %v", test.input, err)
 		}
-		output, err := s.Run(map[Port]*Tensor{inp: t1}, []Port{out}, []*Operation{out.Op})
+		output, err := s.Run(map[Output]*Tensor{inp: t1}, []Output{out}, []*Operation{out.Op})
 		if err != nil {
 			t.Fatalf("Run() for %v: %v", test.input, err)
 		}
@@ -103,7 +103,7 @@ func TestConcurrency(t *testing.T) {
 	}
 	for i := 0; i < 100; i++ {
 		// Session may close before Run() starts, so we don't check the error.
-		go s.Run(map[Port]*Tensor{inp: tensor}, []Port{out}, []*Operation{out.Op})
+		go s.Run(map[Output]*Tensor{inp: tensor}, []Output{out}, []*Operation{out.Op})
 	}
 	if err = s.Close(); err != nil {
 		t.Errorf("Close() 1: %v", err)
-- 
GitLab


From 69c00c90330e8c0351f9d16e7a1aad725522aca5 Mon Sep 17 00:00:00 2001
From: Phil Stahlfeld <philstahlfeld@google.com>
Date: Tue, 23 Aug 2016 13:04:08 -0800
Subject: [PATCH 055/223] Deprecate export.export_estimator in favor of
 BaseEstimator.export. Change: 131093055

---
 .../python/learn/estimators/estimator.py      | 19 +++++++++--------
 .../learn/python/learn/utils/export.py        | 21 +++++++++++++++++--
 2 files changed, 29 insertions(+), 11 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/estimators/estimator.py b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
index c6c894542c..c8511b8c75 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/estimator.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/estimator.py
@@ -382,9 +382,8 @@ class BaseEstimator(
   def model_dir(self):
     return self._model_dir
 
-  def export(self, export_dir, signature_fn=None,
-             input_fn=export.default_input_fn, default_batch_size=1,
-             exports_to_keep=None):
+  def export(self, export_dir, signature_fn=None, input_fn=None,
+             default_batch_size=1, exports_to_keep=None):
     """Exports inference graph into given dir.
 
     Args:
@@ -398,12 +397,14 @@ class BaseEstimator(
       default_batch_size: Default batch size of the `Example` placeholder.
       exports_to_keep: Number of exports to keep.
     """
-    export.export_estimator(estimator=self,
-                            export_dir=export_dir,
-                            signature_fn=signature_fn,
-                            input_fn=input_fn,
-                            default_batch_size=default_batch_size,
-                            exports_to_keep=exports_to_keep)
+    # pylint: disable=protected-access
+    export._export_estimator(estimator=self,
+                             export_dir=export_dir,
+                             signature_fn=signature_fn,
+                             input_fn=input_fn,
+                             default_batch_size=default_batch_size,
+                             exports_to_keep=exports_to_keep)
+    # pylint: enable=protected-access
 
   @abc.abstractproperty
   def _get_train_ops(self, features, targets):
diff --git a/tensorflow/contrib/learn/python/learn/utils/export.py b/tensorflow/contrib/learn/python/learn/utils/export.py
index a67b6fca4a..88ba2a9bf9 100644
--- a/tensorflow/contrib/learn/python/learn/utils/export.py
+++ b/tensorflow/contrib/learn/python/learn/utils/export.py
@@ -20,6 +20,7 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib import layers
+from tensorflow.contrib.framework import deprecated
 from tensorflow.contrib.framework.python.ops import variables as contrib_variables
 from tensorflow.contrib.session_bundle import exporter
 from tensorflow.contrib.session_bundle import gc
@@ -167,18 +168,34 @@ def logistic_regression_signature_fn(examples, unused_features, predictions):
 
 
 # pylint: disable=protected-access
-def default_input_fn(estimator, examples):
+def _default_input_fn(estimator, examples):
   """Creates default input parsing using Estimator's feature signatures."""
   return estimator._get_feature_ops_from_example(examples)
 
 
+@deprecated('2016-09-23', 'Please use BaseEstimator.export')
 def export_estimator(estimator,
                      export_dir,
                      signature_fn=None,
-                     input_fn=default_input_fn,
+                     input_fn=_default_input_fn,
                      default_batch_size=1,
                      exports_to_keep=None):
   """Deprecated, please use BaseEstimator.export."""
+  _export_estimator(estimator=estimator,
+                    export_dir=export_dir,
+                    signature_fn=signature_fn,
+                    input_fn=input_fn,
+                    default_batch_size=default_batch_size,
+                    exports_to_keep=exports_to_keep)
+
+
+def _export_estimator(estimator,
+                      export_dir,
+                      signature_fn,
+                      input_fn,
+                      default_batch_size,
+                      exports_to_keep):
+  input_fn = input_fn or _default_input_fn
   checkpoint_path = tf_saver.latest_checkpoint(estimator._model_dir)
   with ops.Graph().as_default() as g:
     contrib_variables.create_global_step(g)
-- 
GitLab


From be5f854da474fb9373c089302e7b4b18c3b7e6ef Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 13:12:04 -0800
Subject: [PATCH 056/223] Exposes functor::FillPhiloxRandom<CPUDevice, ...>()
 so that other modules can reuse. Change: 131094610

---
 tensorflow/core/kernels/random_op.cc | 103 +++++++++++++--------------
 tensorflow/core/kernels/random_op.h  |  14 ++++
 2 files changed, 65 insertions(+), 52 deletions(-)

diff --git a/tensorflow/core/kernels/random_op.cc b/tensorflow/core/kernels/random_op.cc
index 9b94fc5d60..d378764178 100644
--- a/tensorflow/core/kernels/random_op.cc
+++ b/tensorflow/core/kernels/random_op.cc
@@ -138,32 +138,29 @@ struct FillPhiloxRandomTask<Distribution, true> {
 // Partial specialization for CPU to fill the entire region with randoms
 // It splits the work into several tasks and run them in parallel
 template <class Distribution>
-struct FillPhiloxRandom<CPUDevice, Distribution> {
-  typedef typename Distribution::ResultElementType T;
-  void operator()(OpKernelContext* context, const CPUDevice&,
-                  random::PhiloxRandom gen, T* data, int64 size,
-                  Distribution dist) {
-    const int kGroupSize = Distribution::kResultElementCount;
-
-    auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads());
-
-    int64 total_group_count = (size + kGroupSize - 1) / kGroupSize;
-
-    const int kGroupCost =
-        random::PhiloxRandom::kResultElementCount *
-        (random::PhiloxRandom::kElementCost + Distribution::kElementCost);
-    Shard(worker_threads.num_threads, worker_threads.workers, total_group_count,
-          kGroupCost,
-          [&gen, data, size, dist](int64 start_group, int64 limit_group) {
-            FillPhiloxRandomTask<
-                Distribution,
-                Distribution::kVariableSamplesPerOutput>::Run(gen, data, size,
-                                                              start_group,
-                                                              limit_group,
-                                                              dist);
-          });
-  }
-};
+void FillPhiloxRandom<CPUDevice, Distribution>::operator()(
+    OpKernelContext* context, const CPUDevice&, random::PhiloxRandom gen,
+    typename Distribution::ResultElementType* data, int64 size,
+    Distribution dist) {
+  const int kGroupSize = Distribution::kResultElementCount;
+
+  auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads());
+
+  int64 total_group_count = (size + kGroupSize - 1) / kGroupSize;
+
+  const int kGroupCost =
+      random::PhiloxRandom::kResultElementCount *
+      (random::PhiloxRandom::kElementCost + Distribution::kElementCost);
+  Shard(worker_threads.num_threads, worker_threads.workers, total_group_count,
+        kGroupCost,
+        [&gen, data, size, dist](int64 start_group, int64 limit_group) {
+          FillPhiloxRandomTask<
+              Distribution,
+              Distribution::kVariableSamplesPerOutput>::Run(gen, data, size,
+                                                            start_group,
+                                                            limit_group, dist);
+        });
+}
 
 }  // namespace functor
 
@@ -456,32 +453,34 @@ class RandomGammaOp : public OpKernel {
 
 }  // namespace
 
-#define REGISTER(TYPE)                                                     \
-  REGISTER_KERNEL_BUILDER(                                                 \
-      Name("RandomUniform")                                                \
-          .Device(DEVICE_CPU)                                              \
-          .HostMemory("shape")                                             \
-          .TypeConstraint<TYPE>("dtype"),                                  \
-      PhiloxRandomOp<CPUDevice, random::UniformDistribution<               \
-                                    random::PhiloxRandom, TYPE> >);        \
-  REGISTER_KERNEL_BUILDER(                                                 \
-      Name("RandomStandardNormal")                                         \
-          .Device(DEVICE_CPU)                                              \
-          .HostMemory("shape")                                             \
-          .TypeConstraint<TYPE>("dtype"),                                  \
-      PhiloxRandomOp<CPUDevice, random::NormalDistribution<                \
-                                    random::PhiloxRandom, TYPE> >);        \
-  REGISTER_KERNEL_BUILDER(                                                 \
-      Name("TruncatedNormal")                                              \
-          .Device(DEVICE_CPU)                                              \
-          .HostMemory("shape")                                             \
-          .TypeConstraint<TYPE>("dtype"),                                  \
-      PhiloxRandomOp<                                                      \
-          CPUDevice,                                                       \
-          random::TruncatedNormalDistribution<                             \
-              random::SingleSampleAdapter<random::PhiloxRandom>, TYPE> >); \
-  REGISTER_KERNEL_BUILDER(                                                 \
-      Name("RandomGamma").Device(DEVICE_CPU).TypeConstraint<TYPE>("T"),    \
+#define REGISTER(TYPE)                                                      \
+  template struct functor::FillPhiloxRandom<                                \
+      CPUDevice, random::UniformDistribution<random::PhiloxRandom, TYPE> >; \
+  REGISTER_KERNEL_BUILDER(                                                  \
+      Name("RandomUniform")                                                 \
+          .Device(DEVICE_CPU)                                               \
+          .HostMemory("shape")                                              \
+          .TypeConstraint<TYPE>("dtype"),                                   \
+      PhiloxRandomOp<CPUDevice, random::UniformDistribution<                \
+                                    random::PhiloxRandom, TYPE> >);         \
+  REGISTER_KERNEL_BUILDER(                                                  \
+      Name("RandomStandardNormal")                                          \
+          .Device(DEVICE_CPU)                                               \
+          .HostMemory("shape")                                              \
+          .TypeConstraint<TYPE>("dtype"),                                   \
+      PhiloxRandomOp<CPUDevice, random::NormalDistribution<                 \
+                                    random::PhiloxRandom, TYPE> >);         \
+  REGISTER_KERNEL_BUILDER(                                                  \
+      Name("TruncatedNormal")                                               \
+          .Device(DEVICE_CPU)                                               \
+          .HostMemory("shape")                                              \
+          .TypeConstraint<TYPE>("dtype"),                                   \
+      PhiloxRandomOp<                                                       \
+          CPUDevice,                                                        \
+          random::TruncatedNormalDistribution<                              \
+              random::SingleSampleAdapter<random::PhiloxRandom>, TYPE> >);  \
+  REGISTER_KERNEL_BUILDER(                                                  \
+      Name("RandomGamma").Device(DEVICE_CPU).TypeConstraint<TYPE>("T"),     \
       RandomGammaOp<TYPE>)
 
 #define REGISTER_INT(IntType)                                   \
diff --git a/tensorflow/core/kernels/random_op.h b/tensorflow/core/kernels/random_op.h
index 362600fa34..b52901c38e 100644
--- a/tensorflow/core/kernels/random_op.h
+++ b/tensorflow/core/kernels/random_op.h
@@ -28,6 +28,20 @@ namespace functor {
 template <typename Device, class Distribution>
 struct FillPhiloxRandom;
 
+typedef Eigen::ThreadPoolDevice CPUDevice;
+// Declares the partially CPU-specialized functor struct.
+//
+// NOTE: Due to inlining done by the compiler, you may need to add
+// explicit instantiation of the functor in random_op.cc.  See example
+// functor::FillPhiloxRandom<CPUDevice, random::UniformDistribution>.
+template <class Distribution>
+struct FillPhiloxRandom<CPUDevice, Distribution> {
+  void operator()(OpKernelContext* ctx, const CPUDevice& d,
+                  random::PhiloxRandom gen,
+                  typename Distribution::ResultElementType* data, int64 size,
+                  Distribution dist);
+};
+
 #if GOOGLE_CUDA
 typedef Eigen::GpuDevice GPUDevice;
 // Declares the partially GPU-specialized functor struct.
-- 
GitLab


From a81aa1f091c2a3dad193ac66fff8ea411ebf9008 Mon Sep 17 00:00:00 2001
From: Saurabh Saxena <srbs@google.com>
Date: Tue, 23 Aug 2016 13:17:16 -0800
Subject: [PATCH 057/223] Add buffers for reading and writing snappy compressed
 files. Change: 131095300

---
 tensorflow/core/BUILD                         |   3 +
 .../core/lib/io/snappy/snappy_buffers_test.cc | 185 ++++++++++++++++++
 .../core/lib/io/snappy/snappy_inputbuffer.cc  | 180 +++++++++++++++++
 .../core/lib/io/snappy/snappy_inputbuffer.h   | 124 ++++++++++++
 .../core/lib/io/snappy/snappy_outputbuffer.cc | 182 +++++++++++++++++
 .../core/lib/io/snappy/snappy_outputbuffer.h  | 120 ++++++++++++
 6 files changed, 794 insertions(+)
 create mode 100644 tensorflow/core/lib/io/snappy/snappy_buffers_test.cc
 create mode 100644 tensorflow/core/lib/io/snappy/snappy_inputbuffer.cc
 create mode 100644 tensorflow/core/lib/io/snappy/snappy_inputbuffer.h
 create mode 100644 tensorflow/core/lib/io/snappy/snappy_outputbuffer.cc
 create mode 100644 tensorflow/core/lib/io/snappy/snappy_outputbuffer.h

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index e0a4cc7617..1bbae7d177 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -881,6 +881,8 @@ cc_library(
         "lib/io/iterator.h",
         "lib/io/match.h",
         "lib/io/random_inputstream.h",
+        "lib/io/snappy/snappy_inputbuffer.h",
+        "lib/io/snappy/snappy_outputbuffer.h",
         "lib/io/zlib_compression_options.h",
         "lib/io/zlib_inputbuffer.h",
         "lib/io/zlib_outputbuffer.h",
@@ -1315,6 +1317,7 @@ tf_cc_tests(
         "lib/io/random_inputstream_test.cc",
         "lib/io/record_reader_writer_test.cc",
         "lib/io/recordio_test.cc",
+        "lib/io/snappy/snappy_buffers_test.cc",
         "lib/io/table_test.cc",
         "lib/io/zlib_buffers_test.cc",
         "lib/monitoring/collection_registry_test.cc",
diff --git a/tensorflow/core/lib/io/snappy/snappy_buffers_test.cc b/tensorflow/core/lib/io/snappy/snappy_buffers_test.cc
new file mode 100644
index 0000000000..65bb34d570
--- /dev/null
+++ b/tensorflow/core/lib/io/snappy/snappy_buffers_test.cc
@@ -0,0 +1,185 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/lib/io/inputbuffer.h"
+#include "tensorflow/core/lib/io/snappy/snappy_inputbuffer.h"
+#include "tensorflow/core/lib/io/snappy/snappy_outputbuffer.h"
+
+namespace tensorflow {
+
+// The current implementation of snappy compresses the below block to 619 bytes.
+// We use this to validate the error messages. Please change this number if
+// a new snappy implementation compresses to a different size.
+const int COMPRESSED_RECORD_SIZE = 619;
+
+static string GetRecord() {
+  static const string lorem_ipsum =
+      "Lorem ipsum dolor sit amet, consectetur adipiscing elit."
+      " Fusce vehicula tincidunt libero sit amet ultrices. Vestibulum non "
+      "felis augue. Duis vitae augue id lectus lacinia congue et ut purus. "
+      "Donec auctor, nisl at dapibus volutpat, diam ante lacinia dolor, vel"
+      "dignissim lacus nisi sed purus. Duis fringilla nunc ac lacus sagittis"
+      " efficitur. Praesent tincidunt egestas eros, eu vehicula urna ultrices"
+      " et. Aliquam erat volutpat. Maecenas vehicula risus consequat risus"
+      " dictum, luctus tincidunt nibh imperdiet. Aenean bibendum ac erat"
+      " cursus scelerisque. Cras lacinia in enim dapibus iaculis. Nunc porta"
+      " felis lectus, ac tincidunt massa pharetra quis. Fusce feugiat dolor"
+      " vel ligula rutrum egestas. Donec vulputate quam eros, et commodo"
+      " purus lobortis sed.";
+  return lorem_ipsum;
+}
+
+static string GenTestString(uint copies = 1) {
+  string result = "";
+  for (int i = 0; i < copies; i++) {
+    result += GetRecord();
+  }
+  return result;
+}
+
+Status TestMultipleWrites(size_t compress_input_buf_size,
+                          size_t compress_output_buf_size,
+                          size_t uncompress_input_buf_size,
+                          size_t uncompress_output_buf_size, int num_writes = 1,
+                          bool with_flush = false, int num_copies = 1,
+                          bool corrupt_compressed_file = false) {
+  Env* env = Env::Default();
+
+  string fname = testing::TmpDir() + "/snappy_buffers_test";
+  string data = GenTestString(num_copies);
+  std::unique_ptr<WritableFile> file_writer;
+  string actual_result;
+  string expected_result;
+
+  TF_RETURN_IF_ERROR(env->NewWritableFile(fname, &file_writer));
+  io::SnappyOutputBuffer out(file_writer.get(), compress_input_buf_size,
+                             compress_output_buf_size);
+
+  for (int i = 0; i < num_writes; i++) {
+    TF_RETURN_IF_ERROR(out.Write(StringPiece(data)));
+    if (with_flush) {
+      TF_RETURN_IF_ERROR(out.Flush());
+    }
+    strings::StrAppend(&expected_result, data);
+  }
+  TF_RETURN_IF_ERROR(out.Flush());
+  TF_RETURN_IF_ERROR(file_writer->Flush());
+  TF_RETURN_IF_ERROR(file_writer->Close());
+
+  if (corrupt_compressed_file) {
+    string corrupt_fname = testing::TmpDir() + "/snappy_buffers_test_corrupt";
+    std::unique_ptr<WritableFile> corrupt_file_writer;
+    TF_RETURN_IF_ERROR(
+        env->NewWritableFile(corrupt_fname, &corrupt_file_writer));
+
+    std::unique_ptr<RandomAccessFile> file_reader;
+    TF_RETURN_IF_ERROR(env->NewRandomAccessFile(fname, &file_reader));
+
+    StringPiece data;
+    size_t file_pos = 0;
+    size_t bytes_to_read = 256;
+    char* scratch = new char[bytes_to_read];
+    char* buffer = new char[bytes_to_read];
+    size_t buffer_size = 0;
+
+    while ((file_reader->Read(file_pos, bytes_to_read, &data, scratch)).ok()) {
+      file_pos += data.size();
+      TF_CHECK_OK(
+          corrupt_file_writer->Append(StringPiece(buffer, buffer_size)));
+      memcpy(buffer, data.data(), data.size());
+      buffer_size = data.size();
+    }
+
+    // Drop the last byte. File is now corrupt.
+    TF_CHECK_OK(
+        corrupt_file_writer->Append(StringPiece(buffer, buffer_size - 1)));
+    TF_CHECK_OK(corrupt_file_writer->Flush());
+    TF_CHECK_OK(corrupt_file_writer->Close());
+    delete[] scratch;
+    delete[] buffer;
+    fname = corrupt_fname;
+  }
+
+  std::unique_ptr<RandomAccessFile> file_reader;
+  TF_RETURN_IF_ERROR(env->NewRandomAccessFile(fname, &file_reader));
+  io::SnappyInputBuffer in(file_reader.get(), uncompress_input_buf_size,
+                           uncompress_output_buf_size);
+
+  for (int i = 0; i < num_writes; i++) {
+    string decompressed_output;
+    TF_RETURN_IF_ERROR(in.ReadNBytes(data.size(), &decompressed_output));
+    strings::StrAppend(&actual_result, decompressed_output);
+  }
+
+  if (actual_result.compare(expected_result)) {
+    return errors::DataLoss("Actual and expected results don't match.");
+  }
+  return Status::OK();
+}
+
+static bool SnappyCompressionSupported() {
+  string out;
+  StringPiece in = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa";
+  return port::Snappy_Compress(in.data(), in.size(), &out);
+}
+
+TEST(SnappyBuffers, MultipleWritesWithoutFlush) {
+  if (!SnappyCompressionSupported()) {
+    fprintf(stderr, "Snappy disabled. Skipping test\n");
+    return;
+  }
+  TF_CHECK_OK(TestMultipleWrites(10000, 10000, 10000, 10000, 2));
+}
+
+TEST(SnappyBuffers, MultipleWriteCallsWithFlush) {
+  if (!SnappyCompressionSupported()) {
+    fprintf(stderr, "skipping compression tests\n");
+    return;
+  }
+  TF_CHECK_OK(TestMultipleWrites(10000, 10000, 10000, 10000, 2, true));
+}
+
+TEST(SnappyBuffers, SmallUncompressInputBuffer) {
+  if (!SnappyCompressionSupported()) {
+    fprintf(stderr, "skipping compression tests\n");
+    return;
+  }
+  CHECK_EQ(TestMultipleWrites(10000, 10000, 10, 10000, 2, true),
+           errors::ResourceExhausted("Input buffer(size: 10 bytes) too small. ",
+                                     "Should be larger than ",
+                                     COMPRESSED_RECORD_SIZE, " bytes."));
+}
+
+TEST(SnappyBuffers, CorruptBlock) {
+  if (!SnappyCompressionSupported()) {
+    fprintf(stderr, "skipping compression tests\n");
+    return;
+  }
+  CHECK_EQ(TestMultipleWrites(10000, 10000, 700, 10000, 2, true, 1, true),
+           errors::DataLoss("Failed to read ", COMPRESSED_RECORD_SIZE,
+                            " bytes from file. ", "Possible data corruption."));
+}
+
+TEST(SnappyBuffers, CorruptBlockLargeInputBuffer) {
+  if (!SnappyCompressionSupported()) {
+    fprintf(stderr, "skipping compression tests\n");
+    return;
+  }
+  CHECK_EQ(TestMultipleWrites(10000, 10000, 2000, 10000, 2, true, 1, true),
+           errors::OutOfRange("EOF reached"));
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/lib/io/snappy/snappy_inputbuffer.cc b/tensorflow/core/lib/io/snappy/snappy_inputbuffer.cc
new file mode 100644
index 0000000000..c592d16ebf
--- /dev/null
+++ b/tensorflow/core/lib/io/snappy/snappy_inputbuffer.cc
@@ -0,0 +1,180 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/lib/io/snappy/snappy_inputbuffer.h"
+
+namespace tensorflow {
+namespace io {
+SnappyInputBuffer::SnappyInputBuffer(
+    RandomAccessFile* file,
+    size_t input_buffer_bytes,  // size of input_buffer_
+    size_t output_buffer_bytes  // size of output_buffer_
+    )
+    : file_(file),
+      input_buffer_capacity_(input_buffer_bytes),
+      output_buffer_capacity_(output_buffer_bytes),
+      input_buffer_(new char[input_buffer_capacity_]),
+      output_buffer_(new char[output_buffer_capacity_]),
+      next_in_(input_buffer_.get()) {}
+
+Status SnappyInputBuffer::ReadNBytes(int64 bytes_to_read, string* result) {
+  result->clear();
+  // Read as many bytes as possible from cache.
+  bytes_to_read -= ReadBytesFromCache(bytes_to_read, result);
+
+  while (bytes_to_read > 0) {
+    // At this point we can be sure that cache has been emptied.
+    DCHECK(avail_out_ == 0);
+
+    // Now that the cache is empty we need to inflate more data.
+    TF_RETURN_IF_ERROR(Inflate());
+
+    bytes_to_read -= ReadBytesFromCache(bytes_to_read, result);
+  }
+
+  return Status::OK();
+}
+
+size_t SnappyInputBuffer::ReadBytesFromCache(size_t bytes_to_read,
+                                             string* result) {
+  size_t can_read_bytes = std::min(bytes_to_read, avail_out_);
+  if (can_read_bytes > 0) {
+    result->append(next_out_, can_read_bytes);
+    next_out_ += can_read_bytes;
+    avail_out_ -= can_read_bytes;
+  }
+
+  return can_read_bytes;
+}
+
+Status SnappyInputBuffer::Inflate() {
+  // Read length of compressed block.
+  uint32 compressed_block_length;
+  TF_RETURN_IF_ERROR(ReadCompressedBlockLength(&compressed_block_length));
+
+  // If the entire block is not in cache do a read from file.
+  if (avail_in_ < compressed_block_length) {
+    TF_RETURN_IF_ERROR(ReadFromFile());
+    if (avail_in_ < compressed_block_length) {
+      if (compressed_block_length > input_buffer_capacity_) {
+        return errors::ResourceExhausted(
+            "Input buffer(size: ", input_buffer_capacity_,
+            " bytes) too small. Should be larger ", "than ",
+            compressed_block_length, " bytes.");
+      } else {
+        return errors::DataLoss(
+            strings::StrCat("Failed to read ", compressed_block_length,
+                            " bytes from file. Possible data corruption."));
+      }
+    }
+  }
+
+  size_t uncompressed_length;
+  if (!port::Snappy_GetUncompressedLength(next_in_, compressed_block_length,
+                                          &uncompressed_length)) {
+    return errors::DataLoss("Parsing error in Snappy_GetUncompressedLength");
+  }
+
+  // Output buffer must have been cleared before uncompressing more input.
+  DCHECK_EQ(avail_out_, 0);
+
+  // Output buffer must be large enough to fit the uncompressed block.
+  DCHECK_GE(output_buffer_capacity_, uncompressed_length);
+  next_out_ = (char*)output_buffer_.get();
+
+  bool status = port::Snappy_Uncompress(next_in_, compressed_block_length,
+                                        output_buffer_.get());
+  if (!status) {
+    return errors::DataLoss("Snappy_Uncompress failed");
+  }
+  next_in_ += compressed_block_length;
+  avail_in_ -= compressed_block_length;
+  avail_out_ += uncompressed_length;
+  return Status::OK();
+}
+
+Status SnappyInputBuffer::ReadCompressedBlockLength(uint32* length) {
+  *length = 0;
+  size_t bytes_to_read = 4;
+  while (bytes_to_read > 0) {
+    if (avail_in_ == 0) {
+      TF_RETURN_IF_ERROR(ReadFromFile());
+    }
+    size_t readable = std::min(bytes_to_read, avail_in_);
+
+    for (int i = 0; i < readable; i++) {
+      *length = (*length << 8) | next_in_[0];
+      bytes_to_read--;
+      next_in_++;
+      avail_in_--;
+    }
+  }
+  return Status::OK();
+}
+
+Status SnappyInputBuffer::ReadFromFile() {
+  int bytes_to_read = input_buffer_capacity_;
+  char* read_location = reinterpret_cast<char*>(input_buffer_.get());
+
+  // If there are unread bytes in the input stream we move them to the head
+  // of the stream to maximize the space available to read new data into.
+  // TODO(srbs): A circular buffer would be useful here.
+  if (avail_in_ > 0) {
+    size_t read_bytes = next_in_ - input_buffer_.get();
+    // Remove `read_bytes` from the head of the input stream.
+    // Move unread bytes to the head of the input stream.
+    if (read_bytes > 0) {
+      memmove(input_buffer_.get(), next_in_, avail_in_);
+    }
+
+    bytes_to_read -= avail_in_;
+    read_location += avail_in_;
+  }
+  StringPiece data;
+  // Try to read enough data to fill up input_buffer_.
+  Status s = file_->Read(file_pos_, bytes_to_read, &data, read_location);
+  if (data.data() != read_location) {
+    memmove(read_location, data.data(), data.size());
+  }
+
+  // Since we moved unread data to the head of the input stream we can point
+  // next_in to the head of the input stream.
+  next_in_ = input_buffer_.get();
+
+  // Note: data.size() could be different from bytes_to_read.
+  avail_in_ += data.size();
+  file_pos_ += data.size();
+
+  if (!s.ok() && !errors::IsOutOfRange(s)) {
+    return s;
+  }
+
+  // We throw OutOfRange error iff no new data has been read from file.
+  // Since we never check how much data is remaining in the file, it is
+  // possible that on the last read there isn't enough data in the file to
+  // fill up the buffer in which case file_->ReadNBytes would return an
+  // OutOfRange error.
+  if (data.size() == 0) {
+    return errors::OutOfRange("EOF reached");
+  }
+  if (errors::IsOutOfRange(s)) {
+    return Status::OK();
+  }
+
+  return s;
+}
+
+}  // namespace io
+}  // namespace tensorflow
diff --git a/tensorflow/core/lib/io/snappy/snappy_inputbuffer.h b/tensorflow/core/lib/io/snappy/snappy_inputbuffer.h
new file mode 100644
index 0000000000..862f2aecd4
--- /dev/null
+++ b/tensorflow/core/lib/io/snappy/snappy_inputbuffer.h
@@ -0,0 +1,124 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LIB_IO_SNAPPY_INPUTBUFFER_H_
+#define TENSORFLOW_LIB_IO_SNAPPY_INPUTBUFFER_H_
+
+#include <string>
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/lib/io/inputstream_interface.h"
+#include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/snappy.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+namespace io {
+
+// An SnappyInputBuffer provides support for reading from a file compressed
+// using snappy (https://github.com/google/snappy).
+//
+// A given instance of an SnappyInputBuffer is NOT safe for concurrent use
+// by multiple threads
+class SnappyInputBuffer : public InputStreamInterface {
+ public:
+  // Create a SnappyInputBuffer for `file` with a buffer of size
+  // `input_buffer_bytes` bytes for reading contents from `file` and another
+  // buffer with size `output_buffer_bytes` for caching decompressed contents.
+  // Does *not* take ownership of "file".
+  SnappyInputBuffer(RandomAccessFile* file, size_t input_buffer_bytes,
+                    size_t output_buffer_bytes);
+
+  // Reads bytes_to_read bytes into *result, overwriting *result.
+  //
+  // Return Status codes:
+  // OK:
+  //   If successful.
+  // OUT_OF_RANGE:
+  //   If there are not enough bytes to read before the end of the file.
+  // DATA_LOSS:
+  //   If uncompression failed or if the file is corrupted.
+  // RESOURCE_EXHAUSTED:
+  //   If input_buffer_ is smaller in size than a compressed block.
+  // others:
+  //   If reading from file failed.
+  Status ReadNBytes(int64 bytes_to_read, string* result);
+
+ private:
+  // Reads data from `file_` and tries to fill up `input_buffer_` if enough
+  // unread data is left in `file_`.
+  //
+  // Looks up `next_in_` to check how much data in `input_buffer_`
+  // has already been read. The used data is removed and new data is added to
+  // after any unread data in `input_buffer_`.
+  // After this call `next_in` points to the start of `input_buffer_`
+  // and `avail_in_` stores the number of readable bytes in
+  // `input_buffer_`.
+  //
+  // Returns OutOfRange error if NO data could be read from file. Note that this
+  // won't return an OutOfRange if there wasn't sufficient data in file to
+  // completely fill up `input_buffer_`.
+  Status ReadFromFile();
+
+  // Reads the length of the next compressed block stored in the next 4 bytes at
+  // `next_in_`. Uncompresses the next compressed block and writes the output
+  // produced to the output_buffer_.
+  // Should be called only after the cached output has been consumed.
+  Status Inflate();
+
+  // Starts reading bytes at `next_out_` till either `bytes_to_read`
+  // bytes have been read or `next_out_` is reached.
+  // Returns the number of bytes read and advances the `next_out_`
+  // pointer to the next location to read from.
+  size_t ReadBytesFromCache(size_t bytes_to_read, string* result);
+
+  // Reads the length of the next *compressed* block and stores in `length`.
+  // The length is stored in 4 bytes in little endian notation.
+  Status ReadCompressedBlockLength(uint32* length);
+
+  RandomAccessFile* file_;         // Not owned
+  int64 file_pos_ = 0;             // Next position to read from in `file_`
+  size_t input_buffer_capacity_;   // Size of `input_buffer_`.
+                                   // Must be at least as big as the size of
+                                   // the largest compressed block.
+  size_t output_buffer_capacity_;  // Size of `output_buffer_`
+
+  // Buffer for storing contents read from compressed file.
+  // TODO(srbs): Consider using circular buffers. That would greatly simplify
+  // the implementation.
+  std::unique_ptr<char[]> input_buffer_;
+
+  // Buffer for storing inflated contents of `file_`.
+  std::unique_ptr<char[]> output_buffer_;
+
+  // Next unread byte in `input_buffer_`.
+  char* next_in_;
+
+  // Next unread byte in `output_buffer_`
+  char* next_out_;
+
+  // Number of unread bytes bytes available at `next_in_` in `input_buffer_`.
+  size_t avail_in_ = 0;
+
+  // Number of unread bytes bytes available at `next_out_` in `output_buffer_`.
+  size_t avail_out_ = 0;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(SnappyInputBuffer);
+};
+
+}  // namespace io
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_LIB_IO_SNAPPY_INPUTBUFFER_H_
diff --git a/tensorflow/core/lib/io/snappy/snappy_outputbuffer.cc b/tensorflow/core/lib/io/snappy/snappy_outputbuffer.cc
new file mode 100644
index 0000000000..be1fa22c69
--- /dev/null
+++ b/tensorflow/core/lib/io/snappy/snappy_outputbuffer.cc
@@ -0,0 +1,182 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/lib/io/snappy/snappy_outputbuffer.h"
+
+namespace tensorflow {
+namespace io {
+
+SnappyOutputBuffer::SnappyOutputBuffer(WritableFile* file,
+                                       int32 input_buffer_bytes,
+                                       int32 output_buffer_bytes)
+    : file_(file),
+      input_buffer_(new char[input_buffer_bytes]),
+      input_buffer_capacity_(input_buffer_bytes),
+      next_in_(input_buffer_.get()),
+      output_buffer_(new char[output_buffer_bytes]),
+      output_buffer_capacity_(output_buffer_bytes),
+      next_out_(output_buffer_.get()),
+      avail_out_(output_buffer_bytes) {}
+
+Status SnappyOutputBuffer::Write(StringPiece data) {
+  //
+  // The deflated output is accumulated in output_buffer_ and gets written to
+  // file as and when needed.
+
+  size_t bytes_to_write = data.size();
+
+  // If there is sufficient free space in input_buffer_ to fit data we
+  // add it there and return.
+  if (bytes_to_write <= AvailableInputSpace()) {
+    AddToInputBuffer(data);
+    return Status::OK();
+  }
+
+  // If there isn't enough available space in the input_buffer_ we empty it
+  // by uncompressing its contents. If data now fits in input_buffer_
+  // we add it there else we directly deflate it.
+  TF_RETURN_IF_ERROR(DeflateBuffered());
+
+  // input_buffer_ should be empty at this point.
+  if (bytes_to_write <= AvailableInputSpace()) {
+    AddToInputBuffer(data);
+    return Status::OK();
+  }
+
+  // `data` is too large to fit in input buffer so we deflate it directly.
+  // Note that at this point we have already deflated all existing input so
+  // we do not need to backup next_in and avail_in.
+  next_in_ = const_cast<char*>(data.data());
+  avail_in_ = bytes_to_write;
+
+  TF_RETURN_IF_ERROR(Deflate());
+
+  DCHECK(avail_in_ == 0);  // All input will be used up.
+
+  next_in_ = input_buffer_.get();
+
+  return Status::OK();
+}
+
+Status SnappyOutputBuffer::Flush() {
+  TF_RETURN_IF_ERROR(DeflateBuffered());
+  TF_RETURN_IF_ERROR(FlushOutputBufferToFile());
+  return Status::OK();
+}
+
+int32 SnappyOutputBuffer::AvailableInputSpace() const {
+  return input_buffer_capacity_ - avail_in_;
+}
+
+void SnappyOutputBuffer::AddToInputBuffer(StringPiece data) {
+  size_t bytes_to_write = data.size();
+  DCHECK_LE(bytes_to_write, AvailableInputSpace());
+
+  // Input stream ->
+  // [....................input_buffer_capacity_...............]
+  // [<...read_bytes...><...avail_in...>......empty space......]
+  //  ^                 ^
+  //  |                 |
+  //  input_buffer_   next_in
+  //
+  // Data in the input stream is sharded as shown above. next_in_ could
+  // be pointing to some byte in the buffer with avail_in number of bytes
+  // available to be read.
+  //
+  // In order to avoid shifting the avail_in bytes at next_in to the head of
+  // the buffer we try to fit `data` in the empty space at the tail of the
+  // input stream.
+  // TODO(srbs): This could be avoided if we had a circular buffer.
+  // If it doesn't fit we free the space at the head of the stream and then
+  // append `data` at the end of existing data.
+
+  const int32 read_bytes = next_in_ - input_buffer_.get();
+  const int32 unread_bytes = avail_in_;
+  const int32 free_tail_bytes =
+      input_buffer_capacity_ - (read_bytes + unread_bytes);
+
+  if (bytes_to_write > free_tail_bytes) {
+    memmove(input_buffer_.get(), next_in_, avail_in_);
+    next_in_ = input_buffer_.get();
+  }
+  memcpy(next_in_ + avail_in_, data.data(), bytes_to_write);
+  avail_in_ += bytes_to_write;
+}
+
+Status SnappyOutputBuffer::AddToOutputBuffer(const char* data, size_t length) {
+  while (length > 0) {
+    size_t bytes_to_copy = std::min(length, avail_out_);
+    memcpy(next_out_, data, bytes_to_copy);
+    data += bytes_to_copy;
+    next_out_ += bytes_to_copy;
+    avail_out_ -= bytes_to_copy;
+    length -= bytes_to_copy;
+    if (avail_out_ == 0) {
+      TF_RETURN_IF_ERROR(FlushOutputBufferToFile());
+    }
+  }
+  return Status::OK();
+}
+
+Status SnappyOutputBuffer::DeflateBuffered() {
+  TF_RETURN_IF_ERROR(Deflate());
+  DCHECK(avail_in_ == 0);
+  next_in_ = input_buffer_.get();
+  return Status::OK();
+}
+
+Status SnappyOutputBuffer::FlushOutputBufferToFile() {
+  size_t bytes_to_write = output_buffer_capacity_ - avail_out_;
+  if (bytes_to_write > 0) {
+    Status s = file_->Append(StringPiece(
+        reinterpret_cast<char*>(output_buffer_.get()), bytes_to_write));
+    if (s.ok()) {
+      next_out_ = output_buffer_.get();
+      avail_out_ = output_buffer_capacity_;
+    }
+    return s;
+  }
+  return Status::OK();
+}
+
+Status SnappyOutputBuffer::Deflate() {
+  if (avail_in_ == 0) {
+    return Status::OK();
+  }
+  string output;
+  if (!port::Snappy_Compress(next_in_, avail_in_, &output)) {
+    return errors::DataLoss("Snappy_Compress failed");
+  }
+
+  // Write length of compressed block to output buffer.
+  char* compressed_length_array = new char[4];
+  std::fill(compressed_length_array, compressed_length_array + 4, 0);
+  for (int i = 0; i < 4; i++) {
+    // Little endian.
+    compressed_length_array[i] = output.size() >> (8 * (3 - i));
+  }
+  TF_RETURN_IF_ERROR(AddToOutputBuffer(compressed_length_array, 4));
+
+  // Write compressed output to buffer.
+  TF_RETURN_IF_ERROR(AddToOutputBuffer(output.data(), output.size()));
+  next_in_ += avail_in_;
+  avail_in_ = 0;
+  delete[] compressed_length_array;
+
+  return Status::OK();
+}
+
+}  // namespace io
+}  // namespace tensorflow
diff --git a/tensorflow/core/lib/io/snappy/snappy_outputbuffer.h b/tensorflow/core/lib/io/snappy/snappy_outputbuffer.h
new file mode 100644
index 0000000000..5d330a2c5a
--- /dev/null
+++ b/tensorflow/core/lib/io/snappy/snappy_outputbuffer.h
@@ -0,0 +1,120 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_LIB_IO_SNAPPY_OUTPUTBUFFER_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_LIB_IO_SNAPPY_OUTPUTBUFFER_H_
+
+#include <string>
+#include "tensorflow/core/lib/core/status.h"
+#include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/snappy.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+namespace io {
+
+// Compresses input data using Snappy (https://github.com/google/snappy) and
+// writes to `file`.
+//
+// The input data is cached in a buffer of size `input_buffer_bytes`. When the
+// buffer does not have enough available space to fit new data (in the call to
+// `Write`), the contents of the buffer are compressed and sent to the output
+// buffer.
+//
+// The compressed output is buffered in a buffer of size `output_buffer_bytes`
+// which gets flushed to file when full.
+//
+// Output file format:
+// The output file consists of a sequence of compressed blocks. Each block
+// starts with a 4 byte header which stores the length (in bytes) of the
+// _compressed_ block _excluding_ this header. The compressed
+// block (excluding the 4 byte header) is a valid snappy block and can directly
+// be uncompressed using Snappy_Uncompress.
+class SnappyOutputBuffer {
+ public:
+  // Create an SnappyOutputBuffer for `file` with two buffers that cache the
+  // 1. input data to be deflated
+  // 2. the deflated output
+  // with sizes `input_buffer_bytes` and `output_buffer_bytes` respectively.
+  // Does not take ownership of `file`.
+  SnappyOutputBuffer(WritableFile* file, int32 input_buffer_bytes,
+                     int32 output_buffer_bytes);
+
+  // Adds `data` to the compression pipeline.
+  //
+  // The input data is buffered in `input_buffer_` and is compressed in bulk
+  // when the buffer gets full. The compressed output may not be immediately
+  // written to file but rather buffered in `output_buffer_` and gets written
+  // to file when the buffer is full.
+  //
+  // To immediately write contents to file call `Flush()`.
+  Status Write(StringPiece data);
+
+  // Compresses any cached input and writes all output to file. This must be
+  // called before the destructor to avoid any data loss.
+  Status Flush();
+
+ private:
+  // Appends `data` to `input_buffer_`.
+  // Throws if `data.size()` > AvailableInputSpace().
+  void AddToInputBuffer(StringPiece data);
+
+  // Appends `data` to `output_buffer_`. Flushes buffer contents to file when
+  // buffer gets full.
+  Status AddToOutputBuffer(const char* data, size_t length);
+
+  // Returns the total space available in `input_buffer_`.
+  int32 AvailableInputSpace() const;
+
+  // Deflate contents in input_buffer_ and store results in output_buffer_.
+  // The contents of output stream are written to file if more space is needed.
+  //
+  // Note: This method does not flush contents to file.
+  // Returns non-ok status if writing contents to file fails.
+  Status DeflateBuffered();
+
+  // Appends contents of `output_buffer_` to `file_`.
+  // Returns non-OK status if writing to file fails.
+  Status FlushOutputBufferToFile();
+
+  // Compresses `avail_in_` bytes at `next_in_` location in `input_buffer_` and
+  // writes the results to output using `AddToOutputBuffer`.
+  // Returns non-OK status if writing to file failed.
+  Status Deflate();
+
+  WritableFile* file_;  // Not owned
+
+  // Buffer for storing contents read from input `file_`.
+  // TODO(srbs): Consider using circular buffers. That would greatly simplify
+  // the implementation.
+  std::unique_ptr<char[]> input_buffer_;
+  size_t input_buffer_capacity_;
+  char* next_in_;
+  size_t avail_in_ = 0;
+
+  // Buffer for storing deflated contents of `file_`.
+  std::unique_ptr<char[]> output_buffer_;
+  size_t output_buffer_capacity_;
+  char* next_out_;
+  size_t avail_out_;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(SnappyOutputBuffer);
+};
+
+}  // namespace io
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_LIB_IO_SNAPPY_OUTPUTBUFFER_H_
-- 
GitLab


From d0550db5736f484c12ac7f52dfaf2aa581d3170f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 13:28:42 -0800
Subject: [PATCH 058/223] Adding magic methods __bool__, __eq__, and __ne__ to
 ClusterSpec to facilitate comparisons. Change: 131096794

---
 tensorflow/python/training/server_lib.py      | 12 +++++++
 tensorflow/python/training/server_lib_test.py | 31 ++++++++++++++++++-
 2 files changed, 42 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/training/server_lib.py b/tensorflow/python/training/server_lib.py
index 99e55aa365..b537eea61b 100644
--- a/tensorflow/python/training/server_lib.py
+++ b/tensorflow/python/training/server_lib.py
@@ -282,6 +282,18 @@ class ClusterSpec(object):
                       "job names to lists of network addresses, or a "
                       "`ClusterDef` protocol buffer")
 
+  def __nonzero__(self):
+    return bool(self._cluster_spec)
+
+  # Python 3.x
+  __bool__ = __nonzero__
+
+  def __eq__(self, other):
+    return self._cluster_spec == other
+
+  def __ne__(self, other):
+    return self._cluster_spec != other
+
   def as_dict(self):
     """Returns a dictionary from job names to lists of network addresses."""
     return self._cluster_spec
diff --git a/tensorflow/python/training/server_lib_test.py b/tensorflow/python/training/server_lib_test.py
index 9d71d54909..d5da5dd232 100644
--- a/tensorflow/python/training/server_lib_test.py
+++ b/tensorflow/python/training/server_lib_test.py
@@ -356,7 +356,10 @@ class ServerDefTest(tf.test.TestCase):
     cluster_spec = tf.train.ClusterSpec(cluster_def)
     self.assertProtoEquals(cluster_def, cluster_spec.as_cluster_def())
 
-  def testClusterSpec(self):
+
+class ClusterSpecTest(tf.test.TestCase):
+
+  def testProtoDictDefEquivalences(self):
     cluster_spec = tf.train.ClusterSpec(
         {"ps": ["ps0:2222", "ps1:2222"],
          "worker": ["worker0:2222", "worker1:2222", "worker2:2222"]})
@@ -379,6 +382,32 @@ class ServerDefTest(tf.test.TestCase):
         expected_proto,
         tf.train.ClusterSpec(cluster_spec.as_dict()).as_cluster_def())
 
+  def testEmptyClusterSpecIsFalse(self):
+    self.assertFalse(tf.train.ClusterSpec({}))
+
+  def testNonEmptyClusterSpecIsTrue(self):
+    self.assertTrue(tf.train.ClusterSpec({"job": ["host:port"]}))
+
+  def testEq(self):
+    self.assertEquals(tf.train.ClusterSpec({}), tf.train.ClusterSpec({}))
+    self.assertEquals(
+        tf.train.ClusterSpec({"job": ["host:2222"]}),
+        tf.train.ClusterSpec({"job": ["host:2222"]}),)
+
+  def testNe(self):
+    self.assertNotEquals(
+        tf.train.ClusterSpec({}),
+        tf.train.ClusterSpec({"job": ["host:2223"]}),)
+    self.assertNotEquals(
+        tf.train.ClusterSpec({"job1": ["host:2222"]}),
+        tf.train.ClusterSpec({"job2": ["host:2222"]}),)
+    self.assertNotEquals(
+        tf.train.ClusterSpec({"job": ["host:2222"]}),
+        tf.train.ClusterSpec({"job": ["host:2223"]}),)
+    self.assertNotEquals(
+        tf.train.ClusterSpec({"job": ["host:2222", "host:2223"]}),
+        tf.train.ClusterSpec({"job": ["host:2223", "host:2222"]}),)
+
 
 if __name__ == "__main__":
   tf.test.main()
-- 
GitLab


From 011402d8987e753acd54c6251a7edd2e2d8155ba Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 13:33:04 -0800
Subject: [PATCH 059/223] Allow a python shape inference fn to delegate to the
 cpp shape inference function. Enable this for MatMul and SparseMatMul.
 Change: 131097313

---
 tensorflow/core/framework/shape_inference.cc  |  19 ++++
 tensorflow/core/framework/shape_inference.h   |  11 ++
 tensorflow/python/BUILD                       |  14 +++
 tensorflow/python/framework/common_shapes.py  |  34 ++++++
 .../python/framework/cpp_shape_inference.cc   | 102 ++++++++++++++++++
 .../python/framework/cpp_shape_inference.h    |  48 +++++++++
 .../python/framework/cpp_shape_inference.i    |  28 +++++
 .../python/kernel_tests/matmul_op_test.py     |  28 ++++-
 tensorflow/python/ops/math_ops.py             |   4 +-
 tensorflow/python/platform/base.i             |  24 ++++-
 tensorflow/python/tensorflow.i                |   2 +
 11 files changed, 308 insertions(+), 6 deletions(-)
 create mode 100644 tensorflow/python/framework/cpp_shape_inference.cc
 create mode 100644 tensorflow/python/framework/cpp_shape_inference.h
 create mode 100644 tensorflow/python/framework/cpp_shape_inference.i

diff --git a/tensorflow/core/framework/shape_inference.cc b/tensorflow/core/framework/shape_inference.cc
index 4300784ffe..c6da445165 100644
--- a/tensorflow/core/framework/shape_inference.cc
+++ b/tensorflow/core/framework/shape_inference.cc
@@ -46,6 +46,25 @@ InferenceContext::InferenceContext(
   PostInputInit();
 }
 
+InferenceContext::InferenceContext(
+    const NodeDef* node_def, const OpDef& op_def,
+    const std::vector<string>& input_shapes_string,
+    const std::vector<TensorShapeProto>& input_shapes,
+    const std::vector<const Tensor*>& input_tensors)
+    : node_def_(*CHECK_NOTNULL(node_def)) {
+  PreInputInit(op_def, input_tensors);
+  if (!construction_status_.ok()) return;
+  for (const TensorShapeProto& p : input_shapes) {
+    const Shape* shape;
+    construction_status_.Update(MakeShapeFromShapeProto(p, &shape));
+    if (!construction_status_.ok()) {
+      return;
+    }
+    inputs_.push_back(shape);
+  }
+  PostInputInit();
+}
+
 InferenceContext::InferenceContext(
     const NodeDef* node_def, const OpDef& op_def,
     const std::vector<string>& input_shapes_string,
diff --git a/tensorflow/core/framework/shape_inference.h b/tensorflow/core/framework/shape_inference.h
index b08ffd369e..1d0d4ab471 100644
--- a/tensorflow/core/framework/shape_inference.h
+++ b/tensorflow/core/framework/shape_inference.h
@@ -100,6 +100,17 @@ class InferenceContext {
                    const std::vector<const Shape*>& input_shapes,
                    const std::vector<const Tensor*>& input_tensors);
 
+  // <input_tensors> is NULL-padded to be the same size as <input_shapes>.
+  //
+  // REQUIRES: <node_def> is not NULL, and must outlive the InferenceContext.
+  //
+  // TODO(cwhipkey): Remove 'input_shapes_string' once we can move the creation
+  // of Shapes from strings out of this class (or hide it).
+  InferenceContext(const NodeDef* node_def, const OpDef& op_def,
+                   const std::vector<string>& input_shapes_string,
+                   const std::vector<TensorShapeProto>& input_shapes,
+                   const std::vector<const Tensor*>& input_tensors);
+
   // This is a temporary constructor used for initial testing.
   //
   // TODO(cwhipkey): remove this temporary constructor.
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 21825e1bc2..fa10426ca2 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -168,6 +168,18 @@ cc_library(
     alwayslink = 1,
 )
 
+cc_library(
+    name = "cpp_shape_inference",
+    srcs = ["framework/cpp_shape_inference.cc"],
+    hdrs = ["framework/cpp_shape_inference.h"],
+    visibility = ["//visibility:public"],
+    deps = [
+        "//tensorflow/c:tf_status_helper",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:protos_cc",
+    ],
+)
+
 cc_library(
     name = "python_op_gen_main",
     srcs = [
@@ -1125,6 +1137,7 @@ tf_py_wrap_cc(
         "client/net_lib.i",
         "client/quantize_training.i",
         "client/tf_session.i",
+        "framework/cpp_shape_inference.i",
         "framework/python_op_gen.i",
         "lib/core/py_func.i",
         "lib/core/strings.i",
@@ -1142,6 +1155,7 @@ tf_py_wrap_cc(
         ":py_func_lib",
         ":py_record_reader_lib",
         ":py_record_writer_lib",
+        ":cpp_shape_inference",
         ":python_op_gen",
         ":tf_session_helper",
         "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib",
diff --git a/tensorflow/python/framework/common_shapes.py b/tensorflow/python/framework/common_shapes.py
index 3e034579c0..843317d391 100644
--- a/tensorflow/python/framework/common_shapes.py
+++ b/tensorflow/python/framework/common_shapes.py
@@ -19,6 +19,9 @@ from __future__ import print_function
 
 import six.moves
 
+from tensorflow.core.framework import tensor_shape_pb2
+from tensorflow.python import pywrap_tensorflow
+from tensorflow.python.framework import errors
 from tensorflow.python.framework import tensor_shape
 
 
@@ -586,3 +589,34 @@ def broadcast_shape(shape_x, shape_y):
   return tensor_shape.TensorShape(return_dims)
 
 
+def call_cpp_shape_fn(op):
+  """A shape function that delegates to the registered C++ shape function.
+
+  Args:
+    op: the node in the graph for which to compute output shapes.
+
+  Returns:
+    A TensorShape list of the output shapes of the op, as computed using the
+    C++ shape inference function registered for the op.
+
+  Raises:
+    ValueError: If the C++ shape function returned an error (e.g. because the
+    shapes of the inputs are of the wrong rank or otherwise incompatible
+    according to the shape function).
+  """
+  node_def_str = op.node_def.SerializeToString()
+  input_shapes = [i.get_shape().as_proto().SerializeToString() for i in
+                  op.inputs]
+
+  try:
+    with errors.raise_exception_on_not_ok_status() as status:
+      output_shapes = pywrap_tensorflow.RunCppShapeInference(
+          node_def_str, input_shapes, status)
+  except errors.InvalidArgumentError as err:
+    raise ValueError(err.message)
+
+  # Convert TensorShapeProto values in output_shapes.
+  return [
+      tensor_shape.TensorShape(tensor_shape_pb2.TensorShapeProto.FromString(s))
+      for s in output_shapes
+  ]
diff --git a/tensorflow/python/framework/cpp_shape_inference.cc b/tensorflow/python/framework/cpp_shape_inference.cc
new file mode 100644
index 0000000000..49ef244944
--- /dev/null
+++ b/tensorflow/python/framework/cpp_shape_inference.cc
@@ -0,0 +1,102 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/python/framework/cpp_shape_inference.h"
+
+#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/op.h"
+#include "tensorflow/core/framework/shape_inference.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/strings/strcat.h"
+
+namespace tensorflow {
+namespace swig {
+namespace {
+
+Status RunCppShapeInferenceImpl(
+    const string& serialized_node_def,
+    const std::vector<string>& input_serialized_shapes,
+    std::vector<string>* output_tensor_shape_protos) {
+  tensorflow::NodeDef node;
+  if (!node.ParseFromString(serialized_node_def)) {
+    return errors::InvalidArgument(
+        "Error parsing node_def during cpp shape inference");
+  }
+  DCHECK_EQ(output_tensor_shape_protos->size(), 0);
+
+  const OpRegistrationData* op_reg_data;
+  TF_RETURN_IF_ERROR(OpRegistry::Global()->LookUp(node.op(), &op_reg_data));
+
+  if (op_reg_data->shape_inference_fn == nullptr) {
+    return errors::InvalidArgument(
+        "No shape inference function exists for op '", node.op(),
+        "', did you forget to define it?");
+  }
+
+  std::vector<TensorShapeProto> input_shapes;
+  input_shapes.resize(input_serialized_shapes.size());
+  for (int i = 0; i < input_serialized_shapes.size(); ++i) {
+    if (!input_shapes[i].ParseFromString(input_serialized_shapes[i])) {
+      return errors::InvalidArgument(
+          "Error parsing shape proto during cpp shape inference");
+    }
+  }
+
+  tensorflow::shape_inference::InferenceContext c(
+      &node, op_reg_data->op_def, {} /* input_shape_strings */, input_shapes,
+      {} /* input_tensors */);
+  TF_RETURN_IF_ERROR(c.construction_status());
+  TF_RETURN_IF_ERROR(op_reg_data->shape_inference_fn(&c));
+
+  // Convert output shapes.
+  output_tensor_shape_protos->resize(c.num_outputs());
+  TensorShapeProto out;
+  for (int i = 0; i < c.num_outputs(); ++i) {
+    const shape_inference::Shape* s = c.output(i);
+    out.Clear();
+    if (c.RankKnown(s)) {
+      const int32 rank = c.Rank(s);
+      for (int i = 0; i < rank; ++i) {
+        const shape_inference::Dimension* d = c.Dim(s, i);
+        auto* out_dim = out.add_dim();
+        if (c.ValueKnown(d)) {
+          out_dim->set_size(c.Value(d));
+        } else {
+          out_dim->set_size(-1);
+        }
+      }
+    } else {
+      out.set_unknown_rank(true);
+    }
+    CHECK(out.AppendToString(&(*output_tensor_shape_protos)[i]));
+  }
+  return Status::OK();
+}
+
+}  // namespace
+
+std::vector<string> RunCppShapeInference(
+    const string& serialized_node_def,
+    const std::vector<string>& input_serialized_shapes, TF_Status* out_status) {
+  std::vector<string> output_tensor_shape_protos;
+  tensorflow::Status status =
+      RunCppShapeInferenceImpl(serialized_node_def, input_serialized_shapes,
+                               &output_tensor_shape_protos);
+  Set_TF_Status_from_Status(out_status, status);
+  return status.ok() ? output_tensor_shape_protos : std::vector<string>();
+}
+
+}  // namespace swig
+}  // namespace tensorflow
diff --git a/tensorflow/python/framework/cpp_shape_inference.h b/tensorflow/python/framework/cpp_shape_inference.h
new file mode 100644
index 0000000000..f10ac8cd3b
--- /dev/null
+++ b/tensorflow/python/framework/cpp_shape_inference.h
@@ -0,0 +1,48 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_PYTHON_FRAMEWORK_CPP_SHAPE_INFERENCE_H_
+#define THIRD_PARTY_TENSORFLOW_PYTHON_FRAMEWORK_CPP_SHAPE_INFERENCE_H_
+
+#include <vector>
+#include "tensorflow/c/tf_status_helper.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+namespace swig {
+
+// Calls the registered C++ shape inference function for <node> (a serialized
+// NodeDef).
+// Should not be called for shape functions that access input tensors; constant
+// input tensor values are not made available, and so the inferred shapes will
+// be less precise than they could be.
+//
+// Returns an error, or OK, in <out_status> according to whether the shape
+// inference was successful.
+//
+// On success, <*output_shapes> is populated with the inferred output shapes (as
+// serialized TensorShapeProtos).
+// <*output_shapes> must be empty when this function is called.
+//
+// This is temporary code to be used during the migration
+// from python shape inference functions to C++ shape inference functions.
+std::vector<string> RunCppShapeInference(
+    const string& serialized_node_def,
+    const std::vector<string>& input_serialized_shapes, TF_Status* out_status);
+
+}  // namespace swig
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_PYTHON_FRAMEWORK_CPP_SHAPE_INFERENCE_H_
diff --git a/tensorflow/python/framework/cpp_shape_inference.i b/tensorflow/python/framework/cpp_shape_inference.i
new file mode 100644
index 0000000000..7135f9380b
--- /dev/null
+++ b/tensorflow/python/framework/cpp_shape_inference.i
@@ -0,0 +1,28 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+%include "tensorflow/python/platform/base.i"
+
+%{
+#include "tensorflow/python/framework/cpp_shape_inference.h"
+%}
+
+%ignoreall;
+%unignore tensorflow;
+%unignore tensorflow::swig;
+%unignore tensorflow::swig::RunCppShapeInference;
+%include "tensorflow/python/framework/cpp_shape_inference.h"
+
+%unignoreall
diff --git a/tensorflow/python/kernel_tests/matmul_op_test.py b/tensorflow/python/kernel_tests/matmul_op_test.py
index aefcde0b89..e6d3960980 100644
--- a/tensorflow/python/kernel_tests/matmul_op_test.py
+++ b/tensorflow/python/kernel_tests/matmul_op_test.py
@@ -283,11 +283,35 @@ class MatMulTest(tf.test.TestCase):
     b = tf.placeholder(tf.float32, [36, 2])
     c = tf.placeholder(tf.float32, [37])
     with self.assertRaisesRegexp(
-        ValueError, "Dimensions 37 and 36 are not compatible"):
+        ValueError, "Dimensions must be equal, but are 37 and 36"):
       tf.matmul(a, b)
-    with self.assertRaisesRegexp(ValueError, "must have rank 2"):
+    with self.assertRaisesRegexp(ValueError, "must be rank 2"):
       tf.matmul(a, c)
 
+  def testShapeInference(self):
+    """Tests common_shapes.call_cpp_shape_fn."""
+    a = tf.constant([2] * 6, shape=[3, 2])
+    b = tf.constant([2] * 2, shape=[2, 1])
+    mm = tf.matmul(a, b)
+    self.assertEqual([3, 1], mm.get_shape())
+
+    # Transpose arguments are respected.
+    a = tf.constant([2] * 6, shape=[2, 3])
+    b = tf.constant([2] * 2, shape=[1, 2])
+    mm = tf.matmul(a, b, transpose_a=True, transpose_b=True)
+    self.assertEqual([3, 1], mm.get_shape())
+
+    # Unknown dims come through in output.
+    a = tf.placeholder(np.float32)
+    b = tf.placeholder(np.float32)
+    mm = tf.matmul(a, b)
+    self.assertEqual([None, None], mm.get_shape().as_list())
+
+    a = tf.constant([1] * 6, shape=[2, 3])
+    b = tf.constant([2] * 2, shape=[1, 2])
+    with self.assertRaisesRegexp(ValueError, ".*must be equal.*"):
+      tf.matmul(a, b, transpose_a=False, transpose_b=True)
+
 
 # TODO(zhifengc): Figures out how to test matmul gradients on GPU.
 class MatMulGradientTest(tf.test.TestCase):
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index 2f7c09b7ac..b976e953f8 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -1354,8 +1354,8 @@ def matmul(a, b,
 sparse_matmul = gen_math_ops._sparse_mat_mul
 batch_matmul = gen_math_ops._batch_mat_mul
 
-ops.RegisterShape("MatMul")(common_shapes.matmul_shape)
-ops.RegisterShape("SparseMatMul")(common_shapes.matmul_shape)
+ops.RegisterShape("MatMul")(common_shapes.call_cpp_shape_fn)
+ops.RegisterShape("SparseMatMul")(common_shapes.call_cpp_shape_fn)
 
 
 @ops.RegisterStatistics("MatMul", "flops")
diff --git a/tensorflow/python/platform/base.i b/tensorflow/python/platform/base.i
index df40491ed3..246446893c 100644
--- a/tensorflow/python/platform/base.i
+++ b/tensorflow/python/platform/base.i
@@ -17,6 +17,7 @@ limitations under the License.
 //
 %{
   #include <memory>
+  #include <vector>
   #include "tensorflow/core/platform/types.h"
   using tensorflow::uint64;
   using tensorflow::string;
@@ -75,6 +76,25 @@ limitations under the License.
     return PyUnicode_FromStringAndSize(s.data(), s.size());
 #endif
   }
+
+  template <class T>
+  bool tf_vector_input_helper(PyObject * seq, std::vector<T> * out,
+                              bool (*convert)(PyObject*, T * const)) {
+    PyObject *item, *it = PyObject_GetIter(seq);
+    if (!it) return false;
+    while ((item = PyIter_Next(it))) {
+      T elem;
+      bool success = convert(item, &elem);
+      Py_DECREF(item);
+      if (!success) {
+        Py_DECREF(it);
+        return false;
+      }
+      if (out) out->push_back(elem);
+    }
+    Py_DECREF(it);
+    return static_cast<bool>(!PyErr_Occurred());
+  }
 %}
 
 %typemap(in) string {
@@ -112,7 +132,7 @@ limitations under the License.
 
 %define _LIST_OUTPUT_TYPEMAP(type, py_converter)
     %typemap(in) std::vector<type>(std::vector<type> temp) {
-  if (!vector_input_helper($input, &temp, _PyObjAs<type>)) {
+  if (!tf_vector_input_helper($input, &temp, _PyObjAs<type>)) {
     if (!PyErr_Occurred())
       PyErr_SetString(PyExc_TypeError, "sequence(type) expected");
     return NULL;
@@ -121,7 +141,7 @@ limitations under the License.
 }
 %typemap(in) const std::vector<type>& (std::vector<type> temp),
    const std::vector<type>* (std::vector<type> temp) {
-  if (!vector_input_helper($input, &temp, _PyObjAs<type>)) {
+  if (!tf_vector_input_helper($input, &temp, _PyObjAs<type>)) {
     if (!PyErr_Occurred())
       PyErr_SetString(PyExc_TypeError, "sequence(type) expected");
     return NULL;
diff --git a/tensorflow/python/tensorflow.i b/tensorflow/python/tensorflow.i
index ef82a009f9..7a8fbf7201 100644
--- a/tensorflow/python/tensorflow.i
+++ b/tensorflow/python/tensorflow.i
@@ -35,3 +35,5 @@ limitations under the License.
 %include "tensorflow/python/training/server_lib.i"
 
 %include "tensorflow/python/framework/python_op_gen.i"
+
+%include "tensorflow/python/framework/cpp_shape_inference.i"
-- 
GitLab


From 9b6134d411ed73066acaccdb050b8c403bdd9ab8 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 13:38:25 -0800
Subject: [PATCH 060/223] Update generated Python Op docs. Change: 131097956

---
 .../g3doc/api_docs/python/contrib.learn.md    | 30 +++++++++----------
 .../tf.contrib.learn.LinearRegressor.md       |  2 +-
 .../tf.contrib.learn.LinearClassifier.md      |  2 +-
 .../shard2/tf.contrib.learn.BaseEstimator.md  |  2 +-
 ...tf.contrib.learn.TensorFlowDNNRegressor.md |  2 +-
 .../shard3/tf.contrib.learn.Estimator.md      |  2 +-
 .../shard4/tf.contrib.learn.DNNClassifier.md  |  2 +-
 .../tf.contrib.learn.TensorFlowEstimator.md   |  2 +-
 ...ontrib.learn.TensorFlowLinearClassifier.md |  2 +-
 ...tf.contrib.learn.TensorFlowRNNRegressor.md |  2 +-
 ...f.contrib.learn.TensorFlowDNNClassifier.md |  2 +-
 .../tf.contrib.learn.TensorFlowClassifier.md  |  2 +-
 .../tf.contrib.learn.TensorFlowRegressor.md   |  2 +-
 .../shard9/tf.contrib.learn.DNNRegressor.md   |  2 +-
 ...contrib.learn.TensorFlowLinearRegressor.md |  2 +-
 ...f.contrib.learn.TensorFlowRNNClassifier.md |  2 +-
 16 files changed, 30 insertions(+), 30 deletions(-)

diff --git a/tensorflow/g3doc/api_docs/python/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md
index 211a200659..684badf83a 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.learn.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md
@@ -60,7 +60,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.BaseEstimator.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#BaseEstimator.export}
+#### `tf.contrib.learn.BaseEstimator.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#BaseEstimator.export}
 
 Exports inference graph into given dir.
 
@@ -325,7 +325,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.Estimator.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#Estimator.export}
+#### `tf.contrib.learn.Estimator.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#Estimator.export}
 
 Exports inference graph into given dir.
 
@@ -584,7 +584,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowClassifier.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowClassifier.export}
+#### `tf.contrib.learn.TensorFlowClassifier.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowClassifier.export}
 
 Exports inference graph into given dir.
 
@@ -925,7 +925,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.DNNClassifier.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#DNNClassifier.export}
+#### `tf.contrib.learn.DNNClassifier.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#DNNClassifier.export}
 
 Exports inference graph into given dir.
 
@@ -1296,7 +1296,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.DNNRegressor.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#DNNRegressor.export}
+#### `tf.contrib.learn.DNNRegressor.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#DNNRegressor.export}
 
 Exports inference graph into given dir.
 
@@ -1564,7 +1564,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowDNNClassifier.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowDNNClassifier.export}
+#### `tf.contrib.learn.TensorFlowDNNClassifier.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowDNNClassifier.export}
 
 Exports inference graph into given dir.
 
@@ -1816,7 +1816,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowDNNRegressor.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowDNNRegressor.export}
+#### `tf.contrib.learn.TensorFlowDNNRegressor.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowDNNRegressor.export}
 
 Exports inference graph into given dir.
 
@@ -2096,7 +2096,7 @@ See superclass Estimator for more details.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowEstimator.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowEstimator.export}
+#### `tf.contrib.learn.TensorFlowEstimator.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowEstimator.export}
 
 Exports inference graph into given dir.
 
@@ -2502,7 +2502,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.LinearClassifier.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#LinearClassifier.export}
+#### `tf.contrib.learn.LinearClassifier.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#LinearClassifier.export}
 
 Exports inference graph into given dir.
 
@@ -2856,7 +2856,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.LinearRegressor.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#LinearRegressor.export}
+#### `tf.contrib.learn.LinearRegressor.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#LinearRegressor.export}
 
 Exports inference graph into given dir.
 
@@ -3124,7 +3124,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowLinearClassifier.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowLinearClassifier.export}
+#### `tf.contrib.learn.TensorFlowLinearClassifier.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowLinearClassifier.export}
 
 Exports inference graph into given dir.
 
@@ -3376,7 +3376,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowLinearRegressor.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowLinearRegressor.export}
+#### `tf.contrib.learn.TensorFlowLinearRegressor.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowLinearRegressor.export}
 
 Exports inference graph into given dir.
 
@@ -3670,7 +3670,7 @@ See superclass Estimator for more details.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowRNNClassifier.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowRNNClassifier.export}
+#### `tf.contrib.learn.TensorFlowRNNClassifier.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowRNNClassifier.export}
 
 Exports inference graph into given dir.
 
@@ -4038,7 +4038,7 @@ See superclass Estimator for more details.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowRNNRegressor.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowRNNRegressor.export}
+#### `tf.contrib.learn.TensorFlowRNNRegressor.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowRNNRegressor.export}
 
 Exports inference graph into given dir.
 
@@ -4363,7 +4363,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowRegressor.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowRegressor.export}
+#### `tf.contrib.learn.TensorFlowRegressor.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowRegressor.export}
 
 Exports inference graph into given dir.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.LinearRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.LinearRegressor.md
index 879981f9a6..62dccdf99f 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.LinearRegressor.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.learn.LinearRegressor.md
@@ -120,7 +120,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.LinearRegressor.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#LinearRegressor.export}
+#### `tf.contrib.learn.LinearRegressor.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#LinearRegressor.export}
 
 Exports inference graph into given dir.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.LinearClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.LinearClassifier.md
index 241425383a..321c4a9fb3 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.LinearClassifier.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.learn.LinearClassifier.md
@@ -138,7 +138,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.LinearClassifier.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#LinearClassifier.export}
+#### `tf.contrib.learn.LinearClassifier.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#LinearClassifier.export}
 
 Exports inference graph into given dir.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md
index 45bf50461a..586fb6314a 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.BaseEstimator.md
@@ -45,7 +45,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.BaseEstimator.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#BaseEstimator.export}
+#### `tf.contrib.learn.BaseEstimator.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#BaseEstimator.export}
 
 Exports inference graph into given dir.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.TensorFlowDNNRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.TensorFlowDNNRegressor.md
index ffba46a17b..8fde353604 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.TensorFlowDNNRegressor.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.learn.TensorFlowDNNRegressor.md
@@ -50,7 +50,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowDNNRegressor.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowDNNRegressor.export}
+#### `tf.contrib.learn.TensorFlowDNNRegressor.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowDNNRegressor.export}
 
 Exports inference graph into given dir.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md
index e98d0138fb..c7defcf60a 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.learn.Estimator.md
@@ -68,7 +68,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.Estimator.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#Estimator.export}
+#### `tf.contrib.learn.Estimator.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#Estimator.export}
 
 Exports inference graph into given dir.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md
index a16a419c5c..c9247a5446 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.DNNClassifier.md
@@ -139,7 +139,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.DNNClassifier.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#DNNClassifier.export}
+#### `tf.contrib.learn.DNNClassifier.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#DNNClassifier.export}
 
 Exports inference graph into given dir.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowEstimator.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowEstimator.md
index 10daed3f79..77a96151b6 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowEstimator.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowEstimator.md
@@ -78,7 +78,7 @@ See superclass Estimator for more details.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowEstimator.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowEstimator.export}
+#### `tf.contrib.learn.TensorFlowEstimator.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowEstimator.export}
 
 Exports inference graph into given dir.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowLinearClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowLinearClassifier.md
index d2cc47c993..775ccf8fd5 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowLinearClassifier.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowLinearClassifier.md
@@ -50,7 +50,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowLinearClassifier.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowLinearClassifier.export}
+#### `tf.contrib.learn.TensorFlowLinearClassifier.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowLinearClassifier.export}
 
 Exports inference graph into given dir.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowRNNRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowRNNRegressor.md
index a4f0affc3b..99633b3193 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowRNNRegressor.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowRNNRegressor.md
@@ -93,7 +93,7 @@ See superclass Estimator for more details.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowRNNRegressor.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowRNNRegressor.export}
+#### `tf.contrib.learn.TensorFlowRNNRegressor.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowRNNRegressor.export}
 
 Exports inference graph into given dir.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.TensorFlowDNNClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.TensorFlowDNNClassifier.md
index 16aead9ed8..f9ce477baa 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.TensorFlowDNNClassifier.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.contrib.learn.TensorFlowDNNClassifier.md
@@ -50,7 +50,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowDNNClassifier.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowDNNClassifier.export}
+#### `tf.contrib.learn.TensorFlowDNNClassifier.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowDNNClassifier.export}
 
 Exports inference graph into given dir.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowClassifier.md
index 39f9040f3b..8d70087e97 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowClassifier.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowClassifier.md
@@ -50,7 +50,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowClassifier.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowClassifier.export}
+#### `tf.contrib.learn.TensorFlowClassifier.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowClassifier.export}
 
 Exports inference graph into given dir.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.TensorFlowRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.TensorFlowRegressor.md
index 44b2458369..2471d73521 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.TensorFlowRegressor.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.TensorFlowRegressor.md
@@ -50,7 +50,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowRegressor.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowRegressor.export}
+#### `tf.contrib.learn.TensorFlowRegressor.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowRegressor.export}
 
 Exports inference graph into given dir.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md
index 15f5f79a1d..fd7551e09a 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.DNNRegressor.md
@@ -137,7 +137,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.DNNRegressor.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#DNNRegressor.export}
+#### `tf.contrib.learn.DNNRegressor.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#DNNRegressor.export}
 
 Exports inference graph into given dir.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowLinearRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowLinearRegressor.md
index 1b378411b2..338f1126c9 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowLinearRegressor.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowLinearRegressor.md
@@ -50,7 +50,7 @@ See `Evaluable`.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowLinearRegressor.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowLinearRegressor.export}
+#### `tf.contrib.learn.TensorFlowLinearRegressor.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowLinearRegressor.export}
 
 Exports inference graph into given dir.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRNNClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRNNClassifier.md
index 077d3336d8..94b378b63a 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRNNClassifier.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowRNNClassifier.md
@@ -92,7 +92,7 @@ See superclass Estimator for more details.
 
 - - -
 
-#### `tf.contrib.learn.TensorFlowRNNClassifier.export(export_dir, signature_fn=None, input_fn=default_input_fn, default_batch_size=1, exports_to_keep=None)` {#TensorFlowRNNClassifier.export}
+#### `tf.contrib.learn.TensorFlowRNNClassifier.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowRNNClassifier.export}
 
 Exports inference graph into given dir.
 
-- 
GitLab


From 8b937add81046daedbc3486142ba07cf4c5419ff Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 14:14:38 -0800
Subject: [PATCH 061/223] Fused GRU cell implementation (CPU+GPU support)
 FusedGRUCell kernel and FusedGRUCellGrad kernel are implemented. Change:
 131102163

---
 tensorflow/BUILD                              |   1 +
 tensorflow/contrib/rnn/BUILD                  |  30 +-
 tensorflow/contrib/rnn/__init__.py            |   2 +
 tensorflow/contrib/rnn/kernels/gru_ops.cc     | 508 ++++++++++++++++++
 tensorflow/contrib/rnn/kernels/gru_ops.h      | 243 +++++++++
 .../contrib/rnn/kernels/gru_ops_gpu.cu.cc     |  35 ++
 tensorflow/contrib/rnn/ops/gru_ops.cc         | 178 ++++++
 .../rnn/python/kernel_tests/gru_ops_test.py   | 319 +++++++++++
 tensorflow/contrib/rnn/python/ops/gru_ops.py  | 202 +++++++
 9 files changed, 1517 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/contrib/rnn/kernels/gru_ops.cc
 create mode 100644 tensorflow/contrib/rnn/kernels/gru_ops.h
 create mode 100644 tensorflow/contrib/rnn/kernels/gru_ops_gpu.cu.cc
 create mode 100644 tensorflow/contrib/rnn/ops/gru_ops.cc
 create mode 100644 tensorflow/contrib/rnn/python/kernel_tests/gru_ops_test.py
 create mode 100644 tensorflow/contrib/rnn/python/ops/gru_ops.py

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 5fcdb7aa56..add5d97169 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -101,6 +101,7 @@ filegroup(
         "//tensorflow/contrib/quantization/kernels:all_files",
         "//tensorflow/contrib/quantization/kernels/hexagon:all_files",
         "//tensorflow/contrib/quantization/tools:all_files",
+        "//tensorflow/contrib/rnn:all_files",
         "//tensorflow/contrib/session_bundle:all_files",
         "//tensorflow/contrib/session_bundle/example:all_files",
         "//tensorflow/contrib/slim:all_files",
diff --git a/tensorflow/contrib/rnn/BUILD b/tensorflow/contrib/rnn/BUILD
index f69c656c68..92489033c8 100644
--- a/tensorflow/contrib/rnn/BUILD
+++ b/tensorflow/contrib/rnn/BUILD
@@ -6,7 +6,7 @@ licenses(["notice"])  # Apache 2.0
 
 exports_files(["LICENSE"])
 
-package(default_visibility = ["//tensorflow:__subpackages__"])
+package(default_visibility = ["//visibility:public"])
 
 load("//tensorflow:tensorflow.bzl", "cuda_py_tests")
 load("//tensorflow:tensorflow.bzl", "tf_custom_op_library")
@@ -15,6 +15,7 @@ py_library(
     name = "rnn_py",
     srcs = ["__init__.py"] + glob(["python/ops/*.py"]),
     data = [
+        ":python/ops/_gru_ops.so",
         ":python/ops/_lstm_ops.so",
     ],
     srcs_version = "PY2AND3",
@@ -59,6 +60,33 @@ tf_custom_op_library(
     ],
 )
 
+tf_custom_op_library(
+    name = "python/ops/_gru_ops.so",
+    srcs = [
+        "kernels/gru_ops.cc",
+        "kernels/gru_ops.h",
+        "ops/gru_ops.cc",
+    ],
+    gpu_srcs = [
+        "kernels/gru_ops_gpu.cu.cc",
+        "kernels/gru_ops.h",
+    ],
+    deps = [
+        "//tensorflow/core/kernels:eigen_helpers",
+    ],
+)
+
+cuda_py_tests(
+    name = "gru_ops_test",
+    size = "small",
+    srcs = ["python/kernel_tests/gru_ops_test.py"],
+    additional_deps = [
+        ":rnn_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 filegroup(
     name = "all_files",
     srcs = glob(
diff --git a/tensorflow/contrib/rnn/__init__.py b/tensorflow/contrib/rnn/__init__.py
index 9622b46ff5..627310957a 100644
--- a/tensorflow/contrib/rnn/__init__.py
+++ b/tensorflow/contrib/rnn/__init__.py
@@ -18,6 +18,7 @@
 
 ### Fused RNNCells
 @@LSTMBlockCell
+@@GRUBlockCell
 
 ### LSTM-like cells
 @@CoupledInputForgetGateLSTMCell
@@ -33,5 +34,6 @@ from __future__ import division
 from __future__ import print_function
 
 # pylint: disable=unused-import,wildcard-import, line-too-long
+from tensorflow.contrib.rnn.python.ops.gru_ops import *
 from tensorflow.contrib.rnn.python.ops.lstm_ops import *
 from tensorflow.contrib.rnn.python.ops.rnn_cell import *
diff --git a/tensorflow/contrib/rnn/kernels/gru_ops.cc b/tensorflow/contrib/rnn/kernels/gru_ops.cc
new file mode 100644
index 0000000000..89a4817111
--- /dev/null
+++ b/tensorflow/contrib/rnn/kernels/gru_ops.cc
@@ -0,0 +1,508 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#define EIGEN_USE_THREADS
+
+#if GOOGLE_CUDA
+#include "tensorflow/core/platform/stream_executor.h"
+#endif  // GOOGLE_CUDA
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/contrib/rnn/kernels/gru_ops.h"
+#include "tensorflow/core/framework/op_kernel.h"
+
+namespace tensorflow {
+
+typedef Eigen::ThreadPoolDevice CPUDevice;
+typedef Eigen::GpuDevice GPUDevice;
+
+#if GOOGLE_CUDA
+namespace {
+template <typename T>
+perftools::gputools::DeviceMemory<T> AsDeviceMemory(const T* cuda_memory) {
+  perftools::gputools::DeviceMemoryBase wrapped(const_cast<T*>(cuda_memory));
+  perftools::gputools::DeviceMemory<T> typed(wrapped);
+  return typed;
+}
+}  // namespace
+#endif  // GOOGLE_CUDA
+
+namespace functor {
+template <typename T>
+// TODO(gitegaurav) : Refactor the matmul operation inside the kernel. Make
+// similar changes in the LSTMBlockCell. Create a new file which contains matmul
+// functionality. It should perform matmul operation using CuBlas when the Cuda
+// support is present otherwise using eigentensors.
+void TensorCuBlasGemm<T>::operator()(OpKernelContext* ctx,
+                                     perftools::gputools::Stream* stream,
+                                     bool transa, bool transb, uint64 m,
+                                     uint64 n, uint64 k, T alpha, const T* a,
+                                     int lda, const T* b, int ldb, T beta, T* c,
+                                     int ldc) {
+#if GOOGLE_CUDA
+  perftools::gputools::blas::Transpose trans[] = {
+      perftools::gputools::blas::Transpose::kNoTranspose,
+      perftools::gputools::blas::Transpose::kTranspose};
+
+  auto a_ptr = AsDeviceMemory(a);
+  auto b_ptr = AsDeviceMemory(b);
+  auto c_ptr = AsDeviceMemory(c);
+
+  bool blas_launch_status =
+      stream
+          ->ThenBlasGemm(trans[transa], trans[transb], m, n, k, alpha, a_ptr,
+                         lda, b_ptr, ldb, beta, &c_ptr, ldc)
+          .ok();
+  OP_REQUIRES(ctx, blas_launch_status, errors::Aborted("CuBlasGemm failed!"));
+#else
+  ctx->SetStatus(errors::InvalidArgument("CuBlasGemm needs CUDA."));
+#endif
+}
+
+template struct TensorCuBlasGemm<float>;
+}  // end namespace functor
+
+template <typename Device, typename T, bool USE_CUBLAS>
+class GRUCellBlockOp : public OpKernel {
+ public:
+  explicit GRUCellBlockOp(OpKernelConstruction* ctx) : OpKernel(ctx) {}
+  // TODO(gitegaurav) Replace the input checks with some smarter function.
+  void Compute(OpKernelContext* ctx) override {
+    // Grab the input tensors.
+    const Tensor* x_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->input("x", &x_tensor));
+
+    const Tensor* h_prev_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->input("h_prev", &h_prev_tensor));
+
+    const Tensor* w_ru_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->input("w_ru", &w_ru_tensor));
+
+    const Tensor* w_c_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->input("w_c", &w_c_tensor));
+
+    const Tensor* b_ru_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->input("b_ru", &b_ru_tensor));
+
+    const Tensor* b_c_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->input("b_c", &b_c_tensor));
+
+    const int64 batch_size = x_tensor->dim_size(0);
+    const int64 input_size = x_tensor->dim_size(1);
+    const int64 cell_size = h_prev_tensor->dim_size(1);
+
+    // Sanity checks for input shapes.
+
+    // Shape of 'h' must be [batch_size, cell_size]
+    OP_REQUIRES(ctx, h_prev_tensor->dim_size(0) == batch_size,
+                errors::InvalidArgument("h_prev.dims(0) != batch_size: ",
+                                        h_prev_tensor->dim_size(0), " vs. ",
+                                        batch_size));
+    OP_REQUIRES(ctx, h_prev_tensor->dim_size(1) == cell_size,
+                errors::InvalidArgument("h_prev.dims(1) != cell_size: ",
+                                        h_prev_tensor->dim_size(1), " vs. ",
+                                        cell_size));
+
+    // Shape of 'w_ru' must be [input_size+cell_size, 2*cell_size]
+    OP_REQUIRES(ctx, w_ru_tensor->dim_size(0) == input_size + cell_size,
+                errors::InvalidArgument(
+                    "w_ru.dim_size(0) != input_size + cell_size: ",
+                    w_ru_tensor->dim_size(0), " vs. ", input_size + cell_size));
+
+    OP_REQUIRES(ctx, w_ru_tensor->dim_size(1) == cell_size * 2,
+                errors::InvalidArgument("w_ru.dim_size(1) != cell_size * 2: ",
+                                        w_ru_tensor->dim_size(1), " vs. ",
+                                        cell_size * 2));
+
+    // Shape of 'w_c' must be [input_size+cell_size, cell_size]
+    OP_REQUIRES(ctx, w_c_tensor->dim_size(0) == input_size + cell_size,
+                errors::InvalidArgument(
+                    "w_c.dim_size(0) != input_size + cell_size: ",
+                    w_c_tensor->dim_size(0), " vs. ", input_size + cell_size));
+
+    OP_REQUIRES(
+        ctx, w_c_tensor->dim_size(1) == cell_size,
+        errors::InvalidArgument("w_c.dim_size(1) != cell_size: ",
+                                w_c_tensor->dim_size(1), " vs. ", cell_size));
+
+    // Shape of 'b_ru' must be [2*cell_size]
+    OP_REQUIRES(ctx, b_ru_tensor->dim_size(0) == cell_size * 2,
+                errors::InvalidArgument("b_ru.dim_size(0) != cell_size * 2: ",
+                                        b_ru_tensor->dim_size(0), " vs. ",
+                                        cell_size * 2));
+
+    OP_REQUIRES(ctx, b_ru_tensor->dims() == 1,
+                errors::InvalidArgument("Rank of b_ru must be 1",
+                                        b_ru_tensor->dims(), " vs. 1", 1));
+    // Shape of 'b_c' must be [cell_size]
+    OP_REQUIRES(
+        ctx, b_c_tensor->dim_size(0) == cell_size,
+        errors::InvalidArgument("b_c.dim_size(0) != cell_size: ",
+                                b_c_tensor->dim_size(0), " vs. ", cell_size));
+    OP_REQUIRES(ctx, b_c_tensor->dims() == 1,
+                errors::InvalidArgument("Rank of b_c must be 1",
+                                        b_c_tensor->dims(), " vs. 1"));
+
+    // Create output tensors.
+    Tensor* r_tensor = nullptr;
+    OP_REQUIRES_OK(
+        ctx, ctx->allocate_output("r", TensorShape({batch_size, cell_size}),
+                                  &r_tensor));
+
+    Tensor* u_tensor = nullptr;
+    OP_REQUIRES_OK(
+        ctx, ctx->allocate_output("u", TensorShape({batch_size, cell_size}),
+                                  &u_tensor));
+
+    Tensor* c_tensor = nullptr;
+    OP_REQUIRES_OK(
+        ctx, ctx->allocate_output("c", TensorShape({batch_size, cell_size}),
+                                  &c_tensor));
+
+    Tensor* h_tensor = nullptr;
+    OP_REQUIRES_OK(
+        ctx, ctx->allocate_output("h", TensorShape({batch_size, cell_size}),
+                                  &h_tensor));
+
+    // Allocate temp tensors.
+    Tensor x_h_prev_tensor;
+    OP_REQUIRES_OK(ctx, ctx->allocate_temp(
+                            DataTypeToEnum<T>::v(),
+                            TensorShape({batch_size, input_size + cell_size}),
+                            &x_h_prev_tensor));
+
+    Tensor x_h_prevr_tensor;
+    OP_REQUIRES_OK(ctx, ctx->allocate_temp(
+                            DataTypeToEnum<T>::v(),
+                            TensorShape({batch_size, input_size + cell_size}),
+                            &x_h_prevr_tensor));
+
+    Tensor r_u_bar_tensor;
+    OP_REQUIRES_OK(ctx,
+                   ctx->allocate_temp(DataTypeToEnum<T>::v(),
+                                      TensorShape({batch_size, 2 * cell_size}),
+                                      &r_u_bar_tensor));
+
+    const Device& device = ctx->eigen_device<Device>();
+
+    perftools::gputools::Stream* stream =
+        std::is_same<Device, GPUDevice>::value
+            ? ctx->op_device_context()->stream()
+            : nullptr;
+
+    functor::GRUBlockCellFprop<Device, T, USE_CUBLAS>(batch_size, input_size,
+                                                      cell_size)(
+        ctx, stream, device, x_tensor->matrix<T>(), h_prev_tensor->matrix<T>(),
+        w_ru_tensor->matrix<T>(), w_c_tensor->matrix<T>(),
+        b_ru_tensor->vec<T>(), b_c_tensor->vec<T>(), r_u_bar_tensor.matrix<T>(),
+        r_tensor->matrix<T>(), u_tensor->matrix<T>(), c_tensor->matrix<T>(),
+        h_tensor->matrix<T>(), x_h_prev_tensor.matrix<T>(),
+        x_h_prevr_tensor.matrix<T>());
+  }
+};
+
+// Register the Block GRU cell kernel for CPU.
+#define REGISTER_KERNEL(T)                                            \
+  REGISTER_KERNEL_BUILDER(                                            \
+      Name("GRUBlockCell").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      GRUCellBlockOp<CPUDevice, T, false>);
+
+REGISTER_KERNEL(float);
+#undef REGISTER_KERNEL
+
+template <typename Device, typename T, bool USE_CUBLAS>
+class GRUBlockCellGradOp : public OpKernel {
+ public:
+  explicit GRUBlockCellGradOp(OpKernelConstruction* ctx) : OpKernel(ctx) {}
+
+  void Compute(OpKernelContext* ctx) override {
+    // Grab the input tensors.
+    const Tensor* x_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->input("x", &x_tensor));
+
+    const Tensor* h_prev_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->input("h_prev", &h_prev_tensor));
+
+    const Tensor* w_ru_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->input("w_ru", &w_ru_tensor));
+
+    const Tensor* w_c_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->input("w_c", &w_c_tensor));
+
+    const Tensor* b_ru_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->input("b_ru", &b_ru_tensor));
+
+    const Tensor* b_c_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->input("b_c", &b_c_tensor));
+
+    const Tensor* r_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->input("r", &r_tensor));
+
+    const Tensor* u_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->input("u", &u_tensor));
+
+    const Tensor* c_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->input("c", &c_tensor));
+
+    const Tensor* d_h_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->input("d_h", &d_h_tensor));
+
+    const int64 batch_size = x_tensor->dim_size(0);
+    const int64 input_size = x_tensor->dim_size(1);
+    const int64 cell_size = h_prev_tensor->dim_size(1);
+
+    // Sanity checks for input shapes.
+
+    // Shape of 'h_prev' must be [batch_size, cell_size]
+    OP_REQUIRES(ctx, h_prev_tensor->dim_size(0) == batch_size,
+                errors::InvalidArgument("h_prev.dims(0) != batch_size: ",
+                                        h_prev_tensor->dim_size(0), " vs. ",
+                                        batch_size));
+    OP_REQUIRES(ctx, h_prev_tensor->dim_size(1) == cell_size,
+                errors::InvalidArgument("h_prev.dims(1) != cell_size: ",
+                                        h_prev_tensor->dim_size(1), " vs. ",
+                                        cell_size));
+
+    // Shape of 'w_ru' must be [input_size+cell_size, 2*cell_size]
+    OP_REQUIRES(ctx, w_ru_tensor->dim_size(0) == input_size + cell_size,
+                errors::InvalidArgument(
+                    "w_ru.dim_size(0) != input_size + cell_size: ",
+                    w_ru_tensor->dim_size(0), " vs. ", input_size + cell_size));
+
+    OP_REQUIRES(ctx, w_ru_tensor->dim_size(1) == cell_size * 2,
+                errors::InvalidArgument("w_ru.dim_size(1) != cell_size * 2: ",
+                                        w_ru_tensor->dim_size(1), " vs. ",
+                                        cell_size * 2));
+
+    // Shape of 'w_c' must be [input_size+cell_size, cell_size]
+    OP_REQUIRES(ctx, w_c_tensor->dim_size(0) == input_size + cell_size,
+                errors::InvalidArgument(
+                    "w_c.dim_size(0) != input_size + cell_size: ",
+                    w_c_tensor->dim_size(0), " vs. ", input_size + cell_size));
+
+    OP_REQUIRES(
+        ctx, w_c_tensor->dim_size(1) == cell_size,
+        errors::InvalidArgument("w_c.dim_size(1) != cell_size: ",
+                                w_c_tensor->dim_size(1), " vs. ", cell_size));
+
+    // Shape of 'b_ru' must be [2*cell_size]
+    OP_REQUIRES(ctx, b_ru_tensor->dim_size(0) == cell_size * 2,
+                errors::InvalidArgument("b_ru.dim_size(0) != cell_size * 2: ",
+                                        b_ru_tensor->dim_size(0), " vs. ",
+                                        cell_size * 2));
+
+    OP_REQUIRES(ctx, b_ru_tensor->dims() == 1,
+                errors::InvalidArgument("Rank of b_ru must be 1",
+                                        b_ru_tensor->dims(), " vs. 1"));
+
+    // Shape of 'b_c' must be [cell_size]
+    OP_REQUIRES(
+        ctx, b_c_tensor->dim_size(0) == cell_size,
+        errors::InvalidArgument("b_c.dim_size(0) != cell_size: ",
+                                b_c_tensor->dim_size(0), " vs. ", cell_size));
+
+    OP_REQUIRES(ctx, b_c_tensor->dims() == 1,
+                errors::InvalidArgument("Rank of b_c must be 1 ",
+                                        b_c_tensor->dims(), " vs. 1"));
+
+    // Shape of 'r' must be [batch_size, cell_size]
+    OP_REQUIRES(
+        ctx, r_tensor->dim_size(0) == batch_size,
+        errors::InvalidArgument("r.dims(0) != batch_size: ",
+                                r_tensor->dim_size(0), " vs. ", batch_size));
+    OP_REQUIRES(
+        ctx, r_tensor->dim_size(1) == cell_size,
+        errors::InvalidArgument("r.dims(1) != cell_size: ",
+                                r_tensor->dim_size(1), " vs. ", cell_size));
+
+    // Shape of 'u' must be [batch_size, cell_size]
+    OP_REQUIRES(
+        ctx, u_tensor->dim_size(0) == batch_size,
+        errors::InvalidArgument("u.dims(0) != batch_size: ",
+                                u_tensor->dim_size(0), " vs. ", batch_size));
+    OP_REQUIRES(
+        ctx, u_tensor->dim_size(1) == cell_size,
+        errors::InvalidArgument("u.dims(1) != cell_size: ",
+                                u_tensor->dim_size(1), " vs. ", cell_size));
+
+    // Shape of 'c' must be [batch_size, cell_size]
+    OP_REQUIRES(
+        ctx, c_tensor->dim_size(0) == batch_size,
+        errors::InvalidArgument("c.dims(0) != batch_size: ",
+                                c_tensor->dim_size(0), " vs. ", batch_size));
+    OP_REQUIRES(
+        ctx, c_tensor->dim_size(1) == cell_size,
+        errors::InvalidArgument("c.dims(1) != cell_size: ",
+                                c_tensor->dim_size(1), " vs. ", cell_size));
+
+    // Shape of 'd_h' must be [batch_size, cell_size]
+    OP_REQUIRES(
+        ctx, d_h_tensor->dim_size(0) == batch_size,
+        errors::InvalidArgument("d_h.dims(0) != batch_size: ",
+                                d_h_tensor->dim_size(0), " vs. ", batch_size));
+    OP_REQUIRES(
+        ctx, d_h_tensor->dim_size(1) == cell_size,
+        errors::InvalidArgument("d_h.dims(1) != cell_size: ",
+                                d_h_tensor->dim_size(1), " vs. ", cell_size));
+
+    // Create output tensors.
+    Tensor* d_x_tensor = nullptr;
+    OP_REQUIRES_OK(
+        ctx, ctx->allocate_output("d_x", TensorShape({batch_size, input_size}),
+                                  &d_x_tensor));
+
+    Tensor* d_h_prev_tensor = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(
+                            "d_h_prev", TensorShape({batch_size, cell_size}),
+                            &d_h_prev_tensor));
+
+    Tensor* d_c_bar_tensor;
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(
+                            "d_c_bar", TensorShape({batch_size, cell_size}),
+                            &d_c_bar_tensor));
+
+    Tensor* d_r_bar_u_bar_tensor;
+    OP_REQUIRES_OK(
+        ctx, ctx->allocate_output("d_r_bar_u_bar",
+                                  TensorShape({batch_size, 2 * cell_size}),
+                                  &d_r_bar_u_bar_tensor));
+
+    // Allocate temp tensors.
+    Tensor d_r_bar_tensor;
+    OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::v(),
+                                           TensorShape({batch_size, cell_size}),
+                                           &d_r_bar_tensor));
+
+    Tensor d_u_bar_tensor;
+    OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::v(),
+                                           TensorShape({batch_size, cell_size}),
+                                           &d_u_bar_tensor));
+
+    Tensor d_h_prevr_tensor;
+    OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::v(),
+                                           TensorShape({batch_size, cell_size}),
+                                           &d_h_prevr_tensor));
+
+    Tensor d_x_component_1_h_prev_compenent_1;
+    OP_REQUIRES_OK(ctx, ctx->allocate_temp(
+                            DataTypeToEnum<T>::v(),
+                            TensorShape({batch_size, input_size + cell_size}),
+                            &d_x_component_1_h_prev_compenent_1));
+
+    Tensor d_x_component_2_h_prevr;
+    OP_REQUIRES_OK(ctx, ctx->allocate_temp(
+                            DataTypeToEnum<T>::v(),
+                            TensorShape({batch_size, input_size + cell_size}),
+                            &d_x_component_2_h_prevr));
+
+    const Device& device = ctx->eigen_device<Device>();
+    perftools::gputools::Stream* stream =
+        std::is_same<Device, GPUDevice>::value
+            ? ctx->op_device_context()->stream()
+            : nullptr;
+
+    functor::GRUBlockCellBprop<Device, T, USE_CUBLAS>(batch_size, input_size,
+                                                      cell_size)(
+        ctx, stream, device, x_tensor->matrix<T>(), h_prev_tensor->matrix<T>(),
+        w_ru_tensor->matrix<T>(), w_c_tensor->matrix<T>(),
+        b_ru_tensor->vec<T>(), b_c_tensor->vec<T>(), r_tensor->matrix<T>(),
+        u_tensor->matrix<T>(), c_tensor->matrix<T>(), d_h_tensor->matrix<T>(),
+        d_x_tensor->matrix<T>(), d_h_prev_tensor->matrix<T>(),
+        d_c_bar_tensor->matrix<T>(), d_r_bar_u_bar_tensor->matrix<T>(),
+        d_r_bar_tensor.matrix<T>(), d_u_bar_tensor.matrix<T>(),
+        d_h_prevr_tensor.matrix<T>(),
+        d_x_component_1_h_prev_compenent_1.matrix<T>(),
+        d_x_component_2_h_prevr.matrix<T>());
+  }
+};
+
+// Register the gradient kernel for CPU.
+#define REGISTER_KERNEL(T)                                                \
+  REGISTER_KERNEL_BUILDER(                                                \
+      Name("GRUBlockCellGrad").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      GRUBlockCellGradOp<CPUDevice, T, false>);
+
+REGISTER_KERNEL(float);
+#undef REGISTER_KERNEL
+
+// GPU support.
+#if GOOGLE_CUDA
+#define EIGEN_USE_GPU
+
+// Forward declare the GPU Fprop functor.
+namespace functor {
+#define DECLARE_GPU_SPEC(T)                                                   \
+  template <>                                                                 \
+  void GRUBlockCellFprop<GPUDevice, T, true>::operator()(                     \
+      OpKernelContext* ctx, perftools::gputools::Stream* stream,              \
+      const GPUDevice& d, typename TTypes<T>::ConstMatrix x,                  \
+      typename TTypes<T>::ConstMatrix h_prev,                                 \
+      typename TTypes<T>::ConstMatrix w_ru,                                   \
+      typename TTypes<T>::ConstMatrix w_c, typename TTypes<T>::ConstVec b_ru, \
+      typename TTypes<T>::ConstVec b_c, typename TTypes<T>::Matrix r_u_bar,   \
+      typename TTypes<T>::Matrix r, typename TTypes<T>::Matrix u,             \
+      typename TTypes<T>::Matrix c, typename TTypes<T>::Matrix h,             \
+      typename TTypes<T>::Matrix x_h_prev,                                    \
+      typename TTypes<T>::Matrix x_h_prevr);                                  \
+  extern template struct GRUBlockCellFprop<GPUDevice, T, true>;
+
+DECLARE_GPU_SPEC(float);
+#undef DECLARE_GPU_SPEC
+}  // end namespace functor
+
+// Register the Block GRU cell kernel for GPU.
+#define REGISTER_GPU_KERNEL(T)                                        \
+  REGISTER_KERNEL_BUILDER(                                            \
+      Name("GRUBlockCell").Device(DEVICE_GPU).TypeConstraint<T>("T"), \
+      GRUCellBlockOp<GPUDevice, T, true>);
+
+REGISTER_GPU_KERNEL(float);
+#undef REGISTER_GPU_KERNEL
+
+// Forward declare the GPU Bprop functor.
+namespace functor {
+#define DECLARE_GPU_SPEC(T)                                                    \
+  template <>                                                                  \
+  void GRUBlockCellBprop<GPUDevice, T, true>::operator()(                      \
+      OpKernelContext* ctx, perftools::gputools::Stream* stream,               \
+      const GPUDevice& d, typename TTypes<T>::ConstMatrix x,                   \
+      typename TTypes<T>::ConstMatrix h, typename TTypes<T>::ConstMatrix w_ru, \
+      typename TTypes<T>::ConstMatrix w_c, typename TTypes<T>::ConstVec b_ru,  \
+      typename TTypes<T>::ConstVec b_c, typename TTypes<T>::ConstMatrix r,     \
+      typename TTypes<T>::ConstMatrix u, typename TTypes<T>::ConstMatrix c,    \
+      typename TTypes<T>::ConstMatrix d_h, typename TTypes<T>::Matrix d_x,     \
+      typename TTypes<T>::Matrix d_h_prev, typename TTypes<T>::Matrix d_c_bar, \
+      typename TTypes<T>::Matrix d_r_bar_u_bar,                                \
+      typename TTypes<T>::Matrix d_r_bar, typename TTypes<T>::Matrix d_u_bar,  \
+      typename TTypes<T>::Matrix d_h_prevr,                                    \
+      typename TTypes<T>::Matrix d_x_comp1_h_prev_comp1,                       \
+      typename TTypes<T>::Matrix d_x_comp2_and_h_prevr);                       \
+  extern template struct GRUBlockCellBprop<GPUDevice, T, true>;
+
+DECLARE_GPU_SPEC(float);
+#undef DECLARE_GPU_SPEC
+}  // end namespace functor
+
+// Register the gradient kernel for GPU.
+#define REGISTER_GPU_KERNEL(T)                                            \
+  REGISTER_KERNEL_BUILDER(                                                \
+      Name("GRUBlockCellGrad").Device(DEVICE_GPU).TypeConstraint<T>("T"), \
+      GRUBlockCellGradOp<GPUDevice, T, true>);
+
+REGISTER_GPU_KERNEL(float);
+#undef REGISTER_GPU_KERNEL
+#endif  // GOOGLE_CUDA
+
+}  // end namespace tensorflow
diff --git a/tensorflow/contrib/rnn/kernels/gru_ops.h b/tensorflow/contrib/rnn/kernels/gru_ops.h
new file mode 100644
index 0000000000..b2ff859b8e
--- /dev/null
+++ b/tensorflow/contrib/rnn/kernels/gru_ops.h
@@ -0,0 +1,243 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_CONTRIB_RNN_KERNELS_GRU_OPS_H_
+#define THIRD_PARTY_TENSORFLOW_CONTRIB_RNN_KERNELS_GRU_OPS_H_
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/tensor_types.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace perftools {
+namespace gputools {
+class Stream;
+}  // end namespace gputools
+}  // end namespace perftools
+
+namespace tensorflow {
+
+class OpKernelContext;
+
+namespace functor {
+
+template <typename T>
+struct TensorCuBlasGemm {
+  void operator()(OpKernelContext* ctx, perftools::gputools::Stream* stream,
+                  bool transa, bool transb, uint64 m, uint64 n, uint64 k,
+                  T alpha, const T* a, int lda, const T* b, int ldb, T beta,
+                  T* c, int ldc);
+};
+
+template <typename Device, typename T, bool USE_CUBLAS>
+struct TensorBlasGemm;
+
+template <typename Device, typename T>
+struct TensorBlasGemm<Device, T, true /* USE_CUBLAS */> {
+  static void compute(OpKernelContext* ctx, perftools::gputools::Stream* stream,
+                      const Device& d, bool transa, bool transb, T alpha,
+                      typename TTypes<T>::ConstMatrix a,
+                      typename TTypes<T>::ConstMatrix b, T beta,
+                      typename TTypes<T>::Matrix c) {
+    int64 m = c.dimensions()[0];
+    int64 n = c.dimensions()[1];
+    int64 k = transa ? a.dimensions()[0] : a.dimensions()[1];
+
+    TensorCuBlasGemm<T>()(ctx, stream, transb, transa, n, m, k, alpha, b.data(),
+                          transb ? k : n, a.data(), transa ? m : k, beta,
+                          c.data(), n);
+  }
+};
+
+template <typename Device, typename T>
+struct TensorBlasGemm<Device, T, false /* USE_CUBLAS */> {
+  static void compute(OpKernelContext* ctx, perftools::gputools::Stream* stream,
+                      const Device& d, bool transa, bool transb, T alpha,
+                      typename TTypes<T>::ConstMatrix a,
+                      typename TTypes<T>::ConstMatrix b, T beta,
+                      typename TTypes<T>::Matrix c) {
+    Eigen::array<Eigen::IndexPair<Eigen::DenseIndex>, 1> contract_pairs;
+    contract_pairs[0] =
+        Eigen::IndexPair<Eigen::DenseIndex>(transa == false, transb == true);
+    if (alpha == T(1) && beta == T(0)) {
+      c.device(d) = a.contract(b, contract_pairs);
+    } else if (alpha == T(1) && beta == T(1)) {
+      c.device(d) += a.contract(b, contract_pairs);
+    } else {
+      c.device(d) = c.constant(alpha) * a.contract(b, contract_pairs) +
+                    c.constant(beta) * c;
+    }
+  }
+};
+
+struct GRUCell {
+  GRUCell(const int batch_size, const int input_size, const int cell_size)
+      : batch_size_(batch_size),
+        input_size_(input_size),
+        cell_size_(cell_size) {}
+
+  inline Eigen::array<Eigen::DenseIndex, 2> x_offsets() const { return {0, 0}; }
+
+  inline Eigen::array<Eigen::DenseIndex, 2> x_extends() const {
+    return {batch_size_, input_size_};
+  }
+
+  inline Eigen::array<Eigen::DenseIndex, 2> h_offsets() const {
+    return {0, input_size_};
+  }
+
+  inline Eigen::array<Eigen::DenseIndex, 2> h_extends() const {
+    return {batch_size_, cell_size_};
+  }
+
+  inline Eigen::array<Eigen::DenseIndex, 2> ru_r_offset() const {
+    return {0, 0};
+  }
+
+  inline Eigen::array<Eigen::DenseIndex, 2> ru_u_offset() const {
+    return {0, cell_size_};
+  }
+
+  inline Eigen::array<Eigen::DenseIndex, 2> cell_extents() const {
+    return {batch_size_, cell_size_};
+  }
+
+ protected:
+  const int batch_size_;
+  const int input_size_;
+  const int cell_size_;
+};
+
+template <typename Device, typename T, bool USE_CUBLAS>
+struct GRUBlockCellFprop : public GRUCell {
+  GRUBlockCellFprop(const int batch_size, const int input_size,
+                    const int cell_size)
+      : GRUCell(batch_size, input_size, cell_size) {}
+
+  void operator()(OpKernelContext* ctx, perftools::gputools::Stream* stream,
+                  const Device& d, typename TTypes<T>::ConstMatrix x,
+                  typename TTypes<T>::ConstMatrix h_prev,
+                  typename TTypes<T>::ConstMatrix w_ru,
+                  typename TTypes<T>::ConstMatrix w_c,
+                  typename TTypes<T>::ConstVec b_ru,
+                  typename TTypes<T>::ConstVec b_c,
+                  typename TTypes<T>::Matrix r_u_bar,
+                  typename TTypes<T>::Matrix r, typename TTypes<T>::Matrix u,
+                  typename TTypes<T>::Matrix c, typename TTypes<T>::Matrix h,
+                  typename TTypes<T>::Matrix x_h_prev,
+                  typename TTypes<T>::Matrix x_h_prevr) {
+    // Concat x_h_prev = [x, h_prev].
+    x_h_prev.slice(x_offsets(), x_extends()).device(d) = x;
+    x_h_prev.slice(h_offsets(), h_extends()).device(d) = h_prev;
+
+    // r_u_bar = x_h_prev * w_ru + b_ru
+    typename TTypes<T>::ConstMatrix const_x_h_prev(x_h_prev.data(),
+                                                   x_h_prev.dimensions());
+    TensorBlasGemm<Device, T, USE_CUBLAS>::compute(ctx, stream, d, false, false,
+                                                   T(1), const_x_h_prev, w_ru,
+                                                   T(0), r_u_bar);
+
+    // Creating a bias matrix for adding by broadcasting 'b_ru'
+    Eigen::array<Eigen::DenseIndex, 2> broadcast_shape({batch_size_, 1});
+    Eigen::array<Eigen::DenseIndex, 2> b_ru_shape({1, b_ru.dimensions()[0]});
+    r_u_bar.device(d) += b_ru.reshape(b_ru_shape).broadcast(broadcast_shape);
+
+    // Slice r_u_bar into r, u and apply the sigmoid.
+    r.device(d) = (r_u_bar.slice(ru_r_offset(), cell_extents())).sigmoid();
+    u.device(d) = (r_u_bar.slice(ru_u_offset(), cell_extents())).sigmoid();
+
+    // Concat x_h_prevr = [x,h_prev*r]
+    x_h_prevr.slice(x_offsets(), x_extends()).device(d) = x;
+    x_h_prevr.slice(h_offsets(), h_extends()).device(d) = h_prev * r;
+
+    // c = tanh(x_h_prevr*w_c+b_c), Note b_c is broadcasted before adding.
+    typename TTypes<T>::ConstMatrix const_x_h_prevr(x_h_prevr.data(),
+                                                    x_h_prevr.dimensions());
+    TensorBlasGemm<Device, T, USE_CUBLAS>::compute(
+        ctx, stream, d, false, false, T(1), const_x_h_prevr, w_c, T(0), c);
+
+    Eigen::array<Eigen::DenseIndex, 2> b_c_shape({1, b_c.dimensions()[0]});
+    c.device(d) += (b_c.reshape(b_c_shape).broadcast(broadcast_shape));
+    c.device(d) = c.tanh();
+
+    // h= u*h_prev + (1-u)*c
+    h.device(d) = u * (h_prev - c) + c;
+  }
+};
+
+template <typename Device, typename T, bool USE_CUBLAS>
+struct GRUBlockCellBprop : public GRUCell {
+  GRUBlockCellBprop(const int batch_size, const int input_size,
+                    const int cell_size)
+      : GRUCell(batch_size, input_size, cell_size) {}
+
+  void operator()(
+      OpKernelContext* ctx, perftools::gputools::Stream* stream,
+      const Device& d, typename TTypes<T>::ConstMatrix x,
+      typename TTypes<T>::ConstMatrix h_prev,
+      typename TTypes<T>::ConstMatrix w_ru, typename TTypes<T>::ConstMatrix w_c,
+      typename TTypes<T>::ConstVec b_ru, typename TTypes<T>::ConstVec b_c,
+      typename TTypes<T>::ConstMatrix r, typename TTypes<T>::ConstMatrix u,
+      typename TTypes<T>::ConstMatrix c, typename TTypes<T>::ConstMatrix d_h,
+      typename TTypes<T>::Matrix d_x, typename TTypes<T>::Matrix d_h_prev,
+      typename TTypes<T>::Matrix d_c_bar,
+      typename TTypes<T>::Matrix d_r_bar_u_bar,
+      typename TTypes<T>::Matrix d_r_bar, typename TTypes<T>::Matrix d_u_bar,
+      typename TTypes<T>::Matrix d_hr,
+      typename TTypes<T>::Matrix d_x_comp1_and_h_prev_comp1,
+      typename TTypes<T>::Matrix d_x_comp2_and_h_prevr) {
+    // d_c_bar = d_h*(1-u)*(1-(c*c))
+    d_c_bar.device(d) =
+        ((d_h * (u.constant(T(1)) - u)) * (c.constant(T(1)) - c * c));
+
+    // d_u_bar = d_h*(h-c)*(u*(1-u))
+    d_u_bar.device(d) = d_h * (h_prev - c) * u * (u.constant(T(1)) - u);
+
+    // [2nd_component_of_d_x d_h_prevr] = d_c_bar X w_c^T
+    typename TTypes<T>::ConstMatrix const_d_c_bar(d_c_bar.data(),
+                                                  d_c_bar.dimensions());
+    TensorBlasGemm<Device, T, USE_CUBLAS>::compute(ctx, stream, d, false, true,
+                                                   T(1), const_d_c_bar, w_c,
+                                                   T(0), d_x_comp2_and_h_prevr);
+
+    d_hr.device(d) = d_x_comp2_and_h_prevr.slice(h_offsets(), h_extends());
+    d_r_bar.device(d) = (d_hr * h_prev * r) * (r.constant(T(1)) - r);
+
+    // d_r_bar_u_bar = concatenate(d_r_bar, d_u_bar) along axis = 1.
+    d_r_bar_u_bar.slice(ru_r_offset(), cell_extents()).device(d) = d_r_bar;
+    d_r_bar_u_bar.slice(ru_u_offset(), cell_extents()).device(d) = d_u_bar;
+
+    // [1st_component_of_d_x 1st_component_of_d_h_prev] = [d_r_bar d_u_bar] X
+    // w_ru^T
+    typename TTypes<T>::ConstMatrix const_d_r_bar_u_bar(
+        d_r_bar_u_bar.data(), d_r_bar_u_bar.dimensions());
+    TensorBlasGemm<Device, T, USE_CUBLAS>::compute(
+        ctx, stream, d, false, true, T(1), const_d_r_bar_u_bar, w_ru, T(0),
+        d_x_comp1_and_h_prev_comp1);
+
+    // d_x = d_x_comp1 + d_x_comp2
+    d_x.device(d) = (d_x_comp1_and_h_prev_comp1 + d_x_comp2_and_h_prevr)
+                        .slice(x_offsets(), x_extends());
+
+    // d_h_prev = d_h_comp1 + d_hr*r + d_h*u
+    d_h_prev.device(d) =
+        d_x_comp1_and_h_prev_comp1.slice(h_offsets(), h_extends()) +
+        (d_hr * r) + (d_h * u);
+  }
+};
+
+}  // namespace functor
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CONTRIB_RNN_KERNELS_GRU_OPS_H_
diff --git a/tensorflow/contrib/rnn/kernels/gru_ops_gpu.cu.cc b/tensorflow/contrib/rnn/kernels/gru_ops_gpu.cu.cc
new file mode 100644
index 0000000000..846c7f49e3
--- /dev/null
+++ b/tensorflow/contrib/rnn/kernels/gru_ops_gpu.cu.cc
@@ -0,0 +1,35 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#define EIGEN_USE_GPU
+#include "tensorflow/contrib/rnn/kernels/gru_ops.h"
+
+namespace tensorflow {
+namespace functor {
+
+typedef Eigen::GpuDevice GPUDevice;
+
+#define DEFINE_GPU_SPECS(T)                              \
+  template struct GRUBlockCellFprop<GPUDevice, T, true>; \
+  template struct GRUBlockCellBprop<GPUDevice, T, true>;
+
+DEFINE_GPU_SPECS(float);
+#undef DEFINE_GPU_SPECS
+
+}  // end namespace functor
+}  // end namespace tensorflow
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/contrib/rnn/ops/gru_ops.cc b/tensorflow/contrib/rnn/ops/gru_ops.cc
new file mode 100644
index 0000000000..5accb4fb35
--- /dev/null
+++ b/tensorflow/contrib/rnn/ops/gru_ops.cc
@@ -0,0 +1,178 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/framework/op.h"
+
+REGISTER_OP("GRUBlockCell")
+    .Attr("T: {float}")
+    .Input("x: T")
+    .Input("h_prev: T")
+    .Input("w_ru: T")
+    .Input("w_c: T")
+    .Input("b_ru: T")
+    .Input("b_c: T")
+    .Output("r: T")
+    .Output("u: T")
+    .Output("c: T")
+    .Output("h: T")
+    .Doc(R"doc(
+Computes the GRU cell forward propagation for 1 time step.
+
+Args
+    x: Input to the GRU cell.
+    h_prev: State input from the previous GRU cell.
+    w_ru: Weight matrix for the reset and update gate.
+    w_c: Weight matrix for the cell connection gate.
+    b_ru: Bias vector for the reset and update gate.
+    b_c: Bias vector for the cell connection gate.
+
+Returns
+    r: Output of the reset gate.
+    u: Output of the update gate.
+    c: Output of the cell connection gate.
+    h: Current state of the GRU cell.
+
+Note on notation of the variables:
+
+Concatenation of a and b is represented by a_b
+Element-wise dot product of a and b is represented by ab
+Element-wise dot product is represented by \circ
+Matrix multiplication is represented by *
+
+Baises are initialized with :
+`b_ru` - constant_initializer(1.0)
+`b_c` - constant_initializer(0.0)
+
+This kernel op implements the following mathematical equations:
+
+```
+x_h_prev = [x, h_prev]
+
+[r_bar u_bar] = x_h_prev * w_ru + b_ru
+
+r = sigmoid(r_bar)
+u = sigmoid(u_bar)
+
+h_prevr = h_prev \circ r
+
+x_h_prevr = [x h_prevr]
+
+c_bar = x_h_prevr * w_c + b_c
+c = tanh(c_bar)
+
+h = (1-u) \circ c + u \circ h_prev
+```
+)doc");
+
+REGISTER_OP("GRUBlockCellGrad")
+    .Attr("T: {float}")
+    .Input("x: T")
+    .Input("h_prev: T")
+    .Input("w_ru: T")
+    .Input("w_c: T")
+    .Input("b_ru: T")
+    .Input("b_c: T")
+    .Input("r: T")
+    .Input("u: T")
+    .Input("c: T")
+    .Input("d_h: T")
+    .Output("d_x: T")
+    .Output("d_h_prev: T")
+    .Output("d_c_bar: T")
+    .Output("d_r_bar_u_bar: T")
+    .Doc(R"doc(
+Computes the GRU cell back-propagation for 1 time step.
+
+Args
+    x: Input to the GRU cell.
+    h_prev: State input from the previous GRU cell.
+    w_ru: Weight matrix for the reset and update gate.
+    w_c: Weight matrix for the cell connection gate.
+    b_ru: Bias vector for the reset and update gate.
+    b_c: Bias vector for the cell connection gate.
+    r: Output of the reset gate.
+    u: Output of the update gate.
+    c: Output of the cell connection gate.
+    d_h: Gradients of the h_new wrt to objective function.
+
+Returns
+    d_x: Gradients of the x wrt to objective function.
+    d_h_prev: Gradients of the h wrt to objective function.
+    d_c_bar Gradients of the c_bar wrt to objective function.
+    d_r_bar_u_bar Gradients of the r_bar & u_bar wrt to objective function.
+
+This kernel op implements the following mathematical equations:
+
+Note on notation of the variables:
+
+Concatenation of a and b is represented by a_b
+Element-wise dot product of a and b is represented by ab
+Element-wise dot product is represented by \circ
+Matrix multiplication is represented by *
+
+Additional notes for clarity:
+
+`w_ru` can be segmented into 4 different matrices.
+```
+w_ru = [w_r_x w_u_x
+        w_r_h_prev w_u_h_prev]
+```
+Similarly, `w_c` can be segmented into 2 different matrices.
+```
+w_c = [w_c_x w_c_h_prevr]
+```
+Same goes for biases.
+```
+b_ru = [b_ru_x b_ru_h]
+b_c = [b_c_x b_c_h]
+```
+Another note on notation:
+```
+d_x = d_x_component_1 + d_x_component_2
+
+where d_x_component_1 = d_r_bar * w_r_x^T + d_u_bar * w_r_x^T
+and d_x_component_2 = d_c_bar * w_c_x^T
+
+d_h_prev = d_h_prev_component_1 + d_h_prevr \circ r + d_h \circ u
+where d_h_prev_componenet_1 = d_r_bar * w_r_h_prev^T + d_u_bar * w_r_h_prev^T
+```
+
+Mathematics behind the Gradients below:
+```
+d_c_bar = d_h \circ (1-u) \circ (1-c \circ c)
+d_u_bar = d_h \circ (h-c) \circ u \circ (1-u)
+
+d_r_bar_u_bar = [d_r_bar d_u_bar]
+
+[d_x_component_1 d_h_prev_component_1] = d_r_bar_u_bar * w_ru^T
+
+[d_x_component_2 d_h_prevr] = d_c_bar * w_c^T
+
+d_x = d_x_component_1 + d_x_component_2
+
+d_h_prev = d_h_prev_component_1 + d_h_prevr \circ r + u
+```
+Below calculation is performed in the python wrapper for the Gradients
+(not in the gradient kernel.)
+```
+d_w_ru = x_h_prevr^T * d_c_bar
+
+d_w_c = x_h_prev^T * d_r_bar_u_bar
+
+d_b_ru = sum of d_r_bar_u_bar along axis = 0
+
+d_b_c = sum of d_c_bar along axis = 0
+```
+)doc");
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/gru_ops_test.py b/tensorflow/contrib/rnn/python/kernel_tests/gru_ops_test.py
new file mode 100644
index 0000000000..8a3fd75eab
--- /dev/null
+++ b/tensorflow/contrib/rnn/python/kernel_tests/gru_ops_test.py
@@ -0,0 +1,319 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for Block GRU module."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.contrib.rnn.python.ops import gru_ops
+
+
+class GRUBlockCellTest(tf.test.TestCase):
+  _use_gpu = False
+
+  def testNoneDimsWithDynamicRNN(self):
+    with self.test_session(use_gpu=self._use_gpu, graph=tf.Graph()) as sess:
+      batch_size = 4
+      cell_size = 5
+      input_size = 6
+      num_steps = 7
+
+      cell = gru_ops.GRUBlockCell(cell_size)
+
+      x = tf.placeholder(tf.float32, shape=(None, None, input_size))
+      _, output = tf.nn.dynamic_rnn(cell, x, time_major=True, dtype=tf.float32)
+      sess.run(tf.initialize_all_variables())
+      feed = {}
+      feed[x] = np.random.randn(num_steps, batch_size, input_size)
+      sess.run(output, feed)
+
+  def testBlockGRUToGRUCellSingleStep(self):
+    with self.test_session(use_gpu=self._use_gpu, graph=tf.Graph()) as sess:
+      batch_size = 4
+      cell_size = 5
+      input_size = 6
+
+      seed = 1994
+      initializer = tf.random_uniform_initializer(-0.01, 0.01, seed=seed)
+
+      # Inputs
+      x = tf.zeros([batch_size, input_size])
+      h = tf.zeros([batch_size, cell_size])
+
+      # Values for the inputs.
+      x_value = np.random.rand(batch_size, input_size)
+      h_value = np.random.rand(batch_size, cell_size)
+
+      # Output from the basic GRU cell implementation.
+      with tf.variable_scope("basic", initializer=initializer):
+        output = tf.nn.rnn_cell.GRUCell(cell_size)(x, h)
+        sess.run([tf.initialize_all_variables()])
+        basic_res = sess.run([output], {x: x_value, h: h_value})
+
+      # Output from the block GRU cell implementation.
+      with tf.variable_scope("block", initializer=initializer):
+        output = gru_ops.GRUBlockCell(cell_size)(x, h)
+        sess.run([tf.initialize_all_variables()])
+        block_res = sess.run([output], {x: x_value, h: h_value})
+
+      self.assertEqual(len(block_res), len(basic_res))
+      for block, basic in zip(block_res, basic_res):
+        self.assertAllClose(block, basic)
+
+  def testBlockGRUToGRUCellMultiStep(self):
+    with self.test_session(use_gpu=self._use_gpu, graph=tf.Graph()) as sess:
+      batch_size = 2
+      cell_size = 3
+      input_size = 3
+      time_steps = 4
+
+      # Random initializers.
+      seed = 1994
+      initializer = tf.random_uniform_initializer(-0.01, 0.01, seed=seed)
+      np.random.seed(seed)
+
+      # Inputs
+      concat_x = tf.placeholder(
+          tf.float32, shape=(time_steps, batch_size, input_size))
+      h = tf.zeros([batch_size, cell_size])
+
+      # Values for the inputs.
+      x_values = np.random.rand(time_steps, batch_size, input_size)
+      h_value = np.random.rand(batch_size, cell_size)
+
+      # Output from the block GRU cell implementation.
+      with tf.variable_scope("block", initializer=initializer):
+        cell = gru_ops.GRUBlockCell(cell_size)
+        outputs_dynamic, state_dynamic = tf.nn.dynamic_rnn(
+            cell,
+            inputs=concat_x,
+            initial_state=h,
+            time_major=True,
+            dtype=tf.float32)
+        feeds = {concat_x: x_values, h: h_value}
+        sess.run([tf.initialize_all_variables()])
+        block_res = sess.run([outputs_dynamic, state_dynamic], feeds)
+
+      # Output from the basic GRU cell implementation.
+      with tf.variable_scope("basic", initializer=initializer):
+        cell = tf.nn.rnn_cell.GRUCell(cell_size)
+        outputs_dynamic, state_dynamic = tf.nn.dynamic_rnn(
+            cell,
+            inputs=concat_x,
+            initial_state=h,
+            time_major=True,
+            dtype=tf.float32)
+        feeds = {concat_x: x_values, h: h_value}
+        sess.run([tf.initialize_all_variables()])
+        basic_res = sess.run([outputs_dynamic, state_dynamic], feeds)
+
+      # Check the lengths of the outputs_dynamic, and states.
+      self.assertEqual(len(block_res), len(basic_res))
+      self.assertEqual(len(block_res[0]), len(basic_res[0]))
+      self.assertEqual(len(block_res[1]), len(basic_res[1]))
+
+      # Check the outputs_dynamic values.
+      for block_output, basic_output in zip(block_res[0], basic_res[0]):
+        self.assertAllClose(block_output, basic_output)
+
+      # Check the state_dynamic value.
+      self.assertAllClose(block_res[1], block_res[1])
+
+  def testDerivativeOfBlockGRUToGRUCellSingleStep(self):
+    with self.test_session(use_gpu=self._use_gpu, graph=tf.Graph()) as sess:
+      batch_size = 2
+      cell_size = 3
+      input_size = 4
+
+      seed = 1994
+      initializer = tf.random_uniform_initializer(-0.01, 0.01, seed=seed)
+      np.random.seed(seed)
+
+      # Inputs
+      x = tf.zeros([batch_size, input_size])
+      h = tf.zeros([batch_size, cell_size])
+
+      # Values for the inputs.
+      x_value = np.random.rand(batch_size, input_size)
+      h_value = np.random.rand(batch_size, cell_size)
+
+      # Gradients from the block GRU cell implementation.
+      with tf.variable_scope("block", initializer=initializer):
+        output = gru_ops.GRUBlockCell(cell_size)(x, h)
+        sess.run([tf.initialize_all_variables()])
+
+        all_variables = tf.all_variables()[0:4]
+        [w_ru, b_ru, w_c, b_c] = all_variables
+
+        d_new_h_wrt_x = tf.gradients([output], x)
+        d_new_h_wrt_h = tf.gradients([output], h)
+        d_new_h_wrt_w_ru = tf.gradients([output], w_ru)
+        d_new_h_wrt_w_c = tf.gradients([output], w_c)
+        d_new_h_wrt_b_ru = tf.gradients([output], b_ru)
+        d_new_h_wrt_b_c = tf.gradients([output], b_c)
+
+        d_block_res = sess.run([d_new_h_wrt_x, d_new_h_wrt_h, d_new_h_wrt_w_ru,
+                                d_new_h_wrt_w_c, d_new_h_wrt_b_ru,
+                                d_new_h_wrt_b_c], {x: x_value,
+                                                   h: h_value})
+
+      # Gradients from the basic GRU cell implementation.
+      with tf.variable_scope("basic", initializer=initializer):
+        output = tf.nn.rnn_cell.GRUCell(cell_size)(x, h)
+        sess.run([tf.initialize_all_variables()])
+
+        all_variables = tf.all_variables()[4:8]
+        [w_ru, b_ru, w_c, b_c] = all_variables
+
+        d_new_h_wrt_x = tf.gradients([output], x)
+        d_new_h_wrt_h = tf.gradients([output], h)
+        d_new_h_wrt_w_ru = tf.gradients([output], w_ru)
+        d_new_h_wrt_w_c = tf.gradients([output], w_c)
+        d_new_h_wrt_b_ru = tf.gradients([output], b_ru)
+        d_new_h_wrt_b_c = tf.gradients([output], b_c)
+
+        d_basic_res = sess.run([d_new_h_wrt_x, d_new_h_wrt_h, d_new_h_wrt_w_ru,
+                                d_new_h_wrt_w_c, d_new_h_wrt_b_ru,
+                                d_new_h_wrt_b_c], {x: x_value,
+                                                   h: h_value})
+
+      # Check lengths of derivative results.
+      self.assertEqual(len(d_block_res), len(d_basic_res))
+      # Check the value of every derivative result.
+      for block, basic in zip(d_block_res, d_basic_res):
+        self.assertAllClose(block, basic)
+
+  def testDerivativeOfBlockGRUToGRUCellMultiSteps(self):
+    batch_size = 2
+    cell_size = 3
+    input_size = 4
+    time_steps = 2
+    with self.test_session(use_gpu=self._use_gpu, graph=tf.Graph()) as sess:
+      # Random initializers.
+      seed = 1994
+      initializer = tf.random_uniform_initializer(-0.01, 0.01, seed=seed)
+      np.random.seed(seed)
+
+      # Inputs
+      concat_x = tf.placeholder(
+          tf.float32, shape=(time_steps, batch_size, input_size))
+      h = tf.zeros([batch_size, cell_size])
+
+      # Values for the inputs.
+      x_values = np.random.rand(time_steps, batch_size, input_size)
+      h_value = np.random.rand(batch_size, cell_size)
+      feeds = {concat_x: x_values, h: h_value}
+
+      # Gradients from the block GRU cell implementation.
+      with tf.variable_scope("block", initializer=initializer):
+        cell = gru_ops.GRUBlockCell(cell_size)
+
+        outputs_dynamic, _ = tf.nn.dynamic_rnn(
+            cell,
+            inputs=concat_x,
+            initial_state=h,
+            time_major=True,
+            dtype=tf.float32)
+        grad_output_wrt_x = tf.gradients([outputs_dynamic[0]], concat_x)
+        grad_output_wrt_h = tf.gradients([outputs_dynamic[0]], h)
+
+        sess.run([tf.initialize_all_variables()])
+        block_grad_res_x, block_grad_res_h = sess.run(
+            [grad_output_wrt_x, grad_output_wrt_h], feeds)
+
+      # Gradients from the basic GRU cell implementation.
+      with tf.variable_scope("basic", initializer=initializer):
+        cell = tf.nn.rnn_cell.GRUCell(cell_size)
+
+        outputs_dynamic, _ = tf.nn.dynamic_rnn(
+            cell,
+            inputs=concat_x,
+            initial_state=h,
+            time_major=True,
+            dtype=tf.float32)
+        grad_output_wrt_x = tf.gradients([outputs_dynamic[0]], concat_x)
+        grad_output_wrt_h = tf.gradients([outputs_dynamic[0]], h)
+
+        sess.run([tf.initialize_all_variables()])
+        basic_grad_res_x, basic_grad_res_h = sess.run(
+            [grad_output_wrt_x, grad_output_wrt_h], feeds)
+
+    # Check derivatives values of the outputs wrt to x.
+    self.assertEqual(len(block_grad_res_x), len(basic_grad_res_x))
+
+    # Check derivatives values of the outputs wrt to h.
+    for block, basic in zip(block_grad_res_x, basic_grad_res_x):
+      self.assertAllClose(block, basic)
+
+    # Check derivatives values of the outputs wrt to x.
+    self.assertEqual(len(block_grad_res_h), len(basic_grad_res_h))
+
+    # Check derivatives values of the outputs wrt to h.
+    for block, basic in zip(block_grad_res_h, basic_grad_res_h):
+      self.assertAllClose(block, basic)
+
+  def testGradient(self):
+    with self.test_session(use_gpu=self._use_gpu, graph=tf.Graph()) as sess:
+      batch_size = 1
+      cell_size = 3
+      input_size = 2
+
+      # Inputs
+      x = tf.zeros([batch_size, input_size])
+      h = tf.zeros([batch_size, cell_size])
+      output = gru_ops.GRUBlockCell(cell_size)(x, h)
+
+      sess.run([tf.initialize_all_variables()])
+
+      all_variables = tf.all_variables()
+
+      [w_ru, b_ru, w_c, b_c] = all_variables[:4]
+
+      error_x = tf.test.compute_gradient_error(x, (batch_size, input_size),
+                                               output[0],
+                                               (batch_size, cell_size))
+      error_h = tf.test.compute_gradient_error(h, (batch_size, cell_size),
+                                               output[0],
+                                               (batch_size, cell_size))
+      error_w_ru = tf.test.compute_gradient_error(w_ru, (input_size + cell_size,
+                                                         2 * cell_size),
+                                                  output[0],
+                                                  (batch_size, cell_size))
+      error_w_c = tf.test.compute_gradient_error(w_c, (input_size + cell_size,
+                                                       cell_size), output[0],
+                                                 (batch_size, cell_size))
+      error_b_ru = tf.test.compute_gradient_error(b_ru, (2 * cell_size,),
+                                                  output[0],
+                                                  (batch_size, cell_size))
+      error_b_c = tf.test.compute_gradient_error(b_c, (cell_size,), output[0],
+                                                 (batch_size, cell_size))
+
+    eps = 1e-4
+    self.assertLess(error_x, eps)
+    self.assertLess(error_h, eps)
+    self.assertLess(error_w_ru, eps)
+    self.assertLess(error_w_c, eps)
+    self.assertLess(error_b_ru, eps)
+    self.assertLess(error_b_c, eps)
+
+
+class GRUBlockCellGpuTest(GRUBlockCellTest):
+  _use_gpu = True
+
+
+if __name__ == "__main__":
+  tf.test.main()
diff --git a/tensorflow/contrib/rnn/python/ops/gru_ops.py b/tensorflow/contrib/rnn/python/ops/gru_ops.py
new file mode 100644
index 0000000000..49faad9dd5
--- /dev/null
+++ b/tensorflow/contrib/rnn/python/ops/gru_ops.py
@@ -0,0 +1,202 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Python wrapper for the Block GRU Op."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import load_library
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import init_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import rnn_cell
+from tensorflow.python.ops import variable_scope as vs
+from tensorflow.python.platform import resource_loader
+
+_gru_ops_so = load_library.load_op_library(
+    resource_loader.get_path_to_datafile("_gru_ops.so"))
+assert _gru_ops_so, "Could not load _gru_ops.so."
+
+
+@ops.RegisterShape("GRUBlockCellGrad")
+def _GRUBlockCellGradShape(op):
+  batch_size = op.inputs[0].get_shape().with_rank(2)[0]
+  input_size = op.inputs[0].get_shape().with_rank(2)[1]
+  cell_size = op.inputs[1].get_shape().with_rank(2)[1]
+  twice_cell_size = op.inputs[2].get_shape().with_rank(2)[1]
+
+  return [tensor_shape.TensorShape([batch_size, input_size]),
+          tensor_shape.TensorShape([batch_size, cell_size]),
+          tensor_shape.TensorShape([batch_size, cell_size]),
+          tensor_shape.TensorShape([batch_size, twice_cell_size])]
+
+
+@ops.RegisterGradient("GRUBlockCell")
+def _GRUBlockCellGrad(op, *grad):
+  r"""Gradient for GRUBlockCell.
+
+  Args:
+    op: Op for which the gradient is defined.
+    *grad: Gradients of the optimization function wrt output
+      for the Op.
+
+  Returns:
+    d_x: Gradients wrt to x
+    d_h: Gradients wrt to h
+    d_w_ru: Gradients wrt to w_ru
+    d_w_c: Gradients wrt to w_c
+    d_b_ru: Gradients wrt to b_ru
+    d_b_c: Gradients wrt to b_c
+
+  Mathematics behind the Gradients below:
+  ```
+  d_c_bar = d_h \circ (1-u) \circ (1-c \circ c)
+  d_u_bar = d_h \circ (h-c) \circ u \circ (1-u)
+
+  d_r_bar_u_bar = [d_r_bar d_u_bar]
+
+  [d_x_component_1 d_h_prev_component_1] = d_r_bar_u_bar * w_ru^T
+
+  [d_x_component_2 d_h_prevr] = d_c_bar * w_c^T
+
+  d_x = d_x_component_1 + d_x_component_2
+
+  d_h_prev = d_h_prev_component_1 + d_h_prevr \circ r + u
+  ```
+  Below calculation is performed in the python wrapper for the Gradients
+  (not in the gradient kernel.)
+  ```
+  d_w_ru = x_h_prevr^T * d_c_bar
+
+  d_w_c = x_h_prev^T * d_r_bar_u_bar
+
+  d_b_ru = sum of d_r_bar_u_bar along axis = 0
+
+  d_b_c = sum of d_c_bar along axis = 0
+  ```
+  """
+  x, h_prev, w_ru, w_c, b_ru, b_c = op.inputs
+  r, u, c, _ = op.outputs
+  _, _, _, d_h = grad
+
+  d_x, d_h_prev, d_c_bar, d_r_bar_u_bar = _gru_ops_so.gru_block_cell_grad(
+      x, h_prev, w_ru, w_c, b_ru, b_c, r, u, c, d_h)
+
+  x_h_prev = array_ops.concat(1, [x, h_prev])
+  d_w_ru = math_ops.matmul(x_h_prev, d_r_bar_u_bar, transpose_a=True)
+  d_b_ru = nn_ops.bias_add_grad(d_r_bar_u_bar)
+
+  x_h_prevr = array_ops.concat(1, [x, h_prev * r])
+  d_w_c = math_ops.matmul(x_h_prevr, d_c_bar, transpose_a=True)
+  d_b_c = nn_ops.bias_add_grad(d_c_bar)
+
+  return d_x, d_h_prev, d_w_ru, d_w_c, d_b_ru, d_b_c
+
+
+@ops.RegisterShape("GRUBlockCell")
+def _GRUBlockCellShape(op):
+  batch_size = op.inputs[0].get_shape().with_rank(2)[0]
+  cell_size = op.inputs[1].get_shape().with_rank(2)[1]
+
+  return (tensor_shape.TensorShape([batch_size, cell_size]),
+          tensor_shape.TensorShape([batch_size, cell_size]),
+          tensor_shape.TensorShape([batch_size, cell_size]),
+          tensor_shape.TensorShape([batch_size, cell_size]))
+
+
+class GRUBlockCell(rnn_cell.RNNCell):
+  r"""Block GRU cell implementation.
+
+  The implementation is based on:  http://arxiv.org/abs/1406.1078
+  Computes the LSTM cell forward propagation for 1 time step.
+
+  This kernel op implements the following mathematical equations:
+
+  Baises are initialized with :
+  `b_ru` - constant_initializer(1.0)
+  `b_c` - constant_initializer(0.0)
+  ```
+  x_h_prev = [x, h_prev]
+
+  [r_bar u_bar] = x_h_prev * w_ru + b_ru
+
+  r = sigmoid(r_bar)
+  u = sigmoid(u_bar)
+
+  h_prevr = h_prev \circ r
+
+  x_h_prevr = [x h_prevr]
+
+  c_bar = x_h_prevr * w_c + b_c
+  c = tanh(c_bar)
+
+  h = (1-u) \circ c + u \circ h_prev
+  ```
+
+  """
+
+  def __init__(self, cell_size):
+    """Initialize the Block GRU cell.
+
+    Args:
+      cell_size: int, GRU cell size.
+    """
+    self._cell_size = cell_size
+
+  @property
+  def state_size(self):
+    return self._cell_size
+
+  @property
+  def output_size(self):
+    return self._cell_size
+
+  def __call__(self, x, h_prev, scope=None):
+    """GRU cell."""
+    with vs.variable_scope(scope or type(self).__name__):
+      input_size = x.get_shape().with_rank(2)[1]
+
+      # Check if the input size exist.
+      if input_size is None:
+        raise ValueError("Expecting input_size to be set.")
+
+      # Check cell_size == state_size from h_prev.
+      cell_size = h_prev.get_shape().with_rank(2)[1]
+      if cell_size != self._cell_size:
+        raise ValueError("Shape of h_prev[1] incorrect: cell_size %i vs %s" %
+                         (self._cell_size, cell_size))
+
+      if cell_size is None:
+        raise ValueError("cell_size from `h_prev` should not be None.")
+
+      w_ru = vs.get_variable("w_ru", [input_size + self._cell_size,
+                                      self._cell_size * 2])
+      b_ru = vs.get_variable(
+          "b_ru", [self._cell_size * 2],
+          initializer=init_ops.constant_initializer(1.0))
+      w_c = vs.get_variable("w_c",
+                            [input_size + self._cell_size, self._cell_size])
+      b_c = vs.get_variable(
+          "b_c", [self._cell_size],
+          initializer=init_ops.constant_initializer(0.0))
+
+      _gru_block_cell = _gru_ops_so.gru_block_cell  # pylint: disable=invalid-name
+      _, _, _, new_h = _gru_block_cell(
+          x=x, h_prev=h_prev, w_ru=w_ru, w_c=w_c, b_ru=b_ru, b_c=b_c)
+
+      return new_h, new_h
-- 
GitLab


From 039d4dd37fc176fb4ff3fb96fba798bfa6ad7225 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 14:16:27 -0800
Subject: [PATCH 062/223] Add a utility to obtain cpu cycles for profile on
 hexagon Change: 131102442

---
 tensorflow/core/BUILD                         |  2 +
 .../hexagon/profile_utils/cpu_utils.h         | 89 +++++++++++++++++++
 .../hexagon/profile_utils/cpu_utils_test.cc   | 46 ++++++++++
 3 files changed, 137 insertions(+)
 create mode 100644 tensorflow/core/platform/hexagon/profile_utils/cpu_utils.h
 create mode 100644 tensorflow/core/platform/hexagon/profile_utils/cpu_utils_test.cc

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 1bbae7d177..7bf08a39e6 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -170,6 +170,7 @@ cc_library(
         "platform/env.h",
         "platform/file_system.h",
         "platform/fingerprint.h",
+        "platform/hexagon/profile_utils/cpu_utils.h",
         "platform/host_info.h",  # TODO(josh11b): make internal
         "platform/init_main.h",
         "platform/logging.h",
@@ -1336,6 +1337,7 @@ tf_cc_tests(
         "lib/strings/stringprintf_test.cc",
         "lib/wav/wav_io_test.cc",
         "platform/fingerprint_test.cc",
+        "platform/hexagon/profile_utils/cpu_utils_test.cc",
         "platform/integral_types_test.cc",
         "platform/logging_test.cc",
         "platform/net_test.cc",
diff --git a/tensorflow/core/platform/hexagon/profile_utils/cpu_utils.h b/tensorflow/core/platform/hexagon/profile_utils/cpu_utils.h
new file mode 100644
index 0000000000..d1b174e829
--- /dev/null
+++ b/tensorflow/core/platform/hexagon/profile_utils/cpu_utils.h
@@ -0,0 +1,89 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// This class is designed to get accurate profile for programs.
+
+#ifndef TENSORFLOW_PLATFORM_HEXAGON_PROFILEUTILS_CPU_UTILS_H__
+#define TENSORFLOW_PLATFORM_HEXAGON_PROFILEUTILS_CPU_UTILS_H__
+
+#include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/types.h"
+
+#if defined(ARMV6) || defined(__ARM_ARCH_7A__)
+#include <sys/time.h>
+#endif
+
+namespace tensorflow {
+
+namespace profile_utils {
+
+class CpuUtils {
+ public:
+  static constexpr uint64 DUMMY_CYCLE_CLOCK = 1;
+
+  // Return cpu count obtained by rdtsc. This function is designed to
+  // minimize the overhead to get clock and maximize the accuracy of
+  // time for profile.
+  // This returns unsigned int because there is no guarantee that rdtsc
+  // is less than 2 ^ 61.
+  static inline uint64 GetCurrentCycleClock() {
+// ----------------------------------------------------------------
+#if defined(__x86_64__) || defined(__amd64__)
+    uint64_t high, low;
+    __asm__ volatile("rdtsc" : "=a"(low), "=d"(high));
+    return (high << 32) | low;
+// ----------------------------------------------------------------
+#elif defined(__aarch64__)
+    // System timer of ARMv8 runs at a different frequency than the CPU's.
+    // The frequency is fixed, typically in the range 1-50MHz.  It can because
+    // read at CNTFRQ special register.  We assume the OS has set up
+    // the virtual timer properly.
+    uint64_t virtual_timer_value;
+    asm volatile("mrs %0, cntvct_el0" : "=r"(virtual_timer_value));
+    return virtual_timer_value;
+// ----------------------------------------------------------------
+// V6 is the earliest arm that has a standard cyclecount
+#elif defined(ARMV6) || defined(__ARM_ARCH_7A__)
+    uint32_t pmccntr;
+    uint32_t pmuseren;
+    uint32_t pmcntenset;
+    // Read the user mode perf monitor counter access permissions.
+    asm volatile("mrc p15, 0, %0, c9, c14, 0" : "=r"(pmuseren));
+    if (pmuseren & 1) {  // Allows reading perfmon counters for user mode code.
+      asm volatile("mrc p15, 0, %0, c9, c12, 1" : "=r"(pmcntenset));
+      if (pmcntenset & 0x80000000ul) {  // Is it counting?
+        asm volatile("mrc p15, 0, %0, c9, c13, 0" : "=r"(pmccntr));
+        // The counter is set up to count every 64th cyclecount
+        return static_cast<uint64>(pmccntr) * 64;  // Should optimize to << 64
+      }
+    }
+    // Returning dummy clock when can't access to the counter
+    return DUMMY_CYCLE_CLOCK;
+#else
+    // TODO(satok): Support generic way to emulate clock count.
+    // TODO(satok): Support other architectures if wanted.
+    // Returning dummy clock when can't access to the counter
+    return DUMMY_CYCLE_CLOCK;
+#endif
+  }
+
+ private:
+  TF_DISALLOW_COPY_AND_ASSIGN(CpuUtils);
+};
+
+}  // namespace profile_utils
+
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_PLATFORM_HEXAGON_PROFILEUTILS_CPU_UTILS_H__
diff --git a/tensorflow/core/platform/hexagon/profile_utils/cpu_utils_test.cc b/tensorflow/core/platform/hexagon/profile_utils/cpu_utils_test.cc
new file mode 100644
index 0000000000..b0a2ade37f
--- /dev/null
+++ b/tensorflow/core/platform/hexagon/profile_utils/cpu_utils_test.cc
@@ -0,0 +1,46 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// This class is designed to get accurate profiles for programs
+
+#include "tensorflow/core/platform/hexagon/profile_utils/cpu_utils.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace profile_utils {
+
+static constexpr bool DBG = false;
+
+TEST(CpuUtils, CheckGetCurrentCycleClock) {
+  static constexpr int LOOP_COUNT = 10;
+  const uint64 start_clock_count = CpuUtils::GetCurrentCycleClock();
+  CHECK_GT(start_clock_count, 0);
+  uint64 prev_clock_count = start_clock_count;
+  for (int i = 0; i < LOOP_COUNT; ++i) {
+    const uint64 clock_count = CpuUtils::GetCurrentCycleClock();
+    CHECK_GE(clock_count, prev_clock_count);
+    prev_clock_count = clock_count;
+  }
+  const uint64 end_clock_count = CpuUtils::GetCurrentCycleClock();
+  if (DBG) {
+    LOG(INFO) << "start clock = " << start_clock_count;
+    LOG(INFO) << "end clock = " << end_clock_count;
+    LOG(INFO) << "average clock = "
+              << ((end_clock_count - start_clock_count) / LOOP_COUNT);
+  }
+}
+
+}  // namespace profile_utils
+}  // namespace tensorflow
-- 
GitLab


From 1f3e517baab0f0d0aa0fc1c10f727940d092f1f2 Mon Sep 17 00:00:00 2001
From: Nathan Silberman <nsilberman@google.com>
Date: Tue, 23 Aug 2016 14:19:42 -0800
Subject: [PATCH 063/223] Adding VGG 19, standardizing model function
 signatures to use: model(images, num_classes, is_training, ...) Change:
 131102966

---
 .../contrib/slim/python/slim/nets/alexnet.py  |   4 +-
 .../slim/python/slim/nets/inception_v1.py     |   4 +-
 .../slim/python/slim/nets/inception_v2.py     |   4 +-
 .../slim/python/slim/nets/inception_v3.py     |   4 +-
 .../contrib/slim/python/slim/nets/overfeat.py |   4 +-
 .../contrib/slim/python/slim/nets/vgg.py      |  69 +++++++-
 .../contrib/slim/python/slim/nets/vgg_test.py | 156 +++++++++++++++++-
 7 files changed, 230 insertions(+), 15 deletions(-)

diff --git a/tensorflow/contrib/slim/python/slim/nets/alexnet.py b/tensorflow/contrib/slim/python/slim/nets/alexnet.py
index f6fedf9888..7aca487598 100644
--- a/tensorflow/contrib/slim/python/slim/nets/alexnet.py
+++ b/tensorflow/contrib/slim/python/slim/nets/alexnet.py
@@ -54,8 +54,8 @@ def alexnet_v2_arg_scope(weight_decay=0.0005):
 
 def alexnet_v2(inputs,
                num_classes=1000,
-               dropout_keep_prob=0.5,
                is_training=True,
+               dropout_keep_prob=0.5,
                spatial_squeeze=True,
                scope='alexnet_v2'):
   """AlexNet version 2.
@@ -74,9 +74,9 @@ def alexnet_v2(inputs,
   Args:
     inputs: a tensor of size [batch_size, height, width, channels].
     num_classes: number of predicted classes.
+    is_training: whether or not the model is being trained.
     dropout_keep_prob: the probability that activations are kept in the dropout
       layers during training.
-    is_training: whether or not the model is being trained.
     spatial_squeeze: whether or not should squeeze the spatial dimensions of the
       outputs. Useful to remove unnecessary dimensions for classification.
     scope: Optional scope for the variables.
diff --git a/tensorflow/contrib/slim/python/slim/nets/inception_v1.py b/tensorflow/contrib/slim/python/slim/nets/inception_v1.py
index ede5e300f5..1e4ed212a8 100644
--- a/tensorflow/contrib/slim/python/slim/nets/inception_v1.py
+++ b/tensorflow/contrib/slim/python/slim/nets/inception_v1.py
@@ -245,8 +245,8 @@ def inception_v1_base(inputs,
 
 def inception_v1(inputs,
                  num_classes=1000,
-                 dropout_keep_prob=0.8,
                  is_training=True,
+                 dropout_keep_prob=0.8,
                  prediction_fn=slim.softmax,
                  spatial_squeeze=True,
                  reuse=None,
@@ -265,8 +265,8 @@ def inception_v1(inputs,
   Args:
     inputs: a tensor of size [batch_size, height, width, channels].
     num_classes: number of predicted classes.
-    dropout_keep_prob: the percentage of activation values that are retained.
     is_training: whether is training or not.
+    dropout_keep_prob: the percentage of activation values that are retained.
     prediction_fn: a function to get predictions out of logits.
     spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
         of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
diff --git a/tensorflow/contrib/slim/python/slim/nets/inception_v2.py b/tensorflow/contrib/slim/python/slim/nets/inception_v2.py
index 3e419742e2..cf9372df3a 100644
--- a/tensorflow/contrib/slim/python/slim/nets/inception_v2.py
+++ b/tensorflow/contrib/slim/python/slim/nets/inception_v2.py
@@ -413,8 +413,8 @@ def inception_v2_base(inputs,
 
 def inception_v2(inputs,
                  num_classes=1000,
-                 dropout_keep_prob=0.8,
                  is_training=True,
+                 dropout_keep_prob=0.8,
                  min_depth=16,
                  depth_multiplier=1.0,
                  prediction_fn=slim.softmax,
@@ -431,8 +431,8 @@ def inception_v2(inputs,
   Args:
     inputs: a tensor of shape [batch_size, height, width, channels].
     num_classes: number of predicted classes.
-    dropout_keep_prob: the percentage of activation values that are retained.
     is_training: whether is training or not.
+    dropout_keep_prob: the percentage of activation values that are retained.
     min_depth: Minimum depth value (number of channels) for all convolution ops.
       Enforced when depth_multiplier < 1, and not an active constraint when
       depth_multiplier >= 1.
diff --git a/tensorflow/contrib/slim/python/slim/nets/inception_v3.py b/tensorflow/contrib/slim/python/slim/nets/inception_v3.py
index cba112f703..b938288ce4 100644
--- a/tensorflow/contrib/slim/python/slim/nets/inception_v3.py
+++ b/tensorflow/contrib/slim/python/slim/nets/inception_v3.py
@@ -416,8 +416,8 @@ def inception_v3_base(inputs,
 
 def inception_v3(inputs,
                  num_classes=1000,
-                 dropout_keep_prob=0.8,
                  is_training=True,
+                 dropout_keep_prob=0.8,
                  min_depth=16,
                  depth_multiplier=1.0,
                  prediction_fn=slim.softmax,
@@ -441,8 +441,8 @@ def inception_v3(inputs,
   Args:
     inputs: a tensor of size [batch_size, height, width, channels].
     num_classes: number of predicted classes.
-    dropout_keep_prob: the percentage of activation values that are retained.
     is_training: whether is training or not.
+    dropout_keep_prob: the percentage of activation values that are retained.
     min_depth: Minimum depth value (number of channels) for all convolution ops.
       Enforced when depth_multiplier < 1, and not an active constraint when
       depth_multiplier >= 1.
diff --git a/tensorflow/contrib/slim/python/slim/nets/overfeat.py b/tensorflow/contrib/slim/python/slim/nets/overfeat.py
index c60f21300a..807862469f 100644
--- a/tensorflow/contrib/slim/python/slim/nets/overfeat.py
+++ b/tensorflow/contrib/slim/python/slim/nets/overfeat.py
@@ -49,8 +49,8 @@ def overfeat_arg_scope(weight_decay=0.0005):
 
 def overfeat(inputs,
              num_classes=1000,
-             dropout_keep_prob=0.5,
              is_training=True,
+             dropout_keep_prob=0.5,
              spatial_squeeze=True,
              scope='overfeat'):
   """Contains the model definition for the OverFeat network.
@@ -69,9 +69,9 @@ def overfeat(inputs,
   Args:
     inputs: a tensor of size [batch_size, height, width, channels].
     num_classes: number of predicted classes.
+    is_training: whether or not the model is being trained.
     dropout_keep_prob: the probability that activations are kept in the dropout
       layers during training.
-    is_training: whether or not the model is being trained.
     spatial_squeeze: whether or not should squeeze the spatial dimensions of the
       outputs. Useful to remove unnecessary dimensions for classification.
     scope: Optional scope for the variables.
diff --git a/tensorflow/contrib/slim/python/slim/nets/vgg.py b/tensorflow/contrib/slim/python/slim/nets/vgg.py
index 42c23faa1c..8c1821e9ea 100644
--- a/tensorflow/contrib/slim/python/slim/nets/vgg.py
+++ b/tensorflow/contrib/slim/python/slim/nets/vgg.py
@@ -35,6 +35,7 @@ Usage:
 
 @@vgg_a
 @@vgg_16
+@@vgg_19
 """
 from __future__ import absolute_import
 from __future__ import division
@@ -64,8 +65,8 @@ def vgg_arg_scope(weight_decay=0.0005):
 
 def vgg_a(inputs,
           num_classes=1000,
-          dropout_keep_prob=0.5,
           is_training=True,
+          dropout_keep_prob=0.5,
           spatial_squeeze=True,
           scope='vgg_a'):
   """Oxford Net VGG 11-Layers version A Example.
@@ -76,9 +77,9 @@ def vgg_a(inputs,
   Args:
     inputs: a tensor of size [batch_size, height, width, channels].
     num_classes: number of predicted classes.
+    is_training: whether or not the model is being trained.
     dropout_keep_prob: the probability that activations are kept in the dropout
       layers during training.
-    is_training: whether or not the model is being trained.
     spatial_squeeze: whether or not should squeeze the spatial dimensions of the
       outputs. Useful to remove unnecessary dimensions for classification.
     scope: Optional scope for the variables.
@@ -123,8 +124,8 @@ vgg_a.default_image_size = 224
 
 def vgg_16(inputs,
            num_classes=1000,
-           dropout_keep_prob=0.5,
            is_training=True,
+           dropout_keep_prob=0.5,
            spatial_squeeze=True,
            scope='vgg_16'):
   """Oxford Net VGG 16-Layers version D Example.
@@ -135,9 +136,9 @@ def vgg_16(inputs,
   Args:
     inputs: a tensor of size [batch_size, height, width, channels].
     num_classes: number of predicted classes.
+    is_training: whether or not the model is being trained.
     dropout_keep_prob: the probability that activations are kept in the dropout
       layers during training.
-    is_training: whether or not the model is being trained.
     spatial_squeeze: whether or not should squeeze the spatial dimensions of the
       outputs. Useful to remove unnecessary dimensions for classification.
     scope: Optional scope for the variables.
@@ -179,5 +180,65 @@ def vgg_16(inputs,
       return net, end_points
 vgg_16.default_image_size = 224
 
+
+def vgg_19(inputs,
+           num_classes=1000,
+           is_training=True,
+           dropout_keep_prob=0.5,
+           spatial_squeeze=True,
+           scope='vgg_19'):
+  """Oxford Net VGG 19-Layers version E Example.
+
+  Note: All the fully_connected layers have been transformed to conv2d layers.
+        To use in classification mode, resize input to 224x224.
+
+  Args:
+    inputs: a tensor of size [batch_size, height, width, channels].
+    num_classes: number of predicted classes.
+    is_training: whether or not the model is being trained.
+    dropout_keep_prob: the probability that activations are kept in the dropout
+      layers during training.
+    spatial_squeeze: whether or not should squeeze the spatial dimensions of the
+      outputs. Useful to remove unnecessary dimensions for classification.
+    scope: Optional scope for the variables.
+
+  Returns:
+    the last op containing the log predictions and end_points dict.
+  """
+  with tf.variable_scope(scope, 'vgg_19', [inputs]) as sc:
+    end_points_collection = sc.name + '_end_points'
+    # Collect outputs for conv2d, fully_connected and max_pool2d.
+    with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
+                        outputs_collections=end_points_collection):
+      net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
+      net = slim.max_pool2d(net, [2, 2], scope='pool1')
+      net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
+      net = slim.max_pool2d(net, [2, 2], scope='pool2')
+      net = slim.repeat(net, 4, slim.conv2d, 256, [3, 3], scope='conv3')
+      net = slim.max_pool2d(net, [2, 2], scope='pool3')
+      net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv4')
+      net = slim.max_pool2d(net, [2, 2], scope='pool4')
+      net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv5')
+      net = slim.max_pool2d(net, [2, 2], scope='pool5')
+      # Use conv2d instead of fully_connected layers.
+      net = slim.conv2d(net, 4096, [7, 7], padding='VALID', scope='fc6')
+      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                         scope='dropout6')
+      net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
+      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                         scope='dropout7')
+      net = slim.conv2d(net, num_classes, [1, 1],
+                        activation_fn=None,
+                        normalizer_fn=None,
+                        scope='fc8')
+      # Convert end_points_collection into a end_point dict.
+      end_points = dict(tf.get_collection(end_points_collection))
+      if spatial_squeeze:
+        net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
+        end_points[sc.name + '/fc8'] = net
+      return net, end_points
+vgg_19.default_image_size = 224
+
 # Alias
 vgg_d = vgg_16
+vgg_e = vgg_19
diff --git a/tensorflow/contrib/slim/python/slim/nets/vgg_test.py b/tensorflow/contrib/slim/python/slim/nets/vgg_test.py
index 5bb6562fd0..53837d3216 100644
--- a/tensorflow/contrib/slim/python/slim/nets/vgg_test.py
+++ b/tensorflow/contrib/slim/python/slim/nets/vgg_test.py
@@ -206,7 +206,6 @@ class VGG16Test(tf.test.TestCase):
                         'vgg_16/fc7',
                         'vgg_16/fc8'
                        ]
-      print(end_points.keys())
       self.assertSetEqual(set(end_points.keys()), set(expected_names))
 
   def testModelVariables(self):
@@ -297,5 +296,160 @@ class VGG16Test(tf.test.TestCase):
       output = sess.run(logits)
       self.assertTrue(output.any())
 
+
+class VGG19Test(tf.test.TestCase):
+
+  def testBuild(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_19(inputs, num_classes)
+      self.assertEquals(logits.op.name, 'vgg_19/fc8/squeezed')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+
+  def testFullyConvolutional(self):
+    batch_size = 1
+    height, width = 256, 256
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_19(inputs, num_classes, spatial_squeeze=False)
+      self.assertEquals(logits.op.name, 'vgg_19/fc8/BiasAdd')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, 2, 2, num_classes])
+
+  def testEndPoints(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      _, end_points = vgg.vgg_19(inputs, num_classes)
+      expected_names = [
+          'vgg_19/conv1/conv1_1',
+          'vgg_19/conv1/conv1_2',
+          'vgg_19/pool1',
+          'vgg_19/conv2/conv2_1',
+          'vgg_19/conv2/conv2_2',
+          'vgg_19/pool2',
+          'vgg_19/conv3/conv3_1',
+          'vgg_19/conv3/conv3_2',
+          'vgg_19/conv3/conv3_3',
+          'vgg_19/conv3/conv3_4',
+          'vgg_19/pool3',
+          'vgg_19/conv4/conv4_1',
+          'vgg_19/conv4/conv4_2',
+          'vgg_19/conv4/conv4_3',
+          'vgg_19/conv4/conv4_4',
+          'vgg_19/pool4',
+          'vgg_19/conv5/conv5_1',
+          'vgg_19/conv5/conv5_2',
+          'vgg_19/conv5/conv5_3',
+          'vgg_19/conv5/conv5_4',
+          'vgg_19/pool5',
+          'vgg_19/fc6',
+          'vgg_19/fc7',
+          'vgg_19/fc8'
+      ]
+      self.assertSetEqual(set(end_points.keys()), set(expected_names))
+
+  def testModelVariables(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      vgg.vgg_19(inputs, num_classes)
+      expected_names = [
+          'vgg_19/conv1/conv1_1/weights',
+          'vgg_19/conv1/conv1_1/biases',
+          'vgg_19/conv1/conv1_2/weights',
+          'vgg_19/conv1/conv1_2/biases',
+          'vgg_19/conv2/conv2_1/weights',
+          'vgg_19/conv2/conv2_1/biases',
+          'vgg_19/conv2/conv2_2/weights',
+          'vgg_19/conv2/conv2_2/biases',
+          'vgg_19/conv3/conv3_1/weights',
+          'vgg_19/conv3/conv3_1/biases',
+          'vgg_19/conv3/conv3_2/weights',
+          'vgg_19/conv3/conv3_2/biases',
+          'vgg_19/conv3/conv3_3/weights',
+          'vgg_19/conv3/conv3_3/biases',
+          'vgg_19/conv3/conv3_4/weights',
+          'vgg_19/conv3/conv3_4/biases',
+          'vgg_19/conv4/conv4_1/weights',
+          'vgg_19/conv4/conv4_1/biases',
+          'vgg_19/conv4/conv4_2/weights',
+          'vgg_19/conv4/conv4_2/biases',
+          'vgg_19/conv4/conv4_3/weights',
+          'vgg_19/conv4/conv4_3/biases',
+          'vgg_19/conv4/conv4_4/weights',
+          'vgg_19/conv4/conv4_4/biases',
+          'vgg_19/conv5/conv5_1/weights',
+          'vgg_19/conv5/conv5_1/biases',
+          'vgg_19/conv5/conv5_2/weights',
+          'vgg_19/conv5/conv5_2/biases',
+          'vgg_19/conv5/conv5_3/weights',
+          'vgg_19/conv5/conv5_3/biases',
+          'vgg_19/conv5/conv5_4/weights',
+          'vgg_19/conv5/conv5_4/biases',
+          'vgg_19/fc6/weights',
+          'vgg_19/fc6/biases',
+          'vgg_19/fc7/weights',
+          'vgg_19/fc7/biases',
+          'vgg_19/fc8/weights',
+          'vgg_19/fc8/biases',
+      ]
+      model_variables = [v.op.name for v in slim.get_model_variables()]
+      self.assertSetEqual(set(model_variables), set(expected_names))
+
+  def testEvaluation(self):
+    batch_size = 2
+    height, width = 224, 224
+    num_classes = 1000
+    with self.test_session():
+      eval_inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_19(eval_inputs, is_training=False)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, num_classes])
+      predictions = tf.argmax(logits, 1)
+      self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
+
+  def testTrainEvalWithReuse(self):
+    train_batch_size = 2
+    eval_batch_size = 1
+    train_height, train_width = 224, 224
+    eval_height, eval_width = 256, 256
+    num_classes = 1000
+    with self.test_session():
+      train_inputs = tf.random_uniform(
+          (train_batch_size, train_height, train_width, 3))
+      logits, _ = vgg.vgg_19(train_inputs)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [train_batch_size, num_classes])
+      tf.get_variable_scope().reuse_variables()
+      eval_inputs = tf.random_uniform(
+          (eval_batch_size, eval_height, eval_width, 3))
+      logits, _ = vgg.vgg_19(eval_inputs, is_training=False,
+                             spatial_squeeze=False)
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [eval_batch_size, 2, 2, num_classes])
+      logits = tf.reduce_mean(logits, [1, 2])
+      predictions = tf.argmax(logits, 1)
+      self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
+
+  def testForward(self):
+    batch_size = 1
+    height, width = 224, 224
+    with self.test_session() as sess:
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_19(inputs)
+      sess.run(tf.initialize_all_variables())
+      output = sess.run(logits)
+      self.assertTrue(output.any())
+
 if __name__ == '__main__':
   tf.test.main()
-- 
GitLab


From 5e11e37e912cdac3136aa7601ca16e5d5df74df0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 14:22:01 -0800
Subject: [PATCH 064/223] Update generated Python Op docs. Change: 131103313

---
 .../g3doc/api_docs/python/contrib.rnn.md      | 80 +++++++++++++++++++
 .../shard2/tf.contrib.rnn.GRUBlockCell.md     | 75 +++++++++++++++++
 tensorflow/g3doc/api_docs/python/index.md     |  1 +
 3 files changed, 156 insertions(+)
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.rnn.GRUBlockCell.md

diff --git a/tensorflow/g3doc/api_docs/python/contrib.rnn.md b/tensorflow/g3doc/api_docs/python/contrib.rnn.md
index a77e22dd93..fca5817dd0 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.rnn.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.rnn.md
@@ -73,6 +73,86 @@ the shapes `[batch_size x s]` for each s in `state_size`.
 
 
 
+- - -
+
+### `class tf.contrib.rnn.GRUBlockCell` {#GRUBlockCell}
+
+Block GRU cell implementation.
+
+The implementation is based on:  http://arxiv.org/abs/1406.1078
+Computes the LSTM cell forward propagation for 1 time step.
+
+This kernel op implements the following mathematical equations:
+
+Baises are initialized with :
+`b_ru` - constant_initializer(1.0)
+`b_c` - constant_initializer(0.0)
+```
+x_h_prev = [x, h_prev]
+
+[r_bar u_bar] = x_h_prev * w_ru + b_ru
+
+r = sigmoid(r_bar)
+u = sigmoid(u_bar)
+
+h_prevr = h_prev \circ r
+
+x_h_prevr = [x h_prevr]
+
+c_bar = x_h_prevr * w_c + b_c
+c = tanh(c_bar)
+
+h = (1-u) \circ c + u \circ h_prev
+```
+- - -
+
+#### `tf.contrib.rnn.GRUBlockCell.__init__(cell_size)` {#GRUBlockCell.__init__}
+
+Initialize the Block GRU cell.
+
+##### Args:
+
+
+*  <b>`cell_size`</b>: int, GRU cell size.
+
+
+- - -
+
+#### `tf.contrib.rnn.GRUBlockCell.output_size` {#GRUBlockCell.output_size}
+
+
+
+
+- - -
+
+#### `tf.contrib.rnn.GRUBlockCell.state_size` {#GRUBlockCell.state_size}
+
+
+
+
+- - -
+
+#### `tf.contrib.rnn.GRUBlockCell.zero_state(batch_size, dtype)` {#GRUBlockCell.zero_state}
+
+Return zero-filled state tensor(s).
+
+##### Args:
+
+
+*  <b>`batch_size`</b>: int, float, or unit Tensor representing the batch size.
+*  <b>`dtype`</b>: the data type to use for the state.
+
+##### Returns:
+
+  If `state_size` is an int or TensorShape, then the return value is a
+  `N-D` tensor of shape `[batch_size x state_size]` filled with zeros.
+
+  If `state_size` is a nested list or tuple, then the return value is
+  a nested list or tuple (of the same structure) of `2-D` tensors with
+the shapes `[batch_size x s]` for each s in `state_size`.
+
+
+
 
 ### LSTM-like cells
 - - -
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.rnn.GRUBlockCell.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.rnn.GRUBlockCell.md
new file mode 100644
index 0000000000..b6713ee461
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.rnn.GRUBlockCell.md
@@ -0,0 +1,75 @@
+Block GRU cell implementation.
+
+The implementation is based on:  http://arxiv.org/abs/1406.1078
+Computes the LSTM cell forward propagation for 1 time step.
+
+This kernel op implements the following mathematical equations:
+
+Baises are initialized with :
+`b_ru` - constant_initializer(1.0)
+`b_c` - constant_initializer(0.0)
+```
+x_h_prev = [x, h_prev]
+
+[r_bar u_bar] = x_h_prev * w_ru + b_ru
+
+r = sigmoid(r_bar)
+u = sigmoid(u_bar)
+
+h_prevr = h_prev \circ r
+
+x_h_prevr = [x h_prevr]
+
+c_bar = x_h_prevr * w_c + b_c
+c = tanh(c_bar)
+
+h = (1-u) \circ c + u \circ h_prev
+```
+- - -
+
+#### `tf.contrib.rnn.GRUBlockCell.__init__(cell_size)` {#GRUBlockCell.__init__}
+
+Initialize the Block GRU cell.
+
+##### Args:
+
+
+*  <b>`cell_size`</b>: int, GRU cell size.
+
+
+- - -
+
+#### `tf.contrib.rnn.GRUBlockCell.output_size` {#GRUBlockCell.output_size}
+
+
+
+
+- - -
+
+#### `tf.contrib.rnn.GRUBlockCell.state_size` {#GRUBlockCell.state_size}
+
+
+
+
+- - -
+
+#### `tf.contrib.rnn.GRUBlockCell.zero_state(batch_size, dtype)` {#GRUBlockCell.zero_state}
+
+Return zero-filled state tensor(s).
+
+##### Args:
+
+
+*  <b>`batch_size`</b>: int, float, or unit Tensor representing the batch size.
+*  <b>`dtype`</b>: the data type to use for the state.
+
+##### Returns:
+
+  If `state_size` is an int or TensorShape, then the return value is a
+  `N-D` tensor of shape `[batch_size x state_size]` filled with zeros.
+
+  If `state_size` is a nested list or tuple, then the return value is
+  a nested list or tuple (of the same structure) of `2-D` tensors with
+the shapes `[batch_size x s]` for each s in `state_size`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md
index 8a9c8b95c5..8627c2e2bb 100644
--- a/tensorflow/g3doc/api_docs/python/index.md
+++ b/tensorflow/g3doc/api_docs/python/index.md
@@ -850,6 +850,7 @@
   * [`AttentionCellWrapper`](../../api_docs/python/contrib.rnn.md#AttentionCellWrapper)
   * [`CoupledInputForgetGateLSTMCell`](../../api_docs/python/contrib.rnn.md#CoupledInputForgetGateLSTMCell)
   * [`GridLSTMCell`](../../api_docs/python/contrib.rnn.md#GridLSTMCell)
+  * [`GRUBlockCell`](../../api_docs/python/contrib.rnn.md#GRUBlockCell)
   * [`LSTMBlockCell`](../../api_docs/python/contrib.rnn.md#LSTMBlockCell)
   * [`TimeFreqLSTMCell`](../../api_docs/python/contrib.rnn.md#TimeFreqLSTMCell)
 
-- 
GitLab


From 0c855cc5433eb4ef0ba14564ba8d5ad342ddd195 Mon Sep 17 00:00:00 2001
From: Nathan Silberman <nsilberman@google.com>
Date: Tue, 23 Aug 2016 14:55:20 -0800
Subject: [PATCH 065/223] Adding ability to decode RAW images. Change:
 131107751

---
 .../python/slim/data/tfexample_decoder.py     | 11 ++-
 .../slim/data/tfexample_decoder_test.py       | 75 +++++++++++++++++--
 2 files changed, 77 insertions(+), 9 deletions(-)

diff --git a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py
index 969c2d1e7e..5a5e3c8ce2 100644
--- a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py
+++ b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py
@@ -26,6 +26,7 @@ from __future__ import print_function
 import abc
 
 from tensorflow.contrib.slim.python.slim.data import data_decoder
+from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
@@ -270,7 +271,7 @@ class Image(ItemHandler):
     Args:
       image_key: the name of the TF-Example feature in which the encoded image
         is stored.
-      format_key: the name of the TF-Example feature in which the encoded image
+      format_key: the name of the TF-Example feature in which the image format
         is stored.
       shape: the output shape of the image. If provided, the image is reshaped
         accordingly. If left as None, no reshaping is done. A shape should be
@@ -310,16 +311,22 @@ class Image(ItemHandler):
     """
     def decode_png():
       return image_ops.decode_png(image_buffer, self._channels)
+    def decode_raw():
+      return parsing_ops.decode_raw(image_buffer, dtypes.uint8)
     def decode_jpg():
       return image_ops.decode_jpeg(image_buffer, self._channels)
 
     image = control_flow_ops.case({
-        math_ops.equal(image_format, 'png'): decode_png,
+        math_ops.logical_or(math_ops.equal(image_format, 'png'),
+                            math_ops.equal(image_format, 'PNG')): decode_png,
+        math_ops.logical_or(math_ops.equal(image_format, 'raw'),
+                            math_ops.equal(image_format, 'RAW')): decode_raw,
     }, default=decode_jpg, exclusive=True)
 
     image.set_shape([None, None, self._channels])
     if self._shape is not None:
       image = array_ops.reshape(image, self._shape)
+
     return image
 
 
diff --git a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder_test.py b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder_test.py
index 12c22ff967..cd75db8967 100644
--- a/tensorflow/contrib/slim/python/slim/data/tfexample_decoder_test.py
+++ b/tensorflow/contrib/slim/python/slim/data/tfexample_decoder_test.py
@@ -55,13 +55,15 @@ class TFExampleDecoderTest(tf.test.TestCase):
     return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
 
   def _Encoder(self, image, image_format):
-    assert image_format  in ['jpeg', 'png']
-    if image_format == 'jpeg':
+    assert image_format  in ['jpeg', 'JPEG', 'png', 'PNG', 'raw', 'RAW']
+    if image_format in ['jpeg', 'JPEG']:
       tf_image = tf.constant(image, dtype=tf.uint8)
       return tf.image.encode_jpeg(tf_image)
-    if image_format == 'png':
+    if image_format in ['png', 'PNG']:
       tf_image = tf.constant(image, dtype=tf.uint8)
       return tf.image.encode_png(tf_image)
+    if image_format in ['raw', 'RAW']:
+      return tf.constant(image.tostring(), dtype=tf.string)
 
   def GenerateImage(self, image_format, image_shape):
     """Generates an image and an example containing the encoded image.
@@ -74,7 +76,7 @@ class TFExampleDecoderTest(tf.test.TestCase):
       image: the generated image.
       example: a TF-example with a feature key 'image/encoded' set to the
         serialized image and a feature key 'image/format' set to the image
-        encoding format ['jpeg', 'png'].
+        encoding format ['jpeg', 'JPEG', 'png', 'PNG', 'raw'].
     """
     num_pixels = image_shape[0] * image_shape[1] * image_shape[2]
     image = np.linspace(0, num_pixels-1, num=num_pixels).reshape(
@@ -135,6 +137,22 @@ class TFExampleDecoderTest(tf.test.TestCase):
     # Need to use a tolerance of 1 because of noise in the jpeg encode/decode
     self.assertAllClose(image, decoded_image, atol=1.001)
 
+  def testDecodeExampleWithJPEGEncoding(self):
+    test_image_channels = [1, 3]
+    for channels in test_image_channels:
+      image_shape = (2, 3, channels)
+      image, serialized_example = self.GenerateImage(
+          image_format='JPEG',
+          image_shape=image_shape)
+
+      decoded_image = self.RunDecodeExample(
+          serialized_example,
+          slim.tfexample_decoder.Image(channels=channels),
+          image_format='JPEG')
+
+      # Need to use a tolerance of 1 because of noise in the jpeg encode/decode
+      self.assertAllClose(image, decoded_image, atol=1.001)
+
   def testDecodeExampleWithNoShapeInfo(self):
     test_image_channels = [1, 3]
     for channels in test_image_channels:
@@ -150,15 +168,58 @@ class TFExampleDecoderTest(tf.test.TestCase):
       self.assertEqual(tf_decoded_image.get_shape().ndims, 3)
 
   def testDecodeExampleWithPngEncoding(self):
+    test_image_channels = [1, 3]
+    for channels in test_image_channels:
+      image_shape = (2, 3, channels)
+      image, serialized_example = self.GenerateImage(
+          image_format='png',
+          image_shape=image_shape)
+
+      decoded_image = self.RunDecodeExample(
+          serialized_example,
+          slim.tfexample_decoder.Image(channels=channels),
+          image_format='png')
+
+      self.assertAllClose(image, decoded_image, atol=0)
+
+  def testDecodeExampleWithPNGEncoding(self):
+    test_image_channels = [1, 3]
+    for channels in test_image_channels:
+      image_shape = (2, 3, channels)
+      image, serialized_example = self.GenerateImage(
+          image_format='PNG',
+          image_shape=image_shape)
+
+      decoded_image = self.RunDecodeExample(
+          serialized_example,
+          slim.tfexample_decoder.Image(channels=channels),
+          image_format='PNG')
+
+      self.assertAllClose(image, decoded_image, atol=0)
+
+  def testDecodeExampleWithRawEncoding(self):
     image_shape = (2, 3, 3)
     image, serialized_example = self.GenerateImage(
-        image_format='png',
+        image_format='raw',
         image_shape=image_shape)
 
     decoded_image = self.RunDecodeExample(
         serialized_example,
-        slim.tfexample_decoder.Image(),
-        image_format='png')
+        slim.tfexample_decoder.Image(shape=image_shape),
+        image_format='raw')
+
+    self.assertAllClose(image, decoded_image, atol=0)
+
+  def testDecodeExampleWithRAWEncoding(self):
+    image_shape = (2, 3, 3)
+    image, serialized_example = self.GenerateImage(
+        image_format='RAW',
+        image_shape=image_shape)
+
+    decoded_image = self.RunDecodeExample(
+        serialized_example,
+        slim.tfexample_decoder.Image(shape=image_shape),
+        image_format='RAW')
 
     self.assertAllClose(image, decoded_image, atol=0)
 
-- 
GitLab


From a123d5708dd94bc351d44f805270f0f46a346626 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 15:44:26 -0800
Subject: [PATCH 066/223] Add an option to have LookupTableOp share the table
 using the node name. This is required for MutableHashTable to be correctly
 shared between multiple workers. Change: 131113403

---
 tensorflow/contrib/lookup/lookup_ops.py      |  8 ++-
 tensorflow/contrib/lookup/lookup_ops_test.py | 57 ++++++++++++++++++++
 tensorflow/core/kernels/lookup_table_op.h    |  6 ++-
 tensorflow/core/ops/data_flow_ops.cc         |  7 +++
 4 files changed, 76 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/lookup/lookup_ops.py b/tensorflow/contrib/lookup/lookup_ops.py
index d90a6137b5..d330790c2d 100644
--- a/tensorflow/contrib/lookup/lookup_ops.py
+++ b/tensorflow/contrib/lookup/lookup_ops.py
@@ -726,7 +726,8 @@ class MutableHashTable(LookupInterface):
         the given name across multiple sessions.
       name: A name for the operation (optional).
       checkpoint: if True, the contents of the table are saved to and restored
-        from checkpoints.
+        from checkpoints. If `shared_name` is empty, the table is shared using
+        the table node name.
 
     Returns:
       A `MutableHashTable` object.
@@ -738,16 +739,21 @@ class MutableHashTable(LookupInterface):
                                                 dtype=value_dtype)
     self._value_shape = self._default_value.get_shape()
 
+    # The table must be shared if checkpointing is requested. Use the node name
+    # if no shared_name has been explicitly specified.
+    use_node_name_sharing = checkpoint and shared_name is None
     # pylint: disable=protected-access
     if self._default_value.get_shape().ndims == 0:
       self._table_ref = gen_data_flow_ops._mutable_hash_table(
           shared_name=shared_name,
+          use_node_name_sharing=use_node_name_sharing,
           key_dtype=key_dtype,
           value_dtype=value_dtype,
           name=name)
     else:
       self._table_ref = gen_data_flow_ops._mutable_hash_table_of_tensors(
           shared_name=shared_name,
+          use_node_name_sharing=use_node_name_sharing,
           key_dtype=key_dtype,
           value_dtype=value_dtype,
           value_shape=self._default_value.get_shape(),
diff --git a/tensorflow/contrib/lookup/lookup_ops_test.py b/tensorflow/contrib/lookup/lookup_ops_test.py
index ff97fe7493..9633d10049 100644
--- a/tensorflow/contrib/lookup/lookup_ops_test.py
+++ b/tensorflow/contrib/lookup/lookup_ops_test.py
@@ -235,6 +235,33 @@ class HashTableOpTest(tf.test.TestCase):
             tf.contrib.lookup.KeyValueTensorInitializer(keys, values),
             default_val)
 
+  def testMultipleSessions(self):
+    # Start a server
+    server = tf.train.Server(
+        {"local0": ["localhost:0"]}, protocol="grpc", start=True)
+    # Create two sessions sharing the same state
+    session1 = tf.Session(server.target)
+    session2 = tf.Session(server.target)
+
+    default_val = -1
+    keys = tf.constant(["brain", "salad", "surgery"])
+    values = tf.constant([0, 1, 2], tf.int64)
+    table = tf.contrib.lookup.HashTable(
+        tf.contrib.lookup.KeyValueTensorInitializer(keys, values),
+        default_val,
+        name="t1")
+
+    # Init the table in the first session.
+    with session1:
+      table.init.run()
+      self.assertAllEqual(3, table.size().eval())
+
+    # Init the table in the second session and verify that we do not get a
+    # "Table already initialized" error.
+    with session2:
+      table.init.run()
+      self.assertAllEqual(3, table.size().eval())
+
 
 class MutableHashTableOpTest(tf.test.TestCase):
 
@@ -321,6 +348,36 @@ class MutableHashTableOpTest(tf.test.TestCase):
       output = table.lookup(input_string)
       self.assertAllEqual([-1, 0, 1, 2, -1], output.eval())
 
+  def testSharing(self):
+    # Start a server to store the table state
+    server = tf.train.Server(
+        {"local0": ["localhost:0"]}, protocol="grpc", start=True)
+    # Create two sessions sharing the same state
+    session1 = tf.Session(server.target)
+    session2 = tf.Session(server.target)
+
+    table = tf.contrib.lookup.MutableHashTable(
+        tf.int64, tf.string, "-", name="t1")
+
+    # Populate the table in the first session
+    with session1:
+      self.assertAllEqual(0, table.size().eval())
+
+      keys = tf.constant([11, 12], tf.int64)
+      values = tf.constant(["a", "b"])
+      table.insert(keys, values).run()
+      self.assertAllEqual(2, table.size().eval())
+
+      output = table.lookup(tf.constant([11, 12, 13], tf.int64))
+      self.assertAllEqual([b"a", b"b", b"-"], output.eval())
+
+    # Verify that we can access the shared data from the second session
+    with session2:
+      self.assertAllEqual(2, table.size().eval())
+
+      output = table.lookup(tf.constant([10, 11, 12], tf.int64))
+      self.assertAllEqual([b"-", b"a", b"b"], output.eval())
+
   def testMutableHashTableOfTensors(self):
     with self.test_session():
       default_val = tf.constant([-1, -1], tf.int64)
diff --git a/tensorflow/core/kernels/lookup_table_op.h b/tensorflow/core/kernels/lookup_table_op.h
index cd44fb64c2..ffedbe4a97 100644
--- a/tensorflow/core/kernels/lookup_table_op.h
+++ b/tensorflow/core/kernels/lookup_table_op.h
@@ -42,13 +42,16 @@ class LookupTableOp : public OpKernel {
     OP_REQUIRES_OK(ctx, ctx->allocate_persistent(tensorflow::DT_STRING,
                                                  tensorflow::TensorShape({2}),
                                                  &table_handle_, nullptr));
+    OP_REQUIRES_OK(
+        ctx, ctx->GetAttr("use_node_name_sharing", &use_node_name_sharing_));
   }
 
   // ctx is not owned by this function.
   void Compute(OpKernelContext* ctx) override {
     mutex_lock l(mu_);
     if (!table_handle_set_) {
-      OP_REQUIRES_OK(ctx, cinfo_.Init(ctx->resource_manager(), def()));
+      OP_REQUIRES_OK(ctx, cinfo_.Init(ctx->resource_manager(), def(),
+                                      use_node_name_sharing_));
       auto creator = [ctx, this](lookup::LookupInterface** ret) {
         *ret = new Container(ctx, this);
         return Status::OK();
@@ -87,6 +90,7 @@ class LookupTableOp : public OpKernel {
   PersistentTensor table_handle_ GUARDED_BY(mu_);
   bool table_handle_set_ GUARDED_BY(mu_);
   ContainerInfo cinfo_;
+  bool use_node_name_sharing_;
 
   TF_DISALLOW_COPY_AND_ASSIGN(LookupTableOp);
 };
diff --git a/tensorflow/core/ops/data_flow_ops.cc b/tensorflow/core/ops/data_flow_ops.cc
index fbf43a984b..63af3e28b4 100644
--- a/tensorflow/core/ops/data_flow_ops.cc
+++ b/tensorflow/core/ops/data_flow_ops.cc
@@ -1128,6 +1128,7 @@ REGISTER_OP("HashTable")
     .Output("table_handle: Ref(string)")
     .Attr("container: string = ''")
     .Attr("shared_name: string = ''")
+    .Attr("use_node_name_sharing: bool = false")
     .Attr("key_dtype: type")
     .Attr("value_dtype: type")
     .SetIsStateful()
@@ -1144,6 +1145,8 @@ container: If non-empty, this table is placed in the given container.
   Otherwise, a default container is used.
 shared_name: If non-empty, this table is shared under the given name across
   multiple sessions.
+use_node_name_sharing: If true and shared_name is empty, the table is shared
+  using the node name.
 key_dtype: Type of the table keys.
 value_dtype: Type of the table values.
 )doc");
@@ -1152,6 +1155,7 @@ REGISTER_OP("MutableHashTable")
     .Output("table_handle: Ref(string)")
     .Attr("container: string = ''")
     .Attr("shared_name: string = ''")
+    .Attr("use_node_name_sharing: bool = false")
     .Attr("key_dtype: type")
     .Attr("value_dtype: type")
     .SetIsStateful()
@@ -1168,6 +1172,8 @@ container: If non-empty, this table is placed in the given container.
   Otherwise, a default container is used.
 shared_name: If non-empty, this table is shared under the given name across
   multiple sessions.
+use_node_name_sharing: If true and shared_name is empty, the table is shared
+  using the node name.
 key_dtype: Type of the table keys.
 value_dtype: Type of the table values.
 )doc");
@@ -1176,6 +1182,7 @@ REGISTER_OP("MutableHashTableOfTensors")
     .Output("table_handle: Ref(string)")
     .Attr("container: string = ''")
     .Attr("shared_name: string = ''")
+    .Attr("use_node_name_sharing: bool = false")
     .Attr("key_dtype: type")
     .Attr("value_dtype: type")
     .Attr("value_shape: shape = {}")
-- 
GitLab


From 8252e9c9b9a4f6e16b5200732c6124b442e7efe4 Mon Sep 17 00:00:00 2001
From: Renato Utsch <renatoutsch@google.com>
Date: Tue, 23 Aug 2016 15:44:47 -0800
Subject: [PATCH 067/223] Update the histograms to display the values of the
 other axis together with the x axis value. Change: 131113443

---
 .../vz-histogram-timeseries.html              | 24 +++++++++++++++++--
 1 file changed, 22 insertions(+), 2 deletions(-)

diff --git a/tensorflow/tensorboard/components/vz-histogram-timeseries/vz-histogram-timeseries.html b/tensorflow/tensorboard/components/vz-histogram-timeseries/vz-histogram-timeseries.html
index 35e6a9b203..10db2e2d3b 100644
--- a/tensorflow/tensorboard/components/vz-histogram-timeseries/vz-histogram-timeseries.html
+++ b/tensorflow/tensorboard/components/vz-histogram-timeseries/vz-histogram-timeseries.html
@@ -12,6 +12,7 @@ visualization.
 -->
 <dom-module id="vz-histogram-timeseries">
     <template>
+      <div id="tooltip"><span></span></div>
       <svg id="svg">
         <g>
           <g class="axis x"></g>
@@ -44,6 +45,14 @@ visualization.
           flex-shrink: 1;
         }
 
+        #tooltip {
+          position: absolute;
+          display: block;
+          opacity: 0;
+          font-weight: bold;
+          font-size: 11px;
+        }
+
         .background {
           fill-opacity: 0;
           fill: red;
@@ -312,6 +321,7 @@ visualization.
         var name = this._name;
         var mode = this.mode;
         var color = d3.hcl(this.colorScale(name));
+        var tooltip = d3.select(this.$.tooltip);
 
         var xAccessor = function(d) { return d[xProp] };
         var yAccessor = function(d) { return d[yProp] };
@@ -350,10 +360,10 @@ visualization.
         var yAxisFormat = d3.format(".0f");
 
         if (timeProp === "wall_time") {
-          yAxisFormat = d3.time.format("%X");
+          yAxisFormat = d3.time.format("%m/%d %X");
         } else if (timeProp === "relative") {
           yAxisFormat = function(d) {
-            return d3.format(".1r")(d / 3.6e6); // Convert to hours.
+            return d3.format(".1r")(d / 3.6e6) + 'h'; // Convert to hours.
           };
         }
 
@@ -466,6 +476,7 @@ visualization.
               xAxisHoverUpdate.style("opacity", 1);
               yAxisHoverUpdate.style("opacity", 1);
               ySliceAxisHoverUpdate.style("opacity", 1);
+              tooltip.style("opacity", 1);
             })
             .on("mouseout", function() {
               hoverUpdate.style("opacity", 0);
@@ -474,6 +485,7 @@ visualization.
               ySliceAxisHoverUpdate.style("opacity", 0);
               hoverUpdate.classed("hover-closest", false);
               outlineUpdate.classed("outline-hover", false);
+              tooltip.style("opacity", 0);
             })
             .on("mousemove", onMouseMove);
 
@@ -665,6 +677,14 @@ visualization.
             .select("text")
               .text(function(d) { return fsy(closestSliceData[binsProp][index][yProp]); });
 
+          var svgMouse = d3.mouse(svgNode);
+          tooltip.style("transform", "translate(" + (svgMouse[0] + 15) + "px," +
+              (svgMouse[1] - 15) + "px)")
+            .select('span')
+            .text(mode === "offset" ?
+                fsy(closestSliceData[binsProp][index][yProp]) :
+                (timeProp === "step" ? "step " : "") +
+                fy(timeAccessor(closestSliceData)));
         }
       }
     });
-- 
GitLab


From 6a2140d5e1d54d2c814f3d16ae4d2c212e12abc1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 15:47:09 -0800
Subject: [PATCH 068/223] Update ops-related pbtxt files. Change: 131113726

---
 .../core/ops/compat/ops_history.v0.pbtxt      | 122 ++++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 |  23 ++++
 2 files changed, 145 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v0.pbtxt b/tensorflow/core/ops/compat/ops_history.v0.pbtxt
index 1e84f7d99a..88a6d3cbdb 100644
--- a/tensorflow/core/ops/compat/ops_history.v0.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v0.pbtxt
@@ -10777,6 +10777,44 @@ op {
   }
   is_stateful: true
 }
+op {
+  name: "HashTable"
+  output_arg {
+    name: "table_handle"
+    type: DT_STRING
+    is_ref: true
+  }
+  attr {
+    name: "container"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  attr {
+    name: "shared_name"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  attr {
+    name: "use_node_name_sharing"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  attr {
+    name: "key_dtype"
+    type: "type"
+  }
+  attr {
+    name: "value_dtype"
+    type: "type"
+  }
+  is_stateful: true
+}
 op {
   name: "HistogramSummary"
   input_arg {
@@ -14980,6 +15018,44 @@ op {
   }
   is_stateful: true
 }
+op {
+  name: "MutableHashTable"
+  output_arg {
+    name: "table_handle"
+    type: DT_STRING
+    is_ref: true
+  }
+  attr {
+    name: "container"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  attr {
+    name: "shared_name"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  attr {
+    name: "use_node_name_sharing"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  attr {
+    name: "key_dtype"
+    type: "type"
+  }
+  attr {
+    name: "value_dtype"
+    type: "type"
+  }
+  is_stateful: true
+}
 op {
   name: "MutableHashTableOfTensors"
   output_arg {
@@ -15019,6 +15095,52 @@ op {
   }
   is_stateful: true
 }
+op {
+  name: "MutableHashTableOfTensors"
+  output_arg {
+    name: "table_handle"
+    type: DT_STRING
+    is_ref: true
+  }
+  attr {
+    name: "container"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  attr {
+    name: "shared_name"
+    type: "string"
+    default_value {
+      s: ""
+    }
+  }
+  attr {
+    name: "use_node_name_sharing"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
+  attr {
+    name: "key_dtype"
+    type: "type"
+  }
+  attr {
+    name: "value_dtype"
+    type: "type"
+  }
+  attr {
+    name: "value_shape"
+    type: "shape"
+    default_value {
+      shape {
+      }
+    }
+  }
+  is_stateful: true
+}
 op {
   name: "Neg"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 9be117a4aa..2583128d8d 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -6454,6 +6454,14 @@ op {
     }
     description: "If non-empty, this table is shared under the given name across\nmultiple sessions."
   }
+  attr {
+    name: "use_node_name_sharing"
+    type: "bool"
+    default_value {
+      b: false
+    }
+    description: "If true and shared_name is empty, the table is shared\nusing the node name."
+  }
   attr {
     name: "key_dtype"
     type: "type"
@@ -8860,6 +8868,14 @@ op {
     }
     description: "If non-empty, this table is shared under the given name across\nmultiple sessions."
   }
+  attr {
+    name: "use_node_name_sharing"
+    type: "bool"
+    default_value {
+      b: false
+    }
+    description: "If true and shared_name is empty, the table is shared\nusing the node name."
+  }
   attr {
     name: "key_dtype"
     type: "type"
@@ -8898,6 +8914,13 @@ op {
     }
     description: "If non-empty, this table is shared under the given name across\nmultiple sessions."
   }
+  attr {
+    name: "use_node_name_sharing"
+    type: "bool"
+    default_value {
+      b: false
+    }
+  }
   attr {
     name: "key_dtype"
     type: "type"
-- 
GitLab


From 8945cecc1e78f9d9647eb93f3a2bda0b994d4bb3 Mon Sep 17 00:00:00 2001
From: Vincent Vanhoucke <vanhoucke@google.com>
Date: Tue, 23 Aug 2016 15:55:16 -0800
Subject: [PATCH 069/223] Implement the Concatenated ReLU (CReLu)
 non-linearity. This non-linearity makes it possible to reduce the depth of
 lower layers in a convolution tower by a factor of two at no cost in
 accuracy. See: http://arxiv.org/abs/1603.05201 Change: 131114642

---
 tensorflow/python/ops/nn.py      |  3 ++-
 tensorflow/python/ops/nn_ops.py  | 22 ++++++++++++++++++++++
 tensorflow/python/ops/nn_test.py | 10 ++++++++++
 3 files changed, 34 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/nn.py b/tensorflow/python/ops/nn.py
index 1837ea5a2a..941bbfd271 100644
--- a/tensorflow/python/ops/nn.py
+++ b/tensorflow/python/ops/nn.py
@@ -19,7 +19,7 @@
 The activation ops provide different types of nonlinearities for use in neural
 networks.  These include smooth nonlinearities (`sigmoid`, `tanh`, `elu`,
 `softplus`, and `softsign`), continuous but not everywhere differentiable
-functions (`relu`, `relu6`, and `relu_x`), and random regularization
+functions (`relu`, `relu6`, `crelu` and `relu_x`), and random regularization
 (`dropout`).
 
 All activation ops apply componentwise, and produce a tensor of the same
@@ -27,6 +27,7 @@ shape as the input tensor.
 
 @@relu
 @@relu6
+@@crelu
 @@elu
 @@softplus
 @@softsign
diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py
index 827be1c302..1ac5314ad2 100644
--- a/tensorflow/python/ops/nn_ops.py
+++ b/tensorflow/python/ops/nn_ops.py
@@ -429,6 +429,28 @@ ops.RegisterShape("BiasAddV1")(common_shapes.bias_add_shape)
 ops.RegisterShape("BiasAddGradV1")(common_shapes.bias_add_grad_shape)
 
 
+def crelu(features, name=None):
+  """Computes Concatenated ReLU.
+
+  Concatenates a ReLU which selects only the positive part of the activation
+  with a ReLU which selects only the *negative* part of the activation.
+  Note that as a result this non-linearity doubles the depth of the activations.
+  Source: https://arxiv.org/abs/1603.05201
+
+  Args:
+    features: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`,
+      `int16`, or `int8`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` with the same type as `features`.
+  """
+  with ops.name_scope(name, "CRelu", [features]) as name:
+    features = ops.convert_to_tensor(features, name="features")
+    return gen_nn_ops.relu(array_ops.concat(array_ops.rank(features) - 1,
+                                            [features, -features], name=name))
+
+
 def relu6(features, name=None):
   """Computes Rectified Linear 6: `min(max(features, 0), 6)`.
 
diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py
index d146af478f..3f3bd5e94a 100644
--- a/tensorflow/python/ops/nn_test.py
+++ b/tensorflow/python/ops/nn_test.py
@@ -701,5 +701,15 @@ class ComputeSampledLogitsTest(tf.test.TestCase):
           sampled_softmax_loss_np, sampled_softmax_loss_tf.eval(), 1e-4)
 
 
+class CReluTest(tf.test.TestCase):
+
+  def test(self):
+    x = np.random.rand(3, 4).astype(np.float32)
+    y = np.concatenate([x * (x > 0), -x * (x < 0)], axis=1)
+    with self.test_session():
+      z = tf.nn.crelu(tf.constant(x)).eval()
+      self.assertAllClose(y, z, 1e-4)
+
+
 if __name__ == "__main__":
   tf.test.main()
-- 
GitLab


From 2c90008e49b78d1b1731aacd6b4e28d8c8e690ab Mon Sep 17 00:00:00 2001
From: Alexey Surkov <surkov@google.com>
Date: Tue, 23 Aug 2016 16:01:08 -0800
Subject: [PATCH 070/223] Implements GcsFileSystem::Stat Change: 131115272

---
 tensorflow/core/platform/cloud/BUILD          |  25 +++++
 .../core/platform/cloud/gcs_file_system.cc    | 105 +++++++++++-------
 .../platform/cloud/gcs_file_system_test.cc    |  29 ++++-
 tensorflow/core/platform/cloud/time_util.cc   |  52 +++++++++
 tensorflow/core/platform/cloud/time_util.h    |  29 +++++
 .../core/platform/cloud/time_util_test.cc     |  35 ++++++
 6 files changed, 232 insertions(+), 43 deletions(-)
 create mode 100644 tensorflow/core/platform/cloud/time_util.cc
 create mode 100644 tensorflow/core/platform/cloud/time_util.h
 create mode 100644 tensorflow/core/platform/cloud/time_util_test.cc

diff --git a/tensorflow/core/platform/cloud/BUILD b/tensorflow/core/platform/cloud/BUILD
index 7ee9f4e400..28e1c81e2c 100644
--- a/tensorflow/core/platform/cloud/BUILD
+++ b/tensorflow/core/platform/cloud/BUILD
@@ -38,6 +38,7 @@ cc_library(
         ":google_auth_provider",
         ":http_request",
         ":retrying_file_system",
+        ":time_util",
         "//tensorflow/core:framework_headers_lib",
         "//tensorflow/core:lib_internal",
         "@jsoncpp_git//:jsoncpp",
@@ -122,6 +123,20 @@ cc_library(
     ],
 )
 
+cc_library(
+    name = "time_util",
+    srcs = [
+        "time_util.cc",
+    ],
+    hdrs = [
+        "time_util.h",
+    ],
+    deps = [
+        "//tensorflow/core:framework_headers_lib",
+        "//tensorflow/core:lib_internal",
+    ],
+)
+
 tf_cc_test(
     name = "gcs_file_system_test",
     size = "small",
@@ -191,3 +206,13 @@ tf_cc_test(
         "//tensorflow/core:test_main",
     ],
 )
+
+tf_cc_test(
+    name = "time_util_test",
+    size = "small",
+    deps = [
+        ":time_util",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+    ],
+)
diff --git a/tensorflow/core/platform/cloud/gcs_file_system.cc b/tensorflow/core/platform/cloud/gcs_file_system.cc
index 910e144f35..61aba930f5 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system.cc
@@ -29,6 +29,7 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/cloud/google_auth_provider.h"
+#include "tensorflow/core/platform/cloud/time_util.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/protobuf.h"
 #include "tensorflow/core/platform/regexp.h"
@@ -466,41 +467,9 @@ Status GcsFileSystem::GetChildren(const string& dirname,
 }
 
 Status GcsFileSystem::Stat(const string& fname, FileStatistics* stat) {
-  return errors::Unimplemented("Stat unimplemented");
-}
-
-Status GcsFileSystem::DeleteFile(const string& fname) {
-  string bucket, object;
-  TF_RETURN_IF_ERROR(ParseGcsPath(fname, &bucket, &object));
-
-  string auth_token;
-  TF_RETURN_IF_ERROR(AuthProvider::GetToken(auth_provider_.get(), &auth_token));
-
-  std::unique_ptr<HttpRequest> request(http_request_factory_->Create());
-  TF_RETURN_IF_ERROR(request->Init());
-  TF_RETURN_IF_ERROR(request->SetUri(strings::StrCat(
-      kGcsUriBase, "b/", bucket, "/o/", request->EscapeString(object))));
-  TF_RETURN_IF_ERROR(request->AddAuthBearerHeader(auth_token));
-  TF_RETURN_IF_ERROR(request->SetDeleteRequest());
-  TF_RETURN_WITH_CONTEXT_IF_ERROR(request->Send(), " when deleting ", fname);
-  return Status::OK();
-}
-
-// Does nothing, because directories are not entities in GCS.
-Status GcsFileSystem::CreateDir(const string& dirname) { return Status::OK(); }
-
-// Checks that the directory is empty (i.e no objects with this prefix exist).
-// If it is, does nothing, because directories are not entities in GCS.
-Status GcsFileSystem::DeleteDir(const string& dirname) {
-  std::vector<string> children;
-  TF_RETURN_IF_ERROR(GetChildren(dirname, &children));
-  if (!children.empty()) {
-    return errors::InvalidArgument("Cannot delete a non-empty directory.");
+  if (!stat) {
+    return errors::Internal("'stat' cannot be nullptr.");
   }
-  return Status::OK();
-}
-
-Status GcsFileSystem::GetFileSize(const string& fname, uint64* file_size) {
   string bucket, object_prefix;
   TF_RETURN_IF_ERROR(ParseGcsPath(fname, &bucket, &object_prefix));
 
@@ -512,9 +481,9 @@ Status GcsFileSystem::GetFileSize(const string& fname, uint64* file_size) {
 
   std::unique_ptr<HttpRequest> request(http_request_factory_->Create());
   TF_RETURN_IF_ERROR(request->Init());
-  TF_RETURN_IF_ERROR(request->SetUri(
-      strings::StrCat(kGcsUriBase, "b/", bucket, "/o/",
-                      request->EscapeString(object_prefix), "?fields=size")));
+  TF_RETURN_IF_ERROR(request->SetUri(strings::StrCat(
+      kGcsUriBase, "b/", bucket, "/o/", request->EscapeString(object_prefix),
+      "?fields=size%2Cupdated")));
   TF_RETURN_IF_ERROR(request->AddAuthBearerHeader(auth_token));
   TF_RETURN_IF_ERROR(
       request->SetResultBuffer(scratch.get(), kBufferSize, &response_piece));
@@ -528,19 +497,77 @@ Status GcsFileSystem::GetFileSize(const string& fname, uint64* file_size) {
   if (!reader.parse(response_stream.str(), root)) {
     return errors::Internal("Couldn't parse JSON response from GCS.");
   }
+
+  // Parse file size.
   const auto size = root.get("size", Json::Value::null);
   if (size == Json::Value::null) {
     return errors::Internal("'size' was expected in the JSON response.");
   }
   if (size.isNumeric()) {
-    *file_size = size.asUInt64();
+    stat->length = size.asUInt64();
   } else if (size.isString()) {
-    if (!strings::safe_strtou64(size.asString().c_str(), file_size)) {
+    if (!strings::safe_strto64(size.asString().c_str(), &(stat->length))) {
       return errors::Internal("'size' couldn't be parsed as a nubmer.");
     }
   } else {
     return errors::Internal("'size' is not a number in the JSON response.");
   }
+
+  // Parse file modification time.
+  const auto updated = root.get("updated", Json::Value::null);
+  if (updated == Json::Value::null) {
+    return errors::Internal("'updated' was expected in the JSON response.");
+  }
+  if (!updated.isString()) {
+    return errors::Internal(
+        "'updated' is expected to be a string in the JSON response.");
+  }
+  TF_RETURN_IF_ERROR(ParseRfc3339Time(updated.asString(), &(stat->mtime_nsec)));
+
+  // Converting GCS ACL into mode_t is hard, return -rw------- instead.
+  stat->mode = 0600;
+
+  return Status::OK();
+}
+
+Status GcsFileSystem::DeleteFile(const string& fname) {
+  string bucket, object;
+  TF_RETURN_IF_ERROR(ParseGcsPath(fname, &bucket, &object));
+
+  string auth_token;
+  TF_RETURN_IF_ERROR(AuthProvider::GetToken(auth_provider_.get(), &auth_token));
+
+  std::unique_ptr<HttpRequest> request(http_request_factory_->Create());
+  TF_RETURN_IF_ERROR(request->Init());
+  TF_RETURN_IF_ERROR(request->SetUri(strings::StrCat(
+      kGcsUriBase, "b/", bucket, "/o/", request->EscapeString(object))));
+  TF_RETURN_IF_ERROR(request->AddAuthBearerHeader(auth_token));
+  TF_RETURN_IF_ERROR(request->SetDeleteRequest());
+  TF_RETURN_WITH_CONTEXT_IF_ERROR(request->Send(), " when deleting ", fname);
+  return Status::OK();
+}
+
+// Does nothing, because directories are not entities in GCS.
+Status GcsFileSystem::CreateDir(const string& dirname) { return Status::OK(); }
+
+// Checks that the directory is empty (i.e no objects with this prefix exist).
+// If it is, does nothing, because directories are not entities in GCS.
+Status GcsFileSystem::DeleteDir(const string& dirname) {
+  std::vector<string> children;
+  TF_RETURN_IF_ERROR(GetChildren(dirname, &children));
+  if (!children.empty()) {
+    return errors::InvalidArgument("Cannot delete a non-empty directory.");
+  }
+  return Status::OK();
+}
+
+Status GcsFileSystem::GetFileSize(const string& fname, uint64* file_size) {
+  if (!file_size) {
+    return errors::Internal("'file_size' cannot be nullptr.");
+  }
+  FileStatistics stat;
+  TF_RETURN_IF_ERROR(Stat(fname, &stat));
+  *file_size = stat.length;
   return Status::OK();
 }
 
diff --git a/tensorflow/core/platform/cloud/gcs_file_system_test.cc b/tensorflow/core/platform/cloud/gcs_file_system_test.cc
index b43a5778ee..8b0604b7ee 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system_test.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system_test.cc
@@ -192,9 +192,10 @@ TEST(GcsFileSystemTest, NewReadOnlyMemoryRegionFromFile) {
   std::vector<HttpRequest*> requests(
       {new FakeHttpRequest(
            "Uri: https://www.googleapis.com/storage/v1/b/bucket/o/"
-           "path%2Frandom_access.txt?fields=size\n"
+           "path%2Frandom_access.txt?fields=size%2Cupdated\n"
            "Auth Token: fake_token\n",
-           strings::StrCat("{\"size\": \"", content.size(), "\"}")),
+           strings::StrCat("{\"size\": \"", content.size(),
+                           "\", \"updated\": \"2016-04-29T23:15:24.896Z\"}")),
        new FakeHttpRequest(
            strings::StrCat("Uri: https://bucket.storage.googleapis.com/"
                            "path%2Frandom_access.txt\n"
@@ -398,9 +399,10 @@ TEST(GcsFileSystemTest, DeleteDir_NonEmpty) {
 TEST(GcsFileSystemTest, GetFileSize) {
   std::vector<HttpRequest*> requests({new FakeHttpRequest(
       "Uri: https://www.googleapis.com/storage/v1/b/bucket/o/"
-      "file.txt?fields=size\n"
+      "file.txt?fields=size%2Cupdated\n"
       "Auth Token: fake_token\n",
-      strings::StrCat("{\"size\": \"1010\"}"))});
+      strings::StrCat("{\"size\": \"1010\","
+                      "\"updated\": \"2016-04-29T23:15:24.896Z\"}"))});
   GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
                    std::unique_ptr<HttpRequest::Factory>(
                        new FakeHttpRequestFactory(&requests)),
@@ -434,5 +436,24 @@ TEST(GcsFileSystemTest, RenameFile) {
       fs.RenameFile("gs://bucket/path/src.txt", "gs://bucket/path/dst.txt"));
 }
 
+TEST(GcsFileSystemTest, Stat) {
+  std::vector<HttpRequest*> requests({new FakeHttpRequest(
+      "Uri: https://www.googleapis.com/storage/v1/b/bucket/o/"
+      "file.txt?fields=size%2Cupdated\n"
+      "Auth Token: fake_token\n",
+      strings::StrCat("{\"size\": \"1010\","
+                      "\"updated\": \"2016-04-29T23:15:24.896Z\"}"))});
+  GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
+                   std::unique_ptr<HttpRequest::Factory>(
+                       new FakeHttpRequestFactory(&requests)),
+                   0 /* read ahead bytes */);
+
+  FileStatistics stat;
+  TF_EXPECT_OK(fs.Stat("gs://bucket/file.txt", &stat));
+  EXPECT_EQ(1010, stat.length);
+  EXPECT_EQ(1461971724896, stat.mtime_nsec / 1000 / 1000);
+  EXPECT_EQ(0600, stat.mode);
+}
+
 }  // namespace
 }  // namespace tensorflow
diff --git a/tensorflow/core/platform/cloud/time_util.cc b/tensorflow/core/platform/cloud/time_util.cc
new file mode 100644
index 0000000000..633733a21c
--- /dev/null
+++ b/tensorflow/core/platform/cloud/time_util.cc
@@ -0,0 +1,52 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/platform/cloud/time_util.h"
+#include <time.h>
+#include <cmath>
+#include <cstdio>
+#include <ctime>
+#include "tensorflow/core/lib/core/errors.h"
+
+namespace tensorflow {
+
+namespace {
+constexpr int64 kNanosecondsPerSecond = 1000 * 1000 * 1000;
+
+}  // namespace
+
+// Only implements one special case of RFC 3339 which is returned by
+// GCS API, e.g 2016-04-29T23:15:24.896Z.
+Status ParseRfc3339Time(const string& time, int64* mtime_nsec) {
+  tm parsed{0};
+  float seconds;
+  if (sscanf(time.c_str(), "%4d-%2d-%2dT%2d:%2d:%fZ", &(parsed.tm_year),
+             &(parsed.tm_mon), &(parsed.tm_mday), &(parsed.tm_hour),
+             &(parsed.tm_min), &seconds) != 6) {
+    return errors::Internal(
+        strings::StrCat("Unrecognized RFC 3339 time format: ", time));
+  }
+  const int int_seconds = floor(seconds);
+  parsed.tm_year -= 1900;  // tm_year expects years since 1900.
+  parsed.tm_mon -= 1;      // month is zero-based.
+  parsed.tm_sec = int_seconds;
+
+  *mtime_nsec = timegm(&parsed) * kNanosecondsPerSecond +
+                floor((seconds - int_seconds) * kNanosecondsPerSecond);
+
+  return Status::OK();
+}
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/platform/cloud/time_util.h b/tensorflow/core/platform/cloud/time_util.h
new file mode 100644
index 0000000000..b1bb7f1119
--- /dev/null
+++ b/tensorflow/core/platform/cloud/time_util.h
@@ -0,0 +1,29 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_PLATFORM_CLOUD_TIME_UTIL_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_PLATFORM_CLOUD_TIME_UTIL_H_
+
+#include "tensorflow/core/lib/core/status.h"
+
+namespace tensorflow {
+
+/// Parses the timestamp in RFC 3339 format and returns it
+/// as nanoseconds since epoch.
+Status ParseRfc3339Time(const string& time, int64* mtime_nsec);
+
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_PLATFORM_CLOUD_TIME_UTIL_H_
diff --git a/tensorflow/core/platform/cloud/time_util_test.cc b/tensorflow/core/platform/cloud/time_util_test.cc
new file mode 100644
index 0000000000..3fd8fcdab0
--- /dev/null
+++ b/tensorflow/core/platform/cloud/time_util_test.cc
@@ -0,0 +1,35 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/platform/cloud/time_util.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+
+TEST(TimeUtil, ParseRfc3339Time) {
+  int64 mtime_nsec;
+  TF_EXPECT_OK(ParseRfc3339Time("2016-04-29T23:15:24.896Z", &mtime_nsec));
+  // Compare milliseconds instead of nanoseconds.
+  EXPECT_EQ(1461971724896, mtime_nsec / 1000 / 1000);
+}
+
+TEST(TimeUtil, ParseRfc3339Time_ParseError) {
+  int64 mtime_nsec;
+  EXPECT_EQ("Unrecognized RFC 3339 time format: 2016-04-29",
+            ParseRfc3339Time("2016-04-29", &mtime_nsec).error_message());
+}
+
+}  // namespace tensorflow
-- 
GitLab


From 17af99f07f74ae5edc54113f57e0668065e8edec Mon Sep 17 00:00:00 2001
From: Jingtian Peng <pjt73651@gmail.com>
Date: Wed, 24 Aug 2016 08:23:26 +0800
Subject: [PATCH 071/223] Revert the change of PriorityQueue

---
 tensorflow/g3doc/api_docs/python/io_ops.md | 51 ----------------------
 1 file changed, 51 deletions(-)

diff --git a/tensorflow/g3doc/api_docs/python/io_ops.md b/tensorflow/g3doc/api_docs/python/io_ops.md
index 39eb046845..c1433adbb5 100644
--- a/tensorflow/g3doc/api_docs/python/io_ops.md
+++ b/tensorflow/g3doc/api_docs/python/io_ops.md
@@ -2079,57 +2079,6 @@ queue has been closed.
 
 
 
-- - -
-
-### `class tf.PriorityQueue` {#PriorityQueue}
-
-A queue implementation that dequeues elements in prioritized order.
-
-See [`tf.QueueBase`](#QueueBase) for a description of the methods on
-this class.
-
-- - -
-
-
-#### `tf.PriorityQueue.__init__(capacity, types, shapes=None, names=None, shared_name=None, name='priority_queue')` {#PriorityQueue.__init__}
-
-Creates a queue that dequeues elements in a first-in first-out order.
-
-A `PriorityQueue` has bounded capacity; supports multiple concurrent
-producers and consumers; and provides exactly-once delivery.
-
-A `PriorityQueue` holds a list of up to `capacity` elements. Each
-element is a fixed-length tuple of tensors whose dtypes are
-described by `types`, and whose shapes are optionally described
-by the `shapes` argument.
-
-If the `shapes` argument is specified, each component of a queue
-element must have the respective fixed shape. If it is
-unspecified, different queue elements may have different shapes,
-but the use of `dequeue_many` is disallowed.
-
-Enqueues and Dequeues to the `PriorityQueue` must include an additional
-tuple entry at the beginning: the `priority`.  The priority must be
-an int64 scalar (for `enqueue`) or an int64 vector (for `enqueue_many`).
-
-##### Args:
-*  <b>`capacity`</b>: An integer. The upper bound on the number of elements
-    that may be stored in this queue.
-*  <b>`types`</b>:  A list of `DType` objects. The length of `types` must equal
-    the number of tensors in each queue element, except the first priority
-    element.  The first tensor in each element is the priority,
-    which must be type int64.
-*  <b>`shapes`</b>: (Optional.) A list of fully-defined `TensorShape` objects
-    with the same length as `dtypes`, or `None`.
-*  <b>`names`</b>: (Optional.) A list of string naming the components in the queue
-    with the same length as `dtypes`, or `None`.  If specified the dequeue
-    methods return a dictionary with the names as keys.
-*  <b>`shared_name`</b>: (Optional.) If non-empty, this queue will be shared under
-    the given name across multiple sessions.
-*  <b>`name`</b>: Optional name for the queue operation.
-
-
-
 
 ## Dealing with the filesystem
 
-- 
GitLab


From d6484f778c2f8658e677b8aa853500635f29eb22 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 16:24:09 -0800
Subject: [PATCH 072/223] Add MovingAverageOptimizer which allows to compute
 the moving average of the variables of a graph during training.

This provides a swapping saver that swaps the variables from the averaged ones which allows this solution to be *self-contained* in the training code. The inference/eval codes using the normal savers will use the averaged variables without knowing it.
Change: 131117858
---
 tensorflow/contrib/opt/BUILD                  |  11 ++
 tensorflow/contrib/opt/__init__.py            |   1 +
 .../training/moving_average_optimizer.py      | 130 ++++++++++++++++++
 .../training/moving_average_optimizer_test.py |  88 ++++++++++++
 tensorflow/python/training/saver.py           |  12 +-
 5 files changed, 237 insertions(+), 5 deletions(-)
 create mode 100644 tensorflow/contrib/opt/python/training/moving_average_optimizer.py
 create mode 100644 tensorflow/contrib/opt/python/training/moving_average_optimizer_test.py

diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD
index 0671cb3d80..a0ca0fabd2 100644
--- a/tensorflow/contrib/opt/BUILD
+++ b/tensorflow/contrib/opt/BUILD
@@ -12,6 +12,7 @@ py_library(
     srcs = [
         "__init__.py",
         "python/training/external_optimizer.py",
+        "python/training/moving_average_optimizer.py",
         "python/training/variable_clipping_optimizer.py",
     ],
     srcs_version = "PY2AND3",
@@ -28,6 +29,16 @@ py_test(
     ],
 )
 
+py_test(
+    name = "moving_average_optimizer_test",
+    srcs = ["python/training/moving_average_optimizer_test.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        ":opt_py",
+        "//tensorflow:tensorflow_py",
+    ],
+)
+
 py_test(
     name = "variable_clipping_optimizer_test",
     srcs = ["python/training/variable_clipping_optimizer_test.py"],
diff --git a/tensorflow/contrib/opt/__init__.py b/tensorflow/contrib/opt/__init__.py
index 973d42cbd0..11e1eaea67 100644
--- a/tensorflow/contrib/opt/__init__.py
+++ b/tensorflow/contrib/opt/__init__.py
@@ -20,4 +20,5 @@ from __future__ import print_function
 
 # pylint: disable=wildcard-import
 from tensorflow.contrib.opt.python.training.external_optimizer import *
+from tensorflow.contrib.opt.python.training.moving_average_optimizer import *
 from tensorflow.contrib.opt.python.training.variable_clipping_optimizer import *
diff --git a/tensorflow/contrib/opt/python/training/moving_average_optimizer.py b/tensorflow/contrib/opt/python/training/moving_average_optimizer.py
new file mode 100644
index 0000000000..6d37178a98
--- /dev/null
+++ b/tensorflow/contrib/opt/python/training/moving_average_optimizer.py
@@ -0,0 +1,130 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Optimizer that computes a moving average of the variables.
+
+Empirically it has been found that using the moving average of the trained
+parameters of a deep network is better than using its trained parameters
+directly. This optimizer allows you to compute this moving average and swap the
+variables at save time so that any code outside of the training loop will use by
+default the averaged values instead of the original ones.
+
+Example of usage:
+
+```python
+
+// Encapsulate your favorite optimizer (here the momentum one)
+// inside the MovingAverageOptimizer.
+opt = tf.train.MomentumOptimizer(learning_rate, FLAGS.momentum)
+opt = tf.contrib.opt.MovingAverageOptimizer(opt)
+// Then create your model and all its variables.
+model = build_model()
+// Add the training op that optimizes using opt.
+// This needs to be called before swapping_saver().
+opt.minimize(cost, var_list)
+// Then create your saver like this:
+saver = opt.swapping_saver()
+// Pass it to your training loop.
+    slim.learning.train(
+        model,
+        ...
+        saver=saver)
+```
+
+Note that for evaluation, the normal saver should be used instead of
+swapping_saver().
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import six
+
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import variables
+from tensorflow.python.training import moving_averages
+from tensorflow.python.training import optimizer
+from tensorflow.python.training import saver
+
+
+class MovingAverageOptimizer(optimizer.Optimizer):
+  """Optimizer wrapper that maintains a moving average of parameters."""
+
+  def __init__(self, opt, average_decay=0.9999):
+    """Construct a new MovingAverageOptimizer.
+
+    Args:
+      opt: A tf.Optimizer that will be used to compute and apply gradients.
+      average_decay: Float.  Decay to use to maintain the moving averages
+                     of trained variables.
+                     See tf.train.ExponentialMovingAverage for details.
+    """
+    self._optimizer = opt
+    self._ema = moving_averages.ExponentialMovingAverage(average_decay)
+    self._variable_map = None
+
+  def apply_gradients(self, grads_and_vars, global_step=None, name=None):
+    train_op = self._optimizer.apply_gradients(
+        grads_and_vars, global_step=global_step, name=name)
+    var_list = [x[1] for x in grads_and_vars if x[0] is not None]
+    ma_op = self._ema.apply(var_list)
+    self._variable_map = {}
+    for v in var_list:
+      v_avg = self._ema.average(v)
+      self._variable_map[v.op.name] = v_avg
+      self._variable_map[v_avg.op.name] = v
+    return control_flow_ops.group(train_op, ma_op)
+
+  def swapping_saver(self, var_list=None, name='swapping_saver', **kwargs):
+    """Create a saver swapping moving averages and variables.
+
+    You should use this saver during training.  It will save the moving averages
+    of the trained parameters under the original parameter names.  For
+    evaluations or inference you should use a regular saver and it will
+    automatically use the moving averages for the trained variable.
+
+    You must call this function after all variables have been created and after
+    you have called Optimizer.minimize().
+
+    Args:
+      var_list: List of variables to save, as per `Saver()`.
+                If set to None, will save all the variables that have been
+                created before this call.
+      name: The name of the saver.
+      **kwargs: Keyword arguments of `Saver()`.
+
+    Returns:
+      A `tf.Saver` object.
+
+    Raises:
+      RuntimeError: If apply_gradients or minimize has not been called before.
+    """
+
+    if self._variable_map is None:
+      raise RuntimeError('Must call apply_gradients or minimize before '
+                         'creating the swapping_saver')
+    if var_list is None:
+      var_list = variables.all_variables()
+    if not isinstance(var_list, dict):
+      var_list = saver.BaseSaverBuilder.OpListToDict(var_list)
+    # Now swap variables and moving averages
+    swapped_var_list = {}
+    for k, v in six.iteritems(var_list):
+      v_swap = self._variable_map.get(v.op.name, None)
+      if v_swap:
+        swapped_var_list[k] = v_swap
+      else:
+        swapped_var_list[k] = v
+    # Build the swapping saver.
+    return saver.Saver(swapped_var_list, name=name, **kwargs)
diff --git a/tensorflow/contrib/opt/python/training/moving_average_optimizer_test.py b/tensorflow/contrib/opt/python/training/moving_average_optimizer_test.py
new file mode 100644
index 0000000000..5d1e159374
--- /dev/null
+++ b/tensorflow/contrib/opt/python/training/moving_average_optimizer_test.py
@@ -0,0 +1,88 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for moving_average_optimizer."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os.path
+
+import six
+import tensorflow as tf
+
+
+class MovingAverageOptimizerTest(tf.test.TestCase):
+
+  def testRun(self):
+    for dtype in [tf.half, tf.float32, tf.float64]:
+      with self.test_session() as sess:
+        orig_val0 = [1.0, 2.0]
+        orig_val1 = [3.0, 4.0]
+        var0 = tf.Variable(orig_val0, name='var0', dtype=dtype)
+        var1 = tf.Variable(orig_val1, name='var1', dtype=dtype)
+        grads0 = tf.constant([0.1, 0.1], dtype=dtype)
+        grads1 = tf.constant([0.01, 0.01], dtype=dtype)
+
+        opt = tf.contrib.opt.MovingAverageOptimizer(
+            tf.train.GradientDescentOptimizer(learning_rate=2.0),
+            average_decay=0.5)
+        save_path = os.path.join(self.get_temp_dir(), 'model')
+        var_list = [var0, var1]
+        update = opt.apply_gradients(
+            list(six.moves.zip([grads0, grads1], [var0, var1])))
+        train_saver = opt.swapping_saver(var_list)
+        inference_saver = tf.train.Saver(var_list)
+        tf.initialize_all_variables().run()
+        # Fetch params to validate initial values
+        # Step 1: the momentum accumulators were. So we should see a normal
+        # update: v -= grad * learning_rate
+        update.run()
+        self.assertAllCloseAccordingToType([0.8, 1.8], var0.eval())
+        self.assertAllCloseAccordingToType([2.98, 3.98], var1.eval())
+        update.run()
+        # Test that the swapping saver save/restore operation is identity.
+        self.assertAllCloseAccordingToType([0.6, 1.6], var0.eval())
+        self.assertAllCloseAccordingToType([2.96, 3.96], var1.eval())
+        train_saver.save(sess, save_path)
+        train_saver.restore(sess, save_path)
+        val0 = var0.eval()
+        val1 = var1.eval()
+        self.assertAllCloseAccordingToType([0.6, 1.6], val0)
+        self.assertAllCloseAccordingToType([2.96, 3.96], val1)
+        # Test that the normal saver will have the averaged variables.
+        # We test that the average values are between the original value and the
+        # most recent variable values (since they are an average of the two).
+        inference_saver.restore(sess, save_path)
+        avg_val0 = var0.eval()
+        avg_val1 = var1.eval()
+        for i in six.moves.range(len(val0)):
+          self.assertLess(val0[i], avg_val0[i])
+          self.assertLess(avg_val0[i], orig_val0[i])
+          self.assertLess(val1[i], avg_val1[i])
+          self.assertLess(avg_val1[i], orig_val1[i])
+
+  def testFailWhenSaverCreatedBeforeInitialized(self):
+    with self.test_session():
+      var = tf.Variable([1.0], name='var', dtype=tf.float32)
+      opt = tf.contrib.opt.MovingAverageOptimizer(
+          tf.train.GradientDescentOptimizer(learning_rate=2.0))
+      # We didn't call apply_gradients yet.
+      # This will raise an exception.
+      with self.assertRaises(RuntimeError):
+        _ = opt.swapping_saver([var])
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py
index f66cf5577f..4d3db66ba4 100644
--- a/tensorflow/python/training/saver.py
+++ b/tensorflow/python/training/saver.py
@@ -396,7 +396,8 @@ class BaseSaverBuilder(object):
                 name="restore_shard"))
     return control_flow_ops.group(*sharded_restores, name="restore_all")
 
-  def _IsVariable(self, v):
+  @staticmethod
+  def _IsVariable(v):
     return isinstance(v, ops.Tensor) and (v.op.type == "Variable" or
                                           v.op.type == "AutoReloadVariable")
 
@@ -427,7 +428,8 @@ class BaseSaverBuilder(object):
       per_device[canonical_device.pop()].append(saveable)
     return sorted(per_device.items(), key=lambda t: t[0])
 
-  def _OpListToDict(self, op_list):
+  @staticmethod
+  def OpListToDict(op_list):
     """Create a dictionary of names to operation lists.
 
     Args:
@@ -462,7 +464,7 @@ class BaseSaverBuilder(object):
           names_to_saveables[name] = [var]
       else:
         var = ops.convert_to_tensor(var, as_ref=True)
-        if not self._IsVariable(var):
+        if not BaseSaverBuilder._IsVariable(var):
           raise TypeError("Variable to save is not a Variable: %s" % var)
         name = var.op.name
         if name in names_to_saveables:
@@ -489,7 +491,7 @@ class BaseSaverBuilder(object):
         (this also applies to slices of SlicedVariables).
     """
     if not isinstance(names_to_saveables, dict):
-      names_to_saveables = self._OpListToDict(names_to_saveables)
+      names_to_saveables = BaseSaverBuilder.OpListToDict(names_to_saveables)
 
     saveables = []
     seen_ops = set()
@@ -523,7 +525,7 @@ class BaseSaverBuilder(object):
       else:
         # A variable or tensor.
         variable = ops.convert_to_tensor(op, as_ref=True)
-        if not self._IsVariable(variable):
+        if not BaseSaverBuilder._IsVariable(variable):
           raise TypeError("names_to_saveables must be a dict mapping string "
                           "names to Tensors/Variables. Not a variable: %s" %
                           variable)
-- 
GitLab


From ade1672d60d861c58e1930e93a1b396b22e7a4d9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 16:33:18 -0800
Subject: [PATCH 073/223] Update generated Python Op docs. Change: 131118777

---
 .../shard9/tf.nn.crelu.md                     | 20 +++++++++++++++
 tensorflow/g3doc/api_docs/python/index.md     |  1 +
 tensorflow/g3doc/api_docs/python/nn.md        | 25 ++++++++++++++++++-
 3 files changed, 45 insertions(+), 1 deletion(-)
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.crelu.md

diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.crelu.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.crelu.md
new file mode 100644
index 0000000000..8f6d282c10
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.nn.crelu.md
@@ -0,0 +1,20 @@
+### `tf.nn.crelu(features, name=None)` {#crelu}
+
+Computes Concatenated ReLU.
+
+Concatenates a ReLU which selects only the positive part of the activation
+with a ReLU which selects only the *negative* part of the activation.
+Note that as a result this non-linearity doubles the depth of the activations.
+Source: https://arxiv.org/abs/1603.05201
+
+##### Args:
+
+
+*  <b>`features`</b>: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`,
+    `int16`, or `int8`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` with the same type as `features`.
+
diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md
index 8627c2e2bb..18d77fed93 100644
--- a/tensorflow/g3doc/api_docs/python/index.md
+++ b/tensorflow/g3doc/api_docs/python/index.md
@@ -452,6 +452,7 @@
   * [`conv2d`](../../api_docs/python/nn.md#conv2d)
   * [`conv2d_transpose`](../../api_docs/python/nn.md#conv2d_transpose)
   * [`conv3d`](../../api_docs/python/nn.md#conv3d)
+  * [`crelu`](../../api_docs/python/nn.md#crelu)
   * [`ctc_beam_search_decoder`](../../api_docs/python/nn.md#ctc_beam_search_decoder)
   * [`ctc_greedy_decoder`](../../api_docs/python/nn.md#ctc_greedy_decoder)
   * [`ctc_loss`](../../api_docs/python/nn.md#ctc_loss)
diff --git a/tensorflow/g3doc/api_docs/python/nn.md b/tensorflow/g3doc/api_docs/python/nn.md
index 1e8a37186d..2e4a58e417 100644
--- a/tensorflow/g3doc/api_docs/python/nn.md
+++ b/tensorflow/g3doc/api_docs/python/nn.md
@@ -12,7 +12,7 @@ Note: Functions taking `Tensor` arguments can also take anything accepted by
 The activation ops provide different types of nonlinearities for use in neural
 networks.  These include smooth nonlinearities (`sigmoid`, `tanh`, `elu`,
 `softplus`, and `softsign`), continuous but not everywhere differentiable
-functions (`relu`, `relu6`, and `relu_x`), and random regularization
+functions (`relu`, `relu6`, `crelu` and `relu_x`), and random regularization
 (`dropout`).
 
 All activation ops apply componentwise, and produce a tensor of the same
@@ -44,6 +44,29 @@ Computes Rectified Linear 6: `min(max(features, 0), 6)`.
 ##### Args:
 
 
+*  <b>`features`</b>: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`,
+    `int16`, or `int8`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor` with the same type as `features`.
+
+
+- - -
+
+### `tf.nn.crelu(features, name=None)` {#crelu}
+
+Computes Concatenated ReLU.
+
+Concatenates a ReLU which selects only the positive part of the activation
+with a ReLU which selects only the *negative* part of the activation.
+Note that as a result this non-linearity doubles the depth of the activations.
+Source: https://arxiv.org/abs/1603.05201
+
+##### Args:
+
+
 *  <b>`features`</b>: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`,
     `int16`, or `int8`.
 *  <b>`name`</b>: A name for the operation (optional).
-- 
GitLab


From f6bc8cabbd3ac1fb3acc36d3edbdce672cae7d12 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 16:35:14 -0800
Subject: [PATCH 074/223] Add shape_inference::ShapeHandle and
 shape_inference::DimensionHandle to replace uses of const Shape* and const
 Dimension*.  This change only adds a typedef and updates references.  A later
 change will make DimensionHandle and ShapeHandle real types instead of
 typedefs (to further hide the pointer access). Change: 131118981

---
 tensorflow/contrib/metrics/ops/set_ops.cc     |  28 +-
 .../contrib/quantization/ops/array_ops.cc     |   8 +-
 .../contrib/quantization/ops/math_ops.cc      |   6 +-
 tensorflow/contrib/quantization/ops/nn_ops.cc |  26 +-
 .../tensor_forest/core/ops/best_splits_op.cc  |   6 +-
 .../ops/count_extremely_random_stats_op.cc    |   8 +-
 .../core/ops/sample_inputs_op.cc              |   8 +-
 .../core/ops/tree_predictions_op.cc           |   8 +-
 tensorflow/core/framework/common_shape_fns.cc | 176 ++++++------
 tensorflow/core/framework/common_shape_fns.h  |   8 +-
 .../core/framework/common_shape_fns_test.cc   |  32 +--
 tensorflow/core/framework/shape_inference.cc  | 139 +++++-----
 tensorflow/core/framework/shape_inference.h   | 163 +++++------
 .../core/framework/shape_inference_test.cc    |  58 ++--
 .../framework/shape_inference_testutil.cc     |  10 +-
 tensorflow/core/ops/array_ops.cc              | 262 +++++++++---------
 tensorflow/core/ops/candidate_sampling_ops.cc |  16 +-
 tensorflow/core/ops/control_flow_ops.cc       |  16 +-
 tensorflow/core/ops/ctc_ops.cc                |  32 +--
 tensorflow/core/ops/data_flow_ops.cc          |  92 +++---
 tensorflow/core/ops/image_ops.cc              |  56 ++--
 tensorflow/core/ops/io_ops.cc                 |  28 +-
 tensorflow/core/ops/linalg_ops.cc             | 107 ++++---
 tensorflow/core/ops/math_ops.cc               |  98 +++----
 tensorflow/core/ops/nn_ops.cc                 |  78 +++---
 tensorflow/core/ops/parsing_ops.cc            |  22 +-
 tensorflow/core/ops/random_ops.cc             |  14 +-
 tensorflow/core/ops/sparse_ops.cc             |  72 ++---
 tensorflow/core/ops/state_ops.cc              |  16 +-
 tensorflow/core/ops/string_ops.cc             |   8 +-
 tensorflow/core/ops/training_ops.cc           |  54 ++--
 31 files changed, 828 insertions(+), 827 deletions(-)

diff --git a/tensorflow/contrib/metrics/ops/set_ops.cc b/tensorflow/contrib/metrics/ops/set_ops.cc
index c68a1c964a..2e2bd91367 100644
--- a/tensorflow/contrib/metrics/ops/set_ops.cc
+++ b/tensorflow/contrib/metrics/ops/set_ops.cc
@@ -19,9 +19,9 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 REGISTER_OP("SetSize")
     .Input("set_indices: int64")
@@ -67,15 +67,15 @@ REGISTER_OP("DenseToDenseSetOperation")
       // python/ops/set_ops.py.
       // Dimension n contains the set values to be compared, so ranks and the
       // first n-1 dimensions of inputs and output must match.
-      const Dimension* output_rank;
-      const Shape* input0_shape = c->input(0);
+      DimensionHandle output_rank;
+      ShapeHandle input0_shape = c->input(0);
       if (c->RankKnown(input0_shape)) {
         const int32 input0_rank = c->Rank(input0_shape);
         if (input0_rank < 2) {
           return errors::InvalidArgument("Input 0, expected rank >= 2, got ",
                                          input0_rank, ".");
         }
-        const Shape* input1_shape = c->input(1);
+        ShapeHandle input1_shape = c->input(1);
         if (c->RankKnown(input1_shape)) {
           const int32 rank = c->Rank(input1_shape);
           if (input0_rank != rank) {
@@ -83,19 +83,19 @@ REGISTER_OP("DenseToDenseSetOperation")
                                            input0_rank, ", input 1 ", rank,
                                            ".");
           }
-          const Shape* group0_shape;
+          ShapeHandle group0_shape;
           TF_RETURN_IF_ERROR(
               c->Subshape(input0_shape, 0, rank - 1, &group0_shape));
-          const Shape* group1_shape;
+          ShapeHandle group1_shape;
           TF_RETURN_IF_ERROR(
               c->Subshape(input1_shape, 0, rank - 1, &group1_shape));
-          const Shape* unused_shape;
+          ShapeHandle unused_shape;
           TF_RETURN_IF_ERROR(
               c->Merge(group0_shape, group1_shape, &unused_shape));
         }
         output_rank = c->MakeDim(input0_rank);
       } else {
-        const Shape* input1_shape = c->input(1);
+        ShapeHandle input1_shape = c->input(1);
         if (c->RankKnown(input1_shape)) {
           const int32 input1_rank = c->Rank(input1_shape);
           if (input1_rank < 2) {
@@ -107,9 +107,9 @@ REGISTER_OP("DenseToDenseSetOperation")
           output_rank = c->UnknownDim();
         }
       }
-      const Dimension* output_num_elements = c->Dim(input0_shape, 0);
+      DimensionHandle output_num_elements = c->Dim(input0_shape, 0);
       if (!c->ValueKnown(output_num_elements)) {
-        const Shape* input1_shape = c->input(1);
+        ShapeHandle input1_shape = c->input(1);
         output_num_elements = c->Dim(input1_shape, 0);
         if (!c->ValueKnown(output_num_elements)) {
           output_num_elements = c->UnknownDim();
@@ -163,8 +163,8 @@ REGISTER_OP("DenseToSparseSetOperation")
       // python/ops/set_ops.py.
       // Dimension n contains the set values to be compared, so ranks and the
       // first n-1 dimensions of inputs and output must match.
-      const Dimension* output_rank;
-      const Shape* input0_shape = c->input(0);
+      DimensionHandle output_rank;
+      ShapeHandle input0_shape = c->input(0);
       if (c->RankKnown(input0_shape)) {
         const int32 input0_rank = c->Rank(input0_shape);
         if (input0_rank < 2) {
@@ -177,7 +177,7 @@ REGISTER_OP("DenseToSparseSetOperation")
       }
       TF_RETURN_IF_ERROR(
           c->ValidateSparseTensor(c->input(1), c->input(2), c->input(3)));
-      const Dimension* output_num_elements = c->Dim(input0_shape, 0);
+      DimensionHandle output_num_elements = c->Dim(input0_shape, 0);
       if (!c->ValueKnown(output_num_elements)) {
         output_num_elements = c->UnknownDim();
       }
diff --git a/tensorflow/contrib/quantization/ops/array_ops.cc b/tensorflow/contrib/quantization/ops/array_ops.cc
index 7dd64f82e0..ff636c7957 100644
--- a/tensorflow/contrib/quantization/ops/array_ops.cc
+++ b/tensorflow/contrib/quantization/ops/array_ops.cc
@@ -20,7 +20,7 @@ limitations under the License.
 namespace tensorflow {
 
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 REGISTER_OP("QuantizeV2")
     .Input("input: float")
@@ -33,7 +33,7 @@ REGISTER_OP("QuantizeV2")
     .Attr("mode: {'MIN_COMBINED', 'MIN_FIRST'} = 'MIN_COMBINED'")
     .SetShapeFn([](InferenceContext* c) {
       TF_RETURN_IF_ERROR(shape_inference::UnchangedShape(c));
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
       c->set_output(1, c->Scalar());
@@ -110,7 +110,7 @@ REGISTER_OP("Dequantize")
     .Attr("mode: {'MIN_COMBINED', 'MIN_FIRST'} = 'MIN_COMBINED'")
     .SetShapeFn([](InferenceContext* c) {
       TF_RETURN_IF_ERROR(shape_inference::UnchangedShape(c));
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
       return Status::OK();
@@ -168,7 +168,7 @@ REGISTER_OP("QuantizedConcat")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
       TF_RETURN_IF_ERROR(shape_inference::ConcatShape(c));
-      const Shape* unused;
+      ShapeHandle unused;
       for (int i = 2; i < c->num_inputs(); ++i) {
         TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 0, &unused));
       }
diff --git a/tensorflow/contrib/quantization/ops/math_ops.cc b/tensorflow/contrib/quantization/ops/math_ops.cc
index ed0930c2d6..93bb283630 100644
--- a/tensorflow/contrib/quantization/ops/math_ops.cc
+++ b/tensorflow/contrib/quantization/ops/math_ops.cc
@@ -21,7 +21,7 @@ limitations under the License.
 namespace tensorflow {
 
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 REGISTER_OP("QuantizedMatMul")
     .Input("a: T1")
@@ -40,7 +40,7 @@ REGISTER_OP("QuantizedMatMul")
     .Attr("transpose_b: bool = false")
     .SetShapeFn([](InferenceContext* c) {
       TF_RETURN_IF_ERROR(shape_inference::MatMulShape(c));
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 0, &unused));
@@ -82,7 +82,7 @@ REGISTER_OP("QuantizeDownAndShrinkRange")
     .Attr("out_type: quantizedtype")
     .SetShapeFn([](InferenceContext* c) {
       TF_RETURN_IF_ERROR(shape_inference::UnchangedShape(c));
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
       c->set_output(1, c->Scalar());
diff --git a/tensorflow/contrib/quantization/ops/nn_ops.cc b/tensorflow/contrib/quantization/ops/nn_ops.cc
index c33f318c6e..720377043d 100644
--- a/tensorflow/contrib/quantization/ops/nn_ops.cc
+++ b/tensorflow/contrib/quantization/ops/nn_ops.cc
@@ -21,9 +21,9 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 REGISTER_OP("QuantizedAvgPool")
     .Input("input: T")
@@ -38,7 +38,7 @@ REGISTER_OP("QuantizedAvgPool")
     .Attr(GetPaddingAttrString())
     .SetShapeFn([](InferenceContext* c) {
       TF_RETURN_IF_ERROR(shape_inference::AvgPoolShape(c));
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
       c->set_output(1, c->Scalar());
@@ -76,7 +76,7 @@ REGISTER_OP("QuantizedBiasAdd")
     .Attr("out_type: quantizedtype")
     .SetShapeFn([](InferenceContext* c) {
       TF_RETURN_IF_ERROR(shape_inference::BiasAddShape(c));
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 0, &unused));
@@ -117,7 +117,7 @@ REGISTER_OP("QuantizedConv2D")
     .Attr(GetPaddingAttrString())
     .SetShapeFn([](InferenceContext* c) {
       TF_RETURN_IF_ERROR(shape_inference::Conv2DShape(c));
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 0, &unused));
@@ -159,7 +159,7 @@ REGISTER_OP("QuantizedMaxPool")
     .Attr(GetPaddingAttrString())
     .SetShapeFn([](InferenceContext* c) {
       TF_RETURN_IF_ERROR(shape_inference::MaxPoolShape(c));
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
       c->set_output(1, c->Scalar());
@@ -193,7 +193,7 @@ REGISTER_OP("QuantizedRelu")
     .Attr("out_type: quantizedtype = DT_QUINT8")
     .SetShapeFn([](InferenceContext* c) {
       TF_RETURN_IF_ERROR(shape_inference::UnchangedShape(c));
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
       c->set_output(1, c->Scalar());
@@ -222,7 +222,7 @@ REGISTER_OP("QuantizedRelu6")
     .Attr("out_type: quantizedtype = DT_QUINT8")
     .SetShapeFn([](InferenceContext* c) {
       TF_RETURN_IF_ERROR(shape_inference::UnchangedShape(c));
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
       c->set_output(1, c->Scalar());
@@ -252,7 +252,7 @@ REGISTER_OP("QuantizedReluX")
     .Attr("out_type: quantizedtype = DT_QUINT8")
     .SetShapeFn([](InferenceContext* c) {
       TF_RETURN_IF_ERROR(shape_inference::UnchangedShape(c));
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
       c->set_output(1, c->Scalar());
@@ -294,17 +294,17 @@ REGISTER_OP("QuantizedBatchNormWithGlobalNormalization")
     .Attr("variance_epsilon: float")
     .Attr("scale_after_normalization: bool")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
 
-      const Dimension* last_dim = c->Dim(input, 3);
+      DimensionHandle last_dim = c->Dim(input, 3);
       for (int i = 1; i < 5; ++i) {  // covers m, v, beta, gamma
-        const Shape* vec;
+        ShapeHandle vec;
         TF_RETURN_IF_ERROR(c->WithRank(c->input(i * 3), 1, &vec));
         TF_RETURN_IF_ERROR(c->Merge(last_dim, c->Dim(vec, 0), &last_dim));
       }
 
-      const Shape* out;
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->ReplaceDim(input, 3, last_dim, &out));
       c->set_output(0, out);
       c->set_output(1, c->Scalar());
diff --git a/tensorflow/contrib/tensor_forest/core/ops/best_splits_op.cc b/tensorflow/contrib/tensor_forest/core/ops/best_splits_op.cc
index ecb856c2ff..421f371d9e 100644
--- a/tensorflow/contrib/tensor_forest/core/ops/best_splits_op.cc
+++ b/tensorflow/contrib/tensor_forest/core/ops/best_splits_op.cc
@@ -26,9 +26,9 @@
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 using std::placeholders::_1;
 using tensorforest::BestFeatureClassification;
@@ -45,7 +45,7 @@ REGISTER_OP("BestSplits")
     .Input("accumulator_sqaures: float")
     .Output("split_indices: int32")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* finished_nodes;
+      ShapeHandle finished_nodes;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &finished_nodes));
       c->set_output(0, c->Vector(c->Dim(finished_nodes, 0)));
       return Status::OK();
diff --git a/tensorflow/contrib/tensor_forest/core/ops/count_extremely_random_stats_op.cc b/tensorflow/contrib/tensor_forest/core/ops/count_extremely_random_stats_op.cc
index cd224b0cb9..f49afa025c 100644
--- a/tensorflow/contrib/tensor_forest/core/ops/count_extremely_random_stats_op.cc
+++ b/tensorflow/contrib/tensor_forest/core/ops/count_extremely_random_stats_op.cc
@@ -49,9 +49,9 @@ using tensorforest::Initialize;
 using tensorforest::IsAllInitialized;
 using tensorforest::FeatureSpec;
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 // A data structure to store the results of parallel tree traversal.
 struct InputDataResult {
@@ -174,11 +174,11 @@ REGISTER_OP("CountExtremelyRandomStats")
       bool regression;
       TF_RETURN_IF_ERROR(c->GetAttr("regression", &regression));
 
-      const Dimension* num_points = c->Dim(c->input(0), 0);
+      DimensionHandle num_points = c->Dim(c->input(0), 0);
       if (c->RankKnown(c->input(3)) && c->Rank(c->input(3)) > 0) {
         num_points = c->UnknownDim();
       }
-      const Dimension* num_nodes = c->Dim(c->input(7), 0);
+      DimensionHandle num_nodes = c->Dim(c->input(7), 0);
 
       // Node sums
       c->set_output(0, c->Matrix(num_nodes, num_classes));
diff --git a/tensorflow/contrib/tensor_forest/core/ops/sample_inputs_op.cc b/tensorflow/contrib/tensor_forest/core/ops/sample_inputs_op.cc
index 01d3b30abc..06ddb795ce 100644
--- a/tensorflow/contrib/tensor_forest/core/ops/sample_inputs_op.cc
+++ b/tensorflow/contrib/tensor_forest/core/ops/sample_inputs_op.cc
@@ -29,9 +29,9 @@
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 using tensorforest::CheckTensorBounds;
 using tensorforest::IsAllInitialized;
@@ -51,10 +51,10 @@ REGISTER_OP("SampleInputs")
     .Output("new_split_feature_rows: int32")
     .Output("new_split_threshold_rows: float")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* candidate_split_features;
+      ShapeHandle candidate_split_features;
       TF_RETURN_IF_ERROR(
           c->WithRank(c->input(6), 2, &candidate_split_features));
-      const Dimension* split_dim = c->Dim(candidate_split_features, 1);
+      DimensionHandle split_dim = c->Dim(candidate_split_features, 1);
       c->set_output(0, c->Vector(InferenceContext::kUnknownDim));
       c->set_output(1, c->Matrix(InferenceContext::kUnknownDim, split_dim));
       c->set_output(2, c->Matrix(InferenceContext::kUnknownDim, split_dim));
diff --git a/tensorflow/contrib/tensor_forest/core/ops/tree_predictions_op.cc b/tensorflow/contrib/tensor_forest/core/ops/tree_predictions_op.cc
index dab1aca99e..4a81d6d6c6 100644
--- a/tensorflow/contrib/tensor_forest/core/ops/tree_predictions_op.cc
+++ b/tensorflow/contrib/tensor_forest/core/ops/tree_predictions_op.cc
@@ -35,9 +35,9 @@ using tensorforest::DataColumnTypes;
 using tensorforest::FeatureSpec;
 using tensorforest::Sum;
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 REGISTER_OP("TreePredictions")
     .Attr("valid_leaf_threshold: float")
@@ -55,8 +55,8 @@ REGISTER_OP("TreePredictions")
     .SetShapeFn([](InferenceContext* c) {
       // The output of TreePredictions is
       // [node_pcw(evaluate_tree(x), c) for c in classes for x in input_data].
-      const Dimension* num_points = c->Dim(c->input(0), 0);
-      const Dimension* num_classes = c->Dim(c->input(7), 1);
+      DimensionHandle num_points = c->Dim(c->input(0), 0);
+      DimensionHandle num_classes = c->Dim(c->input(7), 1);
 
       if (c->RankKnown(c->input(3)) && c->Rank(c->input(3)) > 0) {
         num_points = c->UnknownDim();
diff --git a/tensorflow/core/framework/common_shape_fns.cc b/tensorflow/core/framework/common_shape_fns.cc
index 090d9804e2..c345d3c742 100644
--- a/tensorflow/core/framework/common_shape_fns.cc
+++ b/tensorflow/core/framework/common_shape_fns.cc
@@ -75,22 +75,22 @@ Status UnchangedShape(shape_inference::InferenceContext* c) {
 }
 
 Status MatMulShape(shape_inference::InferenceContext* c) {
-  const Shape* a;
+  ShapeHandle a;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &a));
 
-  const Shape* b;
+  ShapeHandle b;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &b));
 
   bool transpose_a, transpose_b;
   TF_RETURN_IF_ERROR(c->GetAttr("transpose_a", &transpose_a));
   TF_RETURN_IF_ERROR(c->GetAttr("transpose_b", &transpose_b));
-  const Dimension* output_rows = transpose_a ? c->Dim(a, 1) : c->Dim(a, 0);
-  const Dimension* output_cols = transpose_b ? c->Dim(b, 0) : c->Dim(b, 1);
+  DimensionHandle output_rows = transpose_a ? c->Dim(a, 1) : c->Dim(a, 0);
+  DimensionHandle output_cols = transpose_b ? c->Dim(b, 0) : c->Dim(b, 1);
 
   // Validate that the inner shapes are compatible.
-  const Dimension* inner_a = transpose_a ? c->Dim(a, 0) : c->Dim(a, 1);
-  const Dimension* inner_b = transpose_b ? c->Dim(b, 1) : c->Dim(b, 0);
-  const Dimension* merged;
+  DimensionHandle inner_a = transpose_a ? c->Dim(a, 0) : c->Dim(a, 1);
+  DimensionHandle inner_b = transpose_b ? c->Dim(b, 1) : c->Dim(b, 0);
+  DimensionHandle merged;
   TF_RETURN_IF_ERROR(c->Merge(inner_a, inner_b, &merged));
 
   c->set_output(0, c->Matrix(output_rows, output_cols));
@@ -98,7 +98,7 @@ Status MatMulShape(shape_inference::InferenceContext* c) {
 }
 
 Status BiasAddShape(shape_inference::InferenceContext* c) {
-  const Shape* input_shape;
+  ShapeHandle input_shape;
 
   // Fetch the data_format attribute, which may not exist.
   string data_format;
@@ -110,9 +110,9 @@ Status BiasAddShape(shape_inference::InferenceContext* c) {
     TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 2, &input_shape));
   }
 
-  const Shape* bias_shape;
+  ShapeHandle bias_shape;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &bias_shape));
-  const Dimension* bias_dim = c->Dim(bias_shape, 0);
+  DimensionHandle bias_dim = c->Dim(bias_shape, 0);
 
   // If rank unknown, return unknown shape.
   if (!c->RankKnown(input_shape)) {
@@ -122,32 +122,32 @@ Status BiasAddShape(shape_inference::InferenceContext* c) {
 
   // Output has the same shape as the input, and matches the length of
   // the bias in its bias dimension.
-  const Shape* output_shape;
+  ShapeHandle output_shape;
   if (s.ok() && data_format == "NCHW") {
     // Merge the length of bias_shape into the third to last dimension
-    const Shape* first;
+    ShapeHandle first;
     TF_RETURN_IF_ERROR(c->Subshape(input_shape, 0, -3, &first));
 
-    const Shape* last;
+    ShapeHandle last;
     TF_RETURN_IF_ERROR(c->Subshape(input_shape, -2, &last));
 
-    const Dimension* input_bias_dim = c->Dim(input_shape, -3);
-    const Dimension* merged_bias_dim;
+    DimensionHandle input_bias_dim = c->Dim(input_shape, -3);
+    DimensionHandle merged_bias_dim;
     TF_RETURN_IF_ERROR(c->Merge(input_bias_dim, bias_dim, &merged_bias_dim));
-    const Shape* merged_bias = c->Vector(merged_bias_dim);
+    ShapeHandle merged_bias = c->Vector(merged_bias_dim);
 
-    const Shape* temp;
+    ShapeHandle temp;
     TF_RETURN_IF_ERROR(c->Concatenate(first, merged_bias, &temp));
     TF_RETURN_IF_ERROR(c->Concatenate(temp, last, &output_shape));
   } else {
-    const Shape* all_but_bias;
+    ShapeHandle all_but_bias;
     TF_RETURN_IF_ERROR(c->Subshape(input_shape, 0, -1, &all_but_bias));
 
-    const Dimension* input_bias_dim = c->Dim(input_shape, -1);
-    const Dimension* merged_bias_dim;
+    DimensionHandle input_bias_dim = c->Dim(input_shape, -1);
+    DimensionHandle merged_bias_dim;
     TF_RETURN_IF_ERROR(c->Merge(input_bias_dim, bias_dim, &merged_bias_dim));
 
-    const Shape* merged_bias = c->Vector(merged_bias_dim);
+    ShapeHandle merged_bias = c->Vector(merged_bias_dim);
     TF_RETURN_IF_ERROR(
         c->Concatenate(all_but_bias, merged_bias, &output_shape));
   }
@@ -157,7 +157,7 @@ Status BiasAddShape(shape_inference::InferenceContext* c) {
 }
 
 Status BiasAddGradShape(shape_inference::InferenceContext* c) {
-  const Shape* input_shape;
+  ShapeHandle input_shape;
   // Fetch the data_format attribute, which may not exist.
   string data_format;
   Status s = c->GetAttr("data_format", &data_format);
@@ -174,9 +174,9 @@ Status BiasAddGradShape(shape_inference::InferenceContext* c) {
 }
 
 Status Conv2DShape(shape_inference::InferenceContext* c) {
-  const Shape* input_shape;
+  ShapeHandle input_shape;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input_shape));
-  const Shape* filter_shape;
+  ShapeHandle filter_shape;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 4, &filter_shape));
 
   string data_format;
@@ -205,12 +205,12 @@ Status Conv2DShape(shape_inference::InferenceContext* c) {
     stride_cols = strides[2];
   }
 
-  const Dimension* batch_size_dim = c->Dim(input_shape, 0);
-  const Dimension* in_rows_dim = c->Dim(input_shape, 1);
-  const Dimension* in_cols_dim = c->Dim(input_shape, 2);
-  const Dimension* filter_rows_dim = c->Dim(filter_shape, 0);
-  const Dimension* filter_cols_dim = c->Dim(filter_shape, 1);
-  const Dimension* output_depth_dim = c->Dim(filter_shape, 3);
+  DimensionHandle batch_size_dim = c->Dim(input_shape, 0);
+  DimensionHandle in_rows_dim = c->Dim(input_shape, 1);
+  DimensionHandle in_cols_dim = c->Dim(input_shape, 2);
+  DimensionHandle filter_rows_dim = c->Dim(filter_shape, 0);
+  DimensionHandle filter_cols_dim = c->Dim(filter_shape, 1);
+  DimensionHandle output_depth_dim = c->Dim(filter_shape, 3);
 
   // At the moment we need to know the values of several fields.
   TF_RETURN_IF_ERROR(c->ValidateKnownDim(in_rows_dim, "in_rows"));
@@ -223,7 +223,7 @@ Status Conv2DShape(shape_inference::InferenceContext* c) {
   auto filter_rows = c->Value(filter_rows_dim);
   auto filter_cols = c->Value(filter_cols_dim);
 
-  const Dimension* unused;
+  DimensionHandle unused;
   TF_RETURN_IF_ERROR(
       c->Merge(c->Dim(input_shape, 3), c->Dim(filter_shape, 2), &unused));
 
@@ -239,7 +239,7 @@ Status Conv2DShape(shape_inference::InferenceContext* c) {
       in_cols, filter_cols, stride_cols, padding, &output_cols, &padding_before,
       &padding_after));
 
-  const Shape* output_shape;
+  ShapeHandle output_shape;
   if (data_format == "NCHW") {
     output_shape = c->MakeShape(
         {batch_size_dim, output_depth_dim, output_rows, output_cols});
@@ -253,9 +253,9 @@ Status Conv2DShape(shape_inference::InferenceContext* c) {
 }
 
 Status Conv3DShape(shape_inference::InferenceContext* c) {
-  const Shape* input_shape;
+  ShapeHandle input_shape;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 5, &input_shape));
-  const Shape* filter_shape;
+  ShapeHandle filter_shape;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 5, &filter_shape));
 
   std::vector<int32> strides;
@@ -270,15 +270,15 @@ Status Conv3DShape(shape_inference::InferenceContext* c) {
   int32 stride_rows = strides[2];
   int32 stride_cols = strides[3];
 
-  const Dimension* batch_size_dim = c->Dim(input_shape, 0);
-  const Dimension* in_planes_dim = c->Dim(input_shape, 1);
-  const Dimension* in_rows_dim = c->Dim(input_shape, 2);
-  const Dimension* in_cols_dim = c->Dim(input_shape, 3);
+  DimensionHandle batch_size_dim = c->Dim(input_shape, 0);
+  DimensionHandle in_planes_dim = c->Dim(input_shape, 1);
+  DimensionHandle in_rows_dim = c->Dim(input_shape, 2);
+  DimensionHandle in_cols_dim = c->Dim(input_shape, 3);
 
-  const Dimension* filter_planes_dim = c->Dim(filter_shape, 0);
-  const Dimension* filter_rows_dim = c->Dim(filter_shape, 1);
-  const Dimension* filter_cols_dim = c->Dim(filter_shape, 2);
-  const Dimension* output_depth_dim = c->Dim(filter_shape, 4);
+  DimensionHandle filter_planes_dim = c->Dim(filter_shape, 0);
+  DimensionHandle filter_rows_dim = c->Dim(filter_shape, 1);
+  DimensionHandle filter_cols_dim = c->Dim(filter_shape, 2);
+  DimensionHandle output_depth_dim = c->Dim(filter_shape, 4);
 
   // At the moment we need to know the values of several fields.
   TF_RETURN_IF_ERROR(c->ValidateKnownDim(in_planes_dim, "in_planes"));
@@ -295,7 +295,7 @@ Status Conv3DShape(shape_inference::InferenceContext* c) {
   auto filter_rows = c->Value(filter_rows_dim);
   auto filter_cols = c->Value(filter_cols_dim);
 
-  const Dimension* unused;
+  DimensionHandle unused;
   TF_RETURN_IF_ERROR(
       c->Merge(c->Dim(input_shape, 4), c->Dim(filter_shape, 3), &unused));
 
@@ -314,7 +314,7 @@ Status Conv3DShape(shape_inference::InferenceContext* c) {
       in_cols, filter_cols, stride_cols, padding, &output_cols, &padding_before,
       &padding_after));
 
-  const Shape* output_shape =
+  ShapeHandle output_shape =
       c->MakeShape({batch_size_dim, output_planes, output_rows, output_cols,
                     output_depth_dim});
   c->set_output(0, output_shape);
@@ -322,9 +322,9 @@ Status Conv3DShape(shape_inference::InferenceContext* c) {
 }
 
 Status DepthwiseConv2DNativeShape(shape_inference::InferenceContext* c) {
-  const Shape* input_shape;
+  ShapeHandle input_shape;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input_shape));
-  const Shape* filter_shape;
+  ShapeHandle filter_shape;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 4, &filter_shape));
 
   std::vector<int32> strides;
@@ -337,13 +337,13 @@ Status DepthwiseConv2DNativeShape(shape_inference::InferenceContext* c) {
         strides.size());
   }
 
-  const Dimension* batch_size_dim = c->Dim(input_shape, 0);
-  const Dimension* in_rows_dim = c->Dim(input_shape, 1);
-  const Dimension* in_cols_dim = c->Dim(input_shape, 2);
-  const Dimension* filter_rows_dim = c->Dim(filter_shape, 0);
-  const Dimension* filter_cols_dim = c->Dim(filter_shape, 1);
-  const Dimension* input_depth = c->Dim(filter_shape, 2);
-  const Dimension* depth_multiplier = c->Dim(filter_shape, 3);
+  DimensionHandle batch_size_dim = c->Dim(input_shape, 0);
+  DimensionHandle in_rows_dim = c->Dim(input_shape, 1);
+  DimensionHandle in_cols_dim = c->Dim(input_shape, 2);
+  DimensionHandle filter_rows_dim = c->Dim(filter_shape, 0);
+  DimensionHandle filter_cols_dim = c->Dim(filter_shape, 1);
+  DimensionHandle input_depth = c->Dim(filter_shape, 2);
+  DimensionHandle depth_multiplier = c->Dim(filter_shape, 3);
 
   // At the moment we need to know the values of several fields.
   TF_RETURN_IF_ERROR(c->ValidateKnownDim(in_rows_dim, "in_rows"));
@@ -357,7 +357,7 @@ Status DepthwiseConv2DNativeShape(shape_inference::InferenceContext* c) {
   TF_RETURN_IF_ERROR(
       c->Merge(c->Dim(input_shape, 3), input_depth, &input_depth));
 
-  const Dimension* output_depth;
+  DimensionHandle output_depth;
   TF_RETURN_IF_ERROR(c->Multiply(input_depth, depth_multiplier, &output_depth));
 
   const int32 stride_rows = strides[1];
@@ -383,14 +383,14 @@ Status DepthwiseConv2DNativeShape(shape_inference::InferenceContext* c) {
       in_cols, filter_cols, stride_cols, padding, &output_cols, &padding_before,
       &padding_after));
 
-  const Shape* output_shape =
+  ShapeHandle output_shape =
       c->MakeShape({batch_size_dim, output_rows, output_cols, output_depth});
   c->set_output(0, output_shape);
   return Status::OK();
 }
 
 Status AvgPoolShape(shape_inference::InferenceContext* c) {
-  const Shape* input_shape;
+  ShapeHandle input_shape;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input_shape));
 
   string data_format;
@@ -432,10 +432,10 @@ Status AvgPoolShape(shape_inference::InferenceContext* c) {
     kernel_cols = kernel_sizes[2];
   }
 
-  const Dimension* batch_size_dim = c->Dim(input_shape, 0);
-  const Dimension* in_rows_dim = c->Dim(input_shape, 1);
-  const Dimension* in_cols_dim = c->Dim(input_shape, 2);
-  const Dimension* output_depth_dim = c->Dim(input_shape, 3);
+  DimensionHandle batch_size_dim = c->Dim(input_shape, 0);
+  DimensionHandle in_rows_dim = c->Dim(input_shape, 1);
+  DimensionHandle in_cols_dim = c->Dim(input_shape, 2);
+  DimensionHandle output_depth_dim = c->Dim(input_shape, 3);
 
   // At the moment we need to know the values of several fields.
   TF_RETURN_IF_ERROR(c->ValidateKnownDim(in_rows_dim, "in_rows"));
@@ -459,7 +459,7 @@ Status AvgPoolShape(shape_inference::InferenceContext* c) {
       in_cols, kernel_cols, stride_cols, padding, &output_cols, &padding_before,
       &padding_after));
 
-  const Shape* output_shape;
+  ShapeHandle output_shape;
   if (data_format == "NCHW") {
     output_shape = c->MakeShape(
         {batch_size_dim, output_depth_dim, output_rows, output_cols});
@@ -473,7 +473,7 @@ Status AvgPoolShape(shape_inference::InferenceContext* c) {
 }
 
 Status MaxPoolShape(shape_inference::InferenceContext* c) {
-  const Shape* input_shape;
+  ShapeHandle input_shape;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input_shape));
 
   string data_format;
@@ -519,10 +519,10 @@ Status MaxPoolShape(shape_inference::InferenceContext* c) {
     kernel_depth = kernel_sizes[3];
   }
 
-  const Dimension* batch_size_dim = c->Dim(input_shape, 0);
-  const Dimension* in_rows_dim = c->Dim(input_shape, 1);
-  const Dimension* in_cols_dim = c->Dim(input_shape, 2);
-  const Dimension* in_depth_dim = c->Dim(input_shape, 3);
+  DimensionHandle batch_size_dim = c->Dim(input_shape, 0);
+  DimensionHandle in_rows_dim = c->Dim(input_shape, 1);
+  DimensionHandle in_cols_dim = c->Dim(input_shape, 2);
+  DimensionHandle in_depth_dim = c->Dim(input_shape, 3);
 
   // At the moment we need to know the values of several fields.
   TF_RETURN_IF_ERROR(c->ValidateKnownDim(in_rows_dim, "in_rows"));
@@ -551,7 +551,7 @@ Status MaxPoolShape(shape_inference::InferenceContext* c) {
       in_depth, kernel_depth, stride_depth, padding, &output_depth,
       &padding_before, &padding_after));
 
-  const Shape* output_shape =
+  ShapeHandle output_shape =
       c->MakeShape({batch_size_dim, output_rows, output_cols, output_depth});
 
   if (data_format == "NCHW") {
@@ -566,7 +566,7 @@ Status MaxPoolShape(shape_inference::InferenceContext* c) {
 }
 
 Status Pool3DShape(shape_inference::InferenceContext* c) {
-  const Shape* input_shape;
+  ShapeHandle input_shape;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 5, &input_shape));
 
   std::vector<int32> strides;
@@ -596,11 +596,11 @@ Status Pool3DShape(shape_inference::InferenceContext* c) {
   kernel_rows = kernel_sizes[2];
   kernel_cols = kernel_sizes[3];
 
-  const Dimension* batch_size_dim = c->Dim(input_shape, 0);
-  const Dimension* in_planes_dim = c->Dim(input_shape, 1);
-  const Dimension* in_rows_dim = c->Dim(input_shape, 2);
-  const Dimension* in_cols_dim = c->Dim(input_shape, 3);
-  const Dimension* output_depth_dim = c->Dim(input_shape, 4);
+  DimensionHandle batch_size_dim = c->Dim(input_shape, 0);
+  DimensionHandle in_planes_dim = c->Dim(input_shape, 1);
+  DimensionHandle in_rows_dim = c->Dim(input_shape, 2);
+  DimensionHandle in_cols_dim = c->Dim(input_shape, 3);
+  DimensionHandle output_depth_dim = c->Dim(input_shape, 4);
 
   // At the moment we need to know the values of several fields.
   TF_RETURN_IF_ERROR(c->ValidateKnownDim(in_planes_dim, "in_planes"));
@@ -629,7 +629,7 @@ Status Pool3DShape(shape_inference::InferenceContext* c) {
       in_cols, kernel_cols, stride_cols, padding, &output_cols, &padding_before,
       &padding_after));
 
-  const Shape* output_shape =
+  ShapeHandle output_shape =
       c->MakeShape({batch_size_dim, output_planes, output_rows, output_cols,
                     output_depth_dim});
 
@@ -645,9 +645,9 @@ Status UnknownShape(shape_inference::InferenceContext* c) {
 }
 
 Status ReductionShape(InferenceContext* c) {
-  const Shape* input = c->input(0);
+  ShapeHandle input = c->input(0);
 
-  const Shape* indices;
+  ShapeHandle indices;
   TF_RETURN_IF_ERROR(c->WithRankAtMost(c->input(1), 1, &indices));
 
   const Tensor* reduction_indices_t = c->input_tensor(1);
@@ -679,7 +679,7 @@ Status ReductionShape(InferenceContext* c) {
       wrapped_index += input_rank;
     }
 
-    const Dimension* reduce_dim = c->Dim(input, wrapped_index);
+    DimensionHandle reduce_dim = c->Dim(input, wrapped_index);
     if (c->ValueKnown(reduce_dim) && c->Value(reduce_dim) == 0) {
       return errors::InvalidArgument("Cannot reduce dimension ",
                                      reduction_index, " with size 0");
@@ -688,7 +688,7 @@ Status ReductionShape(InferenceContext* c) {
     true_indices.insert(wrapped_index);
   }
 
-  std::vector<const Dimension*> dims;
+  std::vector<DimensionHandle> dims;
   bool reduce_all = reduction_indices_t->NumElements() == 0;
   for (int i = 0; i < input_rank; ++i) {
     if (reduce_all || true_indices.count(i) > 0) {
@@ -705,7 +705,7 @@ Status ReductionShape(InferenceContext* c) {
 }
 
 Status ConcatShape(InferenceContext* c) {
-  const Shape* unused;
+  ShapeHandle unused;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
 
   const Tensor* concat_dim_t = c->input_tensor(0);
@@ -730,7 +730,7 @@ Status ConcatShape(InferenceContext* c) {
           "Can't concatenate scalars (use tf.pack instead)");
     }
     // Build result of <rank> different unknown dims.
-    std::vector<const Dimension*> dims;
+    std::vector<DimensionHandle> dims;
     for (int i = 0; i < rank; ++i) dims.push_back(c->UnknownDim());
     c->set_output(0, c->MakeShape(dims));
     return Status::OK();
@@ -744,22 +744,22 @@ Status ConcatShape(InferenceContext* c) {
                                    concat_dim);
   }
 
-  const Shape* output_before;
-  const Shape* output_after;
+  ShapeHandle output_before;
+  ShapeHandle output_after;
 
-  const Shape* input = c->input(c->num_inputs() - 1);
+  ShapeHandle input = c->input(c->num_inputs() - 1);
   TF_RETURN_IF_ERROR(c->WithRankAtLeast(input, concat_dim + 1, &input));
   TF_RETURN_IF_ERROR(c->Subshape(input, 0, concat_dim, &output_before));
-  const Dimension* output_middle = c->Dim(input, concat_dim);
+  DimensionHandle output_middle = c->Dim(input, concat_dim);
   TF_RETURN_IF_ERROR(c->Subshape(input, concat_dim + 1, &output_after));
 
   for (int i = c->num_inputs() - 2; i > 0; --i) {
-    const Shape* before;
-    const Shape* after;
+    ShapeHandle before;
+    ShapeHandle after;
     input = c->input(i);
     TF_RETURN_IF_ERROR(c->WithRankAtLeast(input, concat_dim + 1, &input));
     TF_RETURN_IF_ERROR(c->Subshape(input, 0, concat_dim, &before));
-    const Dimension* middle = c->Dim(input, concat_dim);
+    DimensionHandle middle = c->Dim(input, concat_dim);
     TF_RETURN_IF_ERROR(c->Subshape(input, concat_dim + 1, &after));
 
     TF_RETURN_IF_ERROR(c->Merge(before, output_before, &output_before));
@@ -767,7 +767,7 @@ Status ConcatShape(InferenceContext* c) {
     TF_RETURN_IF_ERROR(c->Merge(after, output_after, &output_after));
   }
 
-  const Shape* s;
+  ShapeHandle s;
   TF_RETURN_IF_ERROR(
       c->Concatenate(output_before, c->Vector(output_middle), &s));
   TF_RETURN_IF_ERROR(c->Concatenate(s, output_after, &s));
diff --git a/tensorflow/core/framework/common_shape_fns.h b/tensorflow/core/framework/common_shape_fns.h
index 71692cec1e..b828b23dfe 100644
--- a/tensorflow/core/framework/common_shape_fns.h
+++ b/tensorflow/core/framework/common_shape_fns.h
@@ -102,7 +102,7 @@ Status UnchangedShape(shape_inference::InferenceContext* c);
 // Transfers shape of input(0) to output(0), after asserting its rank is <rank>.
 inline Status UnchangedShapeWithRank(shape_inference::InferenceContext* c,
                                      int32 rank) {
-  const Shape* out;
+  ShapeHandle out;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(0), rank, &out));
   c->set_output(0, out);
   return Status::OK();
@@ -111,7 +111,7 @@ inline Status UnchangedShapeWithRank(shape_inference::InferenceContext* c,
 // Transfers shape of input(0) to output(0), after asserting its rank >= <rank>.
 inline Status UnchangedShapeWithRankAtLeast(
     shape_inference::InferenceContext* c, int32 rank) {
-  const Shape* out;
+  ShapeHandle out;
   TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), rank, &out));
   c->set_output(0, out);
   return Status::OK();
@@ -120,7 +120,7 @@ inline Status UnchangedShapeWithRankAtLeast(
 // Transfers shape of input(0) to output(0), after asserting its rank <= <rank>.
 inline Status UnchangedShapeWithRankAtMost(shape_inference::InferenceContext* c,
                                            int32 rank) {
-  const Shape* out;
+  ShapeHandle out;
   TF_RETURN_IF_ERROR(c->WithRankAtMost(c->input(0), rank, &out));
   c->set_output(0, out);
   return Status::OK();
@@ -139,7 +139,7 @@ inline Status ScalarShape(shape_inference::InferenceContext* c) {
 
 // Shape function for binary ops where both inputs and the output match.
 inline Status MergeBothInputsShapeFn(InferenceContext* c) {
-  const Shape* out;
+  ShapeHandle out;
   TF_RETURN_IF_ERROR(c->Merge(c->input(0), c->input(1), &out));
   c->set_output(0, out);
   return Status::OK();
diff --git a/tensorflow/core/framework/common_shape_fns_test.cc b/tensorflow/core/framework/common_shape_fns_test.cc
index e12a33adf4..45968fda73 100644
--- a/tensorflow/core/framework/common_shape_fns_test.cc
+++ b/tensorflow/core/framework/common_shape_fns_test.cc
@@ -58,14 +58,14 @@ TEST(CommonShapeFnsTest, ScalarShapeTest) {
   {
     InferenceContext c(&def, op_def, {"[]"}, {});
     TF_EXPECT_OK(ScalarShape(&c));
-    const Shape* output = c.output(0);
+    ShapeHandle output = c.output(0);
     EXPECT_EQ(0, c.Rank(output));
   }
 
   {
     InferenceContext c(&def, op_def, {"[1,23,4,4,2]"}, {});
     TF_EXPECT_OK(ScalarShape(&c));
-    const Shape* output = c.output(0);
+    ShapeHandle output = c.output(0);
     EXPECT_EQ(0, c.Rank(output));
   }
 }
@@ -92,7 +92,7 @@ TEST(CommonShapeFnsTest, MatMulShapeTest) {
   {
     InferenceContext c(&def, op_def, {"[2,3]", "[3,4]"}, {});
     TF_EXPECT_OK(MatMulShape(&c));
-    const Shape* output = c.output(0);
+    ShapeHandle output = c.output(0);
     EXPECT_EQ(2, c.Value(c.Dim(output, 0)));
     EXPECT_EQ(4, c.Value(c.Dim(output, 1)));
   }
@@ -101,7 +101,7 @@ TEST(CommonShapeFnsTest, MatMulShapeTest) {
     // Unknown inner dimension for one
     InferenceContext c(&def, op_def, {"[2,?]", "[3,4]"}, {});
     TF_EXPECT_OK(MatMulShape(&c));
-    const Shape* output = c.output(0);
+    ShapeHandle output = c.output(0);
     EXPECT_EQ(2, c.Value(c.Dim(output, 0)));
     EXPECT_EQ(4, c.Value(c.Dim(output, 1)));
   }
@@ -119,7 +119,7 @@ TEST(CommonShapeFnsTest, MatMulShapeTest) {
     // Unknown outer dimension
     InferenceContext c(&def, op_def, {"[2,3]", "[3,?]"}, {});
     TF_EXPECT_OK(MatMulShape(&c));
-    const Shape* output = c.output(0);
+    ShapeHandle output = c.output(0);
     EXPECT_EQ(2, c.Value(c.Dim(output, 0)));
     EXPECT_FALSE(c.ValueKnown(c.Dim(output, 1)));
   }
@@ -154,7 +154,7 @@ TEST(CommonShapeFnsTest, MatMulShapeTest) {
 
     InferenceContext c(&def, op_def, {"[3,2]", "[3,4]"}, {});
     auto s = MatMulShape(&c);
-    const Shape* output = c.output(0);
+    ShapeHandle output = c.output(0);
     EXPECT_EQ(2, c.Value(c.Dim(output, 0)));
     EXPECT_EQ(4, c.Value(c.Dim(output, 1)));
   }
@@ -171,7 +171,7 @@ TEST(CommonShapeFnsTest, MatMulShapeTest) {
 
     InferenceContext c(&def, op_def, {"[2,3]", "[4,3]"}, {});
     auto s = MatMulShape(&c);
-    const Shape* output = c.output(0);
+    ShapeHandle output = c.output(0);
     EXPECT_EQ(2, c.Value(c.Dim(output, 0)));
     EXPECT_EQ(4, c.Value(c.Dim(output, 1)));
   }
@@ -195,7 +195,7 @@ TEST(CommonShapeFnsTest, BiasAddShapeTest) {
   {
     InferenceContext c(&def, op_def, {"[2,10]", "[10]"}, {});
     TF_EXPECT_OK(BiasAddShape(&c));
-    const Shape* output = c.output(0);
+    ShapeHandle output = c.output(0);
     EXPECT_EQ(2, c.Value(c.Dim(output, 0)));
     EXPECT_EQ(10, c.Value(c.Dim(output, 1)));
   }
@@ -204,7 +204,7 @@ TEST(CommonShapeFnsTest, BiasAddShapeTest) {
     // Unknown ranks.
     InferenceContext c(&def, op_def, {"?", "?"}, {});
     TF_EXPECT_OK(BiasAddShape(&c));
-    const Shape* output = c.output(0);
+    ShapeHandle output = c.output(0);
     EXPECT_FALSE(c.RankKnown(output));
   }
 
@@ -212,7 +212,7 @@ TEST(CommonShapeFnsTest, BiasAddShapeTest) {
     // Rank > 2
     InferenceContext c(&def, op_def, {"[4,3,4,2,15]", "[15]"}, {});
     TF_EXPECT_OK(BiasAddShape(&c));
-    const Shape* output = c.output(0);
+    ShapeHandle output = c.output(0);
     EXPECT_EQ("[4,3,4,2,15]", c.DebugString(output));
   }
 
@@ -225,7 +225,7 @@ TEST(CommonShapeFnsTest, BiasAddShapeTest) {
                     .Finalize(&def));
     InferenceContext c(&def, op_def, {"[2,3,4,5]", "[3]"}, {});
     TF_EXPECT_OK(BiasAddShape(&c));
-    const Shape* output = c.output(0);
+    ShapeHandle output = c.output(0);
     EXPECT_EQ("[2,3,4,5]", c.DebugString(output));
   }
 
@@ -238,7 +238,7 @@ TEST(CommonShapeFnsTest, BiasAddShapeTest) {
                     .Finalize(&def));
     InferenceContext c(&def, op_def, {"[8,6,4,2,3,4,5]", "[3]"}, {});
     TF_EXPECT_OK(BiasAddShape(&c));
-    const Shape* output = c.output(0);
+    ShapeHandle output = c.output(0);
     EXPECT_EQ("[8,6,4,2,3,4,5]", c.DebugString(output));
   }
 
@@ -277,7 +277,7 @@ TEST(CommonShapeFnsTest, BiasAddGradShapeTest) {
   {
     InferenceContext c(&def, op_def, {"[2,10]"}, {});
     TF_EXPECT_OK(BiasAddGradShape(&c));
-    const Shape* output = c.output(0);
+    ShapeHandle output = c.output(0);
     EXPECT_EQ(10, c.Value(c.Dim(output, 0)));
   }
 
@@ -285,7 +285,7 @@ TEST(CommonShapeFnsTest, BiasAddGradShapeTest) {
     // Rank > 2
     InferenceContext c(&def, op_def, {"[5,7,2,10]"}, {});
     TF_EXPECT_OK(BiasAddGradShape(&c));
-    const Shape* output = c.output(0);
+    ShapeHandle output = c.output(0);
     EXPECT_EQ(10, c.Value(c.Dim(output, 0)));
   }
 
@@ -297,7 +297,7 @@ TEST(CommonShapeFnsTest, BiasAddGradShapeTest) {
                     .Finalize(&def));
     InferenceContext c(&def, op_def, {"[2,3,4,5]"}, {});
     TF_EXPECT_OK(BiasAddGradShape(&c));
-    const Shape* output = c.output(0);
+    ShapeHandle output = c.output(0);
     EXPECT_EQ(3, c.Value(c.Dim(output, 0)));
   }
 
@@ -309,7 +309,7 @@ TEST(CommonShapeFnsTest, BiasAddGradShapeTest) {
                     .Finalize(&def));
     InferenceContext c(&def, op_def, {"[8,6,4,2,3,4,5]"}, {});
     TF_EXPECT_OK(BiasAddGradShape(&c));
-    const Shape* output = c.output(0);
+    ShapeHandle output = c.output(0);
     EXPECT_EQ(3, c.Value(c.Dim(output, 0)));
   }
 
diff --git a/tensorflow/core/framework/shape_inference.cc b/tensorflow/core/framework/shape_inference.cc
index c6da445165..90b3a6a688 100644
--- a/tensorflow/core/framework/shape_inference.cc
+++ b/tensorflow/core/framework/shape_inference.cc
@@ -35,7 +35,7 @@ InferenceContext::InferenceContext(
   PreInputInit(op_def, input_tensors);
 
   for (const string& spec : input_shapes) {
-    const Shape* shape;
+    ShapeHandle shape;
     construction_status_.Update(MakeShapeFromString(spec, &shape));
     if (!construction_status_.ok()) {
       return;
@@ -55,7 +55,7 @@ InferenceContext::InferenceContext(
   PreInputInit(op_def, input_tensors);
   if (!construction_status_.ok()) return;
   for (const TensorShapeProto& p : input_shapes) {
-    const Shape* shape;
+    ShapeHandle shape;
     construction_status_.Update(MakeShapeFromShapeProto(p, &shape));
     if (!construction_status_.ok()) {
       return;
@@ -68,7 +68,7 @@ InferenceContext::InferenceContext(
 InferenceContext::InferenceContext(
     const NodeDef* node_def, const OpDef& op_def,
     const std::vector<string>& input_shapes_string,
-    const std::vector<const Shape*>& input_shapes,
+    const std::vector<ShapeHandle>& input_shapes,
     const std::vector<const Tensor*>& input_tensors)
     : node_def_(*CHECK_NOTNULL(node_def)) {
   PreInputInit(op_def, input_tensors);
@@ -118,7 +118,7 @@ void InferenceContext::PostInputInit() {
   requested_input_tensor_.resize(inputs_.size());
 }
 
-bool InferenceContext::FullyDefined(const Shape* s) {
+bool InferenceContext::FullyDefined(ShapeHandle s) {
   if (!RankKnown(s)) return false;
   for (int i = 0; i < Rank(s); ++i) {
     if (!ValueKnown(Dim(s, i))) return false;
@@ -126,7 +126,7 @@ bool InferenceContext::FullyDefined(const Shape* s) {
   return true;
 }
 
-const Dimension* InferenceContext::NumElements(const Shape* s) {
+DimensionHandle InferenceContext::NumElements(ShapeHandle s) {
   const auto rank = Rank(s);
   if (rank == kUnknownRank) return UnknownDim();
   int64 size = 1;
@@ -138,7 +138,7 @@ const Dimension* InferenceContext::NumElements(const Shape* s) {
   return MakeDim(size);
 }
 
-string InferenceContext::DebugString(const Shape* s) {
+string InferenceContext::DebugString(ShapeHandle s) {
   if (RankKnown(s)) {
     std::vector<string> vals;
     for (auto d : s->dims_) vals.push_back(DebugString(d));
@@ -148,19 +148,19 @@ string InferenceContext::DebugString(const Shape* s) {
   }
 }
 
-string InferenceContext::DebugString(const Dimension* d) {
+string InferenceContext::DebugString(DimensionHandle d) {
   return ValueKnown(d) ? strings::StrCat(Value(d)) : "?";
 }
 
-Status InferenceContext::WithRank(const Shape* shape, int32 rank,
-                                  const Shape** out) {
+Status InferenceContext::WithRank(ShapeHandle shape, int32 rank,
+                                  ShapeHandle* out) {
   const int32 existing = Rank(shape);
   if (existing == rank) {
     *out = shape;
     return Status::OK();
   }
   if (existing == kUnknownRank) {
-    std::vector<const Dimension*> dims;
+    std::vector<DimensionHandle> dims;
     dims.reserve(rank);
     for (int i = 0; i < rank; ++i) {
       all_dims_.push_back(new Dimension());
@@ -175,8 +175,8 @@ Status InferenceContext::WithRank(const Shape* shape, int32 rank,
                                  existing);
 }
 
-Status InferenceContext::WithRankAtLeast(const Shape* shape, int32 rank,
-                                         const Shape** out) {
+Status InferenceContext::WithRankAtLeast(ShapeHandle shape, int32 rank,
+                                         ShapeHandle* out) {
   const int32 existing = Rank(shape);
   if (existing >= rank) {
     *out = shape;
@@ -190,8 +190,8 @@ Status InferenceContext::WithRankAtLeast(const Shape* shape, int32 rank,
                                  " but is rank ", existing);
 }
 
-Status InferenceContext::WithRankAtMost(const Shape* shape, int32 rank,
-                                        const Shape** out) {
+Status InferenceContext::WithRankAtMost(ShapeHandle shape, int32 rank,
+                                        ShapeHandle* out) {
   const int32 existing = Rank(shape);
   if (existing == kUnknownRank) {
     return ReturnUnknownShape(out);
@@ -205,8 +205,8 @@ Status InferenceContext::WithRankAtMost(const Shape* shape, int32 rank,
                                  " but is rank ", existing);
 }
 
-Status InferenceContext::WithValue(const Dimension* dim, int64 value,
-                                   const Dimension** out) {
+Status InferenceContext::WithValue(DimensionHandle dim, int64 value,
+                                   DimensionHandle* out) {
   const int64 existing = Value(dim);
   if (existing == value) {
     *out = dim;
@@ -222,8 +222,8 @@ Status InferenceContext::WithValue(const Dimension* dim, int64 value,
                                  existing);
 }
 
-Status InferenceContext::Merge(const Dimension* d0, const Dimension* d1,
-                               const Dimension** out) {
+Status InferenceContext::Merge(DimensionHandle d0, DimensionHandle d1,
+                               DimensionHandle* out) {
   if (d0 == d1 || !ValueKnown(d1)) {
     *out = d0;
     return Status::OK();
@@ -240,9 +240,9 @@ Status InferenceContext::Merge(const Dimension* d0, const Dimension* d1,
   }
 }
 
-Status InferenceContext::MergePrefix(const Shape* s, const Shape* prefix,
-                                     const Shape** s_out,
-                                     const Shape** prefix_out) {
+Status InferenceContext::MergePrefix(ShapeHandle s, ShapeHandle prefix,
+                                     ShapeHandle* s_out,
+                                     ShapeHandle* prefix_out) {
   *s_out = *prefix_out = nullptr;
   if (!RankKnown(prefix) || !RankKnown(s)) {
     *s_out = s;
@@ -253,7 +253,7 @@ Status InferenceContext::MergePrefix(const Shape* s, const Shape* prefix,
   TF_RETURN_IF_ERROR(WithRankAtLeast(s, rank, &s));
 
   // Merge the prefix dims and create the new output shapes.
-  std::vector<const Dimension*> dims;
+  std::vector<DimensionHandle> dims;
   dims.resize(rank);
   for (int i = 0; i < rank; ++i) {
     TF_RETURN_IF_ERROR(Merge(Dim(s, i), Dim(prefix, i), &dims[i]));
@@ -264,8 +264,8 @@ Status InferenceContext::MergePrefix(const Shape* s, const Shape* prefix,
   return Status::OK();
 }
 
-Status InferenceContext::Merge(const Shape* s0, const Shape* s1,
-                               const Shape** out) {
+Status InferenceContext::Merge(ShapeHandle s0, ShapeHandle s1,
+                               ShapeHandle* out) {
   if (s0 == s1 || !RankKnown(s1)) {
     *out = s0;
     return Status::OK();
@@ -309,7 +309,7 @@ Status InferenceContext::Merge(const Shape* s0, const Shape* s1,
   }
 
   // Merge dims.
-  std::vector<const Dimension*> dims(rank, nullptr);
+  std::vector<DimensionHandle> dims(rank, nullptr);
   for (int i = 0; i < rank; ++i) {
     // Invariant for merge was checked earlier, so CHECK is ok.
     TF_CHECK_OK(Merge(Dim(s0, i), Dim(s1, i), &dims[i]));
@@ -317,13 +317,13 @@ Status InferenceContext::Merge(const Shape* s0, const Shape* s1,
   return ReturnCreatedShape(dims, out);
 }
 
-Status InferenceContext::Subshape(const Shape* s, int64 start,
-                                  const Shape** out) {
+Status InferenceContext::Subshape(ShapeHandle s, int64 start,
+                                  ShapeHandle* out) {
   return Subshape(s, start, std::numeric_limits<int64>::max() /* end */, out);
 }
 
-Status InferenceContext::Subshape(const Shape* s, int64 start_in, int64 end_in,
-                                  const Shape** out) {
+Status InferenceContext::Subshape(ShapeHandle s, int64 start_in, int64 end_in,
+                                  ShapeHandle* out) {
   int64 start = start_in;
   int64 end = end_in;
   const int32 rank = Rank(s);
@@ -362,7 +362,7 @@ Status InferenceContext::Subshape(const Shape* s, int64 start_in, int64 end_in,
         end, " (computed from start ", start_in, " and end ", end_in,
         " over shape with rank ", rank, ")");
   }
-  std::vector<const Dimension*> dims;
+  std::vector<DimensionHandle> dims;
   dims.reserve(end - start);
   for (int i = start; i < end; ++i) {
     dims.push_back(Dim(s, i));
@@ -370,24 +370,23 @@ Status InferenceContext::Subshape(const Shape* s, int64 start_in, int64 end_in,
   return ReturnCreatedShape(dims, out);
 }
 
-Status InferenceContext::Concatenate(const Shape* s1, const Shape* s2,
-                                     const Shape** out) {
+Status InferenceContext::Concatenate(ShapeHandle s1, ShapeHandle s2,
+                                     ShapeHandle* out) {
   if (!RankKnown(s1) || !RankKnown(s2)) {
     return ReturnUnknownShape(out);
   }
   const int32 s1_rank = Rank(s1);
   const int32 s2_rank = Rank(s2);
   const int32 rank = s1_rank + s2_rank;
-  std::vector<const Dimension*> dims;
+  std::vector<DimensionHandle> dims;
   dims.reserve(rank);
   for (int i = 0; i < s1_rank; ++i) dims.push_back(Dim(s1, i));
   for (int i = 0; i < s2_rank; ++i) dims.push_back(Dim(s2, i));
   return ReturnCreatedShape(dims, out);
 }
 
-Status InferenceContext::ReplaceDim(const Shape* s, int dim_index_in,
-                                    const Dimension* new_dim,
-                                    const Shape** out) {
+Status InferenceContext::ReplaceDim(ShapeHandle s, int dim_index_in,
+                                    DimensionHandle new_dim, ShapeHandle* out) {
   if (!RankKnown(s)) {
     return ReturnUnknownShape(out);
   }
@@ -401,20 +400,20 @@ Status InferenceContext::ReplaceDim(const Shape* s, int dim_index_in,
                                    " for shape with ", s->dims_.size(),
                                    " dimensions");
   }
-  std::vector<const Dimension*> dims(s->dims_);
+  std::vector<DimensionHandle> dims(s->dims_);
   dims[dim_index] = new_dim;
   return ReturnCreatedShape(dims, out);
 }
 
-const Shape* InferenceContext::MakeShape(
-    const std::vector<const Dimension*>& dims) {
+ShapeHandle InferenceContext::MakeShape(
+    const std::vector<DimensionHandle>& dims) {
   all_shapes_.push_back(new Shape(dims));
   return all_shapes_.back();
 }
 
-const Shape* InferenceContext::MakeShape(
+ShapeHandle InferenceContext::MakeShape(
     std::initializer_list<DimensionOrConstant> dims) {
-  std::vector<const Dimension*> dims_actual;
+  std::vector<DimensionHandle> dims_actual;
   dims_actual.reserve(dims.size());
   for (const DimensionOrConstant& d : dims) {
     dims_actual.push_back(MakeDim(d));
@@ -422,45 +421,45 @@ const Shape* InferenceContext::MakeShape(
   return MakeShape(dims_actual);
 }
 
-const Shape* InferenceContext::UnknownShape() {
+ShapeHandle InferenceContext::UnknownShape() {
   all_shapes_.push_back(new Shape());
   return all_shapes_.back();
 }
 
-const Shape* InferenceContext::UnknownShapeOfRank(int32 rank) {
-  std::vector<const Dimension*> dims(rank);
+ShapeHandle InferenceContext::UnknownShapeOfRank(int32 rank) {
+  std::vector<DimensionHandle> dims(rank);
   for (int32 i = 0; i < rank; ++i) {
     dims[i] = UnknownDim();
   }
   return MakeShape(dims);
 }
 
-const Shape* InferenceContext::Scalar() { return MakeShape({}); }
+ShapeHandle InferenceContext::Scalar() { return MakeShape({}); }
 
-const Shape* InferenceContext::Vector(DimensionOrConstant dim) {
+ShapeHandle InferenceContext::Vector(DimensionOrConstant dim) {
   return MakeShape({dim});
 }
 
-const Shape* InferenceContext::Matrix(DimensionOrConstant dim1,
-                                      DimensionOrConstant dim2) {
+ShapeHandle InferenceContext::Matrix(DimensionOrConstant dim1,
+                                     DimensionOrConstant dim2) {
   return MakeShape({dim1, dim2});
 }
 
 Status InferenceContext::MakeShapeFromShapeTensor(int input_idx,
-                                                  const Shape** out) {
-  const Shape* input_shape;
+                                                  ShapeHandle* out) {
+  ShapeHandle input_shape;
   TF_RETURN_IF_ERROR(WithRank(input(input_idx), 1, &input_shape));
 
   const Tensor* t = input_tensor(input_idx);
   if (t == nullptr) {
     // Shape tensor is not known, but if the shape of the shape tensor is then
     // the right number of unknown dims can be created.
-    const Dimension* shape_dim = Dim(input_shape, 0);
+    DimensionHandle shape_dim = Dim(input_shape, 0);
     if (!ValueKnown(shape_dim)) {
       return ReturnUnknownShape(out);
     }
     const auto num_dims = Value(shape_dim);
-    std::vector<const Dimension*> dims;
+    std::vector<DimensionHandle> dims;
     for (int i = 0; i < num_dims; i++) dims.push_back(UnknownDim());
     return ReturnCreatedShape(dims, out);
   }
@@ -470,7 +469,7 @@ Status InferenceContext::MakeShapeFromShapeTensor(int input_idx,
     return errors::InvalidArgument("Input tensor must be rank 1, but was rank ",
                                    t->shape().dims());
   }
-  std::vector<const Dimension*> dims;
+  std::vector<DimensionHandle> dims;
   if (t->dtype() == DataType::DT_INT32) {
     auto flat_t = t->flat<int32>();
     for (int i = 0; i < flat_t.size(); ++i) {
@@ -492,7 +491,7 @@ Status InferenceContext::MakeShapeFromShapeTensor(int input_idx,
 }
 
 Status InferenceContext::MakeShapeFromShapeProto(const TensorShapeProto& proto,
-                                                 const Shape** out) {
+                                                 ShapeHandle* out) {
   *out = nullptr;
   TF_RETURN_IF_ERROR(PartialTensorShape::IsValidShape(proto));
   PartialTensorShape partial_shape(proto);
@@ -500,7 +499,7 @@ Status InferenceContext::MakeShapeFromShapeProto(const TensorShapeProto& proto,
     return ReturnUnknownShape(out);
   }
   const int num_dims = partial_shape.dims();
-  std::vector<const Dimension*> dims;
+  std::vector<DimensionHandle> dims;
   dims.reserve(partial_shape.dims());
   for (int i = 0; i < num_dims; ++i) {
     // -1 is unknown in proto and in InferenceContext, so this size can be
@@ -511,7 +510,7 @@ Status InferenceContext::MakeShapeFromShapeProto(const TensorShapeProto& proto,
 }
 
 // Returns a new dimension whose value is given by a scalar input tensor.
-Status InferenceContext::MakeDimForScalarInput(int idx, const Dimension** out) {
+Status InferenceContext::MakeDimForScalarInput(int idx, DimensionHandle* out) {
   const Tensor* t = input_tensor(idx);
   if (t == nullptr) {
     *out = UnknownDim();
@@ -539,8 +538,8 @@ Status InferenceContext::MakeDimForScalarInput(int idx, const Dimension** out) {
   return Status::OK();
 }
 
-Status InferenceContext::Divide(const Dimension* dividend, int64 divisor,
-                                const Dimension** out) {
+Status InferenceContext::Divide(DimensionHandle dividend, int64 divisor,
+                                DimensionHandle* out) {
   if (divisor == 1) {
     *out = dividend;
   } else if (!ValueKnown(dividend)) {
@@ -560,8 +559,8 @@ Status InferenceContext::Divide(const Dimension* dividend, int64 divisor,
   return Status::OK();
 }
 
-Status InferenceContext::Add(const Dimension* first, DimensionOrConstant second,
-                             const Dimension** out) {
+Status InferenceContext::Add(DimensionHandle first, DimensionOrConstant second,
+                             DimensionHandle* out) {
   const int64 first_value = Value(first);
   const int64 second_value = Value(second);
   // Special cases.
@@ -583,9 +582,9 @@ Status InferenceContext::Add(const Dimension* first, DimensionOrConstant second,
   return Status::OK();
 }
 
-Status InferenceContext::Subtract(const Dimension* first,
+Status InferenceContext::Subtract(DimensionHandle first,
                                   DimensionOrConstant second,
-                                  const Dimension** out) {
+                                  DimensionHandle* out) {
   const int64 first_value = Value(first);
   const int64 second_value = Value(second);
   // Special cases.
@@ -606,9 +605,9 @@ Status InferenceContext::Subtract(const Dimension* first,
   return Status::OK();
 }
 
-Status InferenceContext::Multiply(const Dimension* first,
+Status InferenceContext::Multiply(DimensionHandle first,
                                   DimensionOrConstant second,
-                                  const Dimension** out) {
+                                  DimensionHandle* out) {
   const int64 first_value = Value(first);
   const int64 second_value = Value(second);
   // Special cases.
@@ -635,8 +634,8 @@ Status InferenceContext::Multiply(const Dimension* first,
   return Status::OK();
 }
 
-Status InferenceContext::Min(const Dimension* first, DimensionOrConstant second,
-                             const Dimension** out) {
+Status InferenceContext::Min(DimensionHandle first, DimensionOrConstant second,
+                             DimensionHandle* out) {
   const int64 first_value = Value(first);
   const int64 second_value = Value(second);
   if (first_value == 0) {
@@ -655,8 +654,8 @@ Status InferenceContext::Min(const Dimension* first, DimensionOrConstant second,
   return Status::OK();
 }
 
-Status InferenceContext::Max(const Dimension* first, DimensionOrConstant second,
-                             const Dimension** out) {
+Status InferenceContext::Max(DimensionHandle first, DimensionOrConstant second,
+                             DimensionHandle* out) {
   const int64 first_value = Value(first);
   const int64 second_value = Value(second);
   if (first_value == kUnknownDim || second_value == kUnknownDim) {
@@ -672,13 +671,13 @@ Status InferenceContext::Max(const Dimension* first, DimensionOrConstant second,
 }
 
 Status InferenceContext::MakeShapeFromString(const string& spec,
-                                             const Shape** output) {
+                                             ShapeHandle* output) {
   if (spec == "?") {
     *output = UnknownShape();
     return Status::OK();
   }
 
-  std::vector<const Dimension*> dims;
+  std::vector<DimensionHandle> dims;
   strings::Scanner scanner(spec);
   scanner.OneLiteral("[");
   while (scanner.Peek() != ']') {
diff --git a/tensorflow/core/framework/shape_inference.h b/tensorflow/core/framework/shape_inference.h
index 1d0d4ab471..6f561f467a 100644
--- a/tensorflow/core/framework/shape_inference.h
+++ b/tensorflow/core/framework/shape_inference.h
@@ -43,38 +43,42 @@ class Dimension {
   TF_DISALLOW_COPY_AND_ASSIGN(Dimension);
 };
 
+typedef const Dimension* DimensionHandle;
+
 // Shape rank and dimensions are accessed through InferenceContext.
 class Shape {
  private:
   Shape();
-  Shape(const std::vector<const Dimension*>& dims);
+  Shape(const std::vector<DimensionHandle>& dims);
   ~Shape() {}
 
   const int32 rank_;
-  const std::vector<const Dimension*> dims_;
+  const std::vector<DimensionHandle> dims_;
 
   friend class InferenceContext;
   TF_DISALLOW_COPY_AND_ASSIGN(Shape);
 };
 
-// Struct used to allow functions to take const Dimension* or a dimension value.
+// Struct used to allow functions to take DimensionHandle or a dimension value.
 // Not meant to be constructed directly.
 struct DimensionOrConstant {
  public:
   // Intentionally not explicit.
-  DimensionOrConstant(const Dimension* dim);
+  DimensionOrConstant(DimensionHandle dim);
 
   // val must be non-negative or InferenceContext::kUnknownDim.
   DimensionOrConstant(int64 val);
 
   // dim takes precedence. If dim != nullptr, val is ignored.
-  const Dimension* dim;
+  DimensionHandle dim;
   int64 val;
 
  private:
   DimensionOrConstant();
 };
 
+typedef const Shape* ShapeHandle;
+
 // Note: This is experimental support for op shape inference in C++.  Shape
 // inference functions are not ready to be implemented yet.
 //
@@ -97,7 +101,7 @@ class InferenceContext {
   // creation of Shapes from strings out of this class (or hide it).
   InferenceContext(const NodeDef* node_def, const OpDef& op_def,
                    const std::vector<string>& input_shapes_string,
-                   const std::vector<const Shape*>& input_shapes,
+                   const std::vector<ShapeHandle>& input_shapes,
                    const std::vector<const Tensor*>& input_tensors);
 
   // <input_tensors> is NULL-padded to be the same size as <input_shapes>.
@@ -131,7 +135,7 @@ class InferenceContext {
 
   ~InferenceContext();
 
-  const Shape* input(int idx) const { return inputs_[idx]; }
+  ShapeHandle input(int idx) const { return inputs_[idx]; }
   int num_inputs() const { return inputs_.size(); }
 
   // Returns the input tensor at index <idx>, or nullptr if the input tensor is
@@ -151,13 +155,13 @@ class InferenceContext {
     input_tensors_ = input_tensors;
   }
 
-  void set_output(int idx, const Shape* shape) { outputs_[idx] = shape; }
+  void set_output(int idx, ShapeHandle shape) { outputs_[idx] = shape; }
   int num_outputs() const { return outputs_.size(); }
-  const Shape* output(int idx) { return outputs_[idx]; }
+  ShapeHandle output(int idx) { return outputs_[idx]; }
 
   // idx can be negative for an offset from end of dimensions.
   // idx must be in the range [-1 * s.rank, s.rank).
-  const Dimension* Dim(const Shape* s, int32 idx) {
+  DimensionHandle Dim(ShapeHandle s, int32 idx) {
     if (s->rank_ == kUnknownRank) {
       return UnknownDim();
     }
@@ -166,8 +170,8 @@ class InferenceContext {
     }
     return s->dims_[idx];
   }
-  int32 Rank(const Shape* s) { return s->rank_; }
-  bool RankKnown(const Shape* s) { return Rank(s) != kUnknownRank; }
+  int32 Rank(ShapeHandle s) { return s->rank_; }
+  bool RankKnown(ShapeHandle s) { return Rank(s) != kUnknownRank; }
   inline int64 Value(DimensionOrConstant d) {
     return d.dim ? d.dim->value_ : d.val;
   }
@@ -176,111 +180,111 @@ class InferenceContext {
   }
 
   // Returns true if the rank and all dimensions of the Shape are known.
-  bool FullyDefined(const Shape* s);
+  bool FullyDefined(ShapeHandle s);
 
   // Returns the total number of elements, or an unknown dimension for an
   // incomplete shape.
-  const Dimension* NumElements(const Shape* s);
+  DimensionHandle NumElements(ShapeHandle s);
 
-  string DebugString(const Shape* s);
-  string DebugString(const Dimension* d);
+  string DebugString(ShapeHandle s);
+  string DebugString(DimensionHandle d);
 
   // If <shape> has rank <rank>, or its rank is unknown, return OK and return
   // the shape with asserted rank in <*out>. Otherwise return an error.
   //
   // Note that <*out> may be set to <shape>.
-  Status WithRank(const Shape* shape, int32 rank,
-                  const Shape** out) TF_MUST_USE_RESULT;
-  Status WithRankAtLeast(const Shape* shape, int32 rank,
-                         const Shape** out) TF_MUST_USE_RESULT;
-  Status WithRankAtMost(const Shape* shape, int32 rank,
-                        const Shape** out) TF_MUST_USE_RESULT;
+  Status WithRank(ShapeHandle shape, int32 rank,
+                  ShapeHandle* out) TF_MUST_USE_RESULT;
+  Status WithRankAtLeast(ShapeHandle shape, int32 rank,
+                         ShapeHandle* out) TF_MUST_USE_RESULT;
+  Status WithRankAtMost(ShapeHandle shape, int32 rank,
+                        ShapeHandle* out) TF_MUST_USE_RESULT;
 
   // If <dim> has value <value>, or its value is unknown, returns OK and returns
   // the dimension with asserted value in <*out>. Otherwise returns an error.
   //
   // Note that <*out> may be set to <dim>.
-  Status WithValue(const Dimension* dim, int64 value,
-                   const Dimension** out) TF_MUST_USE_RESULT;
+  Status WithValue(DimensionHandle dim, int64 value,
+                   DimensionHandle* out) TF_MUST_USE_RESULT;
 
   // Merges <in0> and <in1> and returns the merged shape in <*out>. If <in0> and
   // <in1> are incompatible in rank, or in the value of any dimension, returns
   // an error.
   //
   // Note that <*out> may be set to <in0> or <in1>.
-  Status Merge(const Shape* in0, const Shape* in1,
-               const Shape** out) TF_MUST_USE_RESULT;
+  Status Merge(ShapeHandle in0, ShapeHandle in1,
+               ShapeHandle* out) TF_MUST_USE_RESULT;
 
   // Asserts that <s>'s rank >= <prefix>'s rank, and the first
   // <prefix.rank> dimensions of <s> are compatible with the dimensions of
   // <prefix>.
   // Returns the merged results in <*s_out> and <*prefix_out>.
-  Status MergePrefix(const Shape* s, const Shape* prefix, const Shape** s_out,
-                     const Shape** prefix_out) TF_MUST_USE_RESULT;
+  Status MergePrefix(ShapeHandle s, ShapeHandle prefix, ShapeHandle* s_out,
+                     ShapeHandle* prefix_out) TF_MUST_USE_RESULT;
 
   // Merges <d0> and <d1> and returns the merged dimension in <*out>. If <d0>
   // and <d1> have incompatible values, returns an error.
   //
   // Note that <*out> may be set to <d0> or <d1>.
-  Status Merge(const Dimension* d0, const Dimension* d1,
-               const Dimension** out) TF_MUST_USE_RESULT;
+  Status Merge(DimensionHandle d0, DimensionHandle d1,
+               DimensionHandle* out) TF_MUST_USE_RESULT;
 
   // Returns in <*out> a sub-shape of <s> with dimensions [start:].
   // <start> can be negative to index from the end of the shape. If <start> >
   // rank of <s>, then an empty subshape is returned.
   // Returns an error if the rank of <s> is < <start>.
-  Status Subshape(const Shape* s, int64 start,
-                  const Shape** out) TF_MUST_USE_RESULT;
+  Status Subshape(ShapeHandle s, int64 start,
+                  ShapeHandle* out) TF_MUST_USE_RESULT;
 
   // Returns in <*out> a sub-shape of <s>, with dimensions [start:end].
   // <start> and <end> can be negative, to index from the end of the shape.
   // <start> and <end> are set to the rank of <s> if > rank of <s>.
   // Returns an error if the rank of <s> is insufficient.
-  Status Subshape(const Shape* s, int64 start, int64 end,
-                  const Shape** out) TF_MUST_USE_RESULT;
+  Status Subshape(ShapeHandle s, int64 start, int64 end,
+                  ShapeHandle* out) TF_MUST_USE_RESULT;
 
   // Returns in <*out> the result of appending the dimensions of <s2> to those
   // of <s1>.
-  Status Concatenate(const Shape* s1, const Shape* s2,
-                     const Shape** out) TF_MUST_USE_RESULT;
+  Status Concatenate(ShapeHandle s1, ShapeHandle s2,
+                     ShapeHandle* out) TF_MUST_USE_RESULT;
 
   // Returns in <out> the shape from replacing <s.dim[dim_index]> with
   // <new_dim>.
-  Status ReplaceDim(const Shape* s, int dim_index, const Dimension* new_dim,
-                    const Shape** out) TF_MUST_USE_RESULT;
+  Status ReplaceDim(ShapeHandle s, int dim_index, DimensionHandle new_dim,
+                    ShapeHandle* out) TF_MUST_USE_RESULT;
 
   // Returns a new shape with the given dims. The returned value is owned by
   // this context.
-  const Shape* MakeShape(const std::vector<const Dimension*>& dims);
-  const Shape* MakeShape(std::initializer_list<DimensionOrConstant> dims);
+  ShapeHandle MakeShape(const std::vector<DimensionHandle>& dims);
+  ShapeHandle MakeShape(std::initializer_list<DimensionOrConstant> dims);
 
   // Returns a new unknown shape.
-  const Shape* UnknownShape();
+  ShapeHandle UnknownShape();
 
   // Returns a shape with specified rank but unknown dims.
-  const Shape* UnknownShapeOfRank(int32 rank);
+  ShapeHandle UnknownShapeOfRank(int32 rank);
 
   // Returns a new shape of zero dimensions.
-  const Shape* Scalar();
+  ShapeHandle Scalar();
 
   // Returns a new shape of one dimension.
-  const Shape* Vector(DimensionOrConstant dim);
+  ShapeHandle Vector(DimensionOrConstant dim);
 
   // Returns a new shape of two dimensions.
-  const Shape* Matrix(DimensionOrConstant dim1, DimensionOrConstant dim2);
+  ShapeHandle Matrix(DimensionOrConstant dim1, DimensionOrConstant dim2);
 
   // Returns in <out> a new shape whose dimension sizes come from input tensor
   // <input_idx>. The tensor must be a 1-dimensional int32 or int64 tensor.  If
   // the input tensor is NULL, then an unknown shape is returned.
-  Status MakeShapeFromShapeTensor(int input_idx, const Shape** out);
+  Status MakeShapeFromShapeTensor(int input_idx, ShapeHandle* out);
 
   // Returns in <out> a new shape corresponding to <proto>.
   Status MakeShapeFromShapeProto(const TensorShapeProto& proto,
-                                 const Shape** out);
+                                 ShapeHandle* out);
 
   // Returns a new dimension of the given size.  The returned value is owned by
   // this context.
-  inline const Dimension* MakeDim(DimensionOrConstant d) {
+  inline DimensionHandle MakeDim(DimensionOrConstant d) {
     if (d.dim) {
       return d.dim;
     } else {
@@ -288,12 +292,12 @@ class InferenceContext {
       return all_dims_.back();
     }
   }
-  inline const Dimension* UnknownDim() { return MakeDim(kUnknownDim); }
+  inline DimensionHandle UnknownDim() { return MakeDim(kUnknownDim); }
 
   // Returns a new dimension whose value is given by a scalar input tensor.
   // The input tensor must be in host memory, since it is dereferenced to get
   // the value.
-  Status MakeDimForScalarInput(int idx, const Dimension** out);
+  Status MakeDimForScalarInput(int idx, DimensionHandle* out);
 
   // Look up the attr for the NodeDef being evaluated with name attr_name and
   // set *value to its value.  If no attr with attr_name is found in def(), or
@@ -304,37 +308,36 @@ class InferenceContext {
   // Returns in <out> the result of dividing <dividend> by <divisor>.
   // Returns an error if <divisor>  is not positive or does not evenly
   // divide <dividend>.
-  Status Divide(const Dimension* dividend, int64 divisor,
-                const Dimension** out);
+  Status Divide(DimensionHandle dividend, int64 divisor, DimensionHandle* out);
 
   // Returns in <out> the sum of <first> and <second>.
-  Status Add(const Dimension* first, DimensionOrConstant second,
-             const Dimension** out);
+  Status Add(DimensionHandle first, DimensionOrConstant second,
+             DimensionHandle* out);
 
   // Returns in <out> the dimension that is <first> minus <second>.
-  Status Subtract(const Dimension* first, DimensionOrConstant second,
-                  const Dimension** out);
+  Status Subtract(DimensionHandle first, DimensionOrConstant second,
+                  DimensionHandle* out);
 
   // Returns in <out> the product of <first> and <second>.
-  Status Multiply(const Dimension* first, DimensionOrConstant second,
-                  const Dimension** out);
+  Status Multiply(DimensionHandle first, DimensionOrConstant second,
+                  DimensionHandle* out);
 
   // Returns in <out> the minimum of <first> and <second>. If either <first> or
   // <second> is zero the results is zero. Otherwise, if either <first> or
   // <second> is unknown the results is unknown.
-  Status Min(const Dimension* first, DimensionOrConstant second,
-             const Dimension** out);
+  Status Min(DimensionHandle first, DimensionOrConstant second,
+             DimensionHandle* out);
 
   // Returns in <out> the maximum of <first> and <second>. If either <first> or
   // <second> is unknown the results is unknown.
-  Status Max(const Dimension* first, DimensionOrConstant second,
-             const Dimension** out);
+  Status Max(DimensionHandle first, DimensionOrConstant second,
+             DimensionHandle* out);
 
   Status construction_status() const { return construction_status_; }
 
   // Validates that 'dim' has a known value, and prints an error
   // message containing 'name' if validation fails.
-  Status ValidateKnownDim(const Dimension* dim, const char* name) {
+  Status ValidateKnownDim(DimensionHandle dim, const char* name) {
     if (!ValueKnown(dim)) {
       return errors::InvalidArgument("Cannot infer shape because dimension ",
                                      name, " is not known.");
@@ -344,19 +347,19 @@ class InferenceContext {
 
   // Validates the 3 component tensors of a sparse tensor have the proper
   // shapes. This mimics SparseTensor.__init__ in python/framework/ops.py.
-  Status ValidateSparseTensor(const Shape* indices_shape,
-                              const Shape* values_shape,
-                              const Shape* shape_shape) {
+  Status ValidateSparseTensor(ShapeHandle indices_shape,
+                              ShapeHandle values_shape,
+                              ShapeHandle shape_shape) {
     // Validate ranks.
-    const Shape* unused_shape;
+    ShapeHandle unused_shape;
     TF_RETURN_IF_ERROR(WithRank(indices_shape, 2, &unused_shape));
     TF_RETURN_IF_ERROR(WithRank(values_shape, 1, &unused_shape));
     TF_RETURN_IF_ERROR(WithRank(shape_shape, 1, &unused_shape));
 
     // Number of elements in indices and values must match.
-    const Dimension* num_index_elements_dim = Dim(indices_shape, 0);
+    DimensionHandle num_index_elements_dim = Dim(indices_shape, 0);
     if (ValueKnown(num_index_elements_dim)) {
-      const Dimension* num_values_elements_dim = Dim(values_shape, 0);
+      DimensionHandle num_values_elements_dim = Dim(values_shape, 0);
       if (ValueKnown(num_values_elements_dim)) {
         int64 num_index_elements = Value(num_index_elements_dim);
         int64 num_values_elements = Value(num_values_elements_dim);
@@ -369,9 +372,9 @@ class InferenceContext {
     }
 
     // Rank embedded in indices must match shape.
-    const Dimension* index_rank_dim = Dim(indices_shape, 1);
+    DimensionHandle index_rank_dim = Dim(indices_shape, 1);
     if (ValueKnown(index_rank_dim)) {
-      const Dimension* shape_rank_dim = Dim(shape_shape, 0);
+      DimensionHandle shape_rank_dim = Dim(shape_shape, 0);
       if (ValueKnown(shape_rank_dim)) {
         int64 index_rank = Value(index_rank_dim);
         int32 shape_rank = Value(shape_rank_dim);
@@ -394,16 +397,16 @@ class InferenceContext {
   void PostInputInit();
 
   // Returns a shape from 'shape_string'.
-  Status MakeShapeFromString(const string& shape_string, const Shape** output);
+  Status MakeShapeFromString(const string& shape_string, ShapeHandle* output);
 
-  const Dimension* GetDimension(const DimensionOrConstant& d);
+  DimensionHandle GetDimension(const DimensionOrConstant& d);
 
-  Status ReturnUnknownShape(const Shape** out) {
+  Status ReturnUnknownShape(ShapeHandle* out) {
     *out = UnknownShape();
     return Status::OK();
   }
-  Status ReturnCreatedShape(const std::vector<const Dimension*>& dims,
-                            const Shape** out) {
+  Status ReturnCreatedShape(const std::vector<DimensionHandle>& dims,
+                            ShapeHandle* out) {
     *out = MakeShape(dims);
     return Status::OK();
   }
@@ -412,10 +415,10 @@ class InferenceContext {
   std::vector<Dimension*> all_dims_;  // values are owned.
 
   // inputs_ and outputs_ refer to values from all_shapes_.
-  std::vector<const Shape*> inputs_;
+  std::vector<ShapeHandle> inputs_;
   std::vector<const Tensor*> input_tensors_;
   std::vector<bool> requested_input_tensor_;
-  std::vector<const Shape*> outputs_;
+  std::vector<ShapeHandle> outputs_;
 
   const NodeDef& node_def_;
   NameRangeMap input_name_map_;
@@ -440,10 +443,10 @@ inline Dimension::Dimension(int64 value) : value_(value) {
 }
 
 inline Shape::Shape() : rank_(InferenceContext::kUnknownRank) {}
-inline Shape::Shape(const std::vector<const Dimension*>& dims)
+inline Shape::Shape(const std::vector<DimensionHandle>& dims)
     : rank_(dims.size()), dims_(dims) {}
 
-inline DimensionOrConstant::DimensionOrConstant(const Dimension* dim)
+inline DimensionOrConstant::DimensionOrConstant(DimensionHandle dim)
     : dim(dim) {
   DCHECK(dim != nullptr) << "Internal error: Got nullptr for Dimension.";
 }
diff --git a/tensorflow/core/framework/shape_inference_test.cc b/tensorflow/core/framework/shape_inference_test.cc
index 7be573d249..7b6c9407c4 100644
--- a/tensorflow/core/framework/shape_inference_test.cc
+++ b/tensorflow/core/framework/shape_inference_test.cc
@@ -109,8 +109,8 @@ TEST(ShapeInferenceTest, WithRank) {
 
   auto in0 = c.input(0);
   auto in1 = c.input(1);
-  const Shape* s1 = nullptr;
-  const Shape* s2 = nullptr;
+  ShapeHandle s1 = nullptr;
+  ShapeHandle s2 = nullptr;
 
   // WithRank on a shape with unknown dimensionality always succeeds.
   EXPECT_TRUE(c.WithRank(in0, 1, &s1).ok());
@@ -147,8 +147,8 @@ TEST(ShapeInferenceTest, WithRankAtMost) {
 
   auto in0 = c.input(0);
   auto in1 = c.input(1);
-  const Shape* s1 = nullptr;
-  const Shape* s2 = nullptr;
+  ShapeHandle s1 = nullptr;
+  ShapeHandle s2 = nullptr;
 
   // WithRankAtMost on a shape with unknown dimensionality always succeeds.
   EXPECT_TRUE(c.WithRankAtMost(in0, 1, &s1).ok());
@@ -182,8 +182,8 @@ TEST(ShapeInferenceTest, WithRankAtLeast) {
 
   auto in0 = c.input(0);
   auto in1 = c.input(1);
-  const Shape* s1 = nullptr;
-  const Shape* s2 = nullptr;
+  ShapeHandle s1 = nullptr;
+  ShapeHandle s2 = nullptr;
 
   // WithRankAtLeast on a shape with unknown dimensionality always succeeds.
   EXPECT_TRUE(c.WithRankAtLeast(in0, 1, &s1).ok());
@@ -217,8 +217,8 @@ TEST(ShapeInferenceTest, WithValue) {
 
   auto d0 = c.Dim(c.input(0), 0);
   auto d1 = c.Dim(c.input(0), 1);
-  const Dimension* out1 = nullptr;
-  const Dimension* out2 = nullptr;
+  DimensionHandle out1 = nullptr;
+  DimensionHandle out2 = nullptr;
 
   // WithValue on a dimension with unknown value always succeeds.
   EXPECT_TRUE(c.WithValue(d1, 1, &out1).ok());
@@ -259,7 +259,7 @@ TEST(ShapeInferenceTest, MergeDim) {
   auto d2_b = c.Dim(c.input(0), 2);
   auto d1 = c.Dim(c.input(0), 3);
   auto d_unknown_b = c.Dim(c.input(0), 4);
-  const Dimension* out = nullptr;
+  DimensionHandle out = nullptr;
 
   // Merging anything with unknown returns the same pointer.
   EXPECT_TRUE(c.Merge(d2, d_unknown, &out).ok());
@@ -302,7 +302,7 @@ TEST(ShapeInferenceTest, MergeShape) {
   auto s_1_3 = c.input(4);
   auto s_unknown_b = c.input(5);
   auto s_1 = c.input(6);
-  const Shape* out = nullptr;
+  ShapeHandle out = nullptr;
 
   // Merging any shape with unknown returns the shape.
   EXPECT_TRUE(c.Merge(s_unknown, s_1_2, &out).ok());
@@ -359,8 +359,8 @@ TEST(ShapeInferenceTest, MergePrefix) {
   auto s_1_u_3 = c.input(2);
   auto s_2_4 = c.input(3);
 
-  const Shape* s_out = nullptr;
-  const Shape* s_prefix_out = nullptr;
+  ShapeHandle s_out = nullptr;
+  ShapeHandle s_prefix_out = nullptr;
 
   // Merging with unknown returns the inputs.
   EXPECT_TRUE(c.MergePrefix(s_unknown, s_u_2, &s_out, &s_prefix_out).ok());
@@ -399,8 +399,8 @@ TEST(ShapeInferenceTest, Subshape) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(2, 2), {"[1,2,3,?,5]", "?"}, {});
 
-  const Shape* unknown = c.input(1);
-  const Shape* out;
+  ShapeHandle unknown = c.input(1);
+  ShapeHandle out;
   EXPECT_TRUE(c.Subshape(unknown, 0, &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
   EXPECT_TRUE(out == unknown);
@@ -412,7 +412,7 @@ TEST(ShapeInferenceTest, Subshape) {
   EXPECT_TRUE(out != unknown);
 
   const int kFullRank = 5;
-  const Shape* out_arr[4];
+  ShapeHandle out_arr[4];
   auto in0 = c.input(0);
   EXPECT_TRUE(c.Subshape(in0, 0, &out).ok());
   EXPECT_EQ("[1,2,3,?,5]", c.DebugString(out));
@@ -472,8 +472,8 @@ TEST(ShapeInferenceTest, Concatenate) {
 
   auto in0 = c.input(0);
   auto in1 = c.input(1);
-  const Shape* unknown = c.input(2);
-  const Shape* out;
+  ShapeHandle unknown = c.input(2);
+  ShapeHandle out;
   EXPECT_TRUE(c.Concatenate(unknown, unknown, &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
   EXPECT_TRUE(out != unknown);
@@ -499,7 +499,7 @@ TEST(ShapeInferenceTest, ReplaceDim) {
   auto in = c.input(0);
   auto unknown = c.input(1);
 
-  const Shape* replaced;
+  ShapeHandle replaced;
   EXPECT_TRUE(c.ReplaceDim(in, 0, c.Dim(in, 1), &replaced).ok());
   EXPECT_EQ("[2,2,3]", c.DebugString(replaced));
   EXPECT_TRUE(c.ReplaceDim(in, 2, c.Dim(in, 1), &replaced).ok());
@@ -527,7 +527,7 @@ TEST(ShapeInferenceTest, MakeShape) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(1, 2), {"[1,2,3,?,5]"}, {});
 
-  std::vector<const Dimension*> dims;
+  std::vector<DimensionHandle> dims;
   auto in0 = c.input(0);
   const int rank = c.Rank(in0);
   for (int i = 0; i < rank; ++i) {
@@ -608,7 +608,7 @@ TEST(ShapeInferenceTest, MakeShapeFromShapeTensor) {
   auto create = [](Tensor* t) {
     NodeDef def;
     InferenceContext c(&def, MakeOpDef(1, 0), {"?"}, {t});
-    const Shape* out;
+    ShapeHandle out;
     Status s = c.MakeShapeFromShapeTensor(0, &out);
     if (s.ok()) {
       return c.DebugString(out);
@@ -643,7 +643,7 @@ TEST(ShapeInferenceTest, MakeShapeFromShapeTensor) {
   {
     NodeDef def;
     InferenceContext c(&def, MakeOpDef(1, 0), {"[1,?]"}, {nullptr});
-    const Shape* out;
+    ShapeHandle out;
     EXPECT_EQ("Shape must be rank 1 but is rank 2",
               c.MakeShapeFromShapeTensor(0, &out).error_message());
   }
@@ -655,7 +655,7 @@ TEST(ShapeInferenceTest, MakeShapeFromShapeProto) {
   TensorShapeProto proto;
 
   // With a set unknown rank.
-  const Shape* out;
+  ShapeHandle out;
   proto.set_unknown_rank(true);
   EXPECT_TRUE(c.MakeShapeFromShapeProto(proto, &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
@@ -733,7 +733,7 @@ TEST(ShapeInferenceTest, MakeDimForScalarInput) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(2, 2), {"[]", "[]"}, {&t1, &t2});
 
-  const Dimension* d;
+  DimensionHandle d;
   EXPECT_TRUE(c.MakeDimForScalarInput(0, &d).ok());
   EXPECT_EQ("20", c.DebugString(d));
 
@@ -776,7 +776,7 @@ TEST(ShapeInferenceTest, Divide) {
   auto d_unknown = c.Dim(s, 1);
 
   // Dividing unknown by non-1 gives new unknown.
-  const Dimension* out;
+  DimensionHandle out;
   EXPECT_TRUE(c.Divide(d_unknown, 2, &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
   EXPECT_TRUE(out != d_unknown);
@@ -808,7 +808,7 @@ TEST(ShapeInferenceTest, Add) {
   auto d_0 = c.Dim(s, 2);
 
   // Adding non-zero to unknown gives new unknown.
-  const Dimension* out;
+  DimensionHandle out;
   EXPECT_TRUE(c.Add(d_unknown, 1, &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
   EXPECT_TRUE(out != d_unknown);
@@ -858,7 +858,7 @@ TEST(ShapeInferenceTest, Subtract) {
   auto d_5 = c.Dim(s, 3);
 
   // Subtracting non-zero from unknown gives new unknown.
-  const Dimension* out;
+  DimensionHandle out;
   EXPECT_TRUE(c.Subtract(d_unknown, 1, &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
   EXPECT_TRUE(out != d_unknown);
@@ -906,7 +906,7 @@ TEST(ShapeInferenceTest, Multiply) {
   auto d_1 = c.Dim(s, 3);
 
   // Multiplying non-zero to unknown gives new unknown.
-  const Dimension* out;
+  DimensionHandle out;
   EXPECT_TRUE(c.Multiply(d_unknown, 2, &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
 
@@ -979,7 +979,7 @@ TEST(ShapeInferenceTest, Min) {
   auto d_0 = c.Dim(s, 3);
 
   // Minimum involving zero and unknown returns zero.
-  const Dimension* out;
+  DimensionHandle out;
   EXPECT_TRUE(c.Min(d_0, d_unknown, &out).ok());
   EXPECT_EQ(d_0, out);
   EXPECT_TRUE(c.Min(d_unknown, d_0, &out).ok());
@@ -1026,7 +1026,7 @@ TEST(ShapeInferenceTest, Max) {
   auto d_unknown = c.Dim(s, 2);
 
   // Maximum involving unknowns gives new unknown.
-  const Dimension* out;
+  DimensionHandle out;
   EXPECT_TRUE(c.Max(d_unknown, d_unknown, &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
   EXPECT_TRUE(c.Max(d_unknown, 1, &out).ok());
diff --git a/tensorflow/core/framework/shape_inference_testutil.cc b/tensorflow/core/framework/shape_inference_testutil.cc
index 60a9cb101f..28d2197629 100644
--- a/tensorflow/core/framework/shape_inference_testutil.cc
+++ b/tensorflow/core/framework/shape_inference_testutil.cc
@@ -25,8 +25,8 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
-using shape_inference::Shape;
+using shape_inference::DimensionHandle;
+using shape_inference::ShapeHandle;
 using errors::Unknown;
 
 Status InferShapes(ShapeInferenceTestOp op, const string& ins,
@@ -48,9 +48,9 @@ Status InferShapes(ShapeInferenceTestOp op, const string& ins,
   TF_RETURN_IF_ERROR(op_reg_data->shape_inference_fn(&c));
   const int num_outputs = c.num_outputs();
 
-  std::unordered_map<const Dimension*, std::pair<int, int>>
+  std::unordered_map<DimensionHandle, std::pair<int, int>>
       dim_to_input_and_dim_idx;
-  std::unordered_map<const Shape*, int> shape_to_input_idx;
+  std::unordered_map<ShapeHandle, int> shape_to_input_idx;
   for (int i = 0; i < c.num_inputs(); ++i) {
     auto in = c.input(i);
     shape_to_input_idx[in] = i;
@@ -120,7 +120,7 @@ Status InferShapes(ShapeInferenceTestOp op, const string& ins,
     for (int j = 0; j < expected_dims.size(); ++j) {
       err_prefix = strings::StrCat("Output dim ", i, ",", j);
       StringPiece expected_dim(expected_dims[j]);
-      const Dimension* out_dim = c.Dim(out, j);
+      DimensionHandle out_dim = c.Dim(out, j);
       std::pair<int, int> in_dim_idx = gtl::FindWithDefault(
           dim_to_input_and_dim_idx, out_dim, std::make_pair(-1, -1));
       if (expected_dim == "?") {
diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index 528d6407d4..051e0c1302 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -21,9 +21,9 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 namespace {
 
@@ -41,14 +41,14 @@ Status GetAxisForPackAndUnpack(InferenceContext* c, int32 rank_after_pack,
 
 Status PadShapeFn(InferenceContext* c) {
   // Paddings is a matrix of [input_rank, 2].
-  const Shape* paddings;
+  ShapeHandle paddings;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &paddings));
-  const Dimension* unused;
+  DimensionHandle unused;
   TF_RETURN_IF_ERROR(c->WithValue(c->Dim(paddings, 1), 2, &unused));
 
   // n_dim and input.rank are equivalent.
-  const Shape* input = c->input(0);
-  const Dimension* n_dim = c->Dim(paddings, 0);
+  ShapeHandle input = c->input(0);
+  DimensionHandle n_dim = c->Dim(paddings, 0);
   if (c->ValueKnown(n_dim)) {
     TF_RETURN_IF_ERROR(c->WithRank(input, c->Value(n_dim), &input));
   } else if (c->RankKnown(input)) {
@@ -75,7 +75,7 @@ Status PadShapeFn(InferenceContext* c) {
 
   // paddings_t is known.
   auto paddings_data = paddings_t->matrix<int32>();
-  std::vector<const Dimension*> dims(num_dims);
+  std::vector<DimensionHandle> dims(num_dims);
   for (int i = 0; i < num_dims; ++i) {
     const int32 pad0 = paddings_data(i, 0);
     const int32 pad1 = paddings_data(i, 1);
@@ -98,7 +98,7 @@ REGISTER_OP("Pack")
     .Attr("axis: int = 0")
     .SetShapeFn([](InferenceContext* c) {
       // Validate shapes of all inputs are compatible
-      const Shape* cur = c->input(c->num_inputs() - 1);
+      ShapeHandle cur = c->input(c->num_inputs() - 1);
       for (int i = c->num_inputs() - 2; i >= 0; --i) {
         TF_RETURN_WITH_CONTEXT_IF_ERROR(c->Merge(c->input(i), cur, &cur),
                                         "From merging shape ", i,
@@ -116,7 +116,7 @@ REGISTER_OP("Pack")
 
       // Copy all dimensions over, inserting a dimension of value #inputs
       // at <axis>.
-      std::vector<const Dimension*> dims;
+      std::vector<DimensionHandle> dims;
       int index = 0;
       while (index < axis) dims.push_back(c->Dim(cur, index++));
       dims.push_back(c->MakeDim(c->num_inputs()));
@@ -162,8 +162,8 @@ REGISTER_OP("Unpack")
     .Attr("T: type")
     .Attr("axis: int = 0")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* s = c->input(0);
-      const Shape* out;
+      ShapeHandle s = c->input(0);
+      ShapeHandle out;
       if (c->RankKnown(s)) {
         // Determine the axis that will be removed, converting from negative
         // axes to a positive point per negative indexing rules.
@@ -172,12 +172,12 @@ REGISTER_OP("Unpack")
         TF_RETURN_IF_ERROR(GetAxisForPackAndUnpack(c, rank, &axis));
 
         // The axis dim matches the number of outputs.
-        const Dimension* unused;
+        DimensionHandle unused;
         TF_RETURN_IF_ERROR(
             c->WithValue(c->Dim(s, axis), c->num_outputs(), &unused));
 
         // Copy all dimensions, removing the <axis> dimension.
-        std::vector<const Dimension*> dims;
+        std::vector<DimensionHandle> dims;
         for (int i = 0; i < rank; ++i) {
           if (i != axis) dims.push_back(c->Dim(s, i));
         }
@@ -272,11 +272,11 @@ REGISTER_OP("Split")
     .Attr("num_split: int >= 1")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Dimension* split_dimension;
+      DimensionHandle split_dimension;
       TF_RETURN_IF_ERROR(c->MakeDimForScalarInput(0, &split_dimension));
       int num_split = c->num_outputs();
-      const Shape* input = c->input(1);
-      const Shape* out;
+      ShapeHandle input = c->input(1);
+      ShapeHandle out;
       if (!c->ValueKnown(split_dimension)) {
         if (c->RankKnown(input)) {
           out = c->UnknownShapeOfRank(c->Rank(input));
@@ -286,7 +286,7 @@ REGISTER_OP("Split")
       } else {
         int64 split_dim = c->Value(split_dimension);
         TF_RETURN_IF_ERROR(c->WithRankAtLeast(input, split_dim + 1, &input));
-        const Dimension* split_dim_size;
+        DimensionHandle split_dim_size;
         TF_RETURN_WITH_CONTEXT_IF_ERROR(
             c->Divide(c->Dim(input, split_dim), num_split, &split_dim_size),
             "Number of ways to split should evenly divide the split dimension");
@@ -319,7 +319,7 @@ REGISTER_OP("Const")
       TF_RETURN_IF_ERROR(c->GetAttr("value", &proto));
       TF_RETURN_IF_ERROR(TensorShape::IsValidShape(proto->tensor_shape()));
       TensorShape shape(proto->tensor_shape());
-      std::vector<const Dimension*> dims;
+      std::vector<DimensionHandle> dims;
       for (int i = 0; i < shape.dims(); ++i) {
         dims.push_back(c->MakeDim(shape.dim_size(i)));
       }
@@ -345,7 +345,7 @@ REGISTER_OP("ImmutableConst")
       TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape_from_attr));
       TensorShapeProto shape_proto;
       shape_from_attr.AsProto(&shape_proto);
-      const Shape* output_shape;
+      ShapeHandle output_shape;
       TF_RETURN_IF_ERROR(
           c->MakeShapeFromShapeProto(shape_proto, &output_shape));
       c->set_output(0, output_shape);
@@ -381,10 +381,10 @@ REGISTER_OP("Diag")
     .Output("output: T")
     .Attr("T: {float, double, int32, int64, complex64}")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* in = c->input(0);
+      ShapeHandle in = c->input(0);
       TF_RETURN_IF_ERROR(c->WithRankAtMost(in, 3, &in));
       // Output shape is original concatenated with itself.
-      const Shape* out;
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->Concatenate(in, in, &out));
       c->set_output(0, out);
       return Status::OK();
@@ -419,7 +419,7 @@ REGISTER_OP("DiagPart")
     .Output("diagonal: T")
     .Attr("T: {float, double, int32, int64, complex64}")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* in = c->input(0);
+      ShapeHandle in = c->input(0);
       if (!c->RankKnown(in)) {
         c->set_output(0, c->UnknownShape());
         return Status::OK();
@@ -433,7 +433,7 @@ REGISTER_OP("DiagPart")
       const int32 mid = rank / 2;
 
       // output dim[i] is the merge of in.dim[i] and in.dim[i+mid].
-      std::vector<const Dimension*> dims(mid);
+      std::vector<DimensionHandle> dims(mid);
       for (int i = 0; i < mid; ++i) {
         TF_RETURN_IF_ERROR(
             c->Merge(c->Dim(in, i), c->Dim(in, i + mid), &dims[i]));
@@ -474,14 +474,14 @@ REGISTER_OP("BatchMatrixDiag")
     .Output("output: T")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* in;
+      ShapeHandle in;
       TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &in));
       if (!c->RankKnown(in)) {
         c->set_output(0, c->UnknownShape());
         return Status::OK();
       }
       const int32 rank = c->Rank(in);
-      const Shape* out;
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(
           c->Concatenate(in, c->Vector(c->Dim(in, rank - 1)), &out));
       c->set_output(0, out);
@@ -528,17 +528,17 @@ REGISTER_OP("BatchMatrixSetDiag")
     .Output("output: T")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input;
-      const Shape* diag;
+      ShapeHandle input;
+      ShapeHandle diag;
       TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 2, &input));
       TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(1), 1, &diag));
 
-      const Dimension* square_dim;
+      DimensionHandle square_dim;
       TF_RETURN_IF_ERROR(
           c->Merge(c->Dim(input, -2), c->Dim(input, -1), &square_dim));
       TF_RETURN_IF_ERROR(c->Merge(square_dim, c->Dim(diag, -1), &square_dim));
 
-      const Shape* output;
+      ShapeHandle output;
       TF_RETURN_IF_ERROR(c->Concatenate(diag, c->Vector(square_dim), &output));
       TF_RETURN_IF_ERROR(c->Merge(input, output, &output));
 
@@ -573,7 +573,7 @@ REGISTER_OP("BatchMatrixDiagPart")
     .Output("diagonal: T")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* in;
+      ShapeHandle in;
       TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 2, &in));
       if (!c->RankKnown(in)) {
         c->set_output(0, c->UnknownShape());
@@ -581,12 +581,12 @@ REGISTER_OP("BatchMatrixDiagPart")
       }
       const int32 rank = c->Rank(in);
       // Last two dims must match.
-      const Dimension* unused;
+      DimensionHandle unused;
       TF_RETURN_IF_ERROR(
           c->Merge(c->Dim(in, rank - 1), c->Dim(in, rank - 2), &unused));
 
       // Output shape has all dims but last of input.
-      std::vector<const Dimension*> dims;
+      std::vector<DimensionHandle> dims;
       for (int i = 0; i < rank - 1; ++i) dims.push_back(c->Dim(in, i));
       c->set_output(0, c->MakeShape(dims));
       return Status::OK();
@@ -695,10 +695,10 @@ REGISTER_OP("Reverse")
         "T: {uint8, int8, int32, int64, bool, half, float, double, complex64, "
         "complex128}")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input = c->input(0);
-      const Shape* dims;
+      ShapeHandle input = c->input(0);
+      ShapeHandle dims;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &dims));
-      const Dimension* dims_dim = c->Dim(dims, 0);
+      DimensionHandle dims_dim = c->Dim(dims, 0);
       if (c->ValueKnown(dims_dim)) {
         TF_RETURN_IF_ERROR(c->WithRank(input, c->Value(dims_dim), &input));
       }
@@ -795,7 +795,7 @@ REGISTER_OP("EditDistance")
 
       auto h_values = hypothesis_shape_t->flat<int64>();
       auto t_values = truth_shape_t->flat<int64>();
-      std::vector<const Dimension*> dims(hypothesis_shape_t->NumElements() - 1);
+      std::vector<DimensionHandle> dims(hypothesis_shape_t->NumElements() - 1);
       for (int i = 0; i < dims.size(); ++i) {
         dims[i] = c->MakeDim(std::max(h_values(i), t_values(i)));
       }
@@ -869,7 +869,7 @@ REGISTER_OP("Fill")
     .Output("output: T")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* out;
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(0, &out));
       c->set_output(0, out);
       return Status::OK();
@@ -900,12 +900,12 @@ REGISTER_OP("Gather")
     .Attr("Tparams: type")
     .Attr("Tindices: {int32,int64}")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &unused));
-      const Shape* params_subshape;
+      ShapeHandle params_subshape;
       TF_RETURN_IF_ERROR(c->Subshape(c->input(0), 1, &params_subshape));
-      const Shape* indices_shape = c->input(1);
-      const Shape* out;
+      ShapeHandle indices_shape = c->input(1);
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->Concatenate(indices_shape, params_subshape, &out));
       c->set_output(0, out);
       return Status::OK();
@@ -941,10 +941,10 @@ REGISTER_OP("GatherNd")
     .Attr("Tparams: type")
     .Attr("Tindices: {int32,int64}")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* params = c->input(0);
-      const Shape* indices;
+      ShapeHandle params = c->input(0);
+      ShapeHandle indices;
       TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(1), 1, &indices));
-      const Dimension* r_dim = c->Dim(indices, -1);
+      DimensionHandle r_dim = c->Dim(indices, -1);
 
       if (!c->RankKnown(params) || !c->ValueKnown(r_dim)) {
         c->set_output(0, c->UnknownShape());
@@ -959,11 +959,11 @@ REGISTER_OP("GatherNd")
       }
 
       // Remove r_dim from indices to get output.
-      const Shape* indices_slice;
-      const Shape* params_slice;
+      ShapeHandle indices_slice;
+      ShapeHandle params_slice;
       TF_RETURN_IF_ERROR(c->Subshape(indices, 0, -1, &indices_slice));
       TF_RETURN_IF_ERROR(c->Subshape(params, c->Value(r_dim), &params_slice));
-      const Shape* out;
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->Concatenate(indices_slice, params_slice, &out));
       c->set_output(0, out);
       return Status::OK();
@@ -1131,8 +1131,8 @@ REGISTER_OP("Reshape")
     .Output("output: T")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* in = c->input(0);
-      const Shape* out;
+      ShapeHandle in = c->input(0);
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(1, &out));
 
       // If the rank and all dimensions of the input tensor are known, we may
@@ -1141,8 +1141,8 @@ REGISTER_OP("Reshape")
       // dimension information.
       // Additionally, if the rank of the out shape is unknown we have no shape
       // information to go off of.
-      const Dimension* num_in_elems = c->NumElements(in);
-      const Dimension* num_out_elems = c->NumElements(out);
+      DimensionHandle num_in_elems = c->NumElements(in);
+      DimensionHandle num_out_elems = c->NumElements(out);
       if (!c->ValueKnown(num_in_elems) || !c->RankKnown(out)) {
         // Do nothing. We have no shape information to infer from so we directly
         // return out as our shape.
@@ -1159,9 +1159,9 @@ REGISTER_OP("Reshape")
         // If we don't know the number of output elements, we can infer
         // the missing dimension.
         int32 unknown_idx = -1;
-        const Dimension* known_elems = c->MakeDim(1);
+        DimensionHandle known_elems = c->MakeDim(1);
         for (int32 i = 0; i < c->Rank(out); ++i) {
-          const Dimension* dim = c->Dim(out, i);
+          DimensionHandle dim = c->Dim(out, i);
           if (!c->ValueKnown(dim)) {
             if (unknown_idx >= 0) {
               return errors::InvalidArgument(
@@ -1173,7 +1173,7 @@ REGISTER_OP("Reshape")
             TF_RETURN_IF_ERROR(c->Multiply(known_elems, dim, &known_elems));
           }
         }
-        const Dimension* inferred_dim;
+        DimensionHandle inferred_dim;
         TF_RETURN_IF_ERROR(
             c->Divide(num_in_elems, c->Value(known_elems), &inferred_dim));
         TF_RETURN_IF_ERROR(c->ReplaceDim(out, unknown_idx, inferred_dim, &out));
@@ -1250,7 +1250,7 @@ REGISTER_OP("InvertPermutation")
     .Input("x: int32")
     .Output("y: int32")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* x;
+      ShapeHandle x;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &x));
       c->set_output(0, x);
       return Status::OK();
@@ -1285,10 +1285,10 @@ REGISTER_OP("Transpose")
     .Output("y: T")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input = c->input(0);
-      const Shape* perm_shape = c->input(1);
+      ShapeHandle input = c->input(0);
+      ShapeHandle perm_shape = c->input(1);
       const Tensor* perm = c->input_tensor(1);
-      const Dimension* perm_elems = c->NumElements(perm_shape);
+      DimensionHandle perm_elems = c->NumElements(perm_shape);
       // If we don't have rank information on the input or value information on
       // perm we can't return any shape information, otherwise we have enough
       // information to at least find the rank of the output.
@@ -1307,7 +1307,7 @@ REGISTER_OP("Transpose")
       } else {
         rank = perm->NumElements();
       }
-      std::vector<const Dimension*> dims;
+      std::vector<DimensionHandle> dims;
       dims.resize(rank);
       TF_RETURN_IF_ERROR(c->WithRank(input, rank, &input));
       // Ensure that perm is a vector and has rank elements.
@@ -1423,7 +1423,7 @@ namespace {
 
 Status ShapeShapeFn(InferenceContext* c) {
   for (int i = 0; i < c->num_inputs(); ++i) {
-    const Dimension* dim;
+    DimensionHandle dim;
     if (c->RankKnown(c->input(i))) {
       dim = c->MakeDim(c->Rank(c->input(i)));
     } else {
@@ -1477,8 +1477,8 @@ REGISTER_OP("ReverseSequence")
     .Attr("batch_dim: int = 0")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input = c->input(0);
-      const Shape* seq_lens_shape;
+      ShapeHandle input = c->input(0);
+      ShapeHandle seq_lens_shape;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &seq_lens_shape));
 
       int64 seq_dim;
@@ -1501,12 +1501,12 @@ REGISTER_OP("ReverseSequence")
                                        seq_dim, " vs. ", input_rank);
       }
 
-      const Dimension* batch_dim_dim = c->Dim(input, batch_dim);
+      DimensionHandle batch_dim_dim = c->Dim(input, batch_dim);
       TF_RETURN_IF_ERROR(
           c->Merge(batch_dim_dim, c->Dim(seq_lens_shape, 0), &batch_dim_dim));
 
       // Replace batch_dim of input with batch_size
-      const Shape* output_shape;
+      ShapeHandle output_shape;
       TF_RETURN_IF_ERROR(
           c->ReplaceDim(input, batch_dim, batch_dim_dim, &output_shape));
       c->set_output(0, output_shape);
@@ -1627,11 +1627,11 @@ namespace {
 
 template <typename T>
 Status SliceHelper(InferenceContext* c, const Tensor* begin_t,
-                   const Tensor* sizes_t, std::vector<const Dimension*>* dims) {
+                   const Tensor* sizes_t, std::vector<DimensionHandle>* dims) {
   auto begin_vec = begin_t->vec<T>();
   auto sizes_vec = sizes_t->vec<T>();
   for (int i = 0; i < sizes_t->NumElements(); ++i) {
-    const Dimension* dim = c->Dim(c->input(0), i);
+    DimensionHandle dim = c->Dim(c->input(0), i);
     if (sizes_vec(i) != -1) {
       if (c->ValueKnown(dim)) {
         auto dim_val = c->Value(dim);
@@ -1664,7 +1664,7 @@ Status SliceHelper(InferenceContext* c, const Tensor* begin_t,
 
       dims->emplace_back(c->MakeDim(sizes_vec(i)));
     } else {
-      const Dimension* result;
+      DimensionHandle result;
       TF_RETURN_IF_ERROR(c->Subtract(dim, begin_vec(i), &result));
       dims->emplace_back(result);
     }
@@ -1684,16 +1684,16 @@ REGISTER_OP("Slice")
     .Attr("T: type")
     .Attr("Index: {int32,int64}")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input = c->input(0);
-      const Shape* begin_shape;
+      ShapeHandle input = c->input(0);
+      ShapeHandle begin_shape;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &begin_shape));
-      const Shape* sizes_shape;
+      ShapeHandle sizes_shape;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &sizes_shape));
 
       // Merge to check compatibility of begin and sizes tensors.
       TF_RETURN_IF_ERROR(c->Merge(begin_shape, sizes_shape, &begin_shape));
 
-      const Dimension* ndims = c->Dim(begin_shape, 0);
+      DimensionHandle ndims = c->Dim(begin_shape, 0);
       if (c->ValueKnown(ndims)) {
         TF_RETURN_IF_ERROR(c->WithRank(input, c->Value(ndims), &input));
       }
@@ -1702,7 +1702,7 @@ REGISTER_OP("Slice")
       const Tensor* sizes_t = c->input_tensor(2);
 
       if (sizes_t != nullptr && begin_t != nullptr) {
-        std::vector<const Dimension*> dims;
+        std::vector<DimensionHandle> dims;
         // If the begin and sizes tensors are available, then
         // we can be precise about the shape of the output.
         if (begin_t->dtype() == DT_INT64) {
@@ -1831,10 +1831,10 @@ REGISTER_OP("Tile")
     .Output("output: T")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input;
-      const Shape* multiples;
+      ShapeHandle input;
+      ShapeHandle multiples;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &multiples));
-      const Dimension* multiples_dim0 = c->Dim(multiples, 0);
+      DimensionHandle multiples_dim0 = c->Dim(multiples, 0);
       if (!c->ValueKnown(multiples_dim0)) {
         // Length of multiples vector unknown, so output is unknown.
         //
@@ -1856,7 +1856,7 @@ REGISTER_OP("Tile")
       // Multiply each input dimension by its corresponding value
       // from the multiples tensor.
       auto multiples_data = multiples_t->vec<int32>();
-      std::vector<const Dimension*> dims(rank);
+      std::vector<DimensionHandle> dims(rank);
       for (int i = 0; i < rank; ++i) {
         const int32 multiple = multiples_data(i);
         TF_RETURN_IF_ERROR(c->Multiply(c->Dim(input, i), multiple, &dims[i]));
@@ -1945,7 +1945,7 @@ REGISTER_OP("BroadcastGradientArgs")
     .Output("r1: int32")
     .SetShapeFn([](InferenceContext* c) {
       // TODO(mrry): Implement constant_value for BroadcastGradientArgs?
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &unused));
       c->set_output(0, c->Vector(InferenceContext::kUnknownDim));
@@ -2049,9 +2049,9 @@ REGISTER_OP("MirrorPadGrad")
     .Attr("T: type")
     .Attr(GetMirrorPadModeAttrString())
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* paddings;
+      ShapeHandle paddings;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &paddings));
-      const Dimension* pad_0 = c->Dim(paddings, 0);
+      DimensionHandle pad_0 = c->Dim(paddings, 0);
       if (!c->ValueKnown(pad_0)) {
         // We don't know the rank of the output since the first
         // padding dimension is unknown.
@@ -2060,7 +2060,7 @@ REGISTER_OP("MirrorPadGrad")
       }
 
       int64 input_rank = c->Value(pad_0);
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), input_rank, &input));
       TF_RETURN_IF_ERROR(
           c->Merge(paddings, c->Matrix(input_rank, 2), &paddings));
@@ -2075,7 +2075,7 @@ REGISTER_OP("MirrorPadGrad")
       }
 
       auto paddings_data = paddings_t->matrix<int32>();
-      std::vector<const Dimension*> dims(input_rank);
+      std::vector<DimensionHandle> dims(input_rank);
       for (int i = 0; i < input_rank; ++i) {
         const int64 pad0 = static_cast<int64>(paddings_data(i, 0));
         const int64 pad1 = static_cast<int64>(paddings_data(i, 1));
@@ -2137,7 +2137,7 @@ REGISTER_OP("Placeholder")
 
       TensorShapeProto shape_proto;
       shape.AsProto(&shape_proto);
-      const Shape* out;
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &out));
       c->set_output(0, out);
       return Status::OK();
@@ -2162,17 +2162,17 @@ REGISTER_OP("PlaceholderWithDefault")
     .Attr("dtype: type")
     .Attr("shape: shape")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input = c->input(0);
+      ShapeHandle input = c->input(0);
       PartialTensorShape shape;
       TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape));
       TensorShapeProto shape_proto;
       shape.AsProto(&shape_proto);
-      const Shape* out;
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &out));
 
       // We merge for compatibility checking, but return the output,
       // since output_shape may be less precise than input_shape.
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->Merge(input, out, &unused));
       c->set_output(0, out);
       return Status::OK();
@@ -2193,8 +2193,8 @@ REGISTER_OP("ExpandDims")
     .Output("output: T")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input = c->input(0);
-      const Shape* expand_dim;
+      ShapeHandle input = c->input(0);
+      ShapeHandle expand_dim;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &expand_dim));
 
       const Tensor* dim_t = c->input_tensor(1);
@@ -2207,11 +2207,11 @@ REGISTER_OP("ExpandDims")
         which_dim += c->Rank(input) + 1;
       }
 
-      const Shape* end;
+      ShapeHandle end;
       TF_RETURN_IF_ERROR(c->Subshape(input, which_dim, &end));
 
       // Build output as start + 1 + end.
-      const Shape* output;
+      ShapeHandle output;
       TF_RETURN_IF_ERROR(c->Subshape(input, 0, which_dim, &output));
       TF_RETURN_IF_ERROR(c->Concatenate(output, c->Vector(1), &output));
       TF_RETURN_IF_ERROR(c->Concatenate(output, end, &output));
@@ -2265,7 +2265,7 @@ REGISTER_OP("Squeeze")
     .Attr("T: type")
     .Attr("squeeze_dims: list(int) >= 0 = []")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input = c->input(0);
+      ShapeHandle input = c->input(0);
       if (!c->RankKnown(input)) {
         // Input shape unknown.
         return shape_inference::UnknownShape(c);
@@ -2287,7 +2287,7 @@ REGISTER_OP("Squeeze")
         }
       }
 
-      std::vector<const Dimension*> result_shape;
+      std::vector<DimensionHandle> result_shape;
       for (int i = 0; i < input_rank; ++i) {
         // True if squeeze_dims contains an entry to squeeze this
         // dimension.
@@ -2295,7 +2295,7 @@ REGISTER_OP("Squeeze")
             std::find(squeeze_dims.begin(), squeeze_dims.end(), i) !=
             squeeze_dims.end();
 
-        const Dimension* dim = c->Dim(input, i);
+        DimensionHandle dim = c->Dim(input, i);
 
         if (!c->ValueKnown(dim)) {
           // Assume that the squeezed dimension will be 1 at runtime.
@@ -2362,11 +2362,11 @@ REGISTER_OP("ListDiff")
     .Output("idx: int32")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &unused));
       // TODO(mrry): Indicate that the length falls within an interval?
-      const Shape* out = c->Vector(InferenceContext::kUnknownDim);
+      ShapeHandle out = c->Vector(InferenceContext::kUnknownDim);
       c->set_output(0, out);
       c->set_output(1, out);
       return Status::OK();
@@ -2410,14 +2410,14 @@ REGISTER_OP("SpaceToBatch")
     .Attr("T: type")
     .Attr("block_size: int >= 2")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
 
-      const Shape* paddings;
+      ShapeHandle paddings;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &paddings));
 
-      const Dimension* pad0_dim = c->Dim(paddings, 0);
-      const Dimension* pad1_dim = c->Dim(paddings, 1);
+      DimensionHandle pad0_dim = c->Dim(paddings, 0);
+      DimensionHandle pad1_dim = c->Dim(paddings, 1);
 
       if (!c->ValueKnown(pad0_dim) || !c->ValueKnown(pad1_dim)) {
         return shape_inference::UnknownShape(c);
@@ -2433,8 +2433,8 @@ REGISTER_OP("SpaceToBatch")
       int32 block_size;
       TF_RETURN_IF_ERROR(c->GetAttr("block_size", &block_size));
 
-      const Dimension* output_height;
-      const Dimension* output_width;
+      DimensionHandle output_height;
+      DimensionHandle output_width;
 
       const Tensor* paddings_t = c->input_tensor(1);
       if (paddings_t == nullptr) {
@@ -2457,7 +2457,7 @@ REGISTER_OP("SpaceToBatch")
             c->Add(c->Dim(input, 2), pad_left + pad_right, &output_width));
       }
 
-      const Dimension* batch;
+      DimensionHandle batch;
       TF_RETURN_IF_ERROR(
           c->Multiply(c->Dim(input, 0), block_size * block_size, &batch));
 
@@ -2575,14 +2575,14 @@ REGISTER_OP("BatchToSpace")
     .Attr("T: type")
     .Attr("block_size: int >= 2")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
 
-      const Shape* crops;
+      ShapeHandle crops;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &crops));
 
-      const Dimension* crops0_dim = c->Dim(crops, 0);
-      const Dimension* crops1_dim = c->Dim(crops, 1);
+      DimensionHandle crops0_dim = c->Dim(crops, 0);
+      DimensionHandle crops1_dim = c->Dim(crops, 1);
 
       if (!c->ValueKnown(crops0_dim) || !c->ValueKnown(crops1_dim)) {
         return shape_inference::UnknownShape(c);
@@ -2598,13 +2598,13 @@ REGISTER_OP("BatchToSpace")
       int32 block_size;
       TF_RETURN_IF_ERROR(c->GetAttr("block_size", &block_size));
 
-      const Dimension* batch;
+      DimensionHandle batch;
       // Will return an error if does not evenly divide
       TF_RETURN_IF_ERROR(
           c->Divide(c->Dim(input, 0), block_size * block_size, &batch));
 
-      const Dimension* output_height;
-      const Dimension* output_width;
+      DimensionHandle output_height;
+      DimensionHandle output_width;
 
       const Tensor* crops_t = c->input_tensor(1);
       if (crops_t == nullptr) {
@@ -2733,15 +2733,15 @@ REGISTER_OP("SpaceToDepth")
     .Attr("T: type")
     .Attr("block_size: int >= 2")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
 
       int32 block_size;
       TF_RETURN_IF_ERROR(c->GetAttr("block_size", &block_size));
 
-      const Dimension* output_height;
-      const Dimension* output_width;
-      const Dimension* output_depth;
+      DimensionHandle output_height;
+      DimensionHandle output_width;
+      DimensionHandle output_depth;
       // Will return an error if does not evenly divide
       TF_RETURN_IF_ERROR(
           c->Divide(c->Dim(input, 1), block_size, &output_height));
@@ -2840,15 +2840,15 @@ REGISTER_OP("DepthToSpace")
     .Attr("T: type")
     .Attr("block_size: int >= 2")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
 
       int32 block_size;
       TF_RETURN_IF_ERROR(c->GetAttr("block_size", &block_size));
 
-      const Dimension* output_height;
-      const Dimension* output_width;
-      const Dimension* output_depth;
+      DimensionHandle output_height;
+      DimensionHandle output_width;
+      DimensionHandle output_depth;
       TF_RETURN_IF_ERROR(
           c->Multiply(c->Dim(input, 1), block_size, &output_height));
       TF_RETURN_IF_ERROR(
@@ -2956,7 +2956,7 @@ REGISTER_OP("ExtractImagePatches")
     .Attr("T: realnumbertype")
     .Attr(GetPaddingAttrString())
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input_shape;
+      ShapeHandle input_shape;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input_shape));
 
       std::vector<int32> ksizes;
@@ -2998,10 +2998,10 @@ REGISTER_OP("ExtractImagePatches")
       int32 ksize_rows_eff = ksize_rows + (ksize_rows - 1) * (rate_rows - 1);
       int32 ksize_cols_eff = ksize_cols + (ksize_cols - 1) * (rate_cols - 1);
 
-      const Dimension* batch_size_dim = c->Dim(input_shape, 0);
-      const Dimension* in_rows_dim = c->Dim(input_shape, 1);
-      const Dimension* in_cols_dim = c->Dim(input_shape, 2);
-      const Dimension* output_depth_dim = c->Dim(input_shape, 3);
+      DimensionHandle batch_size_dim = c->Dim(input_shape, 0);
+      DimensionHandle in_rows_dim = c->Dim(input_shape, 1);
+      DimensionHandle in_cols_dim = c->Dim(input_shape, 2);
+      DimensionHandle output_depth_dim = c->Dim(input_shape, 3);
 
       // At the moment we need to know the values of several fields.
       TF_RETURN_IF_ERROR(c->ValidateKnownDim(in_rows_dim, "in_rows"));
@@ -3020,7 +3020,7 @@ REGISTER_OP("ExtractImagePatches")
       TF_RETURN_IF_ERROR(GetWindowedOutputSizeVerbose(
           in_cols, ksize_cols_eff, stride_cols, padding, &output_cols,
           &padding_before, &padding_after));
-      const Shape* output_shape = c->MakeShape(
+      ShapeHandle output_shape = c->MakeShape(
           {batch_size_dim, output_rows, output_cols, output_depth_dim});
       c->set_output(0, output_shape);
       return Status::OK();
@@ -3057,7 +3057,7 @@ REGISTER_OP("Bitcast")
     .Attr("T: numbertype")
     .Attr("type: numbertype")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input = c->input(0);
+      ShapeHandle input = c->input(0);
       if (!c->RankKnown(input)) {
         // Input shape unknown.
         return shape_inference::UnknownShape(c);
@@ -3079,7 +3079,7 @@ REGISTER_OP("Bitcast")
                                        "one of the type sizes is zero.");
       }
 
-      const Shape* new_shape;
+      ShapeHandle new_shape;
       if (input_type_size == output_type_size) {
         // No change in size.
         new_shape = input;
@@ -3087,7 +3087,7 @@ REGISTER_OP("Bitcast")
         TF_RETURN_IF_ERROR(c->WithRankAtLeast(input, 1, &new_shape));
 
         int64 divisor_val = output_type_size / input_type_size;
-        const Dimension* last_dim = c->Dim(new_shape, -1);
+        DimensionHandle last_dim = c->Dim(new_shape, -1);
         if (!c->ValueKnown(last_dim) || c->Value(last_dim) == divisor_val) {
           TF_RETURN_IF_ERROR(c->Subshape(new_shape, 0, -1, &new_shape));
         } else {
@@ -3098,7 +3098,7 @@ REGISTER_OP("Bitcast")
       } else {
         // Input type size is larger than output type size.
         int64 divisor_val = input_type_size / output_type_size;
-        const Shape* extension = c->Vector(divisor_val);
+        ShapeHandle extension = c->Vector(divisor_val);
         TF_RETURN_IF_ERROR(c->Concatenate(input, extension, &new_shape));
       }
 
@@ -3136,10 +3136,10 @@ REGISTER_OP("OneHot")
       TF_RETURN_IF_ERROR(c->GetAttr("axis", &axis));
       if (axis < -1) return errors::InvalidArgument("axis must be >= -1");
 
-      const Dimension* depth;
+      DimensionHandle depth;
       TF_RETURN_IF_ERROR(c->MakeDimForScalarInput(1, &depth));
 
-      const Shape* indices = c->input(0);
+      ShapeHandle indices = c->input(0);
       if (!c->RankKnown(indices)) return shape_inference::UnknownShape(c);
 
       int32 new_rank = c->Rank(indices) + 1;
@@ -3147,9 +3147,9 @@ REGISTER_OP("OneHot")
       // C++ returns negative values from % if the dividend is negative.
       int32 depth_index = (axis + new_rank) % new_rank;
       // Out shape is indices[0:depth_index] + [depth] + indices[depth_index:].
-      const Shape* front;
-      const Shape* back;
-      const Shape* out;
+      ShapeHandle front;
+      ShapeHandle back;
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->Subshape(indices, 0, depth_index, &front));
       TF_RETURN_IF_ERROR(c->Subshape(indices, depth_index, &back));
       TF_RETURN_IF_ERROR(c->Concatenate(front, c->Vector(depth), &front));
diff --git a/tensorflow/core/ops/candidate_sampling_ops.cc b/tensorflow/core/ops/candidate_sampling_ops.cc
index 556090231f..037c393574 100644
--- a/tensorflow/core/ops/candidate_sampling_ops.cc
+++ b/tensorflow/core/ops/candidate_sampling_ops.cc
@@ -18,9 +18,9 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 namespace {
 
@@ -30,11 +30,11 @@ Status CandidateSamplerShapeFn(InferenceContext* c) {
   int64 num_true;
   TF_RETURN_IF_ERROR(c->GetAttr("num_true", &num_true));
 
-  const Shape* true_classes_shape;
+  ShapeHandle true_classes_shape;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &true_classes_shape));
-  const Dimension* batch_size = c->Dim(true_classes_shape, 0);
+  DimensionHandle batch_size = c->Dim(true_classes_shape, 0);
 
-  const Shape* num_sampled_v = c->Vector(num_sampled);
+  ShapeHandle num_sampled_v = c->Vector(num_sampled);
   c->set_output(0, num_sampled_v);
   c->set_output(1, c->Matrix(batch_size, num_true));
   c->set_output(2, num_sampled_v);
@@ -378,14 +378,14 @@ REGISTER_OP("ComputeAccidentalHits")
       TF_RETURN_IF_ERROR(c->GetAttr("num_true", &num_true));
 
       // Validate true_classes.
-      const Shape* true_classes;
+      ShapeHandle true_classes;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &true_classes));
-      const Dimension* unused;
+      DimensionHandle unused;
       TF_RETURN_IF_ERROR(
           c->WithValue(c->Dim(true_classes, 1), num_true, &unused));
 
       // All three outputs are the same shape.
-      const Shape* v = c->Vector(InferenceContext::kUnknownDim);
+      ShapeHandle v = c->Vector(InferenceContext::kUnknownDim);
       c->set_output(0, v);
       c->set_output(1, v);
       c->set_output(2, v);
diff --git a/tensorflow/core/ops/control_flow_ops.cc b/tensorflow/core/ops/control_flow_ops.cc
index 3b1b7c63d3..3214017939 100644
--- a/tensorflow/core/ops/control_flow_ops.cc
+++ b/tensorflow/core/ops/control_flow_ops.cc
@@ -20,14 +20,14 @@ limitations under the License.
 namespace tensorflow {
 
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 // --------------------------------------------------------------------------
 namespace {
 Status SwitchShape(InferenceContext* c) {
-  const Shape* unused;
+  ShapeHandle unused;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
-  const Shape* out = c->input(0);
+  ShapeHandle out = c->input(0);
   c->set_output(0, out);
   c->set_output(1, out);
   return Status::OK();
@@ -85,16 +85,16 @@ REGISTER_OP("RefSelect")
     .Attr("T: type")
     .Attr("N: int >= 1")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
-      const Shape* first_input = c->input(1);
+      ShapeHandle first_input = c->input(1);
       if (!c->FullyDefined(first_input)) {
         c->set_output(0, c->UnknownShape());
         return Status::OK();
       }
       // If any inputs aren't fully defined or don't match, we return unknown.
       for (int i = 2; i < c->num_inputs(); ++i) {
-        const Shape* input = c->input(i);
+        ShapeHandle input = c->input(i);
         if (!c->FullyDefined(input) ||
             !c->Merge(first_input, input, &unused).ok()) {
           c->set_output(0, c->UnknownShape());
@@ -115,13 +115,13 @@ output: The forwarded tensor.
 // --------------------------------------------------------------------------
 namespace {
 Status MergeShape(InferenceContext* c) {
-  const Shape* out = c->input(0);
+  ShapeHandle out = c->input(0);
   if (!c->RankKnown(out)) {
     out = c->UnknownShape();
   } else {
     int32 rank = c->Rank(out);
     for (int i = 1; i < c->num_inputs(); ++i) {
-      const Shape* input = c->input(i);
+      ShapeHandle input = c->input(i);
       if (c->Rank(input) != rank) {
         out = c->UnknownShape();
         break;
diff --git a/tensorflow/core/ops/ctc_ops.cc b/tensorflow/core/ops/ctc_ops.cc
index 7e2313ea3a..0b58a8d817 100644
--- a/tensorflow/core/ops/ctc_ops.cc
+++ b/tensorflow/core/ops/ctc_ops.cc
@@ -18,9 +18,9 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 // CTC is Connectionist Temporal Classification.  See util/ctc/ for details.
 
@@ -34,23 +34,23 @@ REGISTER_OP("CTCLoss")
     .Output("loss: float")
     .Output("gradient: float")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* inputs;
-      const Shape* labels_indices;
-      const Shape* labels_values;
-      const Shape* sequence_length;
+      ShapeHandle inputs;
+      ShapeHandle labels_indices;
+      ShapeHandle labels_values;
+      ShapeHandle sequence_length;
 
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 3, &inputs));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &labels_indices));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &labels_values));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 1, &sequence_length));
 
-      const Dimension* unused;
+      DimensionHandle unused;
       TF_RETURN_IF_ERROR(c->Merge(c->Dim(labels_indices, 0),
                                   c->Dim(labels_values, 0), &unused));
 
       // Get batch size from inputs and sequence_length, and update inputs
       // with the merged batch_size since it is returned.
-      const Dimension* batch_size;
+      DimensionHandle batch_size;
       TF_RETURN_IF_ERROR(
           c->Merge(c->Dim(inputs, 1), c->Dim(sequence_length, 0), &batch_size));
       TF_RETURN_IF_ERROR(c->ReplaceDim(inputs, 1, batch_size, &inputs));
@@ -89,18 +89,18 @@ REGISTER_OP("CTCGreedyDecoder")
     .Output("decoded_shape: int64")
     .Output("log_probability: float")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* inputs;
-      const Shape* sequence_length;
+      ShapeHandle inputs;
+      ShapeHandle sequence_length;
 
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 3, &inputs));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &sequence_length));
 
       // Get batch size from inputs and sequence_length.
-      const Dimension* batch_size;
+      DimensionHandle batch_size;
       TF_RETURN_IF_ERROR(
           c->Merge(c->Dim(inputs, 1), c->Dim(sequence_length, 0), &batch_size));
 
-      const Dimension* total_decoded_outputs = c->UnknownDim();
+      DimensionHandle total_decoded_outputs = c->UnknownDim();
       c->set_output(0, c->Matrix(total_decoded_outputs, 2));
       c->set_output(1, c->Vector(total_decoded_outputs));
       c->set_output(2, c->Vector(2));
@@ -144,14 +144,14 @@ REGISTER_OP("CTCBeamSearchDecoder")
     .Output("decoded_shape: top_paths * int64")
     .Output("log_probability: float")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* inputs;
-      const Shape* sequence_length;
+      ShapeHandle inputs;
+      ShapeHandle sequence_length;
 
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 3, &inputs));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &sequence_length));
 
       // Get batch size from inputs and sequence_length.
-      const Dimension* batch_size;
+      DimensionHandle batch_size;
       TF_RETURN_IF_ERROR(
           c->Merge(c->Dim(inputs, 1), c->Dim(sequence_length, 0), &batch_size));
 
@@ -166,7 +166,7 @@ REGISTER_OP("CTCBeamSearchDecoder")
       for (int i = 0; i < top_paths; ++i) {  // decoded_values
         c->set_output(out_idx++, c->Vector(InferenceContext::kUnknownDim));
       }
-      const Shape* shape_v = c->Vector(2);
+      ShapeHandle shape_v = c->Vector(2);
       for (int i = 0; i < top_paths; ++i) {  // decoded_shape
         c->set_output(out_idx++, shape_v);
       }
diff --git a/tensorflow/core/ops/data_flow_ops.cc b/tensorflow/core/ops/data_flow_ops.cc
index 63af3e28b4..724d83b7f0 100644
--- a/tensorflow/core/ops/data_flow_ops.cc
+++ b/tensorflow/core/ops/data_flow_ops.cc
@@ -20,9 +20,9 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 // --------------------------------------------------------------------------
 
@@ -36,8 +36,8 @@ REGISTER_OP("DynamicPartition")
       int64 num_partitions;
       TF_RETURN_IF_ERROR(c->GetAttr("num_partitions", &num_partitions));
 
-      const Shape* data_shape = c->input(0);
-      const Shape* partitions_shape = c->input(1);
+      ShapeHandle data_shape = c->input(0);
+      ShapeHandle partitions_shape = c->input(1);
 
       if (!c->RankKnown(partitions_shape)) {
         return shape_inference::UnknownShape(c);
@@ -46,17 +46,17 @@ REGISTER_OP("DynamicPartition")
       const int64 rank = c->Rank(partitions_shape);
 
       // data shape must start with partitions_shape
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(
           c->MergePrefix(data_shape, partitions_shape, &unused, &unused));
 
       // The partition shape is dynamic in the 0th dimension, and matches
       // data_shape in the remaining dimensions.
-      const Shape* unknown_dim0 = c->MakeShape({c->UnknownDim()});
+      ShapeHandle unknown_dim0 = c->MakeShape({c->UnknownDim()});
 
-      const Shape* data_suffix_shape;
+      ShapeHandle data_suffix_shape;
       TF_RETURN_IF_ERROR(c->Subshape(data_shape, rank, &data_suffix_shape));
-      const Shape* result_shape;
+      ShapeHandle result_shape;
       TF_RETURN_IF_ERROR(
           c->Concatenate(unknown_dim0, data_suffix_shape, &result_shape));
 
@@ -115,10 +115,10 @@ REGISTER_OP("DynamicStitch")
       int64 num_partitions;
       TF_RETURN_IF_ERROR(c->GetAttr("N", &num_partitions));
 
-      const Shape* extra_shape = c->UnknownShape();
+      ShapeHandle extra_shape = c->UnknownShape();
       for (int i = 0; i < num_partitions; ++i) {
-        const Shape* indices_shape = c->input(i);
-        const Shape* data_shape = c->input(i + num_partitions);
+        ShapeHandle indices_shape = c->input(i);
+        ShapeHandle data_shape = c->input(i + num_partitions);
         if (!c->RankKnown(indices_shape)) {
           continue;
         }
@@ -126,17 +126,17 @@ REGISTER_OP("DynamicStitch")
         const int64 indices_rank = c->Rank(indices_shape);
 
         // Assert that data_shape starts with indices_shape.
-        const Shape* unused;
+        ShapeHandle unused;
         TF_RETURN_IF_ERROR(
             c->MergePrefix(data_shape, indices_shape, &unused, &unused));
 
         // The rest belongs to output.
-        const Shape* rest;
+        ShapeHandle rest;
         TF_RETURN_IF_ERROR(c->Subshape(data_shape, indices_rank, &rest));
         TF_RETURN_IF_ERROR(c->Merge(extra_shape, rest, &extra_shape));
       }
 
-      const Shape* output_shape = c->Vector(c->UnknownDim());
+      ShapeHandle output_shape = c->Vector(c->UnknownDim());
       TF_RETURN_IF_ERROR(
           c->Concatenate(output_shape, extra_shape, &output_shape));
       c->set_output(0, output_shape);
@@ -547,7 +547,7 @@ REGISTER_OP("TensorArray")
     .Output("handle: Ref(string)")
     .SetIsStateful()
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
       c->set_output(0, c->Vector(2));
       return Status::OK();
@@ -576,8 +576,8 @@ REGISTER_OP("TensorArrayGrad")
     .Attr("source: string")
     .SetIsStateful()
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
-      const Dimension* unused_dim;
+      ShapeHandle unused;
+      DimensionHandle unused_dim;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused));
       TF_RETURN_IF_ERROR(c->WithValue(c->Dim(c->input(0), 0), 2, &unused_dim));
       c->set_output(0, c->Vector(2));
@@ -637,8 +637,8 @@ REGISTER_OP("TensorArrayWrite")
     .Output("flow_out: float")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
-      const Dimension* unused_dim;
+      ShapeHandle unused;
+      DimensionHandle unused_dim;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused));
       TF_RETURN_IF_ERROR(c->WithValue(c->Dim(c->input(0), 0), 2, &unused_dim));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
@@ -662,8 +662,8 @@ REGISTER_OP("TensorArrayRead")
     .Output("value: dtype")
     .Attr("dtype: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
-      const Dimension* unused_dim;
+      ShapeHandle unused;
+      DimensionHandle unused_dim;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused));
       TF_RETURN_IF_ERROR(c->WithValue(c->Dim(c->input(0), 0), 2, &unused_dim));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
@@ -686,8 +686,8 @@ REGISTER_OP("TensorArrayPack")
     .Attr("dtype: type")
     .Attr("element_shape: shape = { unknown_rank: true }")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
-      const Dimension* unused_dim;
+      ShapeHandle unused;
+      DimensionHandle unused_dim;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused));
       TF_RETURN_IF_ERROR(c->WithValue(c->Dim(c->input(0), 0), 2, &unused_dim));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
@@ -715,8 +715,8 @@ REGISTER_OP("TensorArrayUnpack")
     .Output("flow_out: float")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
-      const Dimension* unused_dim;
+      ShapeHandle unused;
+      DimensionHandle unused_dim;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused));
       TF_RETURN_IF_ERROR(c->WithValue(c->Dim(c->input(0), 0), 2, &unused_dim));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
@@ -739,8 +739,8 @@ REGISTER_OP("TensorArrayConcat")
     .Attr("dtype: type")
     .Attr("element_shape_except0: shape = { unknown_rank: true }")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
-      const Dimension* unused_dim;
+      ShapeHandle unused;
+      DimensionHandle unused_dim;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused));
       TF_RETURN_IF_ERROR(c->WithValue(c->Dim(c->input(0), 0), 2, &unused_dim));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
@@ -785,8 +785,8 @@ REGISTER_OP("TensorArraySplit")
     .Output("flow_out: float")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
-      const Dimension* unused_dim;
+      ShapeHandle unused;
+      DimensionHandle unused_dim;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused));
       TF_RETURN_IF_ERROR(c->WithValue(c->Dim(c->input(0), 0), 2, &unused_dim));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &unused));
@@ -827,8 +827,8 @@ REGISTER_OP("TensorArraySize")
     .Input("flow_in: float")
     .Output("size: int32")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
-      const Dimension* unused_dim;
+      ShapeHandle unused;
+      DimensionHandle unused_dim;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused));
       TF_RETURN_IF_ERROR(c->WithValue(c->Dim(c->input(0), 0), 2, &unused_dim));
       return shape_inference::ScalarShape(c);
@@ -844,8 +844,8 @@ size: The current size of the TensorArray.
 REGISTER_OP("TensorArrayClose")
     .Input("handle: Ref(string)")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
-      const Dimension* unused_dim;
+      ShapeHandle unused;
+      DimensionHandle unused_dim;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &unused));
       TF_RETURN_IF_ERROR(c->WithValue(c->Dim(c->input(0), 0), 2, &unused_dim));
       return Status::OK();
@@ -900,9 +900,9 @@ REGISTER_OP("BarrierInsertMany")
     .Attr("T: type")
     .Attr("component_index: int")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* keys = c->input(1);
-      const Shape* values = c->input(2);
-      const Shape* unused;
+      ShapeHandle keys = c->input(1);
+      ShapeHandle values = c->input(2);
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(keys, 1, &keys));
       TF_RETURN_IF_ERROR(c->WithRankAtLeast(values, 1, &values));
@@ -1016,7 +1016,7 @@ REGISTER_OP("LookupTableFind")
     .Attr("Tin: type")
     .Attr("Tout: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
       c->set_output(0, c->UnknownShape());
@@ -1044,7 +1044,7 @@ REGISTER_OP("LookupTableInsert")
     .Attr("Tin: type")
     .Attr("Tout: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
       TF_RETURN_IF_ERROR(c->Merge(c->input(1), c->input(2), &unused));
       return Status::OK();
@@ -1064,7 +1064,7 @@ REGISTER_OP("LookupTableSize")
     .Input("table_handle: Ref(string)")
     .Output("size: int64")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
       c->set_output(0, c->Scalar());
       return Status::OK();
@@ -1083,12 +1083,12 @@ REGISTER_OP("LookupTableExport")
     .Attr("Tkeys: type")
     .Attr("Tvalues: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
 
-      const Shape* values = c->UnknownShape();
+      ShapeHandle values = c->UnknownShape();
       TF_RETURN_IF_ERROR(c->WithRankAtLeast(values, 1, &values));
-      const Shape* keys = c->Vector(c->Dim(values, 0));
+      ShapeHandle keys = c->Vector(c->Dim(values, 0));
       c->set_output(0, keys);
       c->set_output(1, values);
       return Status::OK();
@@ -1108,7 +1108,7 @@ REGISTER_OP("LookupTableImport")
     .Attr("Tin: type")
     .Attr("Tout: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
       TF_RETURN_IF_ERROR(c->Merge(c->input(1), c->input(2), &unused));
       return Status::OK();
@@ -1211,9 +1211,9 @@ REGISTER_OP("InitializeTable")
     .Attr("Tkey: type")
     .Attr("Tval: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
-      const Shape* keys;
+      ShapeHandle keys;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &keys));
       TF_RETURN_IF_ERROR(c->Merge(keys, c->input(2), &keys));
       return Status::OK();
@@ -1234,7 +1234,7 @@ REGISTER_OP("InitializeTableFromTextFile")
     .Attr("vocab_size: int >= -1 = -1")
     .Attr("delimiter: string = '\t'")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
       return Status::OK();
diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc
index 5a55493517..7eb380798c 100644
--- a/tensorflow/core/ops/image_ops.cc
+++ b/tensorflow/core/ops/image_ops.cc
@@ -19,26 +19,26 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 namespace {
 
 // Sets output[0] to shape [batch_dim,height,width,channel_dim], where
 // height and width come from the size_tensor.
-Status SetOutputToSizedImage(InferenceContext* c, const Dimension* batch_dim,
-                             int size_input_idx, const Dimension* channel_dim) {
+Status SetOutputToSizedImage(InferenceContext* c, DimensionHandle batch_dim,
+                             int size_input_idx, DimensionHandle channel_dim) {
   // Verify shape of size input.
-  const Shape* size;
+  ShapeHandle size;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(size_input_idx), 1, &size));
-  const Dimension* unused;
+  DimensionHandle unused;
   TF_RETURN_IF_ERROR(c->WithValue(c->Dim(size, 0), 2, &unused));
 
   // Get size values from the size tensor.
   const Tensor* size_tensor = c->input_tensor(size_input_idx);
-  const Dimension* width;
-  const Dimension* height;
+  DimensionHandle width;
+  DimensionHandle height;
   if (size_tensor == nullptr) {
     width = c->UnknownDim();
     height = c->UnknownDim();
@@ -51,16 +51,16 @@ Status SetOutputToSizedImage(InferenceContext* c, const Dimension* batch_dim,
 }
 
 Status ResizeShapeFn(InferenceContext* c) {
-  const Shape* input;
+  ShapeHandle input;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
   return SetOutputToSizedImage(c, c->Dim(input, 0), 1 /* size_input_idx */,
                                c->Dim(input, 3));
 }
 
 Status DecodeImageShapeFn(InferenceContext* c) {
-  const Shape* unused;
+  ShapeHandle unused;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
-  const Dimension* channels_dim;
+  DimensionHandle channels_dim;
   int32 channels;
   Status s = c->GetAttr("channels", &channels);
   if (s.ok()) {
@@ -79,20 +79,20 @@ Status DecodeImageShapeFn(InferenceContext* c) {
 }
 
 Status EncodeImageShapeFn(InferenceContext* c) {
-  const Shape* unused;
+  ShapeHandle unused;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 3, &unused));
   c->set_output(0, c->Scalar());
   return Status::OK();
 }
 
 Status ColorspaceShapeFn(InferenceContext* c) {
-  const Shape* input;
+  ShapeHandle input;
   TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &input));
 
   // The last dimension value is always 3.
-  const Dimension* last_dim;
+  DimensionHandle last_dim;
   TF_RETURN_IF_ERROR(c->WithValue(c->Dim(input, -1), 3, &last_dim));
-  const Shape* out;
+  ShapeHandle out;
   TF_RETURN_IF_ERROR(c->ReplaceDim(input, -1, last_dim, &out));
   c->set_output(0, out);
 
@@ -224,10 +224,10 @@ REGISTER_OP("ResizeNearestNeighborGrad")
     .Attr("T: {uint8, int8, int32, half, float, double}")
     .Attr("align_corners: bool = false")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
-      const Shape* unused;
-      const Dimension* unused_dim;
+      ShapeHandle unused;
+      DimensionHandle unused_dim;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &unused));
       TF_RETURN_IF_ERROR(c->WithValue(c->Dim(unused, 0), 2, &unused_dim));
       const Tensor* size = c->input_tensor(1);
@@ -665,15 +665,15 @@ REGISTER_OP("ExtractGlimpse")
     .Attr("normalized: bool = true")
     .Attr("uniform_noise: bool = true")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
-      const Shape* offsets;
+      ShapeHandle offsets;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 2, &offsets));
 
-      const Dimension* batch_dim;
+      DimensionHandle batch_dim;
       TF_RETURN_IF_ERROR(
           c->Merge(c->Dim(input, 0), c->Dim(offsets, 0), &batch_dim));
-      const Dimension* unused;
+      DimensionHandle unused;
       TF_RETURN_IF_ERROR(c->WithValue(c->Dim(offsets, 1), 2, &unused));
 
       return SetOutputToSizedImage(c, batch_dim, 1 /* size_input_idx */,
@@ -734,20 +734,20 @@ REGISTER_OP("CropAndResize")
     .Attr("extrapolation_value: float = 0")
     .SetShapeFn([](InferenceContext* c) {
       // Get inputs and validate ranks.
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
-      const Shape* boxes;
+      ShapeHandle boxes;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &boxes));
-      const Shape* box_ind;
+      ShapeHandle box_ind;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &box_ind));
 
       // boxes[0] and box_ind[0] are both num_boxes.
-      const Dimension* num_boxes_dim;
+      DimensionHandle num_boxes_dim;
       TF_RETURN_IF_ERROR(
           c->Merge(c->Dim(boxes, 0), c->Dim(box_ind, 0), &num_boxes_dim));
 
       // boxes.dim(1) is 4.
-      const Dimension* unused;
+      DimensionHandle unused;
       TF_RETURN_IF_ERROR(c->WithValue(c->Dim(boxes, 1), 4, &unused));
 
       return SetOutputToSizedImage(c, num_boxes_dim, 3 /* size_input_idx */,
@@ -797,7 +797,7 @@ REGISTER_OP("CropAndResizeGradImage")
     .Attr("T: {float, half, double}")
     .Attr("method: {'bilinear'} = 'bilinear'")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* out;
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(3, &out));
       TF_RETURN_IF_ERROR(c->WithRank(out, 4, &out));
       c->set_output(0, out);
diff --git a/tensorflow/core/ops/io_ops.cc b/tensorflow/core/ops/io_ops.cc
index 1d528660cf..83e4a83897 100644
--- a/tensorflow/core/ops/io_ops.cc
+++ b/tensorflow/core/ops/io_ops.cc
@@ -19,14 +19,14 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 namespace {
 
 Status ScalarInputsAndOutputs(InferenceContext* c) {
-  const Shape* unused;
+  ShapeHandle unused;
   for (int i = 0; i < c->num_inputs(); ++i) {
     TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 0, &unused));
   }
@@ -44,9 +44,9 @@ REGISTER_OP("Save")
     .Input("data: T")
     .Attr("T: list(type)")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
-      const Shape* s;
-      const Dimension* unused_dim;
+      ShapeHandle unused;
+      ShapeHandle s;
+      DimensionHandle unused_dim;
 
       // Validate filename.
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
@@ -79,9 +79,9 @@ REGISTER_OP("SaveSlices")
     .Input("data: T")
     .Attr("T: list(type)")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
-      const Shape* s;
-      const Dimension* unused_dim;
+      ShapeHandle unused;
+      ShapeHandle s;
+      DimensionHandle unused_dim;
 
       // Validate filename.
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
@@ -136,7 +136,7 @@ REGISTER_OP("Restore")
     .Attr("dt: type")
     .Attr("preferred_shard: int = -1")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
       c->set_output(0, c->UnknownShape());
@@ -180,7 +180,7 @@ REGISTER_OP("RestoreSlice")
     .Attr("dt: type")
     .Attr("preferred_shard: int = -1")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
@@ -353,11 +353,11 @@ REGISTER_OP("ReaderReadUpTo")
     .Output("keys: string")
     .Output("values: string")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));
-      const Shape* out = c->Vector(InferenceContext::kUnknownDim);
+      ShapeHandle out = c->Vector(InferenceContext::kUnknownDim);
       c->set_output(0, out);
       c->set_output(1, out);
       return Status::OK();
@@ -451,7 +451,7 @@ REGISTER_OP("MatchingFiles")
     .Input("pattern: string")
     .Output("filenames: string")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &unused));
       c->set_output(0, c->Vector(InferenceContext::kUnknownDim));
       return Status::OK();
diff --git a/tensorflow/core/ops/linalg_ops.cc b/tensorflow/core/ops/linalg_ops.cc
index 54b8e22b7e..d7582bd4cb 100644
--- a/tensorflow/core/ops/linalg_ops.cc
+++ b/tensorflow/core/ops/linalg_ops.cc
@@ -18,62 +18,61 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 namespace {
 
 // Return in <out> the result of making <s> a square matrix.
-Status MakeSquareMatrix(InferenceContext* c, const Shape* s,
-                        const Shape** out) {
+Status MakeSquareMatrix(InferenceContext* c, ShapeHandle s, ShapeHandle* out) {
   TF_RETURN_IF_ERROR(c->WithRank(s, 2, &s));
-  const Dimension* d;
+  DimensionHandle d;
   TF_RETURN_IF_ERROR(c->Merge(c->Dim(s, 0), c->Dim(s, 1), &d));
   *out = c->Matrix(d, d);
   return Status::OK();
 }
 
 Status UnchangedSquareShapeFn(InferenceContext* c) {
-  const Shape* out;
+  ShapeHandle out;
   TF_RETURN_IF_ERROR(MakeSquareMatrix(c, c->input(0), &out));
   c->set_output(0, out);
   return Status::OK();
 }
 
 // Return in <out> the result of making the end of <s> a square matrix.
-Status MakeBatchSquareMatrix(InferenceContext* c, const Shape* input,
-                             const Shape** out) {
-  const Shape* s;
+Status MakeBatchSquareMatrix(InferenceContext* c, ShapeHandle input,
+                             ShapeHandle* out) {
+  ShapeHandle s;
   TF_RETURN_IF_ERROR(c->WithRankAtLeast(input, 2, &s));
 
-  const Dimension* d;
+  DimensionHandle d;
   TF_RETURN_IF_ERROR(c->Merge(c->Dim(s, -2), c->Dim(s, -1), &d));
 
-  const Shape* batch_shape;
+  ShapeHandle batch_shape;
   TF_RETURN_IF_ERROR(c->Subshape(s, 0, -2, &batch_shape));
   TF_RETURN_IF_ERROR(c->Concatenate(batch_shape, c->Matrix(d, d), out));
   return Status::OK();
 }
 
 Status BatchUnchangedSquareShapeFn(InferenceContext* c) {
-  const Shape* out;
+  ShapeHandle out;
   TF_RETURN_IF_ERROR(MakeBatchSquareMatrix(c, c->input(0), &out));
   c->set_output(0, out);
   return Status::OK();
 }
 
 Status SquareMatrixSolveShapeFn(InferenceContext* c) {
-  const Shape* lhs;
-  const Shape* rhs;
+  ShapeHandle lhs;
+  ShapeHandle rhs;
   TF_RETURN_IF_ERROR(MakeSquareMatrix(c, c->input(0), &lhs));
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &rhs));
 
   // lhs and rhs have the same number of rows. Make a new output
   // shape that uses rows to replace rhs.dim[0].
-  const Dimension* rows;
+  DimensionHandle rows;
   TF_RETURN_IF_ERROR(c->Merge(c->Dim(lhs, 0), c->Dim(rhs, 0), &rows));
-  const Shape* out;
+  ShapeHandle out;
   TF_RETURN_IF_ERROR(c->ReplaceDim(rhs, 0, rows, &out));
   c->set_output(0, out);
   return Status::OK();
@@ -82,8 +81,8 @@ Status SquareMatrixSolveShapeFn(InferenceContext* c) {
 // Inputs are [...,M,N] and [...,M,K].  Output is [...,N,K].
 // If <square>, then input is [...,M,M].
 Status BatchMatrixSolveShapeFn(InferenceContext* c, bool square) {
-  const Shape* lhs;
-  const Shape* rhs;
+  ShapeHandle lhs;
+  ShapeHandle rhs;
   if (square) {
     TF_RETURN_IF_ERROR(MakeBatchSquareMatrix(c, c->input(0), &lhs));
   } else {
@@ -92,44 +91,44 @@ Status BatchMatrixSolveShapeFn(InferenceContext* c, bool square) {
   TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(1), 2, &rhs));
 
   // Make the common batch subshape between the two dimensions.
-  const Shape* lhs_batch_shape;
-  const Shape* batch_shape;
+  ShapeHandle lhs_batch_shape;
+  ShapeHandle batch_shape;
   TF_RETURN_IF_ERROR(c->Subshape(lhs, 0, -2, &lhs_batch_shape));
   TF_RETURN_IF_ERROR(c->Subshape(rhs, 0, -2, &batch_shape));
   TF_RETURN_IF_ERROR(c->Merge(lhs_batch_shape, batch_shape, &batch_shape));
 
   // lhs and rhs have the same value for m.
-  const Dimension* m;
+  DimensionHandle m;
   TF_RETURN_IF_ERROR(c->Merge(c->Dim(lhs, -2), c->Dim(rhs, -2), &m));
 
-  const Dimension* n = c->Dim(lhs, -1);
+  DimensionHandle n = c->Dim(lhs, -1);
   if (square) {
     TF_RETURN_IF_ERROR(c->Merge(m, n, &n));
   }
 
   // Build final shape (batch_shape + n + k) in <out>.
-  const Shape* out;
+  ShapeHandle out;
   TF_RETURN_IF_ERROR(c->Concatenate(batch_shape, c->Vector(n), &out));
   TF_RETURN_IF_ERROR(c->Concatenate(out, c->Vector(c->Dim(rhs, -1)), &out));
   c->set_output(0, out);
   return Status::OK();
 }
 
-Status BatchSvdShapeHelperFn(InferenceContext* c, const Shape* input) {
-  const Dimension* m = c->Dim(input, -2);
-  const Dimension* n = c->Dim(input, -1);
-  const Dimension* p;
+Status BatchSvdShapeHelperFn(InferenceContext* c, ShapeHandle input) {
+  DimensionHandle m = c->Dim(input, -2);
+  DimensionHandle n = c->Dim(input, -1);
+  DimensionHandle p;
   TF_RETURN_IF_ERROR(c->Min(m, n, &p));
-  const Shape* batch_shape;
+  ShapeHandle batch_shape;
   TF_RETURN_IF_ERROR(c->Subshape(input, 0, -2, &batch_shape));
-  const Shape* e_shape;
+  ShapeHandle e_shape;
   TF_RETURN_IF_ERROR(c->Concatenate(batch_shape, c->Vector(p), &e_shape));
   c->set_output(0, e_shape);
   bool compute_uv;
   TF_RETURN_IF_ERROR(c->GetAttr("compute_uv", &compute_uv));
   if (compute_uv) {
-    const Shape* u_shape;
-    const Shape* v_shape;
+    ShapeHandle u_shape;
+    ShapeHandle v_shape;
     bool full_matrices;
     TF_RETURN_IF_ERROR(c->GetAttr("full_matrices", &full_matrices));
     if (full_matrices) {
@@ -159,7 +158,7 @@ Status BatchSvdShapeHelperFn(InferenceContext* c, const Shape* input) {
 //   [M,P]; [N,P], if compute_uv is true and full_matrices is false,
 // where P = min(M,N).
 Status SvdShapeFn(InferenceContext* c) {
-  const Shape* input;
+  ShapeHandle input;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &input));
   return BatchSvdShapeHelperFn(c, input);
 }
@@ -171,7 +170,7 @@ Status SvdShapeFn(InferenceContext* c) {
 //   [...,M,P]; [...,N,P], if compute_uv is true and full_matrices is false,
 // where P = min(M,N).
 Status BatchSvdShapeFn(InferenceContext* c) {
-  const Shape* input;
+  ShapeHandle input;
   TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 2, &input));
   return BatchSvdShapeHelperFn(c, input);
 }
@@ -180,9 +179,9 @@ Status BatchSvdShapeFn(InferenceContext* c) {
 //   [N];[0], if compute_v is false,
 //   [N];[N,N], if compute_v is true.
 Status SelfAdjointEigV2ShapeFn(InferenceContext* c) {
-  const Shape* input;
+  ShapeHandle input;
   TF_RETURN_IF_ERROR(MakeSquareMatrix(c, c->input(0), &input));
-  const Dimension* n;
+  DimensionHandle n;
   TF_RETURN_IF_ERROR(c->Merge(c->Dim(input, 0), c->Dim(input, 1), &n));
   c->set_output(0, c->Vector(n));
   bool compute_v;
@@ -199,19 +198,19 @@ Status SelfAdjointEigV2ShapeFn(InferenceContext* c) {
 //   [...,N];[0], if compute_v is false,
 //   [...,N];[...,N,N], if compute_v is true.
 Status BatchSelfAdjointEigV2ShapeFn(InferenceContext* c) {
-  const Shape* input;
+  ShapeHandle input;
   TF_RETURN_IF_ERROR(MakeBatchSquareMatrix(c, c->input(0), &input));
-  const Dimension* n;
+  DimensionHandle n;
   TF_RETURN_IF_ERROR(c->Merge(c->Dim(input, -2), c->Dim(input, -1), &n));
-  const Shape* batch_shape;
+  ShapeHandle batch_shape;
   TF_RETURN_IF_ERROR(c->Subshape(input, 0, -2, &batch_shape));
-  const Shape* e_shape;
+  ShapeHandle e_shape;
   TF_RETURN_IF_ERROR(c->Concatenate(batch_shape, c->Vector(n), &e_shape));
   c->set_output(0, e_shape);
   bool compute_v;
   TF_RETURN_IF_ERROR(c->GetAttr("compute_v", &compute_v));
   if (compute_v) {
-    const Shape* v_shape;
+    ShapeHandle v_shape;
     TF_RETURN_IF_ERROR(c->Concatenate(batch_shape, c->Matrix(n, n), &v_shape));
     c->set_output(1, v_shape);
   } else {
@@ -227,7 +226,7 @@ REGISTER_OP("MatrixDeterminant")
     .Output("output: T")
     .Attr("T: {float, double}")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(MakeSquareMatrix(c, c->input(0), &input));
       c->set_output(0, c->Scalar());
       return Status::OK();
@@ -244,14 +243,14 @@ REGISTER_OP("BatchMatrixDeterminant")
     .Output("output: T")
     .Attr("T: {float, double}")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 2, &input));
 
-      const Dimension* unused;
+      DimensionHandle unused;
       TF_RETURN_IF_ERROR(
           c->Merge(c->Dim(input, -1), c->Dim(input, -2), &unused));
 
-      const Shape* out;
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->Subshape(input, 0, -2, &out));
       c->set_output(0, out);
       return Status::OK();
@@ -395,11 +394,11 @@ REGISTER_OP("SelfAdjointEig")
     .Attr("T: {double, float}")
     .Deprecated(11, "Use SelfAdjointEigV2 instead.")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(MakeSquareMatrix(c, c->input(0), &input));
 
-      const Dimension* d = c->Dim(input, 0);
-      const Dimension* d_plus_1;
+      DimensionHandle d = c->Dim(input, 0);
+      DimensionHandle d_plus_1;
       TF_RETURN_IF_ERROR(c->Add(d, 1, &d_plus_1));
       c->set_output(0, c->Matrix(d_plus_1, d));
       return Status::OK();
@@ -423,14 +422,14 @@ REGISTER_OP("BatchSelfAdjointEig")
     .Attr("T: {double, float}")
     .Deprecated(11, "Use BatchSelfAdjointEigV2 instead.")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(MakeBatchSquareMatrix(c, c->input(0), &input));
 
-      const Dimension* d = c->Dim(input, -1);
-      const Dimension* d_plus_1;
+      DimensionHandle d = c->Dim(input, -1);
+      DimensionHandle d_plus_1;
       TF_RETURN_IF_ERROR(c->Add(d, 1, &d_plus_1));
 
-      const Shape* s;
+      ShapeHandle s;
       TF_RETURN_IF_ERROR(c->Subshape(input, 0, -2, &s));
       TF_RETURN_IF_ERROR(c->Concatenate(s, c->Matrix(d_plus_1, d), &s));
       c->set_output(0, s);
@@ -627,13 +626,13 @@ REGISTER_OP("MatrixSolveLs")
     .Attr("T: {double, float}")
     .Attr("fast: bool = True")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* lhs;
-      const Shape* rhs;
+      ShapeHandle lhs;
+      ShapeHandle rhs;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &lhs));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &rhs));
 
       // The matrix and right-hand side must have the same number of rows.
-      const Dimension* unused;
+      DimensionHandle unused;
       TF_RETURN_IF_ERROR(c->Merge(c->Dim(lhs, 0), c->Dim(rhs, 0), &unused));
 
       c->set_output(0, c->Matrix(c->Dim(lhs, 1), c->Dim(rhs, 1)));
diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index 6b5c450f0e..4d42ab4b47 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -20,9 +20,9 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 REGISTER_OP("AddN")
     .Input("inputs: N * T")
@@ -32,7 +32,7 @@ REGISTER_OP("AddN")
     .SetIsCommutative()
     .SetIsAggregate()
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* cur = c->input(c->num_inputs() - 1);
+      ShapeHandle cur = c->input(c->num_inputs() - 1);
       for (int i = c->num_inputs() - 2; i >= 0; --i) {
         TF_RETURN_WITH_CONTEXT_IF_ERROR(c->Merge(c->input(i), cur, &cur),
                                         "From merging shape ", i,
@@ -51,8 +51,8 @@ namespace {
 
 // Shape inference function for binary operators that broadcast their inputs.
 Status BroadcastBinaryOpShapeFn(InferenceContext* c) {
-  const Shape* shape_x = c->input(0);
-  const Shape* shape_y = c->input(1);
+  ShapeHandle shape_x = c->input(0);
+  ShapeHandle shape_y = c->input(1);
   if (!c->RankKnown(shape_x) || !c->RankKnown(shape_y)) {
     c->set_output(0, c->UnknownShape());
     return Status::OK();
@@ -64,8 +64,8 @@ Status BroadcastBinaryOpShapeFn(InferenceContext* c) {
   // To compute the broadcast dimensions, we zip together shape_x and shape_y
   // and
   // pad with 1 to make them the same length.
-  std::vector<const Dimension*> dims;
-  const Dimension* dim_one = rank_x == rank_y ? nullptr : c->MakeDim(1);
+  std::vector<DimensionHandle> dims;
+  DimensionHandle dim_one = rank_x == rank_y ? nullptr : c->MakeDim(1);
   for (int i = 0; i < rank_out; ++i) {
     const auto* dim_x = i < (rank_out - rank_x)
                             ? dim_one
@@ -103,7 +103,7 @@ Status BroadcastBinaryOpShapeFn(InferenceContext* c) {
         dims.push_back(dim_x);
       }
     } else {
-      const Dimension* dim;
+      DimensionHandle dim;
       TF_RETURN_IF_ERROR(c->Merge(dim_x, dim_y, &dim));
       dims.push_back(dim);
     }
@@ -125,8 +125,8 @@ REGISTER_OP("BatchMatMul")
     .Attr("adj_x: bool = false")
     .Attr("adj_y: bool = false")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* a_shape;
-      const Shape* b_shape;
+      ShapeHandle a_shape;
+      ShapeHandle b_shape;
       TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 3, &a_shape));
       TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(1), 3, &b_shape));
 
@@ -135,23 +135,23 @@ REGISTER_OP("BatchMatMul")
       bool adj_y;
       TF_RETURN_IF_ERROR(c->GetAttr("adj_x", &adj_x));
       TF_RETURN_IF_ERROR(c->GetAttr("adj_y", &adj_y));
-      const Dimension* output_rows = c->Dim(a_shape, adj_x ? -1 : -2);
-      const Dimension* output_cols = c->Dim(b_shape, adj_y ? -2 : -1);
+      DimensionHandle output_rows = c->Dim(a_shape, adj_x ? -1 : -2);
+      DimensionHandle output_cols = c->Dim(b_shape, adj_y ? -2 : -1);
 
       // Batch dims match between inputs.
-      const Shape* a_batch_dims;
-      const Shape* b_batch_dims;
-      const Shape* batch_dims;
+      ShapeHandle a_batch_dims;
+      ShapeHandle b_batch_dims;
+      ShapeHandle batch_dims;
       TF_RETURN_IF_ERROR(c->Subshape(a_shape, 0, -2, &a_batch_dims));
       TF_RETURN_IF_ERROR(c->Subshape(b_shape, 0, -2, &b_batch_dims));
       TF_RETURN_IF_ERROR(c->Merge(a_batch_dims, b_batch_dims, &batch_dims));
 
       // Assert inner dims match.
-      const Dimension* unused;
+      DimensionHandle unused;
       TF_RETURN_IF_ERROR(c->Merge(c->Dim(a_shape, adj_x ? -2 : -1),
                                   c->Dim(b_shape, adj_y ? -1 : -2), &unused));
 
-      const Shape* out;
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->Concatenate(
           batch_dims, c->Matrix(output_rows, output_cols), &out));
       c->set_output(0, out);
@@ -814,8 +814,8 @@ REGISTER_OP("Select")
     .Output("output: T")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* cond = c->input(0);
-      const Shape* data = c->input(1);
+      ShapeHandle cond = c->input(0);
+      ShapeHandle data = c->input(1);
       TF_RETURN_IF_ERROR(c->Merge(data, c->input(2), &data));
 
       // Validate condition's shape if possible.
@@ -830,12 +830,12 @@ REGISTER_OP("Select")
             if (c->Rank(cond) == 1) {
               // Must be a vector whose first dimension matches first dimension
               // of the data vectors.
-              const Dimension* merged_dim;
+              DimensionHandle merged_dim;
               TF_RETURN_IF_ERROR(
                   c->Merge(c->Dim(data, 0), c->Dim(cond, 0), &merged_dim));
               if (merged_dim != c->Dim(data, 0)) {
                 // Merging used the cond dim.  Update data to refer to it.
-                std::vector<const Dimension*> dims{merged_dim};
+                std::vector<DimensionHandle> dims{merged_dim};
                 for (int i = 1; i < data_rank; ++i) {
                   dims.push_back(c->Dim(data, i));
                 }
@@ -1075,10 +1075,10 @@ output: The reduced tensor.
 namespace {
 
 Status ArgOpShape(shape_inference::InferenceContext* c) {
-  const Shape* dimension_shape;
+  ShapeHandle dimension_shape;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &dimension_shape));
 
-  const Shape* input_shape = c->input(0);
+  ShapeHandle input_shape = c->input(0);
   if (!c->RankKnown(input_shape)) {
     return shape_inference::UnknownShape(c);
   }
@@ -1094,7 +1094,7 @@ Status ArgOpShape(shape_inference::InferenceContext* c) {
     // We don't know the value of the dimension, but we
     // know the rank of the input, so return the correct
     // rank with unknown dimensions.
-    std::vector<const Dimension*> dims(input_rank - 1);
+    std::vector<DimensionHandle> dims(input_rank - 1);
     for (int i = 0; i < dims.size(); ++i) {
       dims[i] = c->UnknownDim();
     }
@@ -1112,7 +1112,7 @@ Status ArgOpShape(shape_inference::InferenceContext* c) {
   }
 
   // Return the input shape without the dimension being reduced.
-  std::vector<const Dimension*> dims;
+  std::vector<DimensionHandle> dims;
   for (int i = 0; i < input_rank; ++i) {
     if (dimension_val != i) {
       dims.emplace_back(c->Dim(input_shape, i));
@@ -1153,15 +1153,15 @@ dimension: int32, 0 <= dimension < rank(input).  Describes which dimension
 namespace {
 
 Status SegmentReductionShapeFn(InferenceContext* c) {
-  const Shape* data_shape;
-  const Shape* segment_ids_shape;
+  ShapeHandle data_shape;
+  ShapeHandle segment_ids_shape;
   TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &data_shape));
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &segment_ids_shape));
 
-  const Shape* subshape;
+  ShapeHandle subshape;
   TF_RETURN_IF_ERROR(c->Subshape(data_shape, 1, &subshape));
 
-  const Shape* out;
+  ShapeHandle out;
   TF_RETURN_IF_ERROR(
       c->Concatenate(c->Vector(InferenceContext::kUnknownDim), subshape, &out));
   c->set_output(0, out);
@@ -1169,23 +1169,23 @@ Status SegmentReductionShapeFn(InferenceContext* c) {
 }
 
 Status SparseSegmentReductionShapeFn(InferenceContext* c) {
-  const Shape* data_shape;
+  ShapeHandle data_shape;
   TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &data_shape));
 
-  const Shape* indices_shape;
+  ShapeHandle indices_shape;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &indices_shape));
 
-  const Shape* segment_ids_shape;
+  ShapeHandle segment_ids_shape;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &segment_ids_shape));
 
   // indices and segment_ids should merge cleanly.
-  const Shape* unused;
+  ShapeHandle unused;
   TF_RETURN_IF_ERROR(c->Merge(indices_shape, segment_ids_shape, &unused));
 
-  const Shape* subshape;
+  ShapeHandle subshape;
   TF_RETURN_IF_ERROR(c->Subshape(data_shape, 1, &subshape));
 
-  const Shape* out;
+  ShapeHandle out;
   TF_RETURN_IF_ERROR(
       c->Concatenate(c->Vector(InferenceContext::kUnknownDim), subshape, &out));
   c->set_output(0, out);
@@ -1193,24 +1193,24 @@ Status SparseSegmentReductionShapeFn(InferenceContext* c) {
 }
 
 Status SparseSegmentReductionGradShapeFn(InferenceContext* c) {
-  const Shape* data_shape;
+  ShapeHandle data_shape;
   TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &data_shape));
 
-  const Shape* indices_shape;
+  ShapeHandle indices_shape;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &indices_shape));
 
   // indices and segment_ids should merge cleanly.
-  const Shape* unused;
+  ShapeHandle unused;
   TF_RETURN_IF_ERROR(c->Merge(c->input(2), indices_shape, &unused));
 
   // output_dim0 should be a scalar
   TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused));
 
-  const Shape* subshape;
+  ShapeHandle subshape;
   TF_RETURN_IF_ERROR(c->Subshape(data_shape, 1, &subshape));
 
   const Tensor* dim0 = c->input_tensor(3);
-  const Shape* dim0_shape;
+  ShapeHandle dim0_shape;
   if (dim0 == nullptr) {
     // We don't have the value at inference time, so the output
     // shape is unknown.
@@ -1224,7 +1224,7 @@ Status SparseSegmentReductionGradShapeFn(InferenceContext* c) {
     dim0_shape = c->Vector(dim0_value);
   }
 
-  const Shape* out;
+  ShapeHandle out;
   TF_RETURN_IF_ERROR(c->Concatenate(dim0_shape, subshape, &out));
   c->set_output(0, out);
   return Status::OK();
@@ -1384,12 +1384,12 @@ REGISTER_OP("UnsortedSegmentSum")
     .Attr("T: numbertype")
     .Attr("Tindices: {int32,int64}")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* s_data = c->input(0);
-      const Shape* s_segment_ids = c->input(1);
-      const Shape* s_num_segments = c->input(2);
+      ShapeHandle s_data = c->input(0);
+      ShapeHandle s_segment_ids = c->input(1);
+      ShapeHandle s_num_segments = c->input(2);
       TF_RETURN_IF_ERROR(c->WithRank(s_num_segments, 0, &s_num_segments));
 
-      const Shape* out;
+      ShapeHandle out;
 
       // Leading dimensions of data must be compatible with dimensions of
       // <s_segment_ids>.
@@ -1398,11 +1398,11 @@ REGISTER_OP("UnsortedSegmentSum")
             c->MergePrefix(s_data, s_segment_ids, &s_data, &s_segment_ids));
 
         // Get the value of the num_segments input tensor.
-        const Dimension* num_segments_dim;
+        DimensionHandle num_segments_dim;
         TF_RETURN_IF_ERROR(c->MakeDimForScalarInput(2, &num_segments_dim));
 
         // Output is {segment_id_rank} + s_data[segment_id_rank:].
-        const Shape* s_data_suffix;
+        ShapeHandle s_data_suffix;
         TF_RETURN_IF_ERROR(
             c->Subshape(s_data, c->Rank(s_segment_ids), &s_data_suffix));
         TF_RETURN_IF_ERROR(
@@ -1629,7 +1629,7 @@ REGISTER_OP("Range")
     .Input("delta: int32")
     .Output("output: int32")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_WITH_CONTEXT_IF_ERROR(c->WithRank(c->input(0), 0, &unused),
                                       " for 'start'");
       TF_RETURN_WITH_CONTEXT_IF_ERROR(c->WithRank(c->input(1), 0, &unused),
@@ -1685,7 +1685,7 @@ REGISTER_OP("LinSpace")
     .Output("output: T")
     .Attr("T: {float, double}")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_WITH_CONTEXT_IF_ERROR(c->WithRank(c->input(0), 0, &unused),
                                       " for 'start'");
       TF_RETURN_WITH_CONTEXT_IF_ERROR(c->WithRank(c->input(1), 0, &unused),
diff --git a/tensorflow/core/ops/nn_ops.cc b/tensorflow/core/ops/nn_ops.cc
index 38556f0e35..affbd26966 100644
--- a/tensorflow/core/ops/nn_ops.cc
+++ b/tensorflow/core/ops/nn_ops.cc
@@ -22,9 +22,9 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 namespace {
 
@@ -33,7 +33,7 @@ namespace {
 // unknown dims.
 Status InputTensorShapeOrUnknown(InferenceContext* c, int input_idx,
                                  int ndims) {
-  const Shape* out;
+  ShapeHandle out;
   const Tensor* input = c->input_tensor(input_idx);
   if (input == nullptr) {
     out = c->UnknownShapeOfRank(ndims);
@@ -122,17 +122,17 @@ REGISTER_OP("BatchNormWithGlobalNormalization")
     .Attr("scale_after_normalization: bool")
     .Deprecated(9, "Use tf.nn.batch_normalization()")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
 
-      const Dimension* last_dim = c->Dim(input, 3);
+      DimensionHandle last_dim = c->Dim(input, 3);
       for (int i = 1; i < 5; ++i) {  // covers m, v, beta, gamma
-        const Shape* vec;
+        ShapeHandle vec;
         TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 1, &vec));
         TF_RETURN_IF_ERROR(c->Merge(last_dim, c->Dim(vec, 0), &last_dim));
       }
 
-      const Shape* out;
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->ReplaceDim(input, 3, last_dim, &out));
       c->set_output(0, out);
       return Status::OK();
@@ -175,23 +175,23 @@ REGISTER_OP("BatchNormWithGlobalNormalizationGrad")
     .Attr("scale_after_normalization: bool")
     .Deprecated(9, "Use tf.nn.batch_normalization()")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input));
       TF_RETURN_IF_ERROR(
           c->Merge(input, c->input(4), &input));  // with backprop
 
-      const Dimension* last_dim = c->Dim(input, 3);
+      DimensionHandle last_dim = c->Dim(input, 3);
       for (int i = 1; i < 4; ++i) {  // covers m, v, gamma
-        const Shape* vec;
+        ShapeHandle vec;
         TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 1, &vec));
         TF_RETURN_IF_ERROR(c->Merge(last_dim, c->Dim(vec, 0), &last_dim));
       }
 
-      const Shape* dx;
+      ShapeHandle dx;
       TF_RETURN_IF_ERROR(c->ReplaceDim(input, 3, last_dim, &dx));
       c->set_output(0, dx);
 
-      const Shape* vector_shape = c->Vector(last_dim);
+      ShapeHandle vector_shape = c->Vector(last_dim);
       c->set_output(1, vector_shape);
       c->set_output(2, vector_shape);
       c->set_output(3, vector_shape);
@@ -586,7 +586,7 @@ REGISTER_OP("Conv3DBackpropFilter")
     .Attr(GetPaddingAttrString())
     .Deprecated(10, "Use Conv3DBackpropFilterV2")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* out;
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 5, &out));
       c->set_output(0, out);
       return Status::OK();
@@ -614,7 +614,7 @@ REGISTER_OP("Conv3DBackpropInputV2")
     .Attr("strides: list(int) >= 5")
     .Attr(GetPaddingAttrString())
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* s;
+      ShapeHandle s;
       TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(0, &s));
       TF_RETURN_IF_ERROR(c->WithRank(s, 5, &s));
       c->set_output(0, s);
@@ -645,7 +645,7 @@ REGISTER_OP("Conv3DBackpropFilterV2")
     .Attr("strides: list(int) >= 5")
     .Attr(GetPaddingAttrString())
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* s;
+      ShapeHandle s;
       TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(1, &s));
       TF_RETURN_IF_ERROR(c->WithRank(s, 5, &s));
       c->set_output(0, s);
@@ -698,7 +698,7 @@ REGISTER_OP("AvgPool3DGrad")
     .Attr(GetPaddingAttrString())
     .Attr("T: numbertype")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* s;
+      ShapeHandle s;
       TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(0, &s));
       TF_RETURN_IF_ERROR(c->WithRank(s, 5, &s));
       c->set_output(0, s);
@@ -829,7 +829,7 @@ REGISTER_OP("LRNGrad")
     .Attr("beta: float = 0.5")
     .Attr("T: {float, half} = DT_FLOAT")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* s;
+      ShapeHandle s;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &s));  // input_grads
       TF_RETURN_IF_ERROR(c->Merge(s, c->input(1), &s));     // input_image
       TF_RETURN_IF_ERROR(c->Merge(s, c->input(2), &s));     // output_image
@@ -975,9 +975,9 @@ REGISTER_OP("Dilation2D")
     .Attr("rates: list(int) >= 4")
     .Attr(GetPaddingAttrString())
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input_shape;
+      ShapeHandle input_shape;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 4, &input_shape));
-      const Shape* filter_shape;
+      ShapeHandle filter_shape;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 3, &filter_shape));
 
       std::vector<int32> strides;
@@ -1004,14 +1004,14 @@ REGISTER_OP("Dilation2D")
       int32 rate_rows = rates[1];
       int32 rate_cols = rates[2];
 
-      const Dimension* batch_size_dim = c->Dim(input_shape, 0);
-      const Dimension* in_rows_dim = c->Dim(input_shape, 1);
-      const Dimension* in_cols_dim = c->Dim(input_shape, 2);
-      const Dimension* filter_rows_dim = c->Dim(filter_shape, 0);
-      const Dimension* filter_cols_dim = c->Dim(filter_shape, 1);
-      const Dimension* output_depth_dim = c->Dim(filter_shape, 2);
+      DimensionHandle batch_size_dim = c->Dim(input_shape, 0);
+      DimensionHandle in_rows_dim = c->Dim(input_shape, 1);
+      DimensionHandle in_cols_dim = c->Dim(input_shape, 2);
+      DimensionHandle filter_rows_dim = c->Dim(filter_shape, 0);
+      DimensionHandle filter_cols_dim = c->Dim(filter_shape, 1);
+      DimensionHandle output_depth_dim = c->Dim(filter_shape, 2);
 
-      const Dimension* unused;
+      DimensionHandle unused;
       TF_RETURN_IF_ERROR(
           c->Merge(c->Dim(input_shape, 3), output_depth_dim, &unused));
 
@@ -1040,7 +1040,7 @@ REGISTER_OP("Dilation2D")
           in_cols, filter_cols_eff, stride_cols, padding, &output_cols,
           &padding_before, &padding_after));
 
-      const Shape* output_shape = c->MakeShape(
+      ShapeHandle output_shape = c->MakeShape(
           {batch_size_dim, output_rows, output_cols, output_depth_dim});
       c->set_output(0, output_shape);
       return Status::OK();
@@ -1305,11 +1305,11 @@ REGISTER_OP("SoftmaxCrossEntropyWithLogits")
     .Output("backprop: T")
     .Attr("T: {half, float, double}")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &input));
       TF_RETURN_IF_ERROR(c->Merge(input, c->input(1), &input));
 
-      const Dimension* batch_size = c->Dim(input, 0);
+      DimensionHandle batch_size = c->Dim(input, 0);
       c->set_output(0, c->Vector(batch_size));
       c->set_output(1, input);
       return Status::OK();
@@ -1335,12 +1335,12 @@ REGISTER_OP("SparseSoftmaxCrossEntropyWithLogits")
     .Attr("T: {half, float, double}")
     .Attr("Tlabels: {int32, int64} = DT_INT64")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* features;
-      const Shape* labels;
+      ShapeHandle features;
+      ShapeHandle labels;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &features));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &labels));
 
-      const Dimension* batch_size;
+      DimensionHandle batch_size;
       TF_RETURN_IF_ERROR(
           c->Merge(c->Dim(features, 0), c->Dim(labels, 0), &batch_size));
       TF_RETURN_IF_ERROR(c->ReplaceDim(features, 0, batch_size, &features));
@@ -1375,11 +1375,11 @@ REGISTER_OP("InTopK")
     .Attr("k: int")
     .Attr("T: {int32, int64} = DT_INT32")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* predictions;
-      const Shape* targets;
+      ShapeHandle predictions;
+      ShapeHandle targets;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &predictions));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &targets));
-      const Dimension* batch_size;
+      DimensionHandle batch_size;
       TF_RETURN_IF_ERROR(
           c->Merge(c->Dim(predictions, 0), c->Dim(targets, 0), &batch_size));
       c->set_output(0, c->Vector(batch_size));
@@ -1413,11 +1413,11 @@ precision: Computed Precision at `k` as a `bool Tensor`.
 namespace {
 
 Status TopKShapeFn(InferenceContext* c) {
-  const Shape* input;
+  ShapeHandle input;
   TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), 1, &input));
 
   // Get the k value, either from input tensor or attribute.
-  const Dimension* k_dim;
+  DimensionHandle k_dim;
   if (c->num_inputs() >= 2) {
     TF_RETURN_IF_ERROR(c->MakeDimForScalarInput(1, &k_dim));
   } else {
@@ -1429,7 +1429,7 @@ Status TopKShapeFn(InferenceContext* c) {
     k_dim = c->MakeDim(k);
   }
 
-  const Dimension* last_dim = c->Dim(input, -1);
+  DimensionHandle last_dim = c->Dim(input, -1);
   if (c->ValueKnown(last_dim) && c->ValueKnown(k_dim) &&
       c->Value(last_dim) < c->Value(k_dim)) {
     return errors::InvalidArgument("input must have last dimension >= k = ",
@@ -1438,7 +1438,7 @@ Status TopKShapeFn(InferenceContext* c) {
   }
 
   // Replace last_dim with k_dim.
-  const Shape* s;
+  ShapeHandle s;
   TF_RETURN_IF_ERROR(c->Subshape(input, 0, -1, &s));
   TF_RETURN_IF_ERROR(c->Concatenate(s, c->Vector(k_dim), &s));
   c->set_output(0, s);
diff --git a/tensorflow/core/ops/parsing_ops.cc b/tensorflow/core/ops/parsing_ops.cc
index 1b1af7d68a..0dc743ee3b 100644
--- a/tensorflow/core/ops/parsing_ops.cc
+++ b/tensorflow/core/ops/parsing_ops.cc
@@ -20,9 +20,9 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 REGISTER_OP("DecodeRaw")
     .Input("bytes: string")
@@ -31,7 +31,7 @@ REGISTER_OP("DecodeRaw")
     .Attr("little_endian: bool = true")
     .SetShapeFn([](InferenceContext* c) {
       // Note: last dimension is data dependent.
-      const Shape* out;
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->Concatenate(
           c->input(0), c->Vector(InferenceContext::kUnknownDim), &out));
       c->set_output(0, out);
@@ -68,9 +68,9 @@ REGISTER_OP("ParseExample")
       ParseSingleExampleAttrs attrs;
       TF_RETURN_IF_ERROR(attrs.Init(c));
 
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 1, &input));
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &unused));  // names
 
       // Output sparse_indices, sparse_values, and sparse_shapes.
@@ -89,7 +89,7 @@ REGISTER_OP("ParseExample")
       TensorShapeProto shape_proto;
       for (int i = 0; i < attrs.num_dense; ++i) {
         attrs.dense_shapes[i].AsProto(&shape_proto);
-        const Shape* dense;
+        ShapeHandle dense;
         TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &dense));
         TF_RETURN_IF_ERROR(c->Concatenate(input, dense, &dense));
         c->set_output(output_idx++, dense);
@@ -161,11 +161,11 @@ REGISTER_OP("ParseSingleSequenceExample")
     .Attr("feature_list_sparse_types: list({float,int64,string}) >= 0 = []")
     .Attr("feature_list_dense_shapes: list(shape) >= 0 = []")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       ParseSingleSequenceExampleAttrs attrs;
       TF_RETURN_IF_ERROR(attrs.Init(c));
 
-      const Shape* input;
+      ShapeHandle input;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &input));
 
       // feature_list_dense_missing_assumed_empty
@@ -189,7 +189,7 @@ REGISTER_OP("ParseSingleSequenceExample")
       TensorShapeProto shape_proto;
       for (int i = 0; i < attrs.num_context_dense; ++i) {
         attrs.context_dense_shapes[i].AsProto(&shape_proto);
-        const Shape* s;
+        ShapeHandle s;
         TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &s));
         c->set_output(output_idx++, s);
       }
@@ -209,7 +209,7 @@ REGISTER_OP("ParseSingleSequenceExample")
       // Output feature_list_dense_shapes.
       for (int i = 0; i < attrs.num_feature_list_dense; ++i) {
         attrs.feature_list_dense_shapes[i].AsProto(&shape_proto);
-        const Shape* s;
+        ShapeHandle s;
         TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &s));
         TF_RETURN_IF_ERROR(
             c->Concatenate(c->Vector(InferenceContext::kUnknownDim), s, &s));
@@ -312,7 +312,7 @@ REGISTER_OP("DecodeCSV")
     .SetShapeFn([](InferenceContext* c) {
       // Validate the record_defaults inputs.
       for (int i = 1; i < c->num_inputs(); ++i) {
-        const Shape* v;
+        ShapeHandle v;
         TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 1, &v));
         if (c->Value(c->Dim(v, 0)) > 1) {
           return errors::InvalidArgument(
diff --git a/tensorflow/core/ops/random_ops.cc b/tensorflow/core/ops/random_ops.cc
index 5d648a6a7e..776523f33f 100644
--- a/tensorflow/core/ops/random_ops.cc
+++ b/tensorflow/core/ops/random_ops.cc
@@ -19,14 +19,14 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 namespace {
 
 Status RandomShape(InferenceContext* c) {
-  const Shape* out;
+  ShapeHandle out;
   TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(0, &out));
   c->set_output(0, out);
   return Status::OK();
@@ -217,9 +217,9 @@ REGISTER_OP("Multinomial")
     .Attr("seed2: int = 0")
     .Attr("T: realnumbertype")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* logits_shape;
-      const Shape* unused;
-      const Dimension* num_samples;
+      ShapeHandle logits_shape;
+      ShapeHandle unused;
+      DimensionHandle num_samples;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &logits_shape));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));
       TF_RETURN_IF_ERROR(c->MakeDimForScalarInput(1, &num_samples));
@@ -249,7 +249,7 @@ REGISTER_OP("RandomGamma")
     .Attr("S: {int32, int64}")
     .Attr("T: {half, float, double}")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* out;
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(0, &out));
       TF_RETURN_IF_ERROR(c->Concatenate(out, c->input(1), &out));
       c->set_output(0, out);
diff --git a/tensorflow/core/ops/sparse_ops.cc b/tensorflow/core/ops/sparse_ops.cc
index 17d5983d76..f4544062b7 100644
--- a/tensorflow/core/ops/sparse_ops.cc
+++ b/tensorflow/core/ops/sparse_ops.cc
@@ -19,14 +19,14 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 namespace {
 
 Status SparseSparseMinOrMaxShapeFn(InferenceContext* c) {
-  const Shape* unused;
+  ShapeHandle unused;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &unused));  // a_indices
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &unused));  // a_values
   TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &unused));  // a_shape
@@ -50,8 +50,8 @@ REGISTER_OP("SparseAddGrad")
     .Output("b_val_grad: T")
     .Attr("T: numbertype")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* a_indices;
-      const Shape* b_indices;
+      ShapeHandle a_indices;
+      ShapeHandle b_indices;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &a_indices));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 2, &b_indices));
       c->set_output(0, c->Vector(c->Dim(a_indices, 0)));
@@ -92,7 +92,7 @@ REGISTER_OP("SparseAdd")
     .Attr("T: numbertype")
     .Attr("Treal: realnumbertype")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* a_shape;
+      ShapeHandle a_shape;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &a_shape));
       c->set_output(
           0, c->Matrix(InferenceContext::kUnknownDim, c->Dim(a_shape, 0)));
@@ -137,10 +137,10 @@ REGISTER_OP("SparseTensorDenseMatMul")
     .Attr("adjoint_a: bool = false")
     .Attr("adjoint_b: bool = false")
     .SetShapeFn([](InferenceContext* c) {
-      const Dimension* unused_dim;
-      const Shape* unused;
-      const Shape* b;
-      const Shape* a_shape;
+      DimensionHandle unused_dim;
+      ShapeHandle unused;
+      ShapeHandle b;
+      ShapeHandle a_shape;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &unused));  // a_indices
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &unused));  // a_values
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &a_shape));
@@ -152,7 +152,7 @@ REGISTER_OP("SparseTensorDenseMatMul")
 
       // TODO(zongheng): 1) incorporate adjoint_a. 2) When both attrs are
       // considered, check the inner dimensions match.
-      const Dimension* output_right = c->Dim(b, adjoint_b ? 0 : 1);
+      DimensionHandle output_right = c->Dim(b, adjoint_b ? 0 : 1);
       c->set_output(0, c->Matrix(InferenceContext::kUnknownDim, output_right));
       return Status::OK();
     })
@@ -186,7 +186,7 @@ REGISTER_OP("SerializeSparse")
     .Attr("T: type")
     .Output("serialized_sparse: string")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &unused));
@@ -208,7 +208,7 @@ REGISTER_OP("SerializeManySparse")
     .Attr("T: type")
     .Output("serialized_sparse: string")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
+      ShapeHandle unused;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &unused));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &unused));
@@ -239,9 +239,9 @@ REGISTER_OP("DeserializeManySparse")
     .Output("sparse_shape: int64")
     .SetShapeFn([](InferenceContext* c) {
       // serialized sparse is [?,3] matrix.
-      const Shape* serialized_sparse;
+      ShapeHandle serialized_sparse;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &serialized_sparse));
-      const Dimension* unused;
+      DimensionHandle unused;
       TF_RETURN_IF_ERROR(
           c->WithValue(c->Dim(serialized_sparse, 1), 3, &unused));
 
@@ -311,7 +311,7 @@ REGISTER_OP("SparseToDense")
     .Output("dense: T")
     .Attr("Tindices: {int32, int64}")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* out;
+      ShapeHandle out;
       TF_RETURN_IF_ERROR(c->MakeShapeFromShapeTensor(1, &out));
       c->set_output(0, out);
       return Status::OK();
@@ -363,23 +363,23 @@ REGISTER_OP("SparseConcat")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
       // These accumulates the sum.
-      const Dimension* output_row_count = c->MakeDim(0ll);
+      DimensionHandle output_row_count = c->MakeDim(0ll);
 
       // These are only merged.
-      const Dimension* output_ind_cols = c->UnknownDim();
-      const Shape* output_shape = c->UnknownShape();
+      DimensionHandle output_ind_cols = c->UnknownDim();
+      ShapeHandle output_shape = c->UnknownShape();
 
       const int n = c->num_inputs() / 3;
       for (int i = 0; i < n; i++) {
-        const Shape* ind;
+        ShapeHandle ind;
         TF_RETURN_IF_ERROR(c->WithRank(c->input(i), 2, &ind));
-        const Shape* val;
+        ShapeHandle val;
         TF_RETURN_IF_ERROR(c->WithRank(c->input(i + n), 1, &val));
-        const Shape* shape;
+        ShapeHandle shape;
         TF_RETURN_IF_ERROR(c->WithRank(c->input(i + 2 * n), 1, &shape));
 
         // Add to output_ind_rows.
-        const Dimension* num_dim;
+        DimensionHandle num_dim;
         TF_RETURN_IF_ERROR(c->Merge(c->Dim(ind, 0), c->Dim(val, 0), &num_dim));
         TF_RETURN_IF_ERROR(
             c->Add(output_row_count, num_dim, &output_row_count));
@@ -460,11 +460,11 @@ REGISTER_OP("SparseSplit")
     .Attr("num_split: int >= 1")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* input_shape = c->input(3);
-      const Shape* output_indices =
+      ShapeHandle input_shape = c->input(3);
+      ShapeHandle output_indices =
           c->Matrix(InferenceContext::kUnknownDim, c->NumElements(input_shape));
-      const Shape* output_values = c->Vector(InferenceContext::kUnknownDim);
-      const Shape* output_shape = input_shape;
+      ShapeHandle output_values = c->Vector(InferenceContext::kUnknownDim);
+      ShapeHandle output_shape = input_shape;
 
       // Copy the outputs into the output ranges.
       int num_splits = c->num_outputs() / 3;
@@ -520,9 +520,9 @@ REGISTER_OP("SparseReorder")
     .Output("output_values: T")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* indices;
-      const Shape* values;
-      const Shape* unused;
+      ShapeHandle indices;
+      ShapeHandle values;
+      ShapeHandle unused;
 
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &indices));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &values));
@@ -560,9 +560,9 @@ REGISTER_OP("SparseReshape")
     .Output("output_indices: int64")
     .Output("output_shape: int64")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* indices;
-      const Shape* unused;
-      const Shape* new_shape;
+      ShapeHandle indices;
+      ShapeHandle unused;
+      ShapeHandle new_shape;
 
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &indices));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &unused));
@@ -670,7 +670,7 @@ output: `R-K`-D.  The reduced Tensor.
       .Output("output: T")                                       \
       .Attr("T: numbertype")                                     \
       .SetShapeFn([](InferenceContext* c) {                      \
-        const Shape* input;                                      \
+        ShapeHandle input;                                       \
         TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 2, &input)); \
         c->set_output(0, c->Vector(c->Dim(input, 0)));           \
         return Status::OK();                                     \
@@ -737,8 +737,8 @@ REGISTER_OP("SparseSoftmax")
     .Output("output: T")
     .Attr("T: {float, double}")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unused;
-      const Shape* values;
+      ShapeHandle unused;
+      ShapeHandle values;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &unused));  // sp_indices
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 1, &values));  // sp_values
       TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 1, &unused));
diff --git a/tensorflow/core/ops/state_ops.cc b/tensorflow/core/ops/state_ops.cc
index 684e86a00d..cc0c652107 100644
--- a/tensorflow/core/ops/state_ops.cc
+++ b/tensorflow/core/ops/state_ops.cc
@@ -19,7 +19,7 @@ limitations under the License.
 namespace tensorflow {
 
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 REGISTER_OP("Variable")
     .Output("ref: Ref(dtype)")
@@ -69,7 +69,7 @@ REGISTER_OP("TemporaryVariable")
     .SetShapeFn([](InferenceContext* c) {
       TensorShapeProto shape_proto;
       TF_RETURN_IF_ERROR(c->GetAttr("shape", &shape_proto));
-      const Shape* output;
+      ShapeHandle output;
       TF_RETURN_IF_ERROR(c->MakeShapeFromShapeProto(shape_proto, &output));
       c->set_output(0, output);
       return Status::OK();
@@ -201,12 +201,12 @@ output_ref:= Same as "ref".  Returned as a convenience for operations that want
 namespace {
 
 Status ScatterUpdateShape(InferenceContext* c) {
-  const Shape* var_shape = c->input(0);
-  const Shape* indices_shape = c->input(1);
+  ShapeHandle var_shape = c->input(0);
+  ShapeHandle indices_shape = c->input(1);
 
-  const Shape* unused_updates_shape;
-  const Shape* concat;
-  const Shape* var_subshape;
+  ShapeHandle unused_updates_shape;
+  ShapeHandle concat;
+  ShapeHandle var_subshape;
   TF_RETURN_IF_ERROR(c->Subshape(var_shape, 1, &var_subshape));
   TF_RETURN_IF_ERROR(c->Concatenate(indices_shape, var_subshape, &concat));
   TF_RETURN_IF_ERROR(c->Merge(c->input(2), concat, &unused_updates_shape));
@@ -354,7 +354,7 @@ REGISTER_OP("CountUpTo")
     .Attr("limit: int")
     .Attr("T: {int32, int64}")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* output;
+      ShapeHandle output;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 0, &output));
       c->set_output(0, output);
       return Status::OK();
diff --git a/tensorflow/core/ops/string_ops.cc b/tensorflow/core/ops/string_ops.cc
index 3b9f96e496..dd4cb12f5d 100644
--- a/tensorflow/core/ops/string_ops.cc
+++ b/tensorflow/core/ops/string_ops.cc
@@ -19,9 +19,9 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 REGISTER_OP("StringToHashBucketFast")
     .Input("input: string")
@@ -180,7 +180,7 @@ REGISTER_OP("StringJoin")
       // Merge the non-scalars to find the output shape.
       // Don't merge inputs with unknown rank, as they can actually be scalars
       // or the output shape.
-      const Shape* out = c->UnknownShape();
+      ShapeHandle out = c->UnknownShape();
       for (int i = 0; i < c->num_inputs(); ++i) {
         if (c->RankKnown(c->input(i)) && c->Rank(c->input(i)) != 0) {
           TF_RETURN_IF_ERROR(c->Merge(out, c->input(i), &out));
@@ -206,7 +206,7 @@ REGISTER_OP("StringSplit")
     .Output("values: string")
     .Output("shape: int64")
     .SetShapeFn([](InferenceContext* c) {
-      const Shape* unsed_shape;
+      ShapeHandle unsed_shape;
       TF_RETURN_IF_ERROR(c->WithRank(c->input(0), 2, &unsed_shape));
       TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unsed_shape));
 
diff --git a/tensorflow/core/ops/training_ops.cc b/tensorflow/core/ops/training_ops.cc
index 04f3058651..ab82617a13 100644
--- a/tensorflow/core/ops/training_ops.cc
+++ b/tensorflow/core/ops/training_ops.cc
@@ -18,28 +18,28 @@ limitations under the License.
 
 namespace tensorflow {
 
-using shape_inference::Dimension;
+using shape_inference::DimensionHandle;
 using shape_inference::InferenceContext;
-using shape_inference::Shape;
+using shape_inference::ShapeHandle;
 
 // Handle the gradient and, if <sparse>, indices inputs.
 // <s> is an input+output parameter, containing the current known input shape to
 // the gradient.
 static Status HandleGradAndIndicesInputs(InferenceContext* c, bool sparse,
-                                         int grad_idx, const Shape** s) {
-  const Shape* grad = c->input(grad_idx);
+                                         int grad_idx, ShapeHandle* s) {
+  ShapeHandle grad = c->input(grad_idx);
   if (!sparse) {
     TF_RETURN_IF_ERROR(c->Merge(*s, grad, s));
     return Status::OK();
   }
   // Indices is a vector where indices.dim[0].rank == grad[0].rank.
-  const Shape* indices;
+  ShapeHandle indices;
   TF_RETURN_IF_ERROR(c->WithRank(c->input(grad_idx + 1), 1, &indices));
-  const Dimension* unused;
+  DimensionHandle unused;
   TF_RETURN_IF_ERROR(c->Merge(c->Dim(indices, 0), c->Dim(grad, 0), &unused));
 
   // Trailing part of grad matches *s.
-  const Shape* grad_subshape;
+  ShapeHandle grad_subshape;
   TF_RETURN_IF_ERROR(c->Subshape(grad, 1, &grad_subshape));
   TF_RETURN_IF_ERROR(c->Merge(*s, grad_subshape, s));
 
@@ -47,8 +47,8 @@ static Status HandleGradAndIndicesInputs(InferenceContext* c, bool sparse,
 }
 
 static Status ApplyGradientDescentShapeFn(InferenceContext* c) {
-  const Shape* unused;
-  const Shape* s = c->input(0);                              // var
+  ShapeHandle unused;
+  ShapeHandle s = c->input(0);                               // var
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));  // alpha
   TF_RETURN_IF_ERROR(c->Merge(s, c->input(2), &s));          // delta
   c->set_output(0, s);
@@ -76,8 +76,8 @@ use_locking: If `True`, the subtraction will be protected by a lock;
 
 static Status ApplyProximalGradientDescentShapeFn(InferenceContext* c,
                                                   bool sparse) {
-  const Shape* unused;
-  const Shape* s = c->input(0);                              // var
+  ShapeHandle unused;
+  ShapeHandle s = c->input(0);                               // var
   TF_RETURN_IF_ERROR(c->WithRank(c->input(1), 0, &unused));  // alpha
   TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));  // l1
   TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused));  // l2
@@ -146,8 +146,8 @@ use_locking: If True, the subtraction will be protected by a lock;
   otherwise the behavior is undefined, but may exhibit less contention.
 )doc");
 static Status ApplyAdadeltaShapeFn(InferenceContext* c, bool sparse) {
-  const Shape* unused;
-  const Shape* s = c->input(0);                              // var
+  ShapeHandle unused;
+  ShapeHandle s = c->input(0);                               // var
   TF_RETURN_IF_ERROR(c->Merge(s, c->input(1), &s));          // accum
   TF_RETURN_IF_ERROR(c->Merge(s, c->input(2), &s));          // accum update
   TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused));  // lr
@@ -224,8 +224,8 @@ a lock; otherwise the behavior is undefined, but may exhibit less contention.
 )doc");
 
 static Status ApplyAdagradShapeFn(InferenceContext* c, bool sparse) {
-  const Shape* unused;
-  const Shape* s = c->input(0);                              // var
+  ShapeHandle unused;
+  ShapeHandle s = c->input(0);                               // var
   TF_RETURN_IF_ERROR(c->Merge(s, c->input(1), &s));          // accum
   TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));  // lr
   TF_RETURN_IF_ERROR(
@@ -261,8 +261,8 @@ use_locking: If `True`, updating of the var and accum tensors will be protected
   contention.
 )doc");
 static Status ApplyProximalAdagradShapeFn(InferenceContext* c, bool sparse) {
-  const Shape* unused;
-  const Shape* s = c->input(0);                              // var
+  ShapeHandle unused;
+  ShapeHandle s = c->input(0);                               // var
   TF_RETURN_IF_ERROR(c->Merge(s, c->input(1), &s));          // accum
   TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));  // lr
   TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused));  // l1
@@ -335,8 +335,8 @@ use_locking: If `True`, updating of the var and accum tensors will be protected
 )doc");
 
 static Status ApplyAdagradDAShapeFn(InferenceContext* c, bool sparse) {
-  const Shape* unused;
-  const Shape* s = c->input(0);                      // var
+  ShapeHandle unused;
+  ShapeHandle s = c->input(0);                       // var
   TF_RETURN_IF_ERROR(c->Merge(s, c->input(1), &s));  // grad_accumulator
   TF_RETURN_IF_ERROR(
       c->Merge(s, c->input(2), &s));  // gradient_squared_accumulator
@@ -453,8 +453,8 @@ a lock; otherwise the behavior is undefined, but may exhibit less contention.
 )doc");
 
 static Status ApplyFtrlShapeFn(InferenceContext* c, bool sparse) {
-  const Shape* unused;
-  const Shape* s = c->input(0);                      // var
+  ShapeHandle unused;
+  ShapeHandle s = c->input(0);                       // var
   TF_RETURN_IF_ERROR(c->Merge(s, c->input(1), &s));  // accum
   TF_RETURN_IF_ERROR(c->Merge(s, c->input(2), &s));  // linear
   TF_RETURN_IF_ERROR(
@@ -549,8 +549,8 @@ use_locking: If `True`, updating of the var and accum tensors will be protected
 )doc");
 
 static Status ApplyMomentumShapeFn(InferenceContext* c, bool sparse) {
-  const Shape* unused;
-  const Shape* s = c->input(0);                              // var
+  ShapeHandle unused;
+  ShapeHandle s = c->input(0);                               // var
   TF_RETURN_IF_ERROR(c->Merge(s, c->input(1), &s));          // accum
   TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused));  // lr
   TF_RETURN_IF_ERROR(
@@ -635,8 +635,8 @@ var - lr * momentum * accum.
 )doc");
 
 static Status ApplyAdamShapeFn(InferenceContext* c, bool sparse) {
-  const Shape* unused;
-  const Shape* s = c->input(0);                              // var
+  ShapeHandle unused;
+  ShapeHandle s = c->input(0);                               // var
   TF_RETURN_IF_ERROR(c->Merge(s, c->input(1), &s));          // m
   TF_RETURN_IF_ERROR(c->Merge(s, c->input(2), &s));          // v
   TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused));  // beta1_power
@@ -693,8 +693,8 @@ use_locking: If `True`, updating of the var, m, and v tensors will be protected
 )doc");
 
 static Status ApplyRMSPropShapeFn(InferenceContext* c, bool sparse) {
-  const Shape* unused;
-  const Shape* s = c->input(0);                              // var
+  ShapeHandle unused;
+  ShapeHandle s = c->input(0);                               // var
   TF_RETURN_IF_ERROR(c->Merge(s, c->input(1), &s));          // ms
   TF_RETURN_IF_ERROR(c->Merge(s, c->input(2), &s));          // mom
   TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused));  // lr
-- 
GitLab


From 55826f7906f88f2d03021acf20f6a061c0b96db6 Mon Sep 17 00:00:00 2001
From: Robin Nabel <rnabel@google.com>
Date: Tue, 23 Aug 2016 16:51:42 -0800
Subject: [PATCH 075/223] Move vz-heatmap into TensorBoard. Change: 131120464

---
 .../components/vz-heatmap/demo/index.html     | 143 ++++++++
 .../components/vz-heatmap/index.html          |  14 +
 .../components/vz-heatmap/vz-heatmap.html     | 320 ++++++++++++++++++
 3 files changed, 477 insertions(+)
 create mode 100644 tensorflow/tensorboard/components/vz-heatmap/demo/index.html
 create mode 100644 tensorflow/tensorboard/components/vz-heatmap/index.html
 create mode 100644 tensorflow/tensorboard/components/vz-heatmap/vz-heatmap.html

diff --git a/tensorflow/tensorboard/components/vz-heatmap/demo/index.html b/tensorflow/tensorboard/components/vz-heatmap/demo/index.html
new file mode 100644
index 0000000000..0abc131795
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-heatmap/demo/index.html
@@ -0,0 +1,143 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+  <meta charset="UTF-8">
+  <title>Heatmap example</title>
+  <link rel="import" href="../vz-heatmap.html">
+  <link rel="import" href="../../iron-demo-helpers/demo-snippet.html">
+</head>
+<script>
+  function generateRandomMatrix() {
+    var rows = getRandomArbitrary(10, 20);
+    var columns = getRandomArbitrary(10, 20);
+    var data = [];
+    // Generate new data array.
+    for (var row = 0; row < rows; row++) {
+      var currentRow = [];
+      for (var col = 0; col < columns; col++) {
+        currentRow[col] = getRandomArbitrary(0, 20);
+      }
+      data[row] = currentRow;
+    }
+    return data;
+  }
+
+  // Returns a random number between min (inclusive) and max (exclusive)
+  function getRandomArbitrary(min, max) {
+    return Math.random() * (max - min) + min;
+  }
+
+  function getRandomColorRange() {
+    return [getRandomColor(), getRandomColor()];
+  }
+
+  function getRandomColor() {
+    var letters = '0123456789ABCDEF';
+    var color = '#';
+    for (var i = 0; i < 6; i++) {
+      color += letters[Math.floor(Math.random() * 16)];
+    }
+    return color;
+  }
+</script>
+<body>
+<h1>Initialized with data</h1>
+<div style="width: 30%">
+  <demo-snippet>
+    <template>
+      <vz-heatmap data="[[1,2],[3,4]]"></vz-heatmap>
+    </template>
+  </demo-snippet>
+</div>
+
+<h1>Initialized with data and custom data range</h1>
+<div style="width: 30%">
+  <demo-snippet>
+    <template>
+      <vz-heatmap data="[[1,2],[3,4]]" values="[0,10]"></vz-heatmap>
+    </template>
+  </demo-snippet>
+</div>
+
+<h1>Initialized with data and colors</h1>
+<div style="width: 30%">
+  <demo-snippet>
+    <template>
+      <vz-heatmap data="[[1,2],[3,4]]" colors='["yellow", "red"]'></vz-heatmap>
+    </template>
+  </demo-snippet>
+</div>
+
+<h1>Initialized with data and colors and threshold values</h1>
+<div style="width: 30%">
+  <demo-snippet>
+    <template>
+      <vz-heatmap data="[[1,2],[3,4]]"
+                  values="[0, 10]"
+                  colors='["yellow", "red"]'
+      ></vz-heatmap>
+    </template>
+  </demo-snippet>
+</div>
+<h1>Initialized with data and color function</h1>
+<div style="width: 30%">
+  <demo-snippet>
+    <template>
+      <vz-heatmap id="color_function" data="[[1,2],[3,4]]"
+      ></vz-heatmap>
+    </template>
+  </demo-snippet>
+  <script>
+    setTimeout(function () {
+      var heatmapColor = document.getElementById('color_function');
+      heatmapColor.colorFunction =
+          d3.scale.linear().range(['white', 'black']).domain([0, 5]);
+    }, 1500);
+  </script>
+</div>
+
+<h1>Initialized with data and updated data</h1>
+<h3>Click on the heatmap to create new random data.</h3>
+<div style="width: 30%">
+  <demo-snippet>
+    <template>
+      <vz-heatmap data="[[1,2],[3,4]]"
+                  onclick="this.data = generateRandomMatrix()"
+      >
+      </vz-heatmap>
+    </template>
+  </demo-snippet>
+</div>
+
+<h1>Initialized with data and updated color update</h1>
+<h3>Click on the heatmap to update the color scheme.</h3>
+<div style="width: 30%">
+  <demo-snippet>
+    <template>
+      <vz-heatmap id="data_color_update" data="[[1,2],[3,4]]"
+                  onclick="this.colors = getRandomColorRange();
+                       this.data = generateRandomMatrix()"
+      >
+      </vz-heatmap>
+    </template>
+  </demo-snippet>
+</div>
+
+<h2>Let's try to actually break it... *Puts on fedora*</h2>
+<p>Code below is not meant to be seen, but to ensure that no errors are
+  thrown when invalid data is passed into the Polymer element.</p>
+<demo-snippet>
+  <template>
+    <vz-heatmap id="break_the_heatmap" data="undefined"></vz-heatmap>
+  </template>
+</demo-snippet>
+<script>
+  var bth = document.getElementById('break_the_heatmap');
+  bth.data = []; // Empty 1D array.
+  bth.data = [[]]; // Empty 2D array.
+  bth.colors = ['yellow', 'blue', '']; // More than 2 elements in colors array.
+  bth.values = [1, 2, 3]; // More than 2 elements in values array.
+</script>
+
+</body>
+</html>
diff --git a/tensorflow/tensorboard/components/vz-heatmap/index.html b/tensorflow/tensorboard/components/vz-heatmap/index.html
new file mode 100644
index 0000000000..3384d4cb3f
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-heatmap/index.html
@@ -0,0 +1,14 @@
+<!doctype html>
+<html>
+  <head>
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <script src="../webcomponentsjs/webcomponents-lite.js"></script>
+    <link rel="import" href="../iron-component-page/iron-component-page.html">
+  </head>
+  <body>
+
+    <iron-component-page src="vz-heatmap.html"></iron-component-page>
+
+  </body>
+</html>
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/vz-heatmap/vz-heatmap.html b/tensorflow/tensorboard/components/vz-heatmap/vz-heatmap.html
new file mode 100644
index 0000000000..78c01036e3
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-heatmap/vz-heatmap.html
@@ -0,0 +1,320 @@
+<link rel="import" href="../polymer/polymer.html">
+<link rel="import" href="../tf-imports/d3.html">
+
+<!--
+`vz-heatmap` A simple heatmap to visualize 2D data using predefined or user
+defined colour scheme, with a dependency on d3.js. The heatmap automatically
+fits itself to the width of the container it is placed in.
+
+@element vz-heatmap
+@demo demo/index.html
+-->
+<dom-module id="vz-heatmap">
+  <template>
+    <div>
+      <canvas id="heatmap"
+              style="width: 100%; image-rendering:pixelated"></canvas>
+    </div>
+  </template>
+  <script>
+    Polymer({
+      is: 'vz-heatmap',
+      properties: {
+        colors: {
+          type: Array,
+          value: ['white', 'steelblue'],
+          observer: '_onColorsChange'
+        },
+        colorFunction: {
+          type: Object,
+          observer: '_onColorFunctionChange'
+        },
+        data: {
+          type: Array,
+          value: [],
+          observer: '_onDataChange'
+        },
+        values: {
+          type: Array,
+          observer: '_onValuesChange'
+        },
+        _currentDataValid: {
+          type: Boolean,
+          value: false
+        },
+        _dataExtent: {
+          type: Array,
+          value: [0, 1]
+        },
+        _debug: {
+          type: Boolean,
+          value: false
+        },
+        _generatedColorScale: {
+          type: Object,
+          value: (function () {
+            var retFunction = d3.scale.linear();
+            if (this.colors) {
+              retFunction.range(this.colors)
+            }
+
+            return retFunction;
+          })()
+        },
+        _isReady: {
+          type: Boolean,
+          value: false
+        },
+        _height: {
+          type: Number,
+          value: 0
+        },
+        _width: {
+          type: Number,
+          value: 0
+        },
+        _useUserColorPickerFunction: {
+          type: Boolean,
+          value: false
+        }
+      },
+      behaviors: [
+        Polymer.IronResizableBehavior
+      ],
+      listeners: {
+        'iron-resize': '_onWidthChange'
+      },
+      _onWidthChange: function () {
+        // Re-render the chart if the data has already been set.
+        if (this._currentDataValid) {
+          this._renderData();
+        }
+      },
+      ready: function () {
+        this._isReady = true;
+        this._onWidthChange();
+      },
+      attached: function () {
+        this._onWidthChange();
+      },
+      /**
+       * Data change handler, if new data is valid, sets flag to indicate data
+       * now valid, updates the data extent and renders the new data.
+       */
+      _onDataChange: function () {
+        // Validate new data.
+        if (!this._isDataValid(this.data)) {
+          return;
+        }
+
+        // Set flag to indicate data is now valid.
+        this._currentDataValid = true;
+
+        // Calculate new data extent.
+        this._updateDataExtent();
+
+        if (this._isReady) {
+          this._renderData();
+        }
+      },
+      /**
+       * (Re-)Renders the heatmap. Called when data, color mapping, or width
+       * changes.
+       * @private
+       */
+      _renderData: function () {
+        // Ensure dimensions are up-to-date and valid before starting rendering.
+        this._updateDimensions();
+        if (!this._width || !this._height) return;
+
+        // Ensure the color function is up-to-date.
+        this._updateColorFunction();
+        var width = this._width;
+        var rows = this.data.length;
+        var columns = this.data[0].length;
+
+        // Calculate side length of each tile.
+        var sideLength = width / columns;
+
+        // Set domain and range of the axis.
+        var colorScale = (this._useUserColorPickerFunction) ?
+            this.colorFunction : this._generatedColorScale;
+
+        // Clear the canvas.
+        this.resetCanvas();
+
+        // Render heatmap.
+        var ctx = this.$.heatmap.getContext("2d");
+
+        for (var row = 0; row < rows; row++) {
+          for (var column = 0; column < columns; column++) {
+            var value = this.data[row][column];
+            // Set location and dimensions.
+            ctx.fillStyle = colorScale(value);
+            // Preferred way to set color of individual pixels.
+            ctx.fillRect(column, row, 1, 1);
+          }
+        }
+      },
+      /**
+       * Observer for this.colors.
+       * @private
+       */
+      _onColorsChange: function () {
+        if (Array.isArray(this.colors) && this.colors.length == 2) {
+          this._updateColorFunction();
+        }
+        this._useUserColorPickerFunction = false;
+        this._renderData();
+      },
+      /**
+       * Observer for this.values. Updates the
+       * internal color function. If data invalid, internal color scale uses
+       * the extent of the data as domain.
+       * @private
+       */
+      _onValuesChange: function () {
+        // Verify that validity of the new content of values.
+        // If data not valid, set this.values to undefined, as
+        // _updateColorFunction will then use the data's extent as the color
+        // scale's domain.
+        if (!(Array.isArray(this.values) && this.values.length == 2)) {
+          this.values = undefined;
+        }
+
+        this._updateColorFunction();
+        this._useUserColorPickerFunction = false;
+        this._renderData();
+      },
+      /**
+       * Observer for this.colorFunction. This field allows the user to
+       * provide a custom color scale. Updates to this value are ignored, if the new
+       * value is not a function object.
+       * @private
+       */
+      _onColorFunctionChange: function () {
+        if (typeof this.colorFunction === 'function') {
+          this._useUserColorPickerFunction = true;
+          this._renderData();
+        } else if (this._debug) {
+          console.log('The colorFunction provided is not of function type, and was not set as default.')
+        }
+      },
+      /**
+       * Calculates the extent of the data if it is required by the
+       * current color function. This function is called when
+       * properties change which would result in the color scale
+       * changing, or when the computed color scale is started to be used.
+       * @private
+       */
+      _updateDataExtent: function () {
+        if (this._currentDataValid) {
+          var rows = this.data.length;
+          var columns = this.data[0].length;
+
+          var min = this.data[0][0];
+          var max = this.data[0][0];
+
+          for (var row = 0; row < rows; row++) {
+            for (var col = 0; col < columns; col++) {
+              var currentElement = this.data[row][col];
+
+              if (currentElement < min) {
+                min = currentElement;
+              } else if (currentElement > max) {
+                max = currentElement;
+              }
+            }
+          }
+
+          this._dataExtent = [min, max];
+        } else if (!this._dataExtent) {
+          this._dataExtent = [0, 1];
+        }
+      },
+      /**
+       * Updates the internal color function only when the number of
+       * elements in this.colors is equal to the number of elements in
+       * this.values or if this.values is empty, in which case the
+       * extent of the data is used.
+       * @private
+       */
+      _updateColorFunction: function () {
+        if (Array.isArray(this.colors) && this.colors.length) {
+          this._generatedColorScale = d3.scale.linear().range(this.colors);
+        }
+
+        if (this.values) {
+          // Check that the number of elements in this.values and
+          // this.colors have an identical number of elements.
+          if (this.colors.length === this.values.length) {
+            this._generatedColorScale.domain(this.values);
+          }
+
+          // If values reset, and data field contains valid data, set colour scale
+          // to use the data extent as domain.
+        } else if (this._currentDataValid) {
+          this._generatedColorScale.domain(this._dataExtent);
+        }
+      },
+      _getLinearInterpolation: function (domain, range) {
+        return d3.scale.linear().domain(domain).range(range);
+      },
+      /**
+       * Find side length of each tile in heat map by dividing current width
+       * by number of columns
+       */
+      _findTileSideLength: function () {
+        if (this._currentDataValid) {
+          var numOfColumns = this.data[0].length;
+
+          // Make sure value is finite.
+          if (numOfColumns === 0) {
+            var returnValue = 0;
+          } else {
+            returnValue = this._width / numOfColumns;
+          }
+          return returnValue;
+
+        } else {
+          if (this._debug == true) {
+            console.log("WARNING: vz-heatmap is reverting to zero height as passed data is invalid.")
+          }
+          return 0; // Fall back to 0 height, if data is invalid.
+        }
+      },
+      /**
+       * Verifies whether input data is valid.
+       * @param newData Number[][]
+       * @private
+       */
+      _isDataValid: function (newData) {
+        return Array.isArray(newData) && newData.length &&
+            Array.isArray(newData[0]);
+      },
+      _updateDimensions: function () {
+        var canvasElement = this.$.heatmap;
+
+        var rows = 0;
+        var columns = 0;
+        if (this._currentDataValid) {
+          rows = this.data.length;
+          columns = this.data[0].length;
+        }
+
+        // Recalculate height and width, and ensure data can be rendered.
+        this._width = canvasElement.parentNode.clientWidth;
+        this._height = this._findTileSideLength() * rows;
+        // Update the attribute height and width.
+        canvasElement.setAttribute('height', rows);
+        canvasElement.setAttribute('width', columns);
+      },
+      resetCanvas: function () {
+        // Reset the canvas.
+        var canvas = this.$.heatmap;
+        var ctx = canvas.getContext("2d");
+        ctx.clearRect(0, 0, canvas.width, canvas.height);
+      }
+    });
+  </script>
+</dom-module>
\ No newline at end of file
-- 
GitLab


From 4fb42ba92f92a354d9f9652ba44aadcd36428652 Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Tue, 23 Aug 2016 17:07:34 -0800
Subject: [PATCH 076/223] Add missing headers to gif BUILD definition.

% :.../gif/giflib-5.1.4/lib$ ls
dgif_lib.c  gif_err.c	gif_hash.h	   Makefile.am	 openbsd-reallocarray.c
egif_lib.c  gif_font.c	gif_lib.h	   Makefile.in	 quantize.c
gifalloc.c  gif_hash.c	gif_lib_private.h  Makefile.unx
Change: 131121931
---
 gif.BUILD | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/gif.BUILD b/gif.BUILD
index 8dbea9cc41..cbdcc75f12 100644
--- a/gif.BUILD
+++ b/gif.BUILD
@@ -9,12 +9,18 @@ SOURCES = [
     "quantize.c",
 ]
 
+HEADERS = [
+    "gif_hash.h",
+    "gif_lib.h",
+    "gif_lib_private.h",
+]
+
 prefix_dir = "giflib-5.1.4/lib"
 
 cc_library(
     name = "gif",
     srcs = [prefix_dir + "/" + source for source in SOURCES],
-    hdrs = [prefix_dir + "/gif_lib.h"],
+    hdrs = [prefix_dir + "/" + hdrs for hdrs in HEADERS],
     includes = [prefix_dir],
     defines = [
         "HAVE_CONFIG_H",
-- 
GitLab


From 51bd0bb561b920b455e292a562b966503a3a4125 Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Tue, 23 Aug 2016 17:50:41 -0800
Subject: [PATCH 077/223] Update minimum bazel version to 0.3.0, needed by cuda
 autoconfiguring. Change: 131124948

---
 WORKSPACE | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/WORKSPACE b/WORKSPACE
index b6dbc7463f..c0d148094f 100644
--- a/WORKSPACE
+++ b/WORKSPACE
@@ -20,7 +20,7 @@ tf_workspace()
 
 # Specify the minimum required bazel version.
 load("//tensorflow:tensorflow.bzl", "check_version")
-check_version("0.2.0")
+check_version("0.3.0")
 
 # TENSORBOARD_BOWER_AUTOGENERATED_BELOW_THIS_LINE_DO_NOT_EDIT
 
-- 
GitLab


From 4f9a3a4def1d0e0bcc0c2ca4cd06d993024fd469 Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Fri, 19 Aug 2016 15:23:03 -0700
Subject: [PATCH 078/223] Change app.py() to pass through unparsed flags.

---
 tensorflow/python/BUILD                | 20 ++++++++++++
 tensorflow/python/platform/app.py      |  4 +--
 tensorflow/python/platform/app_test.py | 45 ++++++++++++++++++++++++++
 tensorflow/python/platform/flags.py    |  3 +-
 4 files changed, 69 insertions(+), 3 deletions(-)
 create mode 100644 tensorflow/python/platform/app_test.py

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 21825e1bc2..5fec88726f 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -71,6 +71,26 @@ tf_py_test(
     ],
 )
 
+tf_py_test(
+    name = "flags_test",
+    size = "small",
+    srcs = ["platform/flags_test.py"],
+    additional_deps = [
+        ":platform",
+        ":platform_test",
+    ],
+)
+
+tf_py_test(
+    name = "app_test",
+    size = "small",
+    srcs = ["platform/app_test.py"],
+    additional_deps = [
+        ":platform",
+        ":platform_test",
+    ],
+)
+
 cc_library(
     name = "numpy_lib",
     srcs = ["lib/core/numpy.cc"],
diff --git a/tensorflow/python/platform/app.py b/tensorflow/python/platform/app.py
index ebca21e075..7419d844ff 100644
--- a/tensorflow/python/platform/app.py
+++ b/tensorflow/python/platform/app.py
@@ -25,6 +25,6 @@ from tensorflow.python.platform import flags
 
 def run(main=None):
   f = flags.FLAGS
-  f._parse_flags()
+  flags_passthrough = f._parse_flags()
   main = main or sys.modules['__main__'].main
-  sys.exit(main(sys.argv))
+  sys.exit(main(sys.argv[:1] + flags_passthrough))
diff --git a/tensorflow/python/platform/app_test.py b/tensorflow/python/platform/app_test.py
new file mode 100644
index 0000000000..b7a58dd9fb
--- /dev/null
+++ b/tensorflow/python/platform/app_test.py
@@ -0,0 +1,45 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for our flags implementation."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import sys
+
+from tensorflow.python.platform import app
+from tensorflow.python.platform import flags
+
+FLAGS = flags.FLAGS
+flags.DEFINE_boolean('myflag', False, '')
+
+def main(argv):
+  if (len(argv) != 3):
+    print("Length of argv was not 3: ", argv)
+    sys.exit(-1)
+
+  if argv[1] != "--passthrough":
+    print("--passthrough argument not in argv")
+    sys.exit(-1)
+
+  if argv[2] != "extra":
+    print("'extra' argument not in argv")
+    sys.exit(-1)
+
+
+if __name__ == '__main__':
+  sys.argv.extend(["--myflag", "--passthrough", "extra"])
+  app.run()
diff --git a/tensorflow/python/platform/flags.py b/tensorflow/python/platform/flags.py
index 85f9e2cb86..645f8acecb 100644
--- a/tensorflow/python/platform/flags.py
+++ b/tensorflow/python/platform/flags.py
@@ -30,10 +30,11 @@ class _FlagValues(object):
     self.__dict__['__parsed'] = False
 
   def _parse_flags(self):
-    result, _ = _global_parser.parse_known_args()
+    result, unparsed = _global_parser.parse_known_args()
     for flag_name, val in vars(result).items():
       self.__dict__['__flags'][flag_name] = val
     self.__dict__['__parsed'] = True
+    return unparsed
 
   def __getattr__(self, name):
     """Retrieves the 'value' attribute of the flag --name."""
-- 
GitLab


From d8a92d26d65006ae6ddf56925f4f6d7dece9f6b5 Mon Sep 17 00:00:00 2001
From: Rohan Jain <rohanj@google.com>
Date: Tue, 23 Aug 2016 18:03:40 -0800
Subject: [PATCH 079/223] Fixing a memory / pointer issue with mode_t. Change:
 131125780

---
 tensorflow/python/lib/io/file_io_test.py | 1 +
 tensorflow/python/platform/base.i        | 2 ++
 2 files changed, 3 insertions(+)

diff --git a/tensorflow/python/lib/io/file_io_test.py b/tensorflow/python/lib/io/file_io_test.py
index f985f23d9e..dc79d7142e 100644
--- a/tensorflow/python/lib/io/file_io_test.py
+++ b/tensorflow/python/lib/io/file_io_test.py
@@ -262,6 +262,7 @@ class FileIoTest(tf.test.TestCase):
     self.assertEquals(7, file_statistics.length)
     self.assertEqual(
         int(os_statistics.st_mtime), int(file_statistics.mtime_nsec / 1e9))
+    self.assertEqual(33188, file_statistics.mode)
 
 if __name__ == "__main__":
   tf.test.main()
diff --git a/tensorflow/python/platform/base.i b/tensorflow/python/platform/base.i
index 246446893c..de4828130e 100644
--- a/tensorflow/python/platform/base.i
+++ b/tensorflow/python/platform/base.i
@@ -168,6 +168,7 @@ std::vector<type>* OUTPUT (std::vector<type> temp),
 _LIST_OUTPUT_TYPEMAP(string, _SwigBytes_FromString);
 _LIST_OUTPUT_TYPEMAP(long long, PyLong_FromLongLong);
 _LIST_OUTPUT_TYPEMAP(unsigned long long, PyLong_FromUnsignedLongLong);
+_LIST_OUTPUT_TYPEMAP(unsigned int, PyLong_FromUnsignedLong);
 
 %typemap(in) uint64 {
   // TODO(gps): Check if another implementation
@@ -200,6 +201,7 @@ _LIST_OUTPUT_TYPEMAP(unsigned long long, PyLong_FromUnsignedLongLong);
 
 _COPY_TYPEMAPS(unsigned long long, uint64);
 _COPY_TYPEMAPS(long long, int64);
+_COPY_TYPEMAPS(unsigned int, mode_t);
 
 // SWIG macros for explicit API declaration.
 // Usage:
-- 
GitLab


From 94054e083e03b2e7a298a0efbee560746f04d470 Mon Sep 17 00:00:00 2001
From: Zongheng Yang <zongheng@google.com>
Date: Tue, 23 Aug 2016 19:29:40 -0800
Subject: [PATCH 080/223] io::InputBuffer: add Seek() and ReadNBytes(int64,
 char*, size_t*). Change: 131130218

---
 tensorflow/core/lib/io/inputbuffer.cc      | 59 +++++++++++++----
 tensorflow/core/lib/io/inputbuffer.h       | 12 ++++
 tensorflow/core/lib/io/inputbuffer_test.cc | 76 ++++++++++++++++++++++
 3 files changed, 134 insertions(+), 13 deletions(-)

diff --git a/tensorflow/core/lib/io/inputbuffer.cc b/tensorflow/core/lib/io/inputbuffer.cc
index 39b5d70da6..eedca47ce5 100644
--- a/tensorflow/core/lib/io/inputbuffer.cc
+++ b/tensorflow/core/lib/io/inputbuffer.cc
@@ -27,9 +27,7 @@ InputBuffer::InputBuffer(RandomAccessFile* file, size_t buffer_bytes)
       pos_(buf_),
       limit_(buf_) {}
 
-InputBuffer::~InputBuffer() {
-  delete[] buf_;
-}
+InputBuffer::~InputBuffer() { delete[] buf_; }
 
 Status InputBuffer::FillBuffer() {
   StringPiece data;
@@ -77,26 +75,42 @@ Status InputBuffer::ReadNBytes(int64 bytes_to_read, string* result) {
     return errors::InvalidArgument("Can't read a negative number of bytes: ",
                                    bytes_to_read);
   }
-  result->reserve(bytes_to_read);
-  Status s;
-  while (result->size() < static_cast<size_t>(bytes_to_read)) {
+  result->resize(bytes_to_read);
+  size_t bytes_read = 0;
+  Status status = ReadNBytes(bytes_to_read, &(*result)[0], &bytes_read);
+  if (bytes_read < bytes_to_read) result->resize(bytes_read);
+  return status;
+}
+
+Status InputBuffer::ReadNBytes(int64 bytes_to_read, char* result,
+                               size_t* bytes_read) {
+  if (bytes_to_read < 0) {
+    return errors::InvalidArgument("Can't read a negative number of bytes: ",
+                                   bytes_to_read);
+  }
+  Status status;
+  *bytes_read = 0;
+  while (*bytes_read < static_cast<size_t>(bytes_to_read)) {
     if (pos_ == limit_) {
-      // Get more data into buffer
-      s = FillBuffer();
+      // Get more data into buffer.
+      status = FillBuffer();
       if (limit_ == buf_) {
         break;
       }
     }
+    // Do not go over the buffer boundary.
     const int64 bytes_to_copy =
-        std::min<int64>(limit_ - pos_, bytes_to_read - result->size());
-    result->insert(result->size(), pos_, bytes_to_copy);
+        std::min<int64>(limit_ - pos_, bytes_to_read - *bytes_read);
+    // Copies buffered data into the destination.
+    memcpy(result + *bytes_read, pos_, bytes_to_copy);
     pos_ += bytes_to_copy;
+    *bytes_read += bytes_to_copy;
   }
-  if (errors::IsOutOfRange(s) &&
-      (result->size() == static_cast<size_t>(bytes_to_read))) {
+  if (errors::IsOutOfRange(status) &&
+      (*bytes_read == static_cast<size_t>(bytes_to_read))) {
     return Status::OK();
   }
-  return s;
+  return status;
 }
 
 Status InputBuffer::SkipNBytes(int64 bytes_to_skip) {
@@ -125,5 +139,24 @@ Status InputBuffer::SkipNBytes(int64 bytes_to_skip) {
   return s;
 }
 
+Status InputBuffer::Seek(int64 position) {
+  if (position < 0) {
+    return errors::InvalidArgument("Seeking to a negative position: ",
+                                   position);
+  }
+  // Position of the buffer within file.
+  const int64 bufpos = file_pos_ - static_cast<int64>(limit_ - buf_);
+  if (position >= bufpos && position < file_pos_) {
+    // Seeks to somewhere inside the buffer.
+    pos_ = buf_ + (position - bufpos);
+    DCHECK(pos_ >= buf_ && pos_ < limit_);
+  } else {
+    // Seeks to somewhere outside.  Discards the buffered data.
+    pos_ = limit_ = buf_;
+    file_pos_ = position;
+  }
+  return Status::OK();
+}
+
 }  // namespace io
 }  // namespace tensorflow
diff --git a/tensorflow/core/lib/io/inputbuffer.h b/tensorflow/core/lib/io/inputbuffer.h
index 9665a45241..e19cb7ba33 100644
--- a/tensorflow/core/lib/io/inputbuffer.h
+++ b/tensorflow/core/lib/io/inputbuffer.h
@@ -51,9 +51,21 @@ class InputBuffer {
   // Otherwise, we return some other non-OK status.
   Status ReadNBytes(int64 bytes_to_read, string* result);
 
+  // An overload that writes to char*.  Caller must ensure result[0,
+  // bytes_to_read) is valid to be overwritten.  Returns OK, iff "*bytes_read ==
+  // bytes_to_read".
+  Status ReadNBytes(int64 bytes_to_read, char* result, size_t* bytes_read);
+
   // Like ReadNBytes() without returning the bytes read.
   Status SkipNBytes(int64 bytes_to_skip);
 
+  // Seek to this offset within the file.
+  //
+  // If we seek to somewhere within our pre-buffered data, we will re-use what
+  // data we can.  Otherwise, Seek() throws out the current buffer and the next
+  // read will trigger a File::Read().
+  Status Seek(int64 position);
+
   // Returns the position in the file.
   int64 Tell() const { return file_pos_ - (limit_ - pos_); }
 
diff --git a/tensorflow/core/lib/io/inputbuffer_test.cc b/tensorflow/core/lib/io/inputbuffer_test.cc
index cce9c6d3ee..5dc71c731c 100644
--- a/tensorflow/core/lib/io/inputbuffer_test.cc
+++ b/tensorflow/core/lib/io/inputbuffer_test.cc
@@ -26,6 +26,7 @@ limitations under the License.
 #include "tensorflow/core/platform/test.h"
 
 namespace tensorflow {
+namespace {
 
 static std::vector<int> BufferSizes() {
   return {1,  2,  3,  4,  5,  6,  7,  8,  9,  10,   11,
@@ -147,6 +148,7 @@ TEST(InputBuffer, ReadNBytes) {
   string fname = testing::TmpDir() + "/inputbuffer_test";
   WriteStringToFile(env, fname, "0123456789");
 
+  // ReadNBytes(int64, string*).
   for (auto buf_size : BufferSizes()) {
     std::unique_ptr<RandomAccessFile> file;
     TF_CHECK_OK(env->NewRandomAccessFile(fname, &file));
@@ -175,6 +177,43 @@ TEST(InputBuffer, ReadNBytes) {
     EXPECT_EQ(read, "");
     EXPECT_EQ(10, in.Tell());
   }
+  // ReadNBytes(int64, char*, size_t*).
+  size_t bytes_read;
+  for (auto buf_size : BufferSizes()) {
+    std::unique_ptr<RandomAccessFile> file;
+    TF_CHECK_OK(env->NewRandomAccessFile(fname, &file));
+    char read[5];
+    io::InputBuffer in(file.get(), buf_size);
+
+    EXPECT_EQ(0, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(3, read, &bytes_read));
+    EXPECT_EQ(StringPiece(read, 3), "012");
+
+    EXPECT_EQ(3, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(0, read, &bytes_read));
+    EXPECT_EQ(StringPiece(read, 3), "012");
+
+    EXPECT_EQ(3, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(4, read, &bytes_read));
+    EXPECT_EQ(StringPiece(read, 4), "3456");
+
+    EXPECT_EQ(7, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(0, read, &bytes_read));
+    EXPECT_EQ(StringPiece(read, 4), "3456");
+
+    EXPECT_EQ(7, in.Tell());
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadNBytes(5, read, &bytes_read)));
+    EXPECT_EQ(StringPiece(read, 3), "789");
+
+    EXPECT_EQ(10, in.Tell());
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadNBytes(5, read, &bytes_read)));
+    EXPECT_EQ(StringPiece(read, 3), "789");
+
+    EXPECT_EQ(10, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(0, read, &bytes_read));
+    EXPECT_EQ(StringPiece(read, 3), "789");
+    EXPECT_EQ(10, in.Tell());
+  }
 }
 
 TEST(InputBuffer, SkipNBytes) {
@@ -212,4 +251,41 @@ TEST(InputBuffer, SkipNBytes) {
   }
 }
 
+TEST(InputBuffer, Seek) {
+  Env* env = Env::Default();
+  string fname = testing::TmpDir() + "/inputbuffer_test";
+  WriteStringToFile(env, fname, "0123456789");
+
+  for (auto buf_size : BufferSizes()) {
+    std::unique_ptr<RandomAccessFile> file;
+    TF_CHECK_OK(env->NewRandomAccessFile(fname, &file));
+    string read;
+    io::InputBuffer in(file.get(), buf_size);
+
+    TF_CHECK_OK(in.ReadNBytes(3, &read));
+    EXPECT_EQ(read, "012");
+    TF_CHECK_OK(in.ReadNBytes(3, &read));
+    EXPECT_EQ(read, "345");
+
+    TF_CHECK_OK(in.Seek(0));
+    TF_CHECK_OK(in.ReadNBytes(3, &read));
+    EXPECT_EQ(read, "012");
+
+    TF_CHECK_OK(in.Seek(3));
+    TF_CHECK_OK(in.ReadNBytes(4, &read));
+    EXPECT_EQ(read, "3456");
+
+    TF_CHECK_OK(in.Seek(4));
+    TF_CHECK_OK(in.ReadNBytes(4, &read));
+    EXPECT_EQ(read, "4567");
+
+    TF_CHECK_OK(in.Seek(1 << 25));
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadNBytes(1, &read)));
+
+    EXPECT_TRUE(
+        StringPiece(in.Seek(-1).ToString()).contains("negative position"));
+  }
+}
+
+}  // namespace
 }  // namespace tensorflow
-- 
GitLab


From a2dc1ae9cd5991e248dc8885ed8879bb6ab15096 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 20:01:24 -0800
Subject: [PATCH 081/223] Fix the training losses when examples have weights
 associated with it.

Note: in the unlikely event this causes your relus to die, you can lower the learning rate.
Change: 131131862
---
 .../layers/python/layers/target_column.py     | 47 +++++++++++++++++--
 .../python/layers/target_column_test.py       | 32 ++++++++-----
 .../learn/estimators/dnn_linear_combined.py   | 21 +++++----
 3 files changed, 76 insertions(+), 24 deletions(-)

diff --git a/tensorflow/contrib/layers/python/layers/target_column.py b/tensorflow/contrib/layers/python/layers/target_column.py
index feef4e4a74..27f06ff099 100644
--- a/tensorflow/contrib/layers/python/layers/target_column.py
+++ b/tensorflow/contrib/layers/python/layers/target_column.py
@@ -175,9 +175,51 @@ class _TargetColumn(object):
   def problem_type(self):
     return self._problem_type
 
+  def _weighted_loss(self, loss, weight_tensor):
+    """Returns cumulative weighted loss."""
+    unweighted_loss = array_ops.reshape(loss, shape=(-1,))
+    weighted_loss = math_ops.mul(unweighted_loss,
+                                 array_ops.reshape(
+                                     weight_tensor, shape=(-1,)))
+    return weighted_loss
+
+  def training_loss(self, logits, target, features):
+    """Returns training loss tensor for this head.
+
+    Training loss is different from the loss reported on the tensorboard as we
+    should respect the example weights when computing the gradient.
+
+      L = sum_{i} w_{i} * l_{i} / B
+
+    where B is the number of examples in the batch, l_{i}, w_{i} are individual
+    losses, and example weight.
+
+    Args:
+      logits: logits, a float tensor.
+      target: either a tensor for labels or in multihead case, a dict of string
+        to target tensor.
+      features: features dict.
+
+    Returns:
+      Loss tensor.
+    """
+    target = target[self.name] if isinstance(target, dict) else target
+    loss_unweighted = self._loss_fn(logits, target)
+
+    weight_tensor = self.get_weight_tensor(features)
+    if weight_tensor is None:
+      return math_ops.reduce_mean(loss_unweighted, name="loss")
+    else:
+      loss_weighted = self._weighted_loss(loss_unweighted, weight_tensor)
+      return math_ops.reduce_mean(loss_weighted, name="loss")
+
   def loss(self, logits, target, features):
     """Returns loss tensor for this head.
 
+    The loss returned is the weighted average.
+
+      L = sum_{i} w_{i} * l_{i} / sum_{i} w_{i}
+
     Args:
       logits: logits, a float tensor.
       target: either a tensor for labels or in multihead case, a dict of string
@@ -194,10 +236,7 @@ class _TargetColumn(object):
     if weight_tensor is None:
       return math_ops.reduce_mean(loss_unweighted, name="loss")
     else:
-      loss_unweighted = array_ops.reshape(loss_unweighted, shape=(-1,))
-      loss_weighted = math_ops.mul(
-          loss_unweighted,
-          array_ops.reshape(weight_tensor, shape=(-1,)))
+      loss_weighted = self._weighted_loss(loss_unweighted, weight_tensor)
       return math_ops.div(
           math_ops.reduce_sum(loss_weighted),
           math_ops.to_float(math_ops.reduce_sum(weight_tensor)),
diff --git a/tensorflow/contrib/layers/python/layers/target_column_test.py b/tensorflow/contrib/layers/python/layers/target_column_test.py
index 65e8955d88..11f0919d35 100644
--- a/tensorflow/contrib/layers/python/layers/target_column_test.py
+++ b/tensorflow/contrib/layers/python/layers/target_column_test.py
@@ -27,23 +27,29 @@ class RegressionTargetColumnTest(tf.test.TestCase):
   def testRegression(self):
     target_column = tf.contrib.layers.regression_target()
     with tf.Graph().as_default(), tf.Session() as sess:
-      logits = tf.constant([[1.], [1.], [3.]])
+      prediction = tf.constant([[1.], [1.], [3.]])
       targets = tf.constant([[0.], [1.], [1.]])
-      self.assertAlmostEqual(5. / 3,
-                             sess.run(target_column.loss(logits, targets, {})))
+      self.assertAlmostEqual(
+          5. / 3, sess.run(target_column.loss(prediction, targets, {})))
 
   def testRegressionWithWeights(self):
     target_column = tf.contrib.layers.regression_target(
         weight_column_name="label_weight")
     with tf.Graph().as_default(), tf.Session() as sess:
-      features = {"label_weight": tf.constant([[1.], [0.], [0.]])}
-      logits = tf.constant([[1.], [1.], [3.]])
+      features = {"label_weight": tf.constant([[2.], [5.], [0.]])}
+      prediction = tf.constant([[1.], [1.], [3.]])
       targets = tf.constant([[0.], [1.], [1.]])
       self.assertAlmostEqual(
-          1., sess.run(target_column.loss(logits, targets, features)))
+          2. / 7,
+          sess.run(target_column.loss(prediction, targets, features)),
+          places=3)
+      self.assertAlmostEqual(
+          2. / 3,
+          sess.run(target_column.training_loss(prediction, targets, features)),
+          places=3)
 
 
-class MulltiClassTargetColumnTest(tf.test.TestCase):
+class MultiClassTargetColumnTest(tf.test.TestCase):
 
   def testBinaryClassification(self):
     target_column = tf.contrib.layers.multi_class_target(n_classes=2)
@@ -126,9 +132,9 @@ class MulltiClassTargetColumnTest(tf.test.TestCase):
 
   def testBinarySVMDefaultWeights(self):
     target_column = tf.contrib.layers.binary_svm_target()
-    logits = tf.constant([[-0.5], [1.2]])
+    predictions = tf.constant([[-0.5], [1.2]])
     targets = tf.constant([0, 1])
-    loss = target_column.loss(logits, targets, {})
+    loss = target_column.loss(predictions, targets, {})
     # Prediction for first example is in the right side of the hyperplane (i.e.,
     # < 0) but it is within the [-1,1] margin. There is a 0.5 loss incurred by
     # this example. The 2nd prediction is outside the margin so it incurs no
@@ -139,15 +145,17 @@ class MulltiClassTargetColumnTest(tf.test.TestCase):
   def testBinarySVMWithWeights(self):
     target_column = tf.contrib.layers.binary_svm_target(
         weight_column_name="weights")
-    logits = tf.constant([[-0.7], [0.2]])
+    predictions = tf.constant([[-0.7], [0.2]])
     targets = tf.constant([0, 1])
     features = {"weights": tf.constant([2.0, 10.0])}
-    loss = target_column.loss(logits, targets, features)
+    loss = target_column.loss(predictions, targets, features)
+    training_loss = target_column.training_loss(predictions, targets, features)
     # Prediction for both examples are in the right side of the hyperplane but
     # within the margin. The (weighted) loss incurred is 2*0.3=0.6 and 10*0.8=8
     # respectively. The overall (normalized) loss is therefore 8.6/12.
     with tf.Session() as sess:
-      self.assertAlmostEqual(8.6 / 12, sess.run(loss))
+      self.assertAlmostEqual(8.6 / 12, sess.run(loss), places=3)
+      self.assertAlmostEqual(8.6 / 2, sess.run(training_loss), places=3)
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py b/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py
index 963dcdc457..bfd028deb4 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py
@@ -174,16 +174,20 @@ class _DNNLinearCombinedBaseEstimator(estimator.BaseEstimator):
     else:
       centered_bias_step = []
     with ops.control_dependencies(centered_bias_step):
-      loss = self._target_column.loss(logits, targets, features)
-    logging_ops.scalar_summary("loss", loss)
+      training_loss = self._target_column.training_loss(logits, targets,
+                                                        features)
+      weighted_average_loss = self._target_column.loss(logits, targets,
+                                                       features)
 
-    linear_train_step = self._linear_model.get_train_step(loss)
-    dnn_train_step = (self._dnn_model.get_train_step(loss)
-                      if self._dnn_model else [])
+    logging_ops.scalar_summary("loss", weighted_average_loss)
+
+    linear_train_step = self._linear_model.get_train_step(training_loss)
+    dnn_train_step = (self._dnn_model.get_train_step(training_loss) if
+                      self._dnn_model else [])
 
     with ops.control_dependencies(linear_train_step + dnn_train_step):
       with ops.get_default_graph().colocate_with(global_step):
-        return state_ops.assign_add(global_step, 1).op, loss
+        return state_ops.assign_add(global_step, 1).op, weighted_average_loss
 
   def _get_eval_ops(self, features, targets, metrics=None):
     """See base class."""
@@ -242,10 +246,11 @@ class _DNNLinearCombinedBaseEstimator(estimator.BaseEstimator):
     logits = array_ops.reshape(
         array_ops.tile(centered_bias[0], [batch_size]),
         [batch_size, self._target_column.num_label_columns])
-    loss = self._target_column.loss(logits, targets, features)
+    training_loss = self._target_column.training_loss(logits, targets, features)
     # Learn central bias by an optimizer. 0.1 is a convervative lr for a single
     # variable.
-    return training.AdagradOptimizer(0.1).minimize(loss, var_list=centered_bias)
+    return training.AdagradOptimizer(0.1).minimize(
+        training_loss, var_list=centered_bias)
 
   def _logits(self, features, is_training=False):
     linear_feature_columns = self._get_linear_feature_columns()
-- 
GitLab


From b65a2957ba433b0ed0e529696991af4cc818f1c1 Mon Sep 17 00:00:00 2001
From: Rohan Jain <rohanj@google.com>
Date: Tue, 23 Aug 2016 20:12:32 -0800
Subject: [PATCH 082/223] Adding a Buffered InputStream that implements
 buffering on top of the InputStream Interface.

Also adds a Tell() method to the base InputStream interface.
Change: 131132599
---
 tensorflow/core/BUILD                         |   2 +
 .../core/lib/io/buffered_inputstream.cc       | 135 ++++++++
 tensorflow/core/lib/io/buffered_inputstream.h |  76 +++++
 .../core/lib/io/buffered_inputstream_test.cc  | 298 ++++++++++++++++++
 .../core/lib/io/inputstream_interface.h       |   7 +
 .../core/lib/io/inputstream_interface_test.cc |   2 +
 tensorflow/core/lib/io/random_inputstream.cc  |  22 +-
 tensorflow/core/lib/io/random_inputstream.h   |   2 +-
 .../core/lib/io/random_inputstream_test.cc    |  38 ++-
 .../core/lib/io/snappy/snappy_inputbuffer.cc  |   5 +
 .../core/lib/io/snappy/snappy_inputbuffer.h   |   4 +-
 11 files changed, 556 insertions(+), 35 deletions(-)
 create mode 100644 tensorflow/core/lib/io/buffered_inputstream.cc
 create mode 100644 tensorflow/core/lib/io/buffered_inputstream.h
 create mode 100644 tensorflow/core/lib/io/buffered_inputstream_test.cc

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 7bf08a39e6..91d414edb5 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -878,6 +878,7 @@ cc_library(
         "lib/gtl/stl_util.h",
         "lib/gtl/top_n.h",
         "lib/hash/hash.h",
+        "lib/io/buffered_inputstream.h",
         "lib/io/inputstream_interface.h",
         "lib/io/iterator.h",
         "lib/io/match.h",
@@ -1311,6 +1312,7 @@ tf_cc_tests(
         "lib/hash/crc32c_test.cc",
         "lib/hash/hash_test.cc",
         "lib/histogram/histogram_test.cc",
+        "lib/io/buffered_inputstream_test.cc",
         "lib/io/inputbuffer_test.cc",
         "lib/io/inputstream_interface_test.cc",
         "lib/io/match_test.cc",
diff --git a/tensorflow/core/lib/io/buffered_inputstream.cc b/tensorflow/core/lib/io/buffered_inputstream.cc
new file mode 100644
index 0000000000..2d131546f1
--- /dev/null
+++ b/tensorflow/core/lib/io/buffered_inputstream.cc
@@ -0,0 +1,135 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/lib/io/buffered_inputstream.h"
+
+#include "tensorflow/core/lib/io/random_inputstream.h"
+
+namespace tensorflow {
+namespace io {
+
+BufferedInputStream::BufferedInputStream(InputStreamInterface* input_stream,
+                                         size_t buffer_size,
+                                         bool owns_input_stream)
+    : input_stream_(input_stream),
+      size_(buffer_size),
+      owns_input_stream_(owns_input_stream) {
+  buf_.reserve(size_);
+}
+
+BufferedInputStream::BufferedInputStream(RandomAccessFile* file,
+                                         size_t buffer_size)
+    : BufferedInputStream(new RandomAccessInputStream(file), buffer_size,
+                          true) {}
+
+BufferedInputStream::~BufferedInputStream() {
+  if (owns_input_stream_) {
+    delete input_stream_;
+  }
+}
+
+Status BufferedInputStream::FillBuffer() {
+  Status s = input_stream_->ReadNBytes(size_, &buf_);
+  pos_ = 0;
+  limit_ = buf_.size();
+  return s;
+}
+
+Status BufferedInputStream::ReadLine(string* result) {
+  result->clear();
+  Status s;
+  while (true) {
+    if (pos_ == limit_) {
+      // Get more data into buffer
+      s = FillBuffer();
+      if (limit_ == 0) {
+        break;
+      }
+    }
+    char c = buf_[pos_++];
+    if (c == '\n') {
+      // We don't append the '\n' to *result
+      return Status::OK();
+    }
+    // We don't append '\r' to *result
+    if (c != '\r') {
+      *result += c;
+    }
+  }
+  if (errors::IsOutOfRange(s) && !result->empty()) {
+    return Status::OK();
+  }
+  return s;
+}
+
+Status BufferedInputStream::ReadNBytes(int64 bytes_to_read, string* result) {
+  if (bytes_to_read < 0) {
+    return errors::InvalidArgument("Can't read a negative number of bytes: ",
+                                   bytes_to_read);
+  }
+  result->clear();
+  result->reserve(bytes_to_read);
+
+  Status s;
+  while (result->size() < static_cast<size_t>(bytes_to_read)) {
+    // Check whether the buffer is fully read or not.
+    if (pos_ == limit_) {
+      s = FillBuffer();
+      // If we didn't read any bytes, we're at the end of the file; break out.
+      if (limit_ == 0) {
+        break;
+      }
+    }
+    const int64 bytes_to_copy =
+        std::min<int64>(limit_ - pos_, bytes_to_read - result->size());
+    result->insert(result->size(), buf_, pos_, bytes_to_copy);
+    pos_ += bytes_to_copy;
+  }
+  // Filling the buffer might lead to a situation when we go past the end of
+  // the file leading to an OutOfRange() status return. But we might have
+  // obtained enough data to satisfy the function call. Returning OK then.
+  if (errors::IsOutOfRange(s) &&
+      (result->size() == static_cast<size_t>(bytes_to_read))) {
+    return Status::OK();
+  }
+  return s;
+}
+
+Status BufferedInputStream::SkipNBytes(int64 bytes_to_skip) {
+  if (bytes_to_skip < 0) {
+    return errors::InvalidArgument("Can only skip forward, not ",
+                                   bytes_to_skip);
+  }
+  if (pos_ + bytes_to_skip < limit_) {
+    // If we aren't skipping too much, then we can just move pos_;
+    pos_ += bytes_to_skip;
+  } else {
+    // Otherwise, we already have read limit_ - pos_, so skip the rest. At this
+    // point we need to get fresh data into the buffer, so reset pos_ and
+    // limit_.
+    Status s = input_stream_->SkipNBytes(bytes_to_skip - (limit_ - pos_));
+    pos_ = 0;
+    limit_ = 0;
+    return s;
+  }
+  return Status::OK();
+}
+
+int64 BufferedInputStream::Tell() const {
+  return input_stream_->Tell() - (limit_ - pos_);
+}
+
+}  // namespace io
+}  // namespace tensorflow
diff --git a/tensorflow/core/lib/io/buffered_inputstream.h b/tensorflow/core/lib/io/buffered_inputstream.h
new file mode 100644
index 0000000000..8beaf34a72
--- /dev/null
+++ b/tensorflow/core/lib/io/buffered_inputstream.h
@@ -0,0 +1,76 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_LIB_IO_BUFFERED_INPUTSTREAM_H_
+#define TENSORFLOW_LIB_IO_BUFFERED_INPUTSTREAM_H_
+
+#include "tensorflow/core/lib/io/inputstream_interface.h"
+#include "tensorflow/core/platform/file_system.h"
+
+namespace tensorflow {
+namespace io {
+
+// Provides a buffer on top of an InputStreamInterface. A single instance of
+// BufferedInputStream is NOT safe for concurrent use by multiple threads.
+class BufferedInputStream : public InputStreamInterface {
+ public:
+  // Does not take ownership of input_stream unless owns_input_stream is set
+  // to true. input_stream must outlive *this then.
+  // TODO(rohanj): Remove owns_input_stream once the constructor below is
+  // removed.
+  BufferedInputStream(InputStreamInterface* input_stream, size_t buffer_bytes,
+                      bool owns_input_stream = false);
+
+  // For backwards compatibility, expose an interface that is similar to what
+  // InputBuffer exposes. Does not take ownership of file. file must outlive
+  // *this. This will be removed once we migrate all uses of this class to the
+  // constructor above.
+  BufferedInputStream(RandomAccessFile* file, size_t buffer_bytes);
+
+  ~BufferedInputStream() override;
+
+  Status ReadNBytes(int64 bytes_to_read, string* result) override;
+
+  Status SkipNBytes(int64 bytes_to_skip) override;
+
+  int64 Tell() const override;
+
+  // Read one text line of data into "*result" until end-of-file or a
+  // \n is read.  (The \n is not included in the result.)  Overwrites
+  // any existing data in *result.
+  //
+  // If successful, returns OK.  If we are already at the end of the
+  // file, we return an OUT_OF_RANGE error.  Otherwise, we return
+  // some other non-OK status.
+  Status ReadLine(string* result);
+
+ private:
+  Status FillBuffer();
+
+  InputStreamInterface* input_stream_;  // not owned.
+  size_t size_;                         // buffer size.
+  string buf_;                          // the buffer itself.
+  // buf_[pos_, limit_) holds the valid "read ahead" data in the file.
+  size_t pos_ = 0;    // current position in buf_.
+  size_t limit_ = 0;  // just past the end of valid data in buf_.
+  bool owns_input_stream_ = false;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(BufferedInputStream);
+};
+
+}  // namespace io
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_LIB_IO_BUFFERED_INPUTSTREAM_H_
diff --git a/tensorflow/core/lib/io/buffered_inputstream_test.cc b/tensorflow/core/lib/io/buffered_inputstream_test.cc
new file mode 100644
index 0000000000..1263f0f9aa
--- /dev/null
+++ b/tensorflow/core/lib/io/buffered_inputstream_test.cc
@@ -0,0 +1,298 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/lib/io/buffered_inputstream.h"
+
+#include "tensorflow/core/lib/core/status_test_util.h"
+#include "tensorflow/core/lib/io/random_inputstream.h"
+#include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/platform/test.h"
+
+namespace tensorflow {
+namespace io {
+namespace {
+
+static std::vector<int> BufferSizes() {
+  return {1,  2,  3,  4,  5,  6,  7,  8,  9,  10,   11,
+          12, 13, 14, 15, 16, 17, 18, 19, 20, 65536};
+}
+
+TEST(BufferedInputStream, ReadLine_Empty) {
+  Env* env = Env::Default();
+  string fname = testing::TmpDir() + "/buffered_inputstream_test";
+  WriteStringToFile(env, fname, "");
+  std::unique_ptr<RandomAccessFile> file;
+  TF_ASSERT_OK(env->NewRandomAccessFile(fname, &file));
+
+  for (auto buf_size : BufferSizes()) {
+    std::unique_ptr<RandomAccessInputStream> input_stream(
+        new RandomAccessInputStream(file.get()));
+    BufferedInputStream in(input_stream.get(), buf_size);
+    string line;
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadLine(&line)));
+  }
+}
+
+TEST(BufferedInputStream, ReadLine1) {
+  Env* env = Env::Default();
+  string fname = testing::TmpDir() + "/buffered_inputstream_test";
+  WriteStringToFile(env, fname, "line one\nline two\nline three\n");
+  std::unique_ptr<RandomAccessFile> file;
+  TF_ASSERT_OK(env->NewRandomAccessFile(fname, &file));
+
+  for (auto buf_size : BufferSizes()) {
+    std::unique_ptr<RandomAccessInputStream> input_stream(
+        new RandomAccessInputStream(file.get()));
+    BufferedInputStream in(input_stream.get(), buf_size);
+    string line;
+    TF_ASSERT_OK(in.ReadLine(&line));
+    EXPECT_EQ(line, "line one");
+    TF_ASSERT_OK(in.ReadLine(&line));
+    EXPECT_EQ(line, "line two");
+    TF_ASSERT_OK(in.ReadLine(&line));
+    EXPECT_EQ(line, "line three");
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadLine(&line)));
+    // A second call should also return end of file
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadLine(&line)));
+  }
+}
+
+TEST(BufferedInputStream, ReadLine_NoTrailingNewLine) {
+  Env* env = Env::Default();
+  string fname = testing::TmpDir() + "/buffered_inputstream_test";
+  WriteStringToFile(env, fname, "line one\nline two\nline three");
+  std::unique_ptr<RandomAccessFile> file;
+  TF_ASSERT_OK(env->NewRandomAccessFile(fname, &file));
+
+  for (auto buf_size : BufferSizes()) {
+    std::unique_ptr<RandomAccessInputStream> input_stream(
+        new RandomAccessInputStream(file.get()));
+    BufferedInputStream in(input_stream.get(), buf_size);
+    string line;
+    TF_ASSERT_OK(in.ReadLine(&line));
+    EXPECT_EQ(line, "line one");
+    TF_ASSERT_OK(in.ReadLine(&line));
+    EXPECT_EQ(line, "line two");
+    TF_ASSERT_OK(in.ReadLine(&line));
+    EXPECT_EQ(line, "line three");
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadLine(&line)));
+    // A second call should also return end of file
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadLine(&line)));
+  }
+}
+
+TEST(BufferedInputStream, ReadLine_EmptyLines) {
+  Env* env = Env::Default();
+  string fname = testing::TmpDir() + "/buffered_inputstream_test";
+  WriteStringToFile(env, fname, "line one\n\n\nline two\nline three");
+  std::unique_ptr<RandomAccessFile> file;
+  TF_ASSERT_OK(env->NewRandomAccessFile(fname, &file));
+
+  for (auto buf_size : BufferSizes()) {
+    std::unique_ptr<RandomAccessInputStream> input_stream(
+        new RandomAccessInputStream(file.get()));
+    BufferedInputStream in(input_stream.get(), buf_size);
+    string line;
+    TF_ASSERT_OK(in.ReadLine(&line));
+    EXPECT_EQ(line, "line one");
+    TF_ASSERT_OK(in.ReadLine(&line));
+    EXPECT_EQ(line, "");
+    TF_ASSERT_OK(in.ReadLine(&line));
+    EXPECT_EQ(line, "");
+    TF_ASSERT_OK(in.ReadLine(&line));
+    EXPECT_EQ(line, "line two");
+    TF_ASSERT_OK(in.ReadLine(&line));
+    EXPECT_EQ(line, "line three");
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadLine(&line)));
+    // A second call should also return end of file
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadLine(&line)));
+  }
+}
+
+TEST(BufferedInputStream, ReadLine_CRLF) {
+  Env* env = Env::Default();
+  string fname = testing::TmpDir() + "/buffered_inputstream_test";
+  WriteStringToFile(env, fname, "line one\r\n\r\n\r\nline two\r\nline three");
+  std::unique_ptr<RandomAccessFile> file;
+  TF_ASSERT_OK(env->NewRandomAccessFile(fname, &file));
+
+  for (auto buf_size : BufferSizes()) {
+    std::unique_ptr<RandomAccessInputStream> input_stream(
+        new RandomAccessInputStream(file.get()));
+    BufferedInputStream in(input_stream.get(), buf_size);
+    string line;
+    TF_ASSERT_OK(in.ReadLine(&line));
+    EXPECT_EQ(line, "line one");
+    TF_ASSERT_OK(in.ReadLine(&line));
+    EXPECT_EQ(line, "");
+    TF_ASSERT_OK(in.ReadLine(&line));
+    EXPECT_EQ(line, "");
+    TF_ASSERT_OK(in.ReadLine(&line));
+    EXPECT_EQ(line, "line two");
+    TF_ASSERT_OK(in.ReadLine(&line));
+    EXPECT_EQ(line, "line three");
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadLine(&line)));
+    // A second call should also return end of file
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadLine(&line)));
+  }
+}
+
+TEST(BufferedInputStream, ReadNBytes) {
+  Env* env = Env::Default();
+  string fname = testing::TmpDir() + "/buffer_test";
+  WriteStringToFile(env, fname, "0123456789");
+  std::unique_ptr<RandomAccessFile> file;
+  TF_ASSERT_OK(env->NewRandomAccessFile(fname, &file));
+
+  for (auto buf_size : BufferSizes()) {
+    std::unique_ptr<RandomAccessInputStream> input_stream(
+        new RandomAccessInputStream(file.get()));
+    string read;
+    BufferedInputStream in(input_stream.get(), buf_size);
+    EXPECT_EQ(0, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(3, &read));
+    EXPECT_EQ(read, "012");
+    EXPECT_EQ(3, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(0, &read));
+    EXPECT_EQ(read, "");
+    EXPECT_EQ(3, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(4, &read));
+    EXPECT_EQ(read, "3456");
+    EXPECT_EQ(7, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(0, &read));
+    EXPECT_EQ(read, "");
+    EXPECT_EQ(7, in.Tell());
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadNBytes(5, &read)));
+    EXPECT_EQ(read, "789");
+    EXPECT_EQ(10, in.Tell());
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadNBytes(5, &read)));
+    EXPECT_EQ(read, "");
+    EXPECT_EQ(10, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(0, &read));
+    EXPECT_EQ(read, "");
+    EXPECT_EQ(10, in.Tell());
+  }
+}
+
+TEST(BufferedInputStream, SkipNBytes) {
+  Env* env = Env::Default();
+  string fname = testing::TmpDir() + "/buffered_inputstream_test";
+  WriteStringToFile(env, fname, "0123456789");
+  std::unique_ptr<RandomAccessFile> file;
+  TF_ASSERT_OK(env->NewRandomAccessFile(fname, &file));
+
+  for (auto buf_size : BufferSizes()) {
+    std::unique_ptr<RandomAccessInputStream> input_stream(
+        new RandomAccessInputStream(file.get()));
+    string read;
+    BufferedInputStream in(input_stream.get(), buf_size);
+    EXPECT_EQ(0, in.Tell());
+    TF_ASSERT_OK(in.SkipNBytes(3));
+    EXPECT_EQ(3, in.Tell());
+    TF_ASSERT_OK(in.SkipNBytes(0));
+    EXPECT_EQ(3, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(2, &read));
+    EXPECT_EQ(read, "34");
+    EXPECT_EQ(5, in.Tell());
+    TF_ASSERT_OK(in.SkipNBytes(0));
+    EXPECT_EQ(5, in.Tell());
+    TF_ASSERT_OK(in.SkipNBytes(2));
+    EXPECT_EQ(7, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(1, &read));
+    EXPECT_EQ(read, "7");
+    EXPECT_EQ(8, in.Tell());
+    EXPECT_TRUE(errors::IsOutOfRange(in.SkipNBytes(5)));
+    EXPECT_EQ(10, in.Tell());
+    EXPECT_TRUE(errors::IsOutOfRange(in.SkipNBytes(5)));
+    EXPECT_EQ(10, in.Tell());
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadNBytes(5, &read)));
+    EXPECT_EQ(read, "");
+    EXPECT_EQ(10, in.Tell());
+  }
+}
+
+TEST(BufferedInputStream, ReadNBytesRandomAccessFile) {
+  Env* env = Env::Default();
+  string fname = testing::TmpDir() + "/buffer_test";
+  WriteStringToFile(env, fname, "0123456789");
+  std::unique_ptr<RandomAccessFile> file;
+  TF_ASSERT_OK(env->NewRandomAccessFile(fname, &file));
+
+  for (auto buf_size : BufferSizes()) {
+    string read;
+    BufferedInputStream in(file.get(), buf_size);
+    EXPECT_EQ(0, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(3, &read));
+    EXPECT_EQ(read, "012");
+    EXPECT_EQ(3, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(0, &read));
+    EXPECT_EQ(read, "");
+    EXPECT_EQ(3, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(4, &read));
+    EXPECT_EQ(read, "3456");
+    EXPECT_EQ(7, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(0, &read));
+    EXPECT_EQ(read, "");
+    EXPECT_EQ(7, in.Tell());
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadNBytes(5, &read)));
+    EXPECT_EQ(read, "789");
+    EXPECT_EQ(10, in.Tell());
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadNBytes(5, &read)));
+    EXPECT_EQ(read, "");
+    EXPECT_EQ(10, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(0, &read));
+    EXPECT_EQ(read, "");
+    EXPECT_EQ(10, in.Tell());
+  }
+}
+
+TEST(BufferedInputStream, SkipNBytesRandomAccessFile) {
+  Env* env = Env::Default();
+  string fname = testing::TmpDir() + "/buffered_inputstream_test";
+  WriteStringToFile(env, fname, "0123456789");
+  std::unique_ptr<RandomAccessFile> file;
+  TF_ASSERT_OK(env->NewRandomAccessFile(fname, &file));
+
+  for (auto buf_size : BufferSizes()) {
+    string read;
+    BufferedInputStream in(file.get(), buf_size);
+    EXPECT_EQ(0, in.Tell());
+    TF_ASSERT_OK(in.SkipNBytes(3));
+    EXPECT_EQ(3, in.Tell());
+    TF_ASSERT_OK(in.SkipNBytes(0));
+    EXPECT_EQ(3, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(2, &read));
+    EXPECT_EQ(read, "34");
+    EXPECT_EQ(5, in.Tell());
+    TF_ASSERT_OK(in.SkipNBytes(0));
+    EXPECT_EQ(5, in.Tell());
+    TF_ASSERT_OK(in.SkipNBytes(2));
+    EXPECT_EQ(7, in.Tell());
+    TF_ASSERT_OK(in.ReadNBytes(1, &read));
+    EXPECT_EQ(read, "7");
+    EXPECT_EQ(8, in.Tell());
+    EXPECT_TRUE(errors::IsOutOfRange(in.SkipNBytes(5)));
+    EXPECT_EQ(10, in.Tell());
+    EXPECT_TRUE(errors::IsOutOfRange(in.SkipNBytes(5)));
+    EXPECT_EQ(10, in.Tell());
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadNBytes(5, &read)));
+    EXPECT_EQ(read, "");
+    EXPECT_EQ(10, in.Tell());
+  }
+}
+
+}  // anonymous namespace
+}  // namespace io
+}  // namespace tensorflow
diff --git a/tensorflow/core/lib/io/inputstream_interface.h b/tensorflow/core/lib/io/inputstream_interface.h
index 3d5d690a69..e45e3c7a97 100644
--- a/tensorflow/core/lib/io/inputstream_interface.h
+++ b/tensorflow/core/lib/io/inputstream_interface.h
@@ -39,6 +39,13 @@ class InputStreamInterface {
   //  * OK - in case of success.
   //  * OUT_OF_RANGE - not enough bytes remaining before end of file.
   virtual Status SkipNBytes(int64 bytes_to_skip);
+
+  // Return the offset of the current byte relative to the beginning of the
+  // file.
+  // If we Skip / Read beyond the end of the file, this should return the length
+  // of the file.
+  // If there are any errors, this must return -1.
+  virtual int64 Tell() const = 0;
 };
 
 }  // namespace io
diff --git a/tensorflow/core/lib/io/inputstream_interface_test.cc b/tensorflow/core/lib/io/inputstream_interface_test.cc
index 2b4454bedb..5ccc1455c1 100644
--- a/tensorflow/core/lib/io/inputstream_interface_test.cc
+++ b/tensorflow/core/lib/io/inputstream_interface_test.cc
@@ -37,6 +37,8 @@ class TestStringStream : public InputStreamInterface {
     return Status::OK();
   }
 
+  int64 Tell() const override { return pos_; }
+
  private:
   string content_;
   int64 pos_ = 0;
diff --git a/tensorflow/core/lib/io/random_inputstream.cc b/tensorflow/core/lib/io/random_inputstream.cc
index 0b06bad1a9..bb92f0f018 100644
--- a/tensorflow/core/lib/io/random_inputstream.cc
+++ b/tensorflow/core/lib/io/random_inputstream.cc
@@ -50,27 +50,7 @@ Status RandomAccessInputStream::ReadNBytes(int64 bytes_to_read,
   return Status::OK();
 }
 
-Status RandomAccessInputStream::SkipNBytes(int64 bytes_to_skip) {
-  if (bytes_to_skip < 0) {
-    return errors::InvalidArgument("Can only skip forward, not backwards");
-  }
-  // Tries to read one byte at future location to see whether we've skipped
-  // beyond the end of file or not.
-  StringPiece data;
-  char scratch;
-  Status s = file_->Read(pos_ + bytes_to_skip, 1, &data, &scratch);
-  // Advance pointer if OK or OutOfRange status is returned.
-  if (s.ok() || errors::IsOutOfRange(s)) {
-    pos_ += bytes_to_skip;
-  }
-  // Passing on the return status from file_->Read(). We can get the following
-  //  * OK: We have skipped to some portion in the file. Return OK.
-  //  * OutOfRange: Despite b/30839063 an OutOfRange return status means that
-  //                no bytes were read and we've skipped beyond EOF. The
-  //                return semantics of SkipNBytes dictate we return OutOfRange.
-  //  * other: if some other error is encountered we just pass that along.
-  return s;
-}
+int64 RandomAccessInputStream::Tell() const { return pos_; }
 
 }  // namespace io
 }  // namespace tensorflow
diff --git a/tensorflow/core/lib/io/random_inputstream.h b/tensorflow/core/lib/io/random_inputstream.h
index c45d6ddeb4..c02258a85f 100644
--- a/tensorflow/core/lib/io/random_inputstream.h
+++ b/tensorflow/core/lib/io/random_inputstream.h
@@ -31,7 +31,7 @@ class RandomAccessInputStream : public InputStreamInterface {
 
   Status ReadNBytes(int64 bytes_to_read, string* result) override;
 
-  Status SkipNBytes(int64 bytes_to_skip) override;
+  int64 Tell() const override;
 
  private:
   RandomAccessFile* file_;  // Not owned.
diff --git a/tensorflow/core/lib/io/random_inputstream_test.cc b/tensorflow/core/lib/io/random_inputstream_test.cc
index af92585d32..c25a8efff4 100644
--- a/tensorflow/core/lib/io/random_inputstream_test.cc
+++ b/tensorflow/core/lib/io/random_inputstream_test.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/lib/io/random_inputstream.h"
 
+#include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/test.h"
 
@@ -28,21 +29,27 @@ TEST(RandomInputStream, ReadNBytes) {
   WriteStringToFile(env, fname, "0123456789");
 
   std::unique_ptr<RandomAccessFile> file;
-  TF_CHECK_OK(env->NewRandomAccessFile(fname, &file));
+  TF_ASSERT_OK(env->NewRandomAccessFile(fname, &file));
   string read;
   RandomAccessInputStream in(file.get());
-  TF_CHECK_OK(in.ReadNBytes(3, &read));
+  TF_ASSERT_OK(in.ReadNBytes(3, &read));
   EXPECT_EQ(read, "012");
-  TF_CHECK_OK(in.ReadNBytes(0, &read));
+  EXPECT_EQ(3, in.Tell());
+  TF_ASSERT_OK(in.ReadNBytes(0, &read));
   EXPECT_EQ(read, "");
-  TF_CHECK_OK(in.ReadNBytes(5, &read));
+  EXPECT_EQ(3, in.Tell());
+  TF_ASSERT_OK(in.ReadNBytes(5, &read));
   EXPECT_EQ(read, "34567");
-  TF_CHECK_OK(in.ReadNBytes(0, &read));
+  EXPECT_EQ(8, in.Tell());
+  TF_ASSERT_OK(in.ReadNBytes(0, &read));
   EXPECT_EQ(read, "");
+  EXPECT_EQ(8, in.Tell());
   EXPECT_TRUE(errors::IsOutOfRange(in.ReadNBytes(20, &read)));
   EXPECT_EQ(read, "89");
-  TF_CHECK_OK(in.ReadNBytes(0, &read));
+  EXPECT_EQ(10, in.Tell());
+  TF_ASSERT_OK(in.ReadNBytes(0, &read));
   EXPECT_EQ(read, "");
+  EXPECT_EQ(10, in.Tell());
 }
 
 TEST(RandomInputStream, SkipNBytes) {
@@ -51,22 +58,29 @@ TEST(RandomInputStream, SkipNBytes) {
   WriteStringToFile(env, fname, "0123456789");
 
   std::unique_ptr<RandomAccessFile> file;
-  TF_CHECK_OK(env->NewRandomAccessFile(fname, &file));
+  TF_ASSERT_OK(env->NewRandomAccessFile(fname, &file));
   string read;
   RandomAccessInputStream in(file.get());
-  TF_CHECK_OK(in.SkipNBytes(3));
-  TF_CHECK_OK(in.ReadNBytes(0, &read));
+  TF_ASSERT_OK(in.SkipNBytes(3));
+  EXPECT_EQ(3, in.Tell());
+  TF_ASSERT_OK(in.ReadNBytes(0, &read));
   EXPECT_EQ(read, "");
-  TF_CHECK_OK(in.ReadNBytes(4, &read));
+  EXPECT_EQ(3, in.Tell());
+  TF_ASSERT_OK(in.ReadNBytes(4, &read));
   EXPECT_EQ(read, "3456");
-  TF_CHECK_OK(in.SkipNBytes(0));
-  TF_CHECK_OK(in.ReadNBytes(2, &read));
+  EXPECT_EQ(7, in.Tell());
+  TF_ASSERT_OK(in.SkipNBytes(0));
+  EXPECT_EQ(7, in.Tell());
+  TF_ASSERT_OK(in.ReadNBytes(2, &read));
   EXPECT_EQ(read, "78");
+  EXPECT_EQ(9, in.Tell());
   EXPECT_TRUE(errors::IsOutOfRange(in.SkipNBytes(20)));
+  EXPECT_EQ(10, in.Tell());
   // Making sure that if we read after we've skipped beyond end of file, we get
   // nothing.
   EXPECT_TRUE(errors::IsOutOfRange(in.ReadNBytes(5, &read)));
   EXPECT_EQ(read, "");
+  EXPECT_EQ(10, in.Tell());
 }
 
 }  // anonymous namespace
diff --git a/tensorflow/core/lib/io/snappy/snappy_inputbuffer.cc b/tensorflow/core/lib/io/snappy/snappy_inputbuffer.cc
index c592d16ebf..8a300efbda 100644
--- a/tensorflow/core/lib/io/snappy/snappy_inputbuffer.cc
+++ b/tensorflow/core/lib/io/snappy/snappy_inputbuffer.cc
@@ -47,6 +47,11 @@ Status SnappyInputBuffer::ReadNBytes(int64 bytes_to_read, string* result) {
   return Status::OK();
 }
 
+int64 SnappyInputBuffer::Tell() const {
+  // TODO(srbs): Implement this.
+  return -1;
+}
+
 size_t SnappyInputBuffer::ReadBytesFromCache(size_t bytes_to_read,
                                              string* result) {
   size_t can_read_bytes = std::min(bytes_to_read, avail_out_);
diff --git a/tensorflow/core/lib/io/snappy/snappy_inputbuffer.h b/tensorflow/core/lib/io/snappy/snappy_inputbuffer.h
index 862f2aecd4..6df3c0d9a0 100644
--- a/tensorflow/core/lib/io/snappy/snappy_inputbuffer.h
+++ b/tensorflow/core/lib/io/snappy/snappy_inputbuffer.h
@@ -54,7 +54,9 @@ class SnappyInputBuffer : public InputStreamInterface {
   //   If input_buffer_ is smaller in size than a compressed block.
   // others:
   //   If reading from file failed.
-  Status ReadNBytes(int64 bytes_to_read, string* result);
+  Status ReadNBytes(int64 bytes_to_read, string* result) override;
+
+  int64 Tell() const override;
 
  private:
   // Reads data from `file_` and tries to fill up `input_buffer_` if enough
-- 
GitLab


From dba3200515e71ef99d93209b43345ffbd7051390 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Tue, 23 Aug 2016 20:17:17 -0800
Subject: [PATCH 083/223] Upgrade to latest gRPC (1.0.x branch). Change:
 131132846

---
 grpc.BUILD               | 1998 +++++++++++++++++++++++++++++++-------
 tensorflow/workspace.bzl |    2 +-
 2 files changed, 1662 insertions(+), 338 deletions(-)

diff --git a/grpc.BUILD b/grpc.BUILD
index f79cc8b610..7febe623d3 100644
--- a/grpc.BUILD
+++ b/grpc.BUILD
@@ -1,6 +1,15 @@
 # NOTE(mrry): This file is an edited version of the following file:
-# https://raw.githubusercontent.com/grpc/grpc/release-0_14/BUILD
-# ...with the unnecessary SSL dependencies removed.
+# https://raw.githubusercontent.com/grpc/grpc/d7ff4ff40071d2b486a052183e3e9f9382afb745/BUILD
+# ...with small modifications to fix the build rules for :grpc++_unsecure.
+#
+# TODO(mrry): Upstream these fixes back to the gRPC repository.
+
+# GRPC Bazel BUILD file.
+# This currently builds C, C++ and Objective-C code.
+# This file has been automatically generated from a template file.
+# Please look at the templates directory instead.
+# This file can be regenerated from the template by running
+# tools/buildgen/generate_projects.sh
 
 # Copyright 2015, Google Inc.
 # All rights reserved.
@@ -35,6 +44,10 @@ licenses(["notice"])  # 3-clause BSD
 
 package(default_visibility = ["//visibility:public"])
 
+
+
+
+
 cc_library(
   name = "gpr",
   srcs = [
@@ -42,11 +55,10 @@ cc_library(
     "src/core/lib/support/backoff.h",
     "src/core/lib/support/block_annotate.h",
     "src/core/lib/support/env.h",
-    "src/core/lib/support/load_file.h",
     "src/core/lib/support/murmur_hash.h",
     "src/core/lib/support/stack_lockfree.h",
     "src/core/lib/support/string.h",
-    "src/core/lib/support/string_win32.h",
+    "src/core/lib/support/string_windows.h",
     "src/core/lib/support/thd_internal.h",
     "src/core/lib/support/time_precise.h",
     "src/core/lib/support/tmpfile.h",
@@ -62,39 +74,38 @@ cc_library(
     "src/core/lib/support/cpu_windows.c",
     "src/core/lib/support/env_linux.c",
     "src/core/lib/support/env_posix.c",
-    "src/core/lib/support/env_win32.c",
+    "src/core/lib/support/env_windows.c",
     "src/core/lib/support/histogram.c",
     "src/core/lib/support/host_port.c",
-    "src/core/lib/support/load_file.c",
     "src/core/lib/support/log.c",
     "src/core/lib/support/log_android.c",
     "src/core/lib/support/log_linux.c",
     "src/core/lib/support/log_posix.c",
-    "src/core/lib/support/log_win32.c",
+    "src/core/lib/support/log_windows.c",
     "src/core/lib/support/murmur_hash.c",
     "src/core/lib/support/slice.c",
     "src/core/lib/support/slice_buffer.c",
     "src/core/lib/support/stack_lockfree.c",
     "src/core/lib/support/string.c",
     "src/core/lib/support/string_posix.c",
-    "src/core/lib/support/string_util_win32.c",
-    "src/core/lib/support/string_win32.c",
+    "src/core/lib/support/string_util_windows.c",
+    "src/core/lib/support/string_windows.c",
     "src/core/lib/support/subprocess_posix.c",
     "src/core/lib/support/subprocess_windows.c",
     "src/core/lib/support/sync.c",
     "src/core/lib/support/sync_posix.c",
-    "src/core/lib/support/sync_win32.c",
+    "src/core/lib/support/sync_windows.c",
     "src/core/lib/support/thd.c",
     "src/core/lib/support/thd_posix.c",
-    "src/core/lib/support/thd_win32.c",
+    "src/core/lib/support/thd_windows.c",
     "src/core/lib/support/time.c",
     "src/core/lib/support/time_posix.c",
     "src/core/lib/support/time_precise.c",
-    "src/core/lib/support/time_win32.c",
+    "src/core/lib/support/time_windows.c",
     "src/core/lib/support/tls_pthread.c",
     "src/core/lib/support/tmpfile_msys.c",
     "src/core/lib/support/tmpfile_posix.c",
-    "src/core/lib/support/tmpfile_win32.c",
+    "src/core/lib/support/tmpfile_windows.c",
     "src/core/lib/support/wrap_memcpy.c",
   ],
   hdrs = [
@@ -102,14 +113,14 @@ cc_library(
     "include/grpc/support/atm.h",
     "include/grpc/support/atm_gcc_atomic.h",
     "include/grpc/support/atm_gcc_sync.h",
-    "include/grpc/support/atm_win32.h",
+    "include/grpc/support/atm_windows.h",
     "include/grpc/support/avl.h",
     "include/grpc/support/cmdline.h",
     "include/grpc/support/cpu.h",
     "include/grpc/support/histogram.h",
     "include/grpc/support/host_port.h",
     "include/grpc/support/log.h",
-    "include/grpc/support/log_win32.h",
+    "include/grpc/support/log_windows.h",
     "include/grpc/support/port_platform.h",
     "include/grpc/support/slice.h",
     "include/grpc/support/slice_buffer.h",
@@ -118,7 +129,7 @@ cc_library(
     "include/grpc/support/sync.h",
     "include/grpc/support/sync_generic.h",
     "include/grpc/support/sync_posix.h",
-    "include/grpc/support/sync_win32.h",
+    "include/grpc/support/sync_windows.h",
     "include/grpc/support/thd.h",
     "include/grpc/support/time.h",
     "include/grpc/support/tls.h",
@@ -130,7 +141,7 @@ cc_library(
     "include/grpc/impl/codegen/atm.h",
     "include/grpc/impl/codegen/atm_gcc_atomic.h",
     "include/grpc/impl/codegen/atm_gcc_sync.h",
-    "include/grpc/impl/codegen/atm_win32.h",
+    "include/grpc/impl/codegen/atm_windows.h",
     "include/grpc/impl/codegen/log.h",
     "include/grpc/impl/codegen/port_platform.h",
     "include/grpc/impl/codegen/slice.h",
@@ -138,23 +149,19 @@ cc_library(
     "include/grpc/impl/codegen/sync.h",
     "include/grpc/impl/codegen/sync_generic.h",
     "include/grpc/impl/codegen/sync_posix.h",
-    "include/grpc/impl/codegen/sync_win32.h",
+    "include/grpc/impl/codegen/sync_windows.h",
     "include/grpc/impl/codegen/time.h",
   ],
   includes = [
     "include",
     ".",
   ],
-  defines = [
-    "GPR_BACKWARDS_COMPATIBILITY_MODE",
-  ],
-  copts = [
-    "-std=c99",
-  ],
   deps = [
   ],
 )
 
+
+
 cc_library(
   name = "grpc",
   srcs = [
@@ -175,7 +182,10 @@ cc_library(
     "src/core/lib/iomgr/closure.h",
     "src/core/lib/iomgr/endpoint.h",
     "src/core/lib/iomgr/endpoint_pair.h",
+    "src/core/lib/iomgr/error.h",
+    "src/core/lib/iomgr/ev_epoll_linux.h",
     "src/core/lib/iomgr/ev_poll_and_epoll_posix.h",
+    "src/core/lib/iomgr/ev_poll_posix.h",
     "src/core/lib/iomgr/ev_posix.h",
     "src/core/lib/iomgr/exec_ctx.h",
     "src/core/lib/iomgr/executor.h",
@@ -183,6 +193,9 @@ cc_library(
     "src/core/lib/iomgr/iomgr.h",
     "src/core/lib/iomgr/iomgr_internal.h",
     "src/core/lib/iomgr/iomgr_posix.h",
+    "src/core/lib/iomgr/load_file.h",
+    "src/core/lib/iomgr/network_status_tracker.h",
+    "src/core/lib/iomgr/polling_entity.h",
     "src/core/lib/iomgr/pollset.h",
     "src/core/lib/iomgr/pollset_set.h",
     "src/core/lib/iomgr/pollset_set_windows.h",
@@ -191,7 +204,7 @@ cc_library(
     "src/core/lib/iomgr/sockaddr.h",
     "src/core/lib/iomgr/sockaddr_posix.h",
     "src/core/lib/iomgr/sockaddr_utils.h",
-    "src/core/lib/iomgr/sockaddr_win32.h",
+    "src/core/lib/iomgr/sockaddr_windows.h",
     "src/core/lib/iomgr/socket_utils_posix.h",
     "src/core/lib/iomgr/socket_windows.h",
     "src/core/lib/iomgr/tcp_client.h",
@@ -223,7 +236,6 @@ cc_library(
     "src/core/lib/surface/init.h",
     "src/core/lib/surface/lame_client.h",
     "src/core/lib/surface/server.h",
-    "src/core/lib/surface/surface_trace.h",
     "src/core/lib/transport/byte_stream.h",
     "src/core/lib/transport/connectivity_state.h",
     "src/core/lib/transport/metadata.h",
@@ -231,6 +243,7 @@ cc_library(
     "src/core/lib/transport/static_metadata.h",
     "src/core/lib/transport/transport.h",
     "src/core/lib/transport/transport_impl.h",
+    "src/core/ext/transport/chttp2/transport/bin_decoder.h",
     "src/core/ext/transport/chttp2/transport/bin_encoder.h",
     "src/core/ext/transport/chttp2/transport/chttp2_transport.h",
     "src/core/ext/transport/chttp2/transport/frame.h",
@@ -252,15 +265,25 @@ cc_library(
     "src/core/ext/transport/chttp2/transport/timeout_encoding.h",
     "src/core/ext/transport/chttp2/transport/varint.h",
     "src/core/ext/transport/chttp2/alpn/alpn.h",
-    "src/core/lib/security/auth_filters.h",
-    "src/core/lib/security/b64.h",
-    "src/core/lib/security/credentials.h",
-    "src/core/lib/security/handshake.h",
-    "src/core/lib/security/json_token.h",
-    "src/core/lib/security/jwt_verifier.h",
-    "src/core/lib/security/secure_endpoint.h",
-    "src/core/lib/security/security_connector.h",
-    "src/core/lib/security/security_context.h",
+    "src/core/lib/security/context/security_context.h",
+    "src/core/lib/security/credentials/composite/composite_credentials.h",
+    "src/core/lib/security/credentials/credentials.h",
+    "src/core/lib/security/credentials/fake/fake_credentials.h",
+    "src/core/lib/security/credentials/google_default/google_default_credentials.h",
+    "src/core/lib/security/credentials/iam/iam_credentials.h",
+    "src/core/lib/security/credentials/jwt/json_token.h",
+    "src/core/lib/security/credentials/jwt/jwt_credentials.h",
+    "src/core/lib/security/credentials/jwt/jwt_verifier.h",
+    "src/core/lib/security/credentials/oauth2/oauth2_credentials.h",
+    "src/core/lib/security/credentials/plugin/plugin_credentials.h",
+    "src/core/lib/security/credentials/ssl/ssl_credentials.h",
+    "src/core/lib/security/transport/auth_filters.h",
+    "src/core/lib/security/transport/handshake.h",
+    "src/core/lib/security/transport/secure_endpoint.h",
+    "src/core/lib/security/transport/security_connector.h",
+    "src/core/lib/security/transport/tsi_error.h",
+    "src/core/lib/security/util/b64.h",
+    "src/core/lib/security/util/json_util.h",
     "src/core/lib/tsi/fake_transport_security.h",
     "src/core/lib/tsi/ssl_transport_security.h",
     "src/core/lib/tsi/ssl_types.h",
@@ -283,10 +306,13 @@ cc_library(
     "src/core/ext/client_config/subchannel_index.h",
     "src/core/ext/client_config/uri_parser.h",
     "src/core/ext/lb_policy/grpclb/load_balancer_api.h",
-    "src/core/ext/lb_policy/grpclb/proto/grpc/lb/v0/load_balancer.pb.h",
+    "src/core/ext/lb_policy/grpclb/proto/grpc/lb/v1/load_balancer.pb.h",
+    "src/core/ext/load_reporting/load_reporting.h",
+    "src/core/ext/load_reporting/load_reporting_filter.h",
     "src/core/ext/census/aggregation.h",
     "src/core/ext/census/census_interface.h",
     "src/core/ext/census/census_rpc_stats.h",
+    "src/core/ext/census/gen/census.pb.h",
     "src/core/ext/census/grpc_filter.h",
     "src/core/ext/census/mlog.h",
     "src/core/ext/census/rpc_metric_id.h",
@@ -298,7 +324,7 @@ cc_library(
     "src/core/lib/channel/connected_channel.c",
     "src/core/lib/channel/http_client_filter.c",
     "src/core/lib/channel/http_server_filter.c",
-    "src/core/lib/compression/compression_algorithm.c",
+    "src/core/lib/compression/compression.c",
     "src/core/lib/compression/message_compress.c",
     "src/core/lib/debug/trace.c",
     "src/core/lib/http/format_request.c",
@@ -308,7 +334,10 @@ cc_library(
     "src/core/lib/iomgr/endpoint.c",
     "src/core/lib/iomgr/endpoint_pair_posix.c",
     "src/core/lib/iomgr/endpoint_pair_windows.c",
+    "src/core/lib/iomgr/error.c",
+    "src/core/lib/iomgr/ev_epoll_linux.c",
     "src/core/lib/iomgr/ev_poll_and_epoll_posix.c",
+    "src/core/lib/iomgr/ev_poll_posix.c",
     "src/core/lib/iomgr/ev_posix.c",
     "src/core/lib/iomgr/exec_ctx.c",
     "src/core/lib/iomgr/executor.c",
@@ -316,6 +345,9 @@ cc_library(
     "src/core/lib/iomgr/iomgr.c",
     "src/core/lib/iomgr/iomgr_posix.c",
     "src/core/lib/iomgr/iomgr_windows.c",
+    "src/core/lib/iomgr/load_file.c",
+    "src/core/lib/iomgr/network_status_tracker.c",
+    "src/core/lib/iomgr/polling_entity.c",
     "src/core/lib/iomgr/pollset_set_windows.c",
     "src/core/lib/iomgr/pollset_windows.c",
     "src/core/lib/iomgr/resolve_address_posix.c",
@@ -373,6 +405,7 @@ cc_library(
     "src/core/lib/transport/transport.c",
     "src/core/lib/transport/transport_op_string.c",
     "src/core/ext/transport/chttp2/server/secure/server_secure_chttp2.c",
+    "src/core/ext/transport/chttp2/transport/bin_decoder.c",
     "src/core/ext/transport/chttp2/transport/bin_encoder.c",
     "src/core/ext/transport/chttp2/transport/chttp2_plugin.c",
     "src/core/ext/transport/chttp2/transport/chttp2_transport.c",
@@ -396,20 +429,29 @@ cc_library(
     "src/core/ext/transport/chttp2/transport/writing.c",
     "src/core/ext/transport/chttp2/alpn/alpn.c",
     "src/core/lib/http/httpcli_security_connector.c",
-    "src/core/lib/security/b64.c",
-    "src/core/lib/security/client_auth_filter.c",
-    "src/core/lib/security/credentials.c",
-    "src/core/lib/security/credentials_metadata.c",
-    "src/core/lib/security/credentials_posix.c",
-    "src/core/lib/security/credentials_win32.c",
-    "src/core/lib/security/google_default_credentials.c",
-    "src/core/lib/security/handshake.c",
-    "src/core/lib/security/json_token.c",
-    "src/core/lib/security/jwt_verifier.c",
-    "src/core/lib/security/secure_endpoint.c",
-    "src/core/lib/security/security_connector.c",
-    "src/core/lib/security/security_context.c",
-    "src/core/lib/security/server_auth_filter.c",
+    "src/core/lib/security/context/security_context.c",
+    "src/core/lib/security/credentials/composite/composite_credentials.c",
+    "src/core/lib/security/credentials/credentials.c",
+    "src/core/lib/security/credentials/credentials_metadata.c",
+    "src/core/lib/security/credentials/fake/fake_credentials.c",
+    "src/core/lib/security/credentials/google_default/credentials_posix.c",
+    "src/core/lib/security/credentials/google_default/credentials_windows.c",
+    "src/core/lib/security/credentials/google_default/google_default_credentials.c",
+    "src/core/lib/security/credentials/iam/iam_credentials.c",
+    "src/core/lib/security/credentials/jwt/json_token.c",
+    "src/core/lib/security/credentials/jwt/jwt_credentials.c",
+    "src/core/lib/security/credentials/jwt/jwt_verifier.c",
+    "src/core/lib/security/credentials/oauth2/oauth2_credentials.c",
+    "src/core/lib/security/credentials/plugin/plugin_credentials.c",
+    "src/core/lib/security/credentials/ssl/ssl_credentials.c",
+    "src/core/lib/security/transport/client_auth_filter.c",
+    "src/core/lib/security/transport/handshake.c",
+    "src/core/lib/security/transport/secure_endpoint.c",
+    "src/core/lib/security/transport/security_connector.c",
+    "src/core/lib/security/transport/server_auth_filter.c",
+    "src/core/lib/security/transport/tsi_error.c",
+    "src/core/lib/security/util/b64.c",
+    "src/core/lib/security/util/json_util.c",
     "src/core/lib/surface/init_secure.c",
     "src/core/lib/tsi/fake_transport_security.c",
     "src/core/lib/tsi/ssl_transport_security.c",
@@ -435,14 +477,19 @@ cc_library(
     "src/core/ext/client_config/subchannel_index.c",
     "src/core/ext/client_config/uri_parser.c",
     "src/core/ext/transport/chttp2/server/insecure/server_chttp2.c",
+    "src/core/ext/transport/chttp2/server/insecure/server_chttp2_posix.c",
     "src/core/ext/transport/chttp2/client/insecure/channel_create.c",
+    "src/core/ext/transport/chttp2/client/insecure/channel_create_posix.c",
     "src/core/ext/lb_policy/grpclb/load_balancer_api.c",
-    "src/core/ext/lb_policy/grpclb/proto/grpc/lb/v0/load_balancer.pb.c",
+    "src/core/ext/lb_policy/grpclb/proto/grpc/lb/v1/load_balancer.pb.c",
     "src/core/ext/lb_policy/pick_first/pick_first.c",
     "src/core/ext/lb_policy/round_robin/round_robin.c",
     "src/core/ext/resolver/dns/native/dns_resolver.c",
     "src/core/ext/resolver/sockaddr/sockaddr_resolver.c",
+    "src/core/ext/load_reporting/load_reporting.c",
+    "src/core/ext/load_reporting/load_reporting_filter.c",
     "src/core/ext/census/context.c",
+    "src/core/ext/census/gen/census.pb.c",
     "src/core/ext/census/grpc_context.c",
     "src/core/ext/census/grpc_filter.c",
     "src/core/ext/census/grpc_plugin.c",
@@ -458,6 +505,8 @@ cc_library(
     "include/grpc/byte_buffer_reader.h",
     "include/grpc/compression.h",
     "include/grpc/grpc.h",
+    "include/grpc/grpc_posix.h",
+    "include/grpc/grpc_security_constants.h",
     "include/grpc/status.h",
     "include/grpc/impl/codegen/byte_buffer.h",
     "include/grpc/impl/codegen/byte_buffer_reader.h",
@@ -470,7 +519,7 @@ cc_library(
     "include/grpc/impl/codegen/atm.h",
     "include/grpc/impl/codegen/atm_gcc_atomic.h",
     "include/grpc/impl/codegen/atm_gcc_sync.h",
-    "include/grpc/impl/codegen/atm_win32.h",
+    "include/grpc/impl/codegen/atm_windows.h",
     "include/grpc/impl/codegen/log.h",
     "include/grpc/impl/codegen/port_platform.h",
     "include/grpc/impl/codegen/slice.h",
@@ -478,10 +527,9 @@ cc_library(
     "include/grpc/impl/codegen/sync.h",
     "include/grpc/impl/codegen/sync_generic.h",
     "include/grpc/impl/codegen/sync_posix.h",
-    "include/grpc/impl/codegen/sync_win32.h",
+    "include/grpc/impl/codegen/sync_windows.h",
     "include/grpc/impl/codegen/time.h",
     "include/grpc/grpc_security.h",
-    "include/grpc/grpc_security_constants.h",
     "include/grpc/census.h",
   ],
   includes = [
@@ -499,160 +547,10 @@ cc_library(
   ],
 )
 
-cc_library(
-  name = "grpc++",
-  srcs = [
-    "src/cpp/client/secure_credentials.h",
-    "src/cpp/common/core_codegen.h",
-    "src/cpp/common/secure_auth_context.h",
-    "src/cpp/server/secure_server_credentials.h",
-    "src/cpp/client/create_channel_internal.h",
-    "src/cpp/server/dynamic_thread_pool.h",
-    "src/cpp/server/thread_pool_interface.h",
-    "src/cpp/client/secure_credentials.cc",
-    "src/cpp/common/auth_property_iterator.cc",
-    "src/cpp/common/secure_auth_context.cc",
-    "src/cpp/common/secure_channel_arguments.cc",
-    "src/cpp/common/secure_create_auth_context.cc",
-    "src/cpp/server/secure_server_credentials.cc",
-    "src/cpp/client/channel.cc",
-    "src/cpp/client/client_context.cc",
-    "src/cpp/client/create_channel.cc",
-    "src/cpp/client/create_channel_internal.cc",
-    "src/cpp/client/credentials.cc",
-    "src/cpp/client/generic_stub.cc",
-    "src/cpp/client/insecure_credentials.cc",
-    "src/cpp/common/channel_arguments.cc",
-    "src/cpp/common/completion_queue.cc",
-    "src/cpp/common/core_codegen.cc",
-    "src/cpp/common/rpc_method.cc",
-    "src/cpp/server/async_generic_service.cc",
-    "src/cpp/server/create_default_thread_pool.cc",
-    "src/cpp/server/dynamic_thread_pool.cc",
-    "src/cpp/server/insecure_server_credentials.cc",
-    "src/cpp/server/server.cc",
-    "src/cpp/server/server_builder.cc",
-    "src/cpp/server/server_context.cc",
-    "src/cpp/server/server_credentials.cc",
-    "src/cpp/util/byte_buffer.cc",
-    "src/cpp/util/slice.cc",
-    "src/cpp/util/status.cc",
-    "src/cpp/util/string_ref.cc",
-    "src/cpp/util/time.cc",
-    "src/cpp/codegen/codegen_init.cc",
-  ],
-  hdrs = [
-    "include/grpc++/alarm.h",
-    "include/grpc++/channel.h",
-    "include/grpc++/client_context.h",
-    "include/grpc++/completion_queue.h",
-    "include/grpc++/create_channel.h",
-    "include/grpc++/generic/async_generic_service.h",
-    "include/grpc++/generic/generic_stub.h",
-    "include/grpc++/grpc++.h",
-    "include/grpc++/impl/call.h",
-    "include/grpc++/impl/client_unary_call.h",
-    "include/grpc++/impl/grpc_library.h",
-    "include/grpc++/impl/method_handler_impl.h",
-    "include/grpc++/impl/proto_utils.h",
-    "include/grpc++/impl/rpc_method.h",
-    "include/grpc++/impl/rpc_service_method.h",
-    "include/grpc++/impl/serialization_traits.h",
-    "include/grpc++/impl/server_builder_option.h",
-    "include/grpc++/impl/service_type.h",
-    "include/grpc++/impl/sync.h",
-    "include/grpc++/impl/sync_cxx11.h",
-    "include/grpc++/impl/sync_no_cxx11.h",
-    "include/grpc++/impl/thd.h",
-    "include/grpc++/impl/thd_cxx11.h",
-    "include/grpc++/impl/thd_no_cxx11.h",
-    "include/grpc++/security/auth_context.h",
-    "include/grpc++/security/auth_metadata_processor.h",
-    "include/grpc++/security/credentials.h",
-    "include/grpc++/security/server_credentials.h",
-    "include/grpc++/server.h",
-    "include/grpc++/server_builder.h",
-    "include/grpc++/server_context.h",
-    "include/grpc++/support/async_stream.h",
-    "include/grpc++/support/async_unary_call.h",
-    "include/grpc++/support/byte_buffer.h",
-    "include/grpc++/support/channel_arguments.h",
-    "include/grpc++/support/slice.h",
-    "include/grpc++/support/status.h",
-    "include/grpc++/support/status_code_enum.h",
-    "include/grpc++/support/string_ref.h",
-    "include/grpc++/support/stub_options.h",
-    "include/grpc++/support/sync_stream.h",
-    "include/grpc++/support/time.h",
-    "include/grpc++/impl/codegen/async_stream.h",
-    "include/grpc++/impl/codegen/async_unary_call.h",
-    "include/grpc++/impl/codegen/call.h",
-    "include/grpc++/impl/codegen/call_hook.h",
-    "include/grpc++/impl/codegen/channel_interface.h",
-    "include/grpc++/impl/codegen/client_context.h",
-    "include/grpc++/impl/codegen/client_unary_call.h",
-    "include/grpc++/impl/codegen/completion_queue.h",
-    "include/grpc++/impl/codegen/completion_queue_tag.h",
-    "include/grpc++/impl/codegen/core_codegen_interface.h",
-    "include/grpc++/impl/codegen/create_auth_context.h",
-    "include/grpc++/impl/codegen/grpc_library.h",
-    "include/grpc++/impl/codegen/method_handler_impl.h",
-    "include/grpc++/impl/codegen/proto_utils.h",
-    "include/grpc++/impl/codegen/rpc_method.h",
-    "include/grpc++/impl/codegen/rpc_service_method.h",
-    "include/grpc++/impl/codegen/security/auth_context.h",
-    "include/grpc++/impl/codegen/serialization_traits.h",
-    "include/grpc++/impl/codegen/server_context.h",
-    "include/grpc++/impl/codegen/server_interface.h",
-    "include/grpc++/impl/codegen/service_type.h",
-    "include/grpc++/impl/codegen/status.h",
-    "include/grpc++/impl/codegen/status_code_enum.h",
-    "include/grpc++/impl/codegen/string_ref.h",
-    "include/grpc++/impl/codegen/stub_options.h",
-    "include/grpc++/impl/codegen/sync.h",
-    "include/grpc++/impl/codegen/sync_cxx11.h",
-    "include/grpc++/impl/codegen/sync_no_cxx11.h",
-    "include/grpc++/impl/codegen/sync_stream.h",
-    "include/grpc++/impl/codegen/time.h",
-    "include/grpc/impl/codegen/byte_buffer.h",
-    "include/grpc/impl/codegen/byte_buffer_reader.h",
-    "include/grpc/impl/codegen/compression_types.h",
-    "include/grpc/impl/codegen/connectivity_state.h",
-    "include/grpc/impl/codegen/grpc_types.h",
-    "include/grpc/impl/codegen/propagation_bits.h",
-    "include/grpc/impl/codegen/status.h",
-    "include/grpc/impl/codegen/alloc.h",
-    "include/grpc/impl/codegen/atm.h",
-    "include/grpc/impl/codegen/atm_gcc_atomic.h",
-    "include/grpc/impl/codegen/atm_gcc_sync.h",
-    "include/grpc/impl/codegen/atm_win32.h",
-    "include/grpc/impl/codegen/log.h",
-    "include/grpc/impl/codegen/port_platform.h",
-    "include/grpc/impl/codegen/slice.h",
-    "include/grpc/impl/codegen/slice_buffer.h",
-    "include/grpc/impl/codegen/sync.h",
-    "include/grpc/impl/codegen/sync_generic.h",
-    "include/grpc/impl/codegen/sync_posix.h",
-    "include/grpc/impl/codegen/sync_win32.h",
-    "include/grpc/impl/codegen/time.h",
-    "include/grpc++/impl/codegen/config.h",
-    "include/grpc++/impl/codegen/config_protobuf.h",
-    "include/grpc++/support/config.h",
-    "include/grpc++/support/config_protobuf.h",
-  ],
-  includes = [
-    "include",
-    ".",
-  ],
-  deps = [
-    "//external:libssl",
-    "//external:protobuf_clib",
-    ":grpc",
-  ],
-)
+
 
 cc_library(
-  name = "grpc_unsecure",
+  name = "grpc_cronet",
   srcs = [
     "src/core/lib/channel/channel_args.h",
     "src/core/lib/channel/channel_stack.h",
@@ -671,7 +569,10 @@ cc_library(
     "src/core/lib/iomgr/closure.h",
     "src/core/lib/iomgr/endpoint.h",
     "src/core/lib/iomgr/endpoint_pair.h",
+    "src/core/lib/iomgr/error.h",
+    "src/core/lib/iomgr/ev_epoll_linux.h",
     "src/core/lib/iomgr/ev_poll_and_epoll_posix.h",
+    "src/core/lib/iomgr/ev_poll_posix.h",
     "src/core/lib/iomgr/ev_posix.h",
     "src/core/lib/iomgr/exec_ctx.h",
     "src/core/lib/iomgr/executor.h",
@@ -679,6 +580,9 @@ cc_library(
     "src/core/lib/iomgr/iomgr.h",
     "src/core/lib/iomgr/iomgr_internal.h",
     "src/core/lib/iomgr/iomgr_posix.h",
+    "src/core/lib/iomgr/load_file.h",
+    "src/core/lib/iomgr/network_status_tracker.h",
+    "src/core/lib/iomgr/polling_entity.h",
     "src/core/lib/iomgr/pollset.h",
     "src/core/lib/iomgr/pollset_set.h",
     "src/core/lib/iomgr/pollset_set_windows.h",
@@ -687,7 +591,7 @@ cc_library(
     "src/core/lib/iomgr/sockaddr.h",
     "src/core/lib/iomgr/sockaddr_posix.h",
     "src/core/lib/iomgr/sockaddr_utils.h",
-    "src/core/lib/iomgr/sockaddr_win32.h",
+    "src/core/lib/iomgr/sockaddr_windows.h",
     "src/core/lib/iomgr/socket_utils_posix.h",
     "src/core/lib/iomgr/socket_windows.h",
     "src/core/lib/iomgr/tcp_client.h",
@@ -719,7 +623,6 @@ cc_library(
     "src/core/lib/surface/init.h",
     "src/core/lib/surface/lame_client.h",
     "src/core/lib/surface/server.h",
-    "src/core/lib/surface/surface_trace.h",
     "src/core/lib/transport/byte_stream.h",
     "src/core/lib/transport/connectivity_state.h",
     "src/core/lib/transport/metadata.h",
@@ -727,6 +630,8 @@ cc_library(
     "src/core/lib/transport/static_metadata.h",
     "src/core/lib/transport/transport.h",
     "src/core/lib/transport/transport_impl.h",
+    "third_party/objective_c/Cronet/cronet_c_for_grpc.h",
+    "src/core/ext/transport/chttp2/transport/bin_decoder.h",
     "src/core/ext/transport/chttp2/transport/bin_encoder.h",
     "src/core/ext/transport/chttp2/transport/chttp2_transport.h",
     "src/core/ext/transport/chttp2/transport/frame.h",
@@ -764,16 +669,31 @@ cc_library(
     "src/core/ext/client_config/subchannel_call_holder.h",
     "src/core/ext/client_config/subchannel_index.h",
     "src/core/ext/client_config/uri_parser.h",
-    "src/core/ext/lb_policy/grpclb/load_balancer_api.h",
-    "src/core/ext/lb_policy/grpclb/proto/grpc/lb/v0/load_balancer.pb.h",
-    "src/core/ext/census/aggregation.h",
-    "src/core/ext/census/census_interface.h",
-    "src/core/ext/census/census_rpc_stats.h",
-    "src/core/ext/census/grpc_filter.h",
-    "src/core/ext/census/mlog.h",
-    "src/core/ext/census/rpc_metric_id.h",
+    "src/core/lib/security/context/security_context.h",
+    "src/core/lib/security/credentials/composite/composite_credentials.h",
+    "src/core/lib/security/credentials/credentials.h",
+    "src/core/lib/security/credentials/fake/fake_credentials.h",
+    "src/core/lib/security/credentials/google_default/google_default_credentials.h",
+    "src/core/lib/security/credentials/iam/iam_credentials.h",
+    "src/core/lib/security/credentials/jwt/json_token.h",
+    "src/core/lib/security/credentials/jwt/jwt_credentials.h",
+    "src/core/lib/security/credentials/jwt/jwt_verifier.h",
+    "src/core/lib/security/credentials/oauth2/oauth2_credentials.h",
+    "src/core/lib/security/credentials/plugin/plugin_credentials.h",
+    "src/core/lib/security/credentials/ssl/ssl_credentials.h",
+    "src/core/lib/security/transport/auth_filters.h",
+    "src/core/lib/security/transport/handshake.h",
+    "src/core/lib/security/transport/secure_endpoint.h",
+    "src/core/lib/security/transport/security_connector.h",
+    "src/core/lib/security/transport/tsi_error.h",
+    "src/core/lib/security/util/b64.h",
+    "src/core/lib/security/util/json_util.h",
+    "src/core/lib/tsi/fake_transport_security.h",
+    "src/core/lib/tsi/ssl_transport_security.h",
+    "src/core/lib/tsi/ssl_types.h",
+    "src/core/lib/tsi/transport_security.h",
+    "src/core/lib/tsi/transport_security_interface.h",
     "src/core/lib/surface/init.c",
-    "src/core/lib/surface/init_unsecure.c",
     "src/core/lib/channel/channel_args.c",
     "src/core/lib/channel/channel_stack.c",
     "src/core/lib/channel/channel_stack_builder.c",
@@ -781,7 +701,7 @@ cc_library(
     "src/core/lib/channel/connected_channel.c",
     "src/core/lib/channel/http_client_filter.c",
     "src/core/lib/channel/http_server_filter.c",
-    "src/core/lib/compression/compression_algorithm.c",
+    "src/core/lib/compression/compression.c",
     "src/core/lib/compression/message_compress.c",
     "src/core/lib/debug/trace.c",
     "src/core/lib/http/format_request.c",
@@ -791,7 +711,10 @@ cc_library(
     "src/core/lib/iomgr/endpoint.c",
     "src/core/lib/iomgr/endpoint_pair_posix.c",
     "src/core/lib/iomgr/endpoint_pair_windows.c",
+    "src/core/lib/iomgr/error.c",
+    "src/core/lib/iomgr/ev_epoll_linux.c",
     "src/core/lib/iomgr/ev_poll_and_epoll_posix.c",
+    "src/core/lib/iomgr/ev_poll_posix.c",
     "src/core/lib/iomgr/ev_posix.c",
     "src/core/lib/iomgr/exec_ctx.c",
     "src/core/lib/iomgr/executor.c",
@@ -799,6 +722,9 @@ cc_library(
     "src/core/lib/iomgr/iomgr.c",
     "src/core/lib/iomgr/iomgr_posix.c",
     "src/core/lib/iomgr/iomgr_windows.c",
+    "src/core/lib/iomgr/load_file.c",
+    "src/core/lib/iomgr/network_status_tracker.c",
+    "src/core/lib/iomgr/polling_entity.c",
     "src/core/lib/iomgr/pollset_set_windows.c",
     "src/core/lib/iomgr/pollset_windows.c",
     "src/core/lib/iomgr/resolve_address_posix.c",
@@ -855,7 +781,11 @@ cc_library(
     "src/core/lib/transport/static_metadata.c",
     "src/core/lib/transport/transport.c",
     "src/core/lib/transport/transport_op_string.c",
-    "src/core/ext/transport/chttp2/server/insecure/server_chttp2.c",
+    "src/core/ext/transport/cronet/client/secure/cronet_channel_create.c",
+    "src/core/ext/transport/cronet/transport/cronet_api_dummy.c",
+    "src/core/ext/transport/cronet/transport/cronet_transport.c",
+    "src/core/ext/transport/chttp2/client/secure/secure_channel_create.c",
+    "src/core/ext/transport/chttp2/transport/bin_decoder.c",
     "src/core/ext/transport/chttp2/transport/bin_encoder.c",
     "src/core/ext/transport/chttp2/transport/chttp2_plugin.c",
     "src/core/ext/transport/chttp2/transport/chttp2_transport.c",
@@ -878,7 +808,6 @@ cc_library(
     "src/core/ext/transport/chttp2/transport/varint.c",
     "src/core/ext/transport/chttp2/transport/writing.c",
     "src/core/ext/transport/chttp2/alpn/alpn.c",
-    "src/core/ext/transport/chttp2/client/insecure/channel_create.c",
     "src/core/ext/client_config/channel_connectivity.c",
     "src/core/ext/client_config/client_channel.c",
     "src/core/ext/client_config/client_channel_factory.c",
@@ -898,28 +827,43 @@ cc_library(
     "src/core/ext/client_config/subchannel_call_holder.c",
     "src/core/ext/client_config/subchannel_index.c",
     "src/core/ext/client_config/uri_parser.c",
-    "src/core/ext/resolver/dns/native/dns_resolver.c",
-    "src/core/ext/resolver/sockaddr/sockaddr_resolver.c",
-    "src/core/ext/lb_policy/grpclb/load_balancer_api.c",
-    "src/core/ext/lb_policy/grpclb/proto/grpc/lb/v0/load_balancer.pb.c",
-    "src/core/ext/lb_policy/pick_first/pick_first.c",
-    "src/core/ext/lb_policy/round_robin/round_robin.c",
-    "src/core/ext/census/context.c",
-    "src/core/ext/census/grpc_context.c",
-    "src/core/ext/census/grpc_filter.c",
-    "src/core/ext/census/grpc_plugin.c",
-    "src/core/ext/census/initialize.c",
-    "src/core/ext/census/mlog.c",
-    "src/core/ext/census/operation.c",
-    "src/core/ext/census/placeholders.c",
-    "src/core/ext/census/tracing.c",
-    "src/core/plugin_registry/grpc_unsecure_plugin_registry.c",
+    "src/core/lib/http/httpcli_security_connector.c",
+    "src/core/lib/security/context/security_context.c",
+    "src/core/lib/security/credentials/composite/composite_credentials.c",
+    "src/core/lib/security/credentials/credentials.c",
+    "src/core/lib/security/credentials/credentials_metadata.c",
+    "src/core/lib/security/credentials/fake/fake_credentials.c",
+    "src/core/lib/security/credentials/google_default/credentials_posix.c",
+    "src/core/lib/security/credentials/google_default/credentials_windows.c",
+    "src/core/lib/security/credentials/google_default/google_default_credentials.c",
+    "src/core/lib/security/credentials/iam/iam_credentials.c",
+    "src/core/lib/security/credentials/jwt/json_token.c",
+    "src/core/lib/security/credentials/jwt/jwt_credentials.c",
+    "src/core/lib/security/credentials/jwt/jwt_verifier.c",
+    "src/core/lib/security/credentials/oauth2/oauth2_credentials.c",
+    "src/core/lib/security/credentials/plugin/plugin_credentials.c",
+    "src/core/lib/security/credentials/ssl/ssl_credentials.c",
+    "src/core/lib/security/transport/client_auth_filter.c",
+    "src/core/lib/security/transport/handshake.c",
+    "src/core/lib/security/transport/secure_endpoint.c",
+    "src/core/lib/security/transport/security_connector.c",
+    "src/core/lib/security/transport/server_auth_filter.c",
+    "src/core/lib/security/transport/tsi_error.c",
+    "src/core/lib/security/util/b64.c",
+    "src/core/lib/security/util/json_util.c",
+    "src/core/lib/surface/init_secure.c",
+    "src/core/lib/tsi/fake_transport_security.c",
+    "src/core/lib/tsi/ssl_transport_security.c",
+    "src/core/lib/tsi/transport_security.c",
+    "src/core/plugin_registry/grpc_cronet_plugin_registry.c",
   ],
   hdrs = [
     "include/grpc/byte_buffer.h",
     "include/grpc/byte_buffer_reader.h",
     "include/grpc/compression.h",
     "include/grpc/grpc.h",
+    "include/grpc/grpc_posix.h",
+    "include/grpc/grpc_security_constants.h",
     "include/grpc/status.h",
     "include/grpc/impl/codegen/byte_buffer.h",
     "include/grpc/impl/codegen/byte_buffer_reader.h",
@@ -932,7 +876,7 @@ cc_library(
     "include/grpc/impl/codegen/atm.h",
     "include/grpc/impl/codegen/atm_gcc_atomic.h",
     "include/grpc/impl/codegen/atm_gcc_sync.h",
-    "include/grpc/impl/codegen/atm_win32.h",
+    "include/grpc/impl/codegen/atm_windows.h",
     "include/grpc/impl/codegen/log.h",
     "include/grpc/impl/codegen/port_platform.h",
     "include/grpc/impl/codegen/slice.h",
@@ -940,75 +884,818 @@ cc_library(
     "include/grpc/impl/codegen/sync.h",
     "include/grpc/impl/codegen/sync_generic.h",
     "include/grpc/impl/codegen/sync_posix.h",
-    "include/grpc/impl/codegen/sync_win32.h",
+    "include/grpc/impl/codegen/sync_windows.h",
     "include/grpc/impl/codegen/time.h",
-    "include/grpc/census.h",
+    "include/grpc/grpc_cronet.h",
+    "include/grpc/grpc_security.h",
   ],
   includes = [
     "include",
     ".",
   ],
   deps = [
+    "//external:libssl",
     ":gpr",
-    "//external:nanopb",
-  ],
-  copts = [
-    "-std=gnu99",
   ],
 )
 
+
+
 cc_library(
-  name = "grpc++_unsecure",
+  name = "grpc_unsecure",
   srcs = [
-    "src/cpp/client/create_channel_internal.h",
-    "src/cpp/common/core_codegen.h",
-    "src/cpp/server/dynamic_thread_pool.h",
-    "src/cpp/server/thread_pool_interface.h",
-    "src/cpp/common/insecure_create_auth_context.cc",
-    "src/cpp/client/channel.cc",
-    "src/cpp/client/client_context.cc",
-    "src/cpp/client/create_channel.cc",
-    "src/cpp/client/create_channel_internal.cc",
-    "src/cpp/client/credentials.cc",
-    "src/cpp/client/generic_stub.cc",
-    "src/cpp/client/insecure_credentials.cc",
-    "src/cpp/common/channel_arguments.cc",
-    "src/cpp/common/completion_queue.cc",
-    "src/cpp/common/core_codegen.cc",
-    "src/cpp/common/rpc_method.cc",
-    "src/cpp/server/async_generic_service.cc",
-    "src/cpp/server/create_default_thread_pool.cc",
-    "src/cpp/server/dynamic_thread_pool.cc",
-    "src/cpp/server/insecure_server_credentials.cc",
-    "src/cpp/server/server.cc",
-    "src/cpp/server/server_builder.cc",
-    "src/cpp/server/server_context.cc",
-    "src/cpp/server/server_credentials.cc",
-    "src/cpp/util/byte_buffer.cc",
-    "src/cpp/util/slice.cc",
-    "src/cpp/util/status.cc",
-    "src/cpp/util/string_ref.cc",
-    "src/cpp/util/time.cc",
-    "src/cpp/codegen/codegen_init.cc",
-  ],
-  hdrs = [
-    "include/grpc++/alarm.h",
-    "include/grpc++/channel.h",
-    "include/grpc++/client_context.h",
-    "include/grpc++/completion_queue.h",
-    "include/grpc++/create_channel.h",
-    "include/grpc++/generic/async_generic_service.h",
-    "include/grpc++/generic/generic_stub.h",
-    "include/grpc++/grpc++.h",
-    "include/grpc++/impl/call.h",
-    "include/grpc++/impl/client_unary_call.h",
-    "include/grpc++/impl/grpc_library.h",
-    "include/grpc++/impl/method_handler_impl.h",
-    "include/grpc++/impl/proto_utils.h",
-    "include/grpc++/impl/rpc_method.h",
-    "include/grpc++/impl/rpc_service_method.h",
-    "include/grpc++/impl/serialization_traits.h",
-    "include/grpc++/impl/server_builder_option.h",
+    "src/core/lib/channel/channel_args.h",
+    "src/core/lib/channel/channel_stack.h",
+    "src/core/lib/channel/channel_stack_builder.h",
+    "src/core/lib/channel/compress_filter.h",
+    "src/core/lib/channel/connected_channel.h",
+    "src/core/lib/channel/context.h",
+    "src/core/lib/channel/http_client_filter.h",
+    "src/core/lib/channel/http_server_filter.h",
+    "src/core/lib/compression/algorithm_metadata.h",
+    "src/core/lib/compression/message_compress.h",
+    "src/core/lib/debug/trace.h",
+    "src/core/lib/http/format_request.h",
+    "src/core/lib/http/httpcli.h",
+    "src/core/lib/http/parser.h",
+    "src/core/lib/iomgr/closure.h",
+    "src/core/lib/iomgr/endpoint.h",
+    "src/core/lib/iomgr/endpoint_pair.h",
+    "src/core/lib/iomgr/error.h",
+    "src/core/lib/iomgr/ev_epoll_linux.h",
+    "src/core/lib/iomgr/ev_poll_and_epoll_posix.h",
+    "src/core/lib/iomgr/ev_poll_posix.h",
+    "src/core/lib/iomgr/ev_posix.h",
+    "src/core/lib/iomgr/exec_ctx.h",
+    "src/core/lib/iomgr/executor.h",
+    "src/core/lib/iomgr/iocp_windows.h",
+    "src/core/lib/iomgr/iomgr.h",
+    "src/core/lib/iomgr/iomgr_internal.h",
+    "src/core/lib/iomgr/iomgr_posix.h",
+    "src/core/lib/iomgr/load_file.h",
+    "src/core/lib/iomgr/network_status_tracker.h",
+    "src/core/lib/iomgr/polling_entity.h",
+    "src/core/lib/iomgr/pollset.h",
+    "src/core/lib/iomgr/pollset_set.h",
+    "src/core/lib/iomgr/pollset_set_windows.h",
+    "src/core/lib/iomgr/pollset_windows.h",
+    "src/core/lib/iomgr/resolve_address.h",
+    "src/core/lib/iomgr/sockaddr.h",
+    "src/core/lib/iomgr/sockaddr_posix.h",
+    "src/core/lib/iomgr/sockaddr_utils.h",
+    "src/core/lib/iomgr/sockaddr_windows.h",
+    "src/core/lib/iomgr/socket_utils_posix.h",
+    "src/core/lib/iomgr/socket_windows.h",
+    "src/core/lib/iomgr/tcp_client.h",
+    "src/core/lib/iomgr/tcp_posix.h",
+    "src/core/lib/iomgr/tcp_server.h",
+    "src/core/lib/iomgr/tcp_windows.h",
+    "src/core/lib/iomgr/time_averaged_stats.h",
+    "src/core/lib/iomgr/timer.h",
+    "src/core/lib/iomgr/timer_heap.h",
+    "src/core/lib/iomgr/udp_server.h",
+    "src/core/lib/iomgr/unix_sockets_posix.h",
+    "src/core/lib/iomgr/wakeup_fd_pipe.h",
+    "src/core/lib/iomgr/wakeup_fd_posix.h",
+    "src/core/lib/iomgr/workqueue.h",
+    "src/core/lib/iomgr/workqueue_posix.h",
+    "src/core/lib/iomgr/workqueue_windows.h",
+    "src/core/lib/json/json.h",
+    "src/core/lib/json/json_common.h",
+    "src/core/lib/json/json_reader.h",
+    "src/core/lib/json/json_writer.h",
+    "src/core/lib/surface/api_trace.h",
+    "src/core/lib/surface/call.h",
+    "src/core/lib/surface/call_test_only.h",
+    "src/core/lib/surface/channel.h",
+    "src/core/lib/surface/channel_init.h",
+    "src/core/lib/surface/channel_stack_type.h",
+    "src/core/lib/surface/completion_queue.h",
+    "src/core/lib/surface/event_string.h",
+    "src/core/lib/surface/init.h",
+    "src/core/lib/surface/lame_client.h",
+    "src/core/lib/surface/server.h",
+    "src/core/lib/transport/byte_stream.h",
+    "src/core/lib/transport/connectivity_state.h",
+    "src/core/lib/transport/metadata.h",
+    "src/core/lib/transport/metadata_batch.h",
+    "src/core/lib/transport/static_metadata.h",
+    "src/core/lib/transport/transport.h",
+    "src/core/lib/transport/transport_impl.h",
+    "src/core/ext/transport/chttp2/transport/bin_decoder.h",
+    "src/core/ext/transport/chttp2/transport/bin_encoder.h",
+    "src/core/ext/transport/chttp2/transport/chttp2_transport.h",
+    "src/core/ext/transport/chttp2/transport/frame.h",
+    "src/core/ext/transport/chttp2/transport/frame_data.h",
+    "src/core/ext/transport/chttp2/transport/frame_goaway.h",
+    "src/core/ext/transport/chttp2/transport/frame_ping.h",
+    "src/core/ext/transport/chttp2/transport/frame_rst_stream.h",
+    "src/core/ext/transport/chttp2/transport/frame_settings.h",
+    "src/core/ext/transport/chttp2/transport/frame_window_update.h",
+    "src/core/ext/transport/chttp2/transport/hpack_encoder.h",
+    "src/core/ext/transport/chttp2/transport/hpack_parser.h",
+    "src/core/ext/transport/chttp2/transport/hpack_table.h",
+    "src/core/ext/transport/chttp2/transport/http2_errors.h",
+    "src/core/ext/transport/chttp2/transport/huffsyms.h",
+    "src/core/ext/transport/chttp2/transport/incoming_metadata.h",
+    "src/core/ext/transport/chttp2/transport/internal.h",
+    "src/core/ext/transport/chttp2/transport/status_conversion.h",
+    "src/core/ext/transport/chttp2/transport/stream_map.h",
+    "src/core/ext/transport/chttp2/transport/timeout_encoding.h",
+    "src/core/ext/transport/chttp2/transport/varint.h",
+    "src/core/ext/transport/chttp2/alpn/alpn.h",
+    "src/core/ext/client_config/client_channel.h",
+    "src/core/ext/client_config/client_channel_factory.h",
+    "src/core/ext/client_config/client_config.h",
+    "src/core/ext/client_config/connector.h",
+    "src/core/ext/client_config/initial_connect_string.h",
+    "src/core/ext/client_config/lb_policy.h",
+    "src/core/ext/client_config/lb_policy_factory.h",
+    "src/core/ext/client_config/lb_policy_registry.h",
+    "src/core/ext/client_config/parse_address.h",
+    "src/core/ext/client_config/resolver.h",
+    "src/core/ext/client_config/resolver_factory.h",
+    "src/core/ext/client_config/resolver_registry.h",
+    "src/core/ext/client_config/subchannel.h",
+    "src/core/ext/client_config/subchannel_call_holder.h",
+    "src/core/ext/client_config/subchannel_index.h",
+    "src/core/ext/client_config/uri_parser.h",
+    "src/core/ext/load_reporting/load_reporting.h",
+    "src/core/ext/load_reporting/load_reporting_filter.h",
+    "src/core/ext/lb_policy/grpclb/load_balancer_api.h",
+    "src/core/ext/lb_policy/grpclb/proto/grpc/lb/v1/load_balancer.pb.h",
+    "src/core/ext/census/aggregation.h",
+    "src/core/ext/census/census_interface.h",
+    "src/core/ext/census/census_rpc_stats.h",
+    "src/core/ext/census/gen/census.pb.h",
+    "src/core/ext/census/grpc_filter.h",
+    "src/core/ext/census/mlog.h",
+    "src/core/ext/census/rpc_metric_id.h",
+    "src/core/lib/surface/init.c",
+    "src/core/lib/surface/init_unsecure.c",
+    "src/core/lib/channel/channel_args.c",
+    "src/core/lib/channel/channel_stack.c",
+    "src/core/lib/channel/channel_stack_builder.c",
+    "src/core/lib/channel/compress_filter.c",
+    "src/core/lib/channel/connected_channel.c",
+    "src/core/lib/channel/http_client_filter.c",
+    "src/core/lib/channel/http_server_filter.c",
+    "src/core/lib/compression/compression.c",
+    "src/core/lib/compression/message_compress.c",
+    "src/core/lib/debug/trace.c",
+    "src/core/lib/http/format_request.c",
+    "src/core/lib/http/httpcli.c",
+    "src/core/lib/http/parser.c",
+    "src/core/lib/iomgr/closure.c",
+    "src/core/lib/iomgr/endpoint.c",
+    "src/core/lib/iomgr/endpoint_pair_posix.c",
+    "src/core/lib/iomgr/endpoint_pair_windows.c",
+    "src/core/lib/iomgr/error.c",
+    "src/core/lib/iomgr/ev_epoll_linux.c",
+    "src/core/lib/iomgr/ev_poll_and_epoll_posix.c",
+    "src/core/lib/iomgr/ev_poll_posix.c",
+    "src/core/lib/iomgr/ev_posix.c",
+    "src/core/lib/iomgr/exec_ctx.c",
+    "src/core/lib/iomgr/executor.c",
+    "src/core/lib/iomgr/iocp_windows.c",
+    "src/core/lib/iomgr/iomgr.c",
+    "src/core/lib/iomgr/iomgr_posix.c",
+    "src/core/lib/iomgr/iomgr_windows.c",
+    "src/core/lib/iomgr/load_file.c",
+    "src/core/lib/iomgr/network_status_tracker.c",
+    "src/core/lib/iomgr/polling_entity.c",
+    "src/core/lib/iomgr/pollset_set_windows.c",
+    "src/core/lib/iomgr/pollset_windows.c",
+    "src/core/lib/iomgr/resolve_address_posix.c",
+    "src/core/lib/iomgr/resolve_address_windows.c",
+    "src/core/lib/iomgr/sockaddr_utils.c",
+    "src/core/lib/iomgr/socket_utils_common_posix.c",
+    "src/core/lib/iomgr/socket_utils_linux.c",
+    "src/core/lib/iomgr/socket_utils_posix.c",
+    "src/core/lib/iomgr/socket_windows.c",
+    "src/core/lib/iomgr/tcp_client_posix.c",
+    "src/core/lib/iomgr/tcp_client_windows.c",
+    "src/core/lib/iomgr/tcp_posix.c",
+    "src/core/lib/iomgr/tcp_server_posix.c",
+    "src/core/lib/iomgr/tcp_server_windows.c",
+    "src/core/lib/iomgr/tcp_windows.c",
+    "src/core/lib/iomgr/time_averaged_stats.c",
+    "src/core/lib/iomgr/timer.c",
+    "src/core/lib/iomgr/timer_heap.c",
+    "src/core/lib/iomgr/udp_server.c",
+    "src/core/lib/iomgr/unix_sockets_posix.c",
+    "src/core/lib/iomgr/unix_sockets_posix_noop.c",
+    "src/core/lib/iomgr/wakeup_fd_eventfd.c",
+    "src/core/lib/iomgr/wakeup_fd_nospecial.c",
+    "src/core/lib/iomgr/wakeup_fd_pipe.c",
+    "src/core/lib/iomgr/wakeup_fd_posix.c",
+    "src/core/lib/iomgr/workqueue_posix.c",
+    "src/core/lib/iomgr/workqueue_windows.c",
+    "src/core/lib/json/json.c",
+    "src/core/lib/json/json_reader.c",
+    "src/core/lib/json/json_string.c",
+    "src/core/lib/json/json_writer.c",
+    "src/core/lib/surface/alarm.c",
+    "src/core/lib/surface/api_trace.c",
+    "src/core/lib/surface/byte_buffer.c",
+    "src/core/lib/surface/byte_buffer_reader.c",
+    "src/core/lib/surface/call.c",
+    "src/core/lib/surface/call_details.c",
+    "src/core/lib/surface/call_log_batch.c",
+    "src/core/lib/surface/channel.c",
+    "src/core/lib/surface/channel_init.c",
+    "src/core/lib/surface/channel_ping.c",
+    "src/core/lib/surface/channel_stack_type.c",
+    "src/core/lib/surface/completion_queue.c",
+    "src/core/lib/surface/event_string.c",
+    "src/core/lib/surface/lame_client.c",
+    "src/core/lib/surface/metadata_array.c",
+    "src/core/lib/surface/server.c",
+    "src/core/lib/surface/validate_metadata.c",
+    "src/core/lib/surface/version.c",
+    "src/core/lib/transport/byte_stream.c",
+    "src/core/lib/transport/connectivity_state.c",
+    "src/core/lib/transport/metadata.c",
+    "src/core/lib/transport/metadata_batch.c",
+    "src/core/lib/transport/static_metadata.c",
+    "src/core/lib/transport/transport.c",
+    "src/core/lib/transport/transport_op_string.c",
+    "src/core/ext/transport/chttp2/server/insecure/server_chttp2.c",
+    "src/core/ext/transport/chttp2/server/insecure/server_chttp2_posix.c",
+    "src/core/ext/transport/chttp2/transport/bin_decoder.c",
+    "src/core/ext/transport/chttp2/transport/bin_encoder.c",
+    "src/core/ext/transport/chttp2/transport/chttp2_plugin.c",
+    "src/core/ext/transport/chttp2/transport/chttp2_transport.c",
+    "src/core/ext/transport/chttp2/transport/frame_data.c",
+    "src/core/ext/transport/chttp2/transport/frame_goaway.c",
+    "src/core/ext/transport/chttp2/transport/frame_ping.c",
+    "src/core/ext/transport/chttp2/transport/frame_rst_stream.c",
+    "src/core/ext/transport/chttp2/transport/frame_settings.c",
+    "src/core/ext/transport/chttp2/transport/frame_window_update.c",
+    "src/core/ext/transport/chttp2/transport/hpack_encoder.c",
+    "src/core/ext/transport/chttp2/transport/hpack_parser.c",
+    "src/core/ext/transport/chttp2/transport/hpack_table.c",
+    "src/core/ext/transport/chttp2/transport/huffsyms.c",
+    "src/core/ext/transport/chttp2/transport/incoming_metadata.c",
+    "src/core/ext/transport/chttp2/transport/parsing.c",
+    "src/core/ext/transport/chttp2/transport/status_conversion.c",
+    "src/core/ext/transport/chttp2/transport/stream_lists.c",
+    "src/core/ext/transport/chttp2/transport/stream_map.c",
+    "src/core/ext/transport/chttp2/transport/timeout_encoding.c",
+    "src/core/ext/transport/chttp2/transport/varint.c",
+    "src/core/ext/transport/chttp2/transport/writing.c",
+    "src/core/ext/transport/chttp2/alpn/alpn.c",
+    "src/core/ext/transport/chttp2/client/insecure/channel_create.c",
+    "src/core/ext/transport/chttp2/client/insecure/channel_create_posix.c",
+    "src/core/ext/client_config/channel_connectivity.c",
+    "src/core/ext/client_config/client_channel.c",
+    "src/core/ext/client_config/client_channel_factory.c",
+    "src/core/ext/client_config/client_config.c",
+    "src/core/ext/client_config/client_config_plugin.c",
+    "src/core/ext/client_config/connector.c",
+    "src/core/ext/client_config/default_initial_connect_string.c",
+    "src/core/ext/client_config/initial_connect_string.c",
+    "src/core/ext/client_config/lb_policy.c",
+    "src/core/ext/client_config/lb_policy_factory.c",
+    "src/core/ext/client_config/lb_policy_registry.c",
+    "src/core/ext/client_config/parse_address.c",
+    "src/core/ext/client_config/resolver.c",
+    "src/core/ext/client_config/resolver_factory.c",
+    "src/core/ext/client_config/resolver_registry.c",
+    "src/core/ext/client_config/subchannel.c",
+    "src/core/ext/client_config/subchannel_call_holder.c",
+    "src/core/ext/client_config/subchannel_index.c",
+    "src/core/ext/client_config/uri_parser.c",
+    "src/core/ext/resolver/dns/native/dns_resolver.c",
+    "src/core/ext/resolver/sockaddr/sockaddr_resolver.c",
+    "src/core/ext/load_reporting/load_reporting.c",
+    "src/core/ext/load_reporting/load_reporting_filter.c",
+    "src/core/ext/lb_policy/grpclb/load_balancer_api.c",
+    "src/core/ext/lb_policy/grpclb/proto/grpc/lb/v1/load_balancer.pb.c",
+    "src/core/ext/lb_policy/pick_first/pick_first.c",
+    "src/core/ext/lb_policy/round_robin/round_robin.c",
+    "src/core/ext/census/context.c",
+    "src/core/ext/census/gen/census.pb.c",
+    "src/core/ext/census/grpc_context.c",
+    "src/core/ext/census/grpc_filter.c",
+    "src/core/ext/census/grpc_plugin.c",
+    "src/core/ext/census/initialize.c",
+    "src/core/ext/census/mlog.c",
+    "src/core/ext/census/operation.c",
+    "src/core/ext/census/placeholders.c",
+    "src/core/ext/census/tracing.c",
+    "src/core/plugin_registry/grpc_unsecure_plugin_registry.c",
+  ],
+  hdrs = [
+    "include/grpc/byte_buffer.h",
+    "include/grpc/byte_buffer_reader.h",
+    "include/grpc/compression.h",
+    "include/grpc/grpc.h",
+    "include/grpc/grpc_posix.h",
+    "include/grpc/grpc_security_constants.h",
+    "include/grpc/status.h",
+    "include/grpc/impl/codegen/byte_buffer.h",
+    "include/grpc/impl/codegen/byte_buffer_reader.h",
+    "include/grpc/impl/codegen/compression_types.h",
+    "include/grpc/impl/codegen/connectivity_state.h",
+    "include/grpc/impl/codegen/grpc_types.h",
+    "include/grpc/impl/codegen/propagation_bits.h",
+    "include/grpc/impl/codegen/status.h",
+    "include/grpc/impl/codegen/alloc.h",
+    "include/grpc/impl/codegen/atm.h",
+    "include/grpc/impl/codegen/atm_gcc_atomic.h",
+    "include/grpc/impl/codegen/atm_gcc_sync.h",
+    "include/grpc/impl/codegen/atm_windows.h",
+    "include/grpc/impl/codegen/log.h",
+    "include/grpc/impl/codegen/port_platform.h",
+    "include/grpc/impl/codegen/slice.h",
+    "include/grpc/impl/codegen/slice_buffer.h",
+    "include/grpc/impl/codegen/sync.h",
+    "include/grpc/impl/codegen/sync_generic.h",
+    "include/grpc/impl/codegen/sync_posix.h",
+    "include/grpc/impl/codegen/sync_windows.h",
+    "include/grpc/impl/codegen/time.h",
+    "include/grpc/census.h",
+  ],
+  includes = [
+    "include",
+    ".",
+  ],
+  deps = [
+    ":gpr",
+    "//external:nanopb",
+  ],
+  copts = [
+    "-std=gnu99",
+  ],
+)
+
+
+
+cc_library(
+  name = "grpc++",
+  srcs = [
+    "include/grpc++/impl/codegen/core_codegen.h",
+    "src/cpp/client/secure_credentials.h",
+    "src/cpp/common/secure_auth_context.h",
+    "src/cpp/server/secure_server_credentials.h",
+    "src/cpp/client/create_channel_internal.h",
+    "src/cpp/server/dynamic_thread_pool.h",
+    "src/cpp/server/thread_pool_interface.h",
+    "src/core/lib/channel/channel_args.h",
+    "src/core/lib/channel/channel_stack.h",
+    "src/core/lib/channel/channel_stack_builder.h",
+    "src/core/lib/channel/compress_filter.h",
+    "src/core/lib/channel/connected_channel.h",
+    "src/core/lib/channel/context.h",
+    "src/core/lib/channel/http_client_filter.h",
+    "src/core/lib/channel/http_server_filter.h",
+    "src/core/lib/compression/algorithm_metadata.h",
+    "src/core/lib/compression/message_compress.h",
+    "src/core/lib/debug/trace.h",
+    "src/core/lib/http/format_request.h",
+    "src/core/lib/http/httpcli.h",
+    "src/core/lib/http/parser.h",
+    "src/core/lib/iomgr/closure.h",
+    "src/core/lib/iomgr/endpoint.h",
+    "src/core/lib/iomgr/endpoint_pair.h",
+    "src/core/lib/iomgr/error.h",
+    "src/core/lib/iomgr/ev_epoll_linux.h",
+    "src/core/lib/iomgr/ev_poll_and_epoll_posix.h",
+    "src/core/lib/iomgr/ev_poll_posix.h",
+    "src/core/lib/iomgr/ev_posix.h",
+    "src/core/lib/iomgr/exec_ctx.h",
+    "src/core/lib/iomgr/executor.h",
+    "src/core/lib/iomgr/iocp_windows.h",
+    "src/core/lib/iomgr/iomgr.h",
+    "src/core/lib/iomgr/iomgr_internal.h",
+    "src/core/lib/iomgr/iomgr_posix.h",
+    "src/core/lib/iomgr/load_file.h",
+    "src/core/lib/iomgr/network_status_tracker.h",
+    "src/core/lib/iomgr/polling_entity.h",
+    "src/core/lib/iomgr/pollset.h",
+    "src/core/lib/iomgr/pollset_set.h",
+    "src/core/lib/iomgr/pollset_set_windows.h",
+    "src/core/lib/iomgr/pollset_windows.h",
+    "src/core/lib/iomgr/resolve_address.h",
+    "src/core/lib/iomgr/sockaddr.h",
+    "src/core/lib/iomgr/sockaddr_posix.h",
+    "src/core/lib/iomgr/sockaddr_utils.h",
+    "src/core/lib/iomgr/sockaddr_windows.h",
+    "src/core/lib/iomgr/socket_utils_posix.h",
+    "src/core/lib/iomgr/socket_windows.h",
+    "src/core/lib/iomgr/tcp_client.h",
+    "src/core/lib/iomgr/tcp_posix.h",
+    "src/core/lib/iomgr/tcp_server.h",
+    "src/core/lib/iomgr/tcp_windows.h",
+    "src/core/lib/iomgr/time_averaged_stats.h",
+    "src/core/lib/iomgr/timer.h",
+    "src/core/lib/iomgr/timer_heap.h",
+    "src/core/lib/iomgr/udp_server.h",
+    "src/core/lib/iomgr/unix_sockets_posix.h",
+    "src/core/lib/iomgr/wakeup_fd_pipe.h",
+    "src/core/lib/iomgr/wakeup_fd_posix.h",
+    "src/core/lib/iomgr/workqueue.h",
+    "src/core/lib/iomgr/workqueue_posix.h",
+    "src/core/lib/iomgr/workqueue_windows.h",
+    "src/core/lib/json/json.h",
+    "src/core/lib/json/json_common.h",
+    "src/core/lib/json/json_reader.h",
+    "src/core/lib/json/json_writer.h",
+    "src/core/lib/surface/api_trace.h",
+    "src/core/lib/surface/call.h",
+    "src/core/lib/surface/call_test_only.h",
+    "src/core/lib/surface/channel.h",
+    "src/core/lib/surface/channel_init.h",
+    "src/core/lib/surface/channel_stack_type.h",
+    "src/core/lib/surface/completion_queue.h",
+    "src/core/lib/surface/event_string.h",
+    "src/core/lib/surface/init.h",
+    "src/core/lib/surface/lame_client.h",
+    "src/core/lib/surface/server.h",
+    "src/core/lib/transport/byte_stream.h",
+    "src/core/lib/transport/connectivity_state.h",
+    "src/core/lib/transport/metadata.h",
+    "src/core/lib/transport/metadata_batch.h",
+    "src/core/lib/transport/static_metadata.h",
+    "src/core/lib/transport/transport.h",
+    "src/core/lib/transport/transport_impl.h",
+    "src/cpp/client/secure_credentials.cc",
+    "src/cpp/common/auth_property_iterator.cc",
+    "src/cpp/common/secure_auth_context.cc",
+    "src/cpp/common/secure_channel_arguments.cc",
+    "src/cpp/common/secure_create_auth_context.cc",
+    "src/cpp/server/secure_server_credentials.cc",
+    "src/cpp/client/channel.cc",
+    "src/cpp/client/client_context.cc",
+    "src/cpp/client/create_channel.cc",
+    "src/cpp/client/create_channel_internal.cc",
+    "src/cpp/client/create_channel_posix.cc",
+    "src/cpp/client/credentials.cc",
+    "src/cpp/client/generic_stub.cc",
+    "src/cpp/client/insecure_credentials.cc",
+    "src/cpp/common/channel_arguments.cc",
+    "src/cpp/common/completion_queue.cc",
+    "src/cpp/common/core_codegen.cc",
+    "src/cpp/common/rpc_method.cc",
+    "src/cpp/server/async_generic_service.cc",
+    "src/cpp/server/create_default_thread_pool.cc",
+    "src/cpp/server/dynamic_thread_pool.cc",
+    "src/cpp/server/insecure_server_credentials.cc",
+    "src/cpp/server/server.cc",
+    "src/cpp/server/server_builder.cc",
+    "src/cpp/server/server_context.cc",
+    "src/cpp/server/server_credentials.cc",
+    "src/cpp/server/server_posix.cc",
+    "src/cpp/util/byte_buffer.cc",
+    "src/cpp/util/slice.cc",
+    "src/cpp/util/status.cc",
+    "src/cpp/util/string_ref.cc",
+    "src/cpp/util/time.cc",
+    "src/core/lib/channel/channel_args.c",
+    "src/core/lib/channel/channel_stack.c",
+    "src/core/lib/channel/channel_stack_builder.c",
+    "src/core/lib/channel/compress_filter.c",
+    "src/core/lib/channel/connected_channel.c",
+    "src/core/lib/channel/http_client_filter.c",
+    "src/core/lib/channel/http_server_filter.c",
+    "src/core/lib/compression/compression.c",
+    "src/core/lib/compression/message_compress.c",
+    "src/core/lib/debug/trace.c",
+    "src/core/lib/http/format_request.c",
+    "src/core/lib/http/httpcli.c",
+    "src/core/lib/http/parser.c",
+    "src/core/lib/iomgr/closure.c",
+    "src/core/lib/iomgr/endpoint.c",
+    "src/core/lib/iomgr/endpoint_pair_posix.c",
+    "src/core/lib/iomgr/endpoint_pair_windows.c",
+    "src/core/lib/iomgr/error.c",
+    "src/core/lib/iomgr/ev_epoll_linux.c",
+    "src/core/lib/iomgr/ev_poll_and_epoll_posix.c",
+    "src/core/lib/iomgr/ev_poll_posix.c",
+    "src/core/lib/iomgr/ev_posix.c",
+    "src/core/lib/iomgr/exec_ctx.c",
+    "src/core/lib/iomgr/executor.c",
+    "src/core/lib/iomgr/iocp_windows.c",
+    "src/core/lib/iomgr/iomgr.c",
+    "src/core/lib/iomgr/iomgr_posix.c",
+    "src/core/lib/iomgr/iomgr_windows.c",
+    "src/core/lib/iomgr/load_file.c",
+    "src/core/lib/iomgr/network_status_tracker.c",
+    "src/core/lib/iomgr/polling_entity.c",
+    "src/core/lib/iomgr/pollset_set_windows.c",
+    "src/core/lib/iomgr/pollset_windows.c",
+    "src/core/lib/iomgr/resolve_address_posix.c",
+    "src/core/lib/iomgr/resolve_address_windows.c",
+    "src/core/lib/iomgr/sockaddr_utils.c",
+    "src/core/lib/iomgr/socket_utils_common_posix.c",
+    "src/core/lib/iomgr/socket_utils_linux.c",
+    "src/core/lib/iomgr/socket_utils_posix.c",
+    "src/core/lib/iomgr/socket_windows.c",
+    "src/core/lib/iomgr/tcp_client_posix.c",
+    "src/core/lib/iomgr/tcp_client_windows.c",
+    "src/core/lib/iomgr/tcp_posix.c",
+    "src/core/lib/iomgr/tcp_server_posix.c",
+    "src/core/lib/iomgr/tcp_server_windows.c",
+    "src/core/lib/iomgr/tcp_windows.c",
+    "src/core/lib/iomgr/time_averaged_stats.c",
+    "src/core/lib/iomgr/timer.c",
+    "src/core/lib/iomgr/timer_heap.c",
+    "src/core/lib/iomgr/udp_server.c",
+    "src/core/lib/iomgr/unix_sockets_posix.c",
+    "src/core/lib/iomgr/unix_sockets_posix_noop.c",
+    "src/core/lib/iomgr/wakeup_fd_eventfd.c",
+    "src/core/lib/iomgr/wakeup_fd_nospecial.c",
+    "src/core/lib/iomgr/wakeup_fd_pipe.c",
+    "src/core/lib/iomgr/wakeup_fd_posix.c",
+    "src/core/lib/iomgr/workqueue_posix.c",
+    "src/core/lib/iomgr/workqueue_windows.c",
+    "src/core/lib/json/json.c",
+    "src/core/lib/json/json_reader.c",
+    "src/core/lib/json/json_string.c",
+    "src/core/lib/json/json_writer.c",
+    "src/core/lib/surface/alarm.c",
+    "src/core/lib/surface/api_trace.c",
+    "src/core/lib/surface/byte_buffer.c",
+    "src/core/lib/surface/byte_buffer_reader.c",
+    "src/core/lib/surface/call.c",
+    "src/core/lib/surface/call_details.c",
+    "src/core/lib/surface/call_log_batch.c",
+    "src/core/lib/surface/channel.c",
+    "src/core/lib/surface/channel_init.c",
+    "src/core/lib/surface/channel_ping.c",
+    "src/core/lib/surface/channel_stack_type.c",
+    "src/core/lib/surface/completion_queue.c",
+    "src/core/lib/surface/event_string.c",
+    "src/core/lib/surface/lame_client.c",
+    "src/core/lib/surface/metadata_array.c",
+    "src/core/lib/surface/server.c",
+    "src/core/lib/surface/validate_metadata.c",
+    "src/core/lib/surface/version.c",
+    "src/core/lib/transport/byte_stream.c",
+    "src/core/lib/transport/connectivity_state.c",
+    "src/core/lib/transport/metadata.c",
+    "src/core/lib/transport/metadata_batch.c",
+    "src/core/lib/transport/static_metadata.c",
+    "src/core/lib/transport/transport.c",
+    "src/core/lib/transport/transport_op_string.c",
+    "src/cpp/codegen/codegen_init.cc",
+  ],
+  hdrs = [
+    "include/grpc++/alarm.h",
+    "include/grpc++/channel.h",
+    "include/grpc++/client_context.h",
+    "include/grpc++/completion_queue.h",
+    "include/grpc++/create_channel.h",
+    "include/grpc++/create_channel_posix.h",
+    "include/grpc++/generic/async_generic_service.h",
+    "include/grpc++/generic/generic_stub.h",
+    "include/grpc++/grpc++.h",
+    "include/grpc++/impl/call.h",
+    "include/grpc++/impl/client_unary_call.h",
+    "include/grpc++/impl/codegen/core_codegen.h",
+    "include/grpc++/impl/grpc_library.h",
+    "include/grpc++/impl/method_handler_impl.h",
+    "include/grpc++/impl/rpc_method.h",
+    "include/grpc++/impl/rpc_service_method.h",
+    "include/grpc++/impl/serialization_traits.h",
+    "include/grpc++/impl/server_builder_option.h",
+    "include/grpc++/impl/server_builder_plugin.h",
+    "include/grpc++/impl/server_initializer.h",
+    "include/grpc++/impl/service_type.h",
+    "include/grpc++/impl/sync.h",
+    "include/grpc++/impl/sync_cxx11.h",
+    "include/grpc++/impl/sync_no_cxx11.h",
+    "include/grpc++/impl/thd.h",
+    "include/grpc++/impl/thd_cxx11.h",
+    "include/grpc++/impl/thd_no_cxx11.h",
+    "include/grpc++/security/auth_context.h",
+    "include/grpc++/security/auth_metadata_processor.h",
+    "include/grpc++/security/credentials.h",
+    "include/grpc++/security/server_credentials.h",
+    "include/grpc++/server.h",
+    "include/grpc++/server_builder.h",
+    "include/grpc++/server_context.h",
+    "include/grpc++/server_posix.h",
+    "include/grpc++/support/async_stream.h",
+    "include/grpc++/support/async_unary_call.h",
+    "include/grpc++/support/byte_buffer.h",
+    "include/grpc++/support/channel_arguments.h",
+    "include/grpc++/support/config.h",
+    "include/grpc++/support/slice.h",
+    "include/grpc++/support/status.h",
+    "include/grpc++/support/status_code_enum.h",
+    "include/grpc++/support/string_ref.h",
+    "include/grpc++/support/stub_options.h",
+    "include/grpc++/support/sync_stream.h",
+    "include/grpc++/support/time.h",
+    "include/grpc/byte_buffer.h",
+    "include/grpc/byte_buffer_reader.h",
+    "include/grpc/compression.h",
+    "include/grpc/grpc.h",
+    "include/grpc/grpc_posix.h",
+    "include/grpc/grpc_security_constants.h",
+    "include/grpc/status.h",
+    "include/grpc/impl/codegen/byte_buffer.h",
+    "include/grpc/impl/codegen/byte_buffer_reader.h",
+    "include/grpc/impl/codegen/compression_types.h",
+    "include/grpc/impl/codegen/connectivity_state.h",
+    "include/grpc/impl/codegen/grpc_types.h",
+    "include/grpc/impl/codegen/propagation_bits.h",
+    "include/grpc/impl/codegen/status.h",
+    "include/grpc/impl/codegen/alloc.h",
+    "include/grpc/impl/codegen/atm.h",
+    "include/grpc/impl/codegen/atm_gcc_atomic.h",
+    "include/grpc/impl/codegen/atm_gcc_sync.h",
+    "include/grpc/impl/codegen/atm_windows.h",
+    "include/grpc/impl/codegen/log.h",
+    "include/grpc/impl/codegen/port_platform.h",
+    "include/grpc/impl/codegen/slice.h",
+    "include/grpc/impl/codegen/slice_buffer.h",
+    "include/grpc/impl/codegen/sync.h",
+    "include/grpc/impl/codegen/sync_generic.h",
+    "include/grpc/impl/codegen/sync_posix.h",
+    "include/grpc/impl/codegen/sync_windows.h",
+    "include/grpc/impl/codegen/time.h",
+    "include/grpc++/impl/codegen/async_stream.h",
+    "include/grpc++/impl/codegen/async_unary_call.h",
+    "include/grpc++/impl/codegen/call.h",
+    "include/grpc++/impl/codegen/call_hook.h",
+    "include/grpc++/impl/codegen/channel_interface.h",
+    "include/grpc++/impl/codegen/client_context.h",
+    "include/grpc++/impl/codegen/client_unary_call.h",
+    "include/grpc++/impl/codegen/completion_queue.h",
+    "include/grpc++/impl/codegen/completion_queue_tag.h",
+    "include/grpc++/impl/codegen/config.h",
+    "include/grpc++/impl/codegen/core_codegen_interface.h",
+    "include/grpc++/impl/codegen/create_auth_context.h",
+    "include/grpc++/impl/codegen/grpc_library.h",
+    "include/grpc++/impl/codegen/method_handler_impl.h",
+    "include/grpc++/impl/codegen/rpc_method.h",
+    "include/grpc++/impl/codegen/rpc_service_method.h",
+    "include/grpc++/impl/codegen/security/auth_context.h",
+    "include/grpc++/impl/codegen/serialization_traits.h",
+    "include/grpc++/impl/codegen/server_context.h",
+    "include/grpc++/impl/codegen/server_interface.h",
+    "include/grpc++/impl/codegen/service_type.h",
+    "include/grpc++/impl/codegen/status.h",
+    "include/grpc++/impl/codegen/status_code_enum.h",
+    "include/grpc++/impl/codegen/string_ref.h",
+    "include/grpc++/impl/codegen/stub_options.h",
+    "include/grpc++/impl/codegen/sync.h",
+    "include/grpc++/impl/codegen/sync_cxx11.h",
+    "include/grpc++/impl/codegen/sync_no_cxx11.h",
+    "include/grpc++/impl/codegen/sync_stream.h",
+    "include/grpc++/impl/codegen/time.h",
+  ],
+  includes = [
+    "include",
+    ".",
+  ],
+  deps = [
+    "//external:libssl",
+    "//external:protobuf_clib",
+    ":grpc",
+    ":gpr",
+  ],
+)
+
+
+
+cc_library(
+  name = "grpc++_reflection",
+  srcs = [
+    "src/cpp/ext/proto_server_reflection.h",
+    "src/cpp/ext/proto_server_reflection.cc",
+    "src/cpp/ext/proto_server_reflection_plugin.cc",
+    "src/cpp/ext/reflection.grpc.pb.cc",
+    "src/cpp/ext/reflection.pb.cc",
+  ],
+  hdrs = [
+    "include/grpc++/ext/proto_server_reflection_plugin.h",
+    "include/grpc++/ext/reflection.grpc.pb.h",
+    "include/grpc++/ext/reflection.pb.h",
+    "include/grpc++/impl/codegen/proto_utils.h",
+    "include/grpc++/impl/codegen/async_stream.h",
+    "include/grpc++/impl/codegen/async_unary_call.h",
+    "include/grpc++/impl/codegen/call.h",
+    "include/grpc++/impl/codegen/call_hook.h",
+    "include/grpc++/impl/codegen/channel_interface.h",
+    "include/grpc++/impl/codegen/client_context.h",
+    "include/grpc++/impl/codegen/client_unary_call.h",
+    "include/grpc++/impl/codegen/completion_queue.h",
+    "include/grpc++/impl/codegen/completion_queue_tag.h",
+    "include/grpc++/impl/codegen/config.h",
+    "include/grpc++/impl/codegen/core_codegen_interface.h",
+    "include/grpc++/impl/codegen/create_auth_context.h",
+    "include/grpc++/impl/codegen/grpc_library.h",
+    "include/grpc++/impl/codegen/method_handler_impl.h",
+    "include/grpc++/impl/codegen/rpc_method.h",
+    "include/grpc++/impl/codegen/rpc_service_method.h",
+    "include/grpc++/impl/codegen/security/auth_context.h",
+    "include/grpc++/impl/codegen/serialization_traits.h",
+    "include/grpc++/impl/codegen/server_context.h",
+    "include/grpc++/impl/codegen/server_interface.h",
+    "include/grpc++/impl/codegen/service_type.h",
+    "include/grpc++/impl/codegen/status.h",
+    "include/grpc++/impl/codegen/status_code_enum.h",
+    "include/grpc++/impl/codegen/string_ref.h",
+    "include/grpc++/impl/codegen/stub_options.h",
+    "include/grpc++/impl/codegen/sync.h",
+    "include/grpc++/impl/codegen/sync_cxx11.h",
+    "include/grpc++/impl/codegen/sync_no_cxx11.h",
+    "include/grpc++/impl/codegen/sync_stream.h",
+    "include/grpc++/impl/codegen/time.h",
+    "include/grpc/impl/codegen/byte_buffer.h",
+    "include/grpc/impl/codegen/byte_buffer_reader.h",
+    "include/grpc/impl/codegen/compression_types.h",
+    "include/grpc/impl/codegen/connectivity_state.h",
+    "include/grpc/impl/codegen/grpc_types.h",
+    "include/grpc/impl/codegen/propagation_bits.h",
+    "include/grpc/impl/codegen/status.h",
+    "include/grpc/impl/codegen/alloc.h",
+    "include/grpc/impl/codegen/atm.h",
+    "include/grpc/impl/codegen/atm_gcc_atomic.h",
+    "include/grpc/impl/codegen/atm_gcc_sync.h",
+    "include/grpc/impl/codegen/atm_windows.h",
+    "include/grpc/impl/codegen/log.h",
+    "include/grpc/impl/codegen/port_platform.h",
+    "include/grpc/impl/codegen/slice.h",
+    "include/grpc/impl/codegen/slice_buffer.h",
+    "include/grpc/impl/codegen/sync.h",
+    "include/grpc/impl/codegen/sync_generic.h",
+    "include/grpc/impl/codegen/sync_posix.h",
+    "include/grpc/impl/codegen/sync_windows.h",
+    "include/grpc/impl/codegen/time.h",
+    "include/grpc++/impl/codegen/config_protobuf.h",
+  ],
+  includes = [
+    "include",
+    ".",
+  ],
+  deps = [
+    ":grpc++",
+  ],
+)
+
+
+
+cc_library(
+  name = "grpc++_unsecure",
+  srcs = [
+    "src/cpp/client/create_channel_internal.h",
+    "src/cpp/server/dynamic_thread_pool.h",
+    "src/cpp/server/thread_pool_interface.h",
+    "src/cpp/common/insecure_create_auth_context.cc",
+    "src/cpp/client/channel.cc",
+    "src/cpp/client/client_context.cc",
+    "src/cpp/client/create_channel.cc",
+    "src/cpp/client/create_channel_internal.cc",
+    "src/cpp/client/create_channel_posix.cc",
+    "src/cpp/client/credentials.cc",
+    "src/cpp/client/generic_stub.cc",
+    "src/cpp/client/insecure_credentials.cc",
+    "src/cpp/common/channel_arguments.cc",
+    "src/cpp/common/completion_queue.cc",
+    "src/cpp/common/core_codegen.cc",
+    "src/cpp/common/rpc_method.cc",
+    "src/cpp/server/async_generic_service.cc",
+    "src/cpp/server/create_default_thread_pool.cc",
+    "src/cpp/server/dynamic_thread_pool.cc",
+    "src/cpp/server/insecure_server_credentials.cc",
+    "src/cpp/server/server.cc",
+    "src/cpp/server/server_builder.cc",
+    "src/cpp/server/server_context.cc",
+    "src/cpp/server/server_credentials.cc",
+    "src/cpp/server/server_posix.cc",
+    "src/cpp/util/byte_buffer.cc",
+    "src/cpp/util/slice.cc",
+    "src/cpp/util/status.cc",
+    "src/cpp/util/string_ref.cc",
+    "src/cpp/util/time.cc",
+    "src/cpp/codegen/codegen_init.cc",
+  ],
+  hdrs = [
+    "include/grpc++/alarm.h",
+    "include/grpc++/channel.h",
+    "include/grpc++/client_context.h",
+    "include/grpc++/completion_queue.h",
+    "include/grpc++/create_channel.h",
+    "include/grpc++/create_channel_posix.h",
+    "include/grpc++/generic/async_generic_service.h",
+    "include/grpc++/generic/generic_stub.h",
+    "include/grpc++/grpc++.h",
+    "include/grpc++/impl/call.h",
+    "include/grpc++/impl/client_unary_call.h",
+    "include/grpc++/impl/codegen/core_codegen.h",
+    "include/grpc++/impl/grpc_library.h",
+    "include/grpc++/impl/method_handler_impl.h",
+    "include/grpc++/impl/rpc_method.h",
+    "include/grpc++/impl/rpc_service_method.h",
+    "include/grpc++/impl/serialization_traits.h",
+    "include/grpc++/impl/server_builder_option.h",
+    "include/grpc++/impl/server_builder_plugin.h",
+    "include/grpc++/impl/server_initializer.h",
     "include/grpc++/impl/service_type.h",
     "include/grpc++/impl/sync.h",
     "include/grpc++/impl/sync_cxx11.h",
@@ -1023,10 +1710,12 @@ cc_library(
     "include/grpc++/server.h",
     "include/grpc++/server_builder.h",
     "include/grpc++/server_context.h",
+    "include/grpc++/server_posix.h",
     "include/grpc++/support/async_stream.h",
     "include/grpc++/support/async_unary_call.h",
     "include/grpc++/support/byte_buffer.h",
     "include/grpc++/support/channel_arguments.h",
+    "include/grpc++/support/config.h",
     "include/grpc++/support/slice.h",
     "include/grpc++/support/status.h",
     "include/grpc++/support/status_code_enum.h",
@@ -1043,11 +1732,11 @@ cc_library(
     "include/grpc++/impl/codegen/client_unary_call.h",
     "include/grpc++/impl/codegen/completion_queue.h",
     "include/grpc++/impl/codegen/completion_queue_tag.h",
+    "include/grpc++/impl/codegen/config.h",
     "include/grpc++/impl/codegen/core_codegen_interface.h",
     "include/grpc++/impl/codegen/create_auth_context.h",
     "include/grpc++/impl/codegen/grpc_library.h",
     "include/grpc++/impl/codegen/method_handler_impl.h",
-    "include/grpc++/impl/codegen/proto_utils.h",
     "include/grpc++/impl/codegen/rpc_method.h",
     "include/grpc++/impl/codegen/rpc_service_method.h",
     "include/grpc++/impl/codegen/security/auth_context.h",
@@ -1064,6 +1753,391 @@ cc_library(
     "include/grpc++/impl/codegen/sync_no_cxx11.h",
     "include/grpc++/impl/codegen/sync_stream.h",
     "include/grpc++/impl/codegen/time.h",
+  ],
+  includes = [
+    "include",
+    ".",
+  ],
+  deps = [
+    "//external:protobuf_clib",
+    ":gpr",
+    ":grpc_unsecure",
+  ],
+  copts = [
+    "-std=gnu99",
+  ],
+)
+
+
+
+cc_library(
+  name = "grpc_plugin_support",
+  srcs = [
+    "src/compiler/config.h",
+    "src/compiler/cpp_generator.h",
+    "src/compiler/cpp_generator_helpers.h",
+    "src/compiler/csharp_generator.h",
+    "src/compiler/csharp_generator_helpers.h",
+    "src/compiler/generator_helpers.h",
+    "src/compiler/node_generator.h",
+    "src/compiler/node_generator_helpers.h",
+    "src/compiler/objective_c_generator.h",
+    "src/compiler/objective_c_generator_helpers.h",
+    "src/compiler/python_generator.h",
+    "src/compiler/ruby_generator.h",
+    "src/compiler/ruby_generator_helpers-inl.h",
+    "src/compiler/ruby_generator_map-inl.h",
+    "src/compiler/ruby_generator_string-inl.h",
+    "src/compiler/cpp_generator.cc",
+    "src/compiler/csharp_generator.cc",
+    "src/compiler/node_generator.cc",
+    "src/compiler/objective_c_generator.cc",
+    "src/compiler/python_generator.cc",
+    "src/compiler/ruby_generator.cc",
+  ],
+  hdrs = [
+    "include/grpc++/impl/codegen/config_protobuf.h",
+  ],
+  includes = [
+    "include",
+    ".",
+  ],
+  deps = [
+    "//external:protobuf_compiler",
+  ],
+)
+
+
+
+cc_library(
+  name = "grpc_csharp_ext",
+  srcs = [
+    "src/csharp/ext/grpc_csharp_ext.c",
+  ],
+  hdrs = [
+  ],
+  includes = [
+    "include",
+    ".",
+  ],
+  deps = [
+    ":grpc",
+    ":gpr",
+  ],
+)
+
+
+
+
+objc_library(
+  name = "gpr_objc",
+  srcs = [
+    "src/core/lib/profiling/basic_timers.c",
+    "src/core/lib/profiling/stap_timers.c",
+    "src/core/lib/support/alloc.c",
+    "src/core/lib/support/avl.c",
+    "src/core/lib/support/backoff.c",
+    "src/core/lib/support/cmdline.c",
+    "src/core/lib/support/cpu_iphone.c",
+    "src/core/lib/support/cpu_linux.c",
+    "src/core/lib/support/cpu_posix.c",
+    "src/core/lib/support/cpu_windows.c",
+    "src/core/lib/support/env_linux.c",
+    "src/core/lib/support/env_posix.c",
+    "src/core/lib/support/env_windows.c",
+    "src/core/lib/support/histogram.c",
+    "src/core/lib/support/host_port.c",
+    "src/core/lib/support/log.c",
+    "src/core/lib/support/log_android.c",
+    "src/core/lib/support/log_linux.c",
+    "src/core/lib/support/log_posix.c",
+    "src/core/lib/support/log_windows.c",
+    "src/core/lib/support/murmur_hash.c",
+    "src/core/lib/support/slice.c",
+    "src/core/lib/support/slice_buffer.c",
+    "src/core/lib/support/stack_lockfree.c",
+    "src/core/lib/support/string.c",
+    "src/core/lib/support/string_posix.c",
+    "src/core/lib/support/string_util_windows.c",
+    "src/core/lib/support/string_windows.c",
+    "src/core/lib/support/subprocess_posix.c",
+    "src/core/lib/support/subprocess_windows.c",
+    "src/core/lib/support/sync.c",
+    "src/core/lib/support/sync_posix.c",
+    "src/core/lib/support/sync_windows.c",
+    "src/core/lib/support/thd.c",
+    "src/core/lib/support/thd_posix.c",
+    "src/core/lib/support/thd_windows.c",
+    "src/core/lib/support/time.c",
+    "src/core/lib/support/time_posix.c",
+    "src/core/lib/support/time_precise.c",
+    "src/core/lib/support/time_windows.c",
+    "src/core/lib/support/tls_pthread.c",
+    "src/core/lib/support/tmpfile_msys.c",
+    "src/core/lib/support/tmpfile_posix.c",
+    "src/core/lib/support/tmpfile_windows.c",
+    "src/core/lib/support/wrap_memcpy.c",
+  ],
+  hdrs = [
+    "include/grpc/support/alloc.h",
+    "include/grpc/support/atm.h",
+    "include/grpc/support/atm_gcc_atomic.h",
+    "include/grpc/support/atm_gcc_sync.h",
+    "include/grpc/support/atm_windows.h",
+    "include/grpc/support/avl.h",
+    "include/grpc/support/cmdline.h",
+    "include/grpc/support/cpu.h",
+    "include/grpc/support/histogram.h",
+    "include/grpc/support/host_port.h",
+    "include/grpc/support/log.h",
+    "include/grpc/support/log_windows.h",
+    "include/grpc/support/port_platform.h",
+    "include/grpc/support/slice.h",
+    "include/grpc/support/slice_buffer.h",
+    "include/grpc/support/string_util.h",
+    "include/grpc/support/subprocess.h",
+    "include/grpc/support/sync.h",
+    "include/grpc/support/sync_generic.h",
+    "include/grpc/support/sync_posix.h",
+    "include/grpc/support/sync_windows.h",
+    "include/grpc/support/thd.h",
+    "include/grpc/support/time.h",
+    "include/grpc/support/tls.h",
+    "include/grpc/support/tls_gcc.h",
+    "include/grpc/support/tls_msvc.h",
+    "include/grpc/support/tls_pthread.h",
+    "include/grpc/support/useful.h",
+    "include/grpc/impl/codegen/alloc.h",
+    "include/grpc/impl/codegen/atm.h",
+    "include/grpc/impl/codegen/atm_gcc_atomic.h",
+    "include/grpc/impl/codegen/atm_gcc_sync.h",
+    "include/grpc/impl/codegen/atm_windows.h",
+    "include/grpc/impl/codegen/log.h",
+    "include/grpc/impl/codegen/port_platform.h",
+    "include/grpc/impl/codegen/slice.h",
+    "include/grpc/impl/codegen/slice_buffer.h",
+    "include/grpc/impl/codegen/sync.h",
+    "include/grpc/impl/codegen/sync_generic.h",
+    "include/grpc/impl/codegen/sync_posix.h",
+    "include/grpc/impl/codegen/sync_windows.h",
+    "include/grpc/impl/codegen/time.h",
+    "src/core/lib/profiling/timers.h",
+    "src/core/lib/support/backoff.h",
+    "src/core/lib/support/block_annotate.h",
+    "src/core/lib/support/env.h",
+    "src/core/lib/support/murmur_hash.h",
+    "src/core/lib/support/stack_lockfree.h",
+    "src/core/lib/support/string.h",
+    "src/core/lib/support/string_windows.h",
+    "src/core/lib/support/thd_internal.h",
+    "src/core/lib/support/time_precise.h",
+    "src/core/lib/support/tmpfile.h",
+  ],
+  includes = [
+    "include",
+    ".",
+  ],
+  deps = [
+  ],
+)
+
+
+
+objc_library(
+  name = "grpc_objc",
+  srcs = [
+    "src/core/lib/surface/init.c",
+    "src/core/lib/channel/channel_args.c",
+    "src/core/lib/channel/channel_stack.c",
+    "src/core/lib/channel/channel_stack_builder.c",
+    "src/core/lib/channel/compress_filter.c",
+    "src/core/lib/channel/connected_channel.c",
+    "src/core/lib/channel/http_client_filter.c",
+    "src/core/lib/channel/http_server_filter.c",
+    "src/core/lib/compression/compression.c",
+    "src/core/lib/compression/message_compress.c",
+    "src/core/lib/debug/trace.c",
+    "src/core/lib/http/format_request.c",
+    "src/core/lib/http/httpcli.c",
+    "src/core/lib/http/parser.c",
+    "src/core/lib/iomgr/closure.c",
+    "src/core/lib/iomgr/endpoint.c",
+    "src/core/lib/iomgr/endpoint_pair_posix.c",
+    "src/core/lib/iomgr/endpoint_pair_windows.c",
+    "src/core/lib/iomgr/error.c",
+    "src/core/lib/iomgr/ev_epoll_linux.c",
+    "src/core/lib/iomgr/ev_poll_and_epoll_posix.c",
+    "src/core/lib/iomgr/ev_poll_posix.c",
+    "src/core/lib/iomgr/ev_posix.c",
+    "src/core/lib/iomgr/exec_ctx.c",
+    "src/core/lib/iomgr/executor.c",
+    "src/core/lib/iomgr/iocp_windows.c",
+    "src/core/lib/iomgr/iomgr.c",
+    "src/core/lib/iomgr/iomgr_posix.c",
+    "src/core/lib/iomgr/iomgr_windows.c",
+    "src/core/lib/iomgr/load_file.c",
+    "src/core/lib/iomgr/network_status_tracker.c",
+    "src/core/lib/iomgr/polling_entity.c",
+    "src/core/lib/iomgr/pollset_set_windows.c",
+    "src/core/lib/iomgr/pollset_windows.c",
+    "src/core/lib/iomgr/resolve_address_posix.c",
+    "src/core/lib/iomgr/resolve_address_windows.c",
+    "src/core/lib/iomgr/sockaddr_utils.c",
+    "src/core/lib/iomgr/socket_utils_common_posix.c",
+    "src/core/lib/iomgr/socket_utils_linux.c",
+    "src/core/lib/iomgr/socket_utils_posix.c",
+    "src/core/lib/iomgr/socket_windows.c",
+    "src/core/lib/iomgr/tcp_client_posix.c",
+    "src/core/lib/iomgr/tcp_client_windows.c",
+    "src/core/lib/iomgr/tcp_posix.c",
+    "src/core/lib/iomgr/tcp_server_posix.c",
+    "src/core/lib/iomgr/tcp_server_windows.c",
+    "src/core/lib/iomgr/tcp_windows.c",
+    "src/core/lib/iomgr/time_averaged_stats.c",
+    "src/core/lib/iomgr/timer.c",
+    "src/core/lib/iomgr/timer_heap.c",
+    "src/core/lib/iomgr/udp_server.c",
+    "src/core/lib/iomgr/unix_sockets_posix.c",
+    "src/core/lib/iomgr/unix_sockets_posix_noop.c",
+    "src/core/lib/iomgr/wakeup_fd_eventfd.c",
+    "src/core/lib/iomgr/wakeup_fd_nospecial.c",
+    "src/core/lib/iomgr/wakeup_fd_pipe.c",
+    "src/core/lib/iomgr/wakeup_fd_posix.c",
+    "src/core/lib/iomgr/workqueue_posix.c",
+    "src/core/lib/iomgr/workqueue_windows.c",
+    "src/core/lib/json/json.c",
+    "src/core/lib/json/json_reader.c",
+    "src/core/lib/json/json_string.c",
+    "src/core/lib/json/json_writer.c",
+    "src/core/lib/surface/alarm.c",
+    "src/core/lib/surface/api_trace.c",
+    "src/core/lib/surface/byte_buffer.c",
+    "src/core/lib/surface/byte_buffer_reader.c",
+    "src/core/lib/surface/call.c",
+    "src/core/lib/surface/call_details.c",
+    "src/core/lib/surface/call_log_batch.c",
+    "src/core/lib/surface/channel.c",
+    "src/core/lib/surface/channel_init.c",
+    "src/core/lib/surface/channel_ping.c",
+    "src/core/lib/surface/channel_stack_type.c",
+    "src/core/lib/surface/completion_queue.c",
+    "src/core/lib/surface/event_string.c",
+    "src/core/lib/surface/lame_client.c",
+    "src/core/lib/surface/metadata_array.c",
+    "src/core/lib/surface/server.c",
+    "src/core/lib/surface/validate_metadata.c",
+    "src/core/lib/surface/version.c",
+    "src/core/lib/transport/byte_stream.c",
+    "src/core/lib/transport/connectivity_state.c",
+    "src/core/lib/transport/metadata.c",
+    "src/core/lib/transport/metadata_batch.c",
+    "src/core/lib/transport/static_metadata.c",
+    "src/core/lib/transport/transport.c",
+    "src/core/lib/transport/transport_op_string.c",
+    "src/core/ext/transport/chttp2/server/secure/server_secure_chttp2.c",
+    "src/core/ext/transport/chttp2/transport/bin_decoder.c",
+    "src/core/ext/transport/chttp2/transport/bin_encoder.c",
+    "src/core/ext/transport/chttp2/transport/chttp2_plugin.c",
+    "src/core/ext/transport/chttp2/transport/chttp2_transport.c",
+    "src/core/ext/transport/chttp2/transport/frame_data.c",
+    "src/core/ext/transport/chttp2/transport/frame_goaway.c",
+    "src/core/ext/transport/chttp2/transport/frame_ping.c",
+    "src/core/ext/transport/chttp2/transport/frame_rst_stream.c",
+    "src/core/ext/transport/chttp2/transport/frame_settings.c",
+    "src/core/ext/transport/chttp2/transport/frame_window_update.c",
+    "src/core/ext/transport/chttp2/transport/hpack_encoder.c",
+    "src/core/ext/transport/chttp2/transport/hpack_parser.c",
+    "src/core/ext/transport/chttp2/transport/hpack_table.c",
+    "src/core/ext/transport/chttp2/transport/huffsyms.c",
+    "src/core/ext/transport/chttp2/transport/incoming_metadata.c",
+    "src/core/ext/transport/chttp2/transport/parsing.c",
+    "src/core/ext/transport/chttp2/transport/status_conversion.c",
+    "src/core/ext/transport/chttp2/transport/stream_lists.c",
+    "src/core/ext/transport/chttp2/transport/stream_map.c",
+    "src/core/ext/transport/chttp2/transport/timeout_encoding.c",
+    "src/core/ext/transport/chttp2/transport/varint.c",
+    "src/core/ext/transport/chttp2/transport/writing.c",
+    "src/core/ext/transport/chttp2/alpn/alpn.c",
+    "src/core/lib/http/httpcli_security_connector.c",
+    "src/core/lib/security/context/security_context.c",
+    "src/core/lib/security/credentials/composite/composite_credentials.c",
+    "src/core/lib/security/credentials/credentials.c",
+    "src/core/lib/security/credentials/credentials_metadata.c",
+    "src/core/lib/security/credentials/fake/fake_credentials.c",
+    "src/core/lib/security/credentials/google_default/credentials_posix.c",
+    "src/core/lib/security/credentials/google_default/credentials_windows.c",
+    "src/core/lib/security/credentials/google_default/google_default_credentials.c",
+    "src/core/lib/security/credentials/iam/iam_credentials.c",
+    "src/core/lib/security/credentials/jwt/json_token.c",
+    "src/core/lib/security/credentials/jwt/jwt_credentials.c",
+    "src/core/lib/security/credentials/jwt/jwt_verifier.c",
+    "src/core/lib/security/credentials/oauth2/oauth2_credentials.c",
+    "src/core/lib/security/credentials/plugin/plugin_credentials.c",
+    "src/core/lib/security/credentials/ssl/ssl_credentials.c",
+    "src/core/lib/security/transport/client_auth_filter.c",
+    "src/core/lib/security/transport/handshake.c",
+    "src/core/lib/security/transport/secure_endpoint.c",
+    "src/core/lib/security/transport/security_connector.c",
+    "src/core/lib/security/transport/server_auth_filter.c",
+    "src/core/lib/security/transport/tsi_error.c",
+    "src/core/lib/security/util/b64.c",
+    "src/core/lib/security/util/json_util.c",
+    "src/core/lib/surface/init_secure.c",
+    "src/core/lib/tsi/fake_transport_security.c",
+    "src/core/lib/tsi/ssl_transport_security.c",
+    "src/core/lib/tsi/transport_security.c",
+    "src/core/ext/transport/chttp2/client/secure/secure_channel_create.c",
+    "src/core/ext/client_config/channel_connectivity.c",
+    "src/core/ext/client_config/client_channel.c",
+    "src/core/ext/client_config/client_channel_factory.c",
+    "src/core/ext/client_config/client_config.c",
+    "src/core/ext/client_config/client_config_plugin.c",
+    "src/core/ext/client_config/connector.c",
+    "src/core/ext/client_config/default_initial_connect_string.c",
+    "src/core/ext/client_config/initial_connect_string.c",
+    "src/core/ext/client_config/lb_policy.c",
+    "src/core/ext/client_config/lb_policy_factory.c",
+    "src/core/ext/client_config/lb_policy_registry.c",
+    "src/core/ext/client_config/parse_address.c",
+    "src/core/ext/client_config/resolver.c",
+    "src/core/ext/client_config/resolver_factory.c",
+    "src/core/ext/client_config/resolver_registry.c",
+    "src/core/ext/client_config/subchannel.c",
+    "src/core/ext/client_config/subchannel_call_holder.c",
+    "src/core/ext/client_config/subchannel_index.c",
+    "src/core/ext/client_config/uri_parser.c",
+    "src/core/ext/transport/chttp2/server/insecure/server_chttp2.c",
+    "src/core/ext/transport/chttp2/server/insecure/server_chttp2_posix.c",
+    "src/core/ext/transport/chttp2/client/insecure/channel_create.c",
+    "src/core/ext/transport/chttp2/client/insecure/channel_create_posix.c",
+    "src/core/ext/lb_policy/grpclb/load_balancer_api.c",
+    "src/core/ext/lb_policy/grpclb/proto/grpc/lb/v1/load_balancer.pb.c",
+    "src/core/ext/lb_policy/pick_first/pick_first.c",
+    "src/core/ext/lb_policy/round_robin/round_robin.c",
+    "src/core/ext/resolver/dns/native/dns_resolver.c",
+    "src/core/ext/resolver/sockaddr/sockaddr_resolver.c",
+    "src/core/ext/load_reporting/load_reporting.c",
+    "src/core/ext/load_reporting/load_reporting_filter.c",
+    "src/core/ext/census/context.c",
+    "src/core/ext/census/gen/census.pb.c",
+    "src/core/ext/census/grpc_context.c",
+    "src/core/ext/census/grpc_filter.c",
+    "src/core/ext/census/grpc_plugin.c",
+    "src/core/ext/census/initialize.c",
+    "src/core/ext/census/mlog.c",
+    "src/core/ext/census/operation.c",
+    "src/core/ext/census/placeholders.c",
+    "src/core/ext/census/tracing.c",
+    "src/core/plugin_registry/grpc_plugin_registry.c",
+  ],
+  hdrs = [
+    "include/grpc/byte_buffer.h",
+    "include/grpc/byte_buffer_reader.h",
+    "include/grpc/compression.h",
+    "include/grpc/grpc.h",
+    "include/grpc/grpc_posix.h",
+    "include/grpc/grpc_security_constants.h",
+    "include/grpc/status.h",
     "include/grpc/impl/codegen/byte_buffer.h",
     "include/grpc/impl/codegen/byte_buffer_reader.h",
     "include/grpc/impl/codegen/compression_types.h",
@@ -1075,7 +2149,7 @@ cc_library(
     "include/grpc/impl/codegen/atm.h",
     "include/grpc/impl/codegen/atm_gcc_atomic.h",
     "include/grpc/impl/codegen/atm_gcc_sync.h",
-    "include/grpc/impl/codegen/atm_win32.h",
+    "include/grpc/impl/codegen/atm_windows.h",
     "include/grpc/impl/codegen/log.h",
     "include/grpc/impl/codegen/port_platform.h",
     "include/grpc/impl/codegen/slice.h",
@@ -1083,71 +2157,321 @@ cc_library(
     "include/grpc/impl/codegen/sync.h",
     "include/grpc/impl/codegen/sync_generic.h",
     "include/grpc/impl/codegen/sync_posix.h",
-    "include/grpc/impl/codegen/sync_win32.h",
+    "include/grpc/impl/codegen/sync_windows.h",
     "include/grpc/impl/codegen/time.h",
-    "include/grpc++/impl/codegen/config.h",
-    "include/grpc++/impl/codegen/config_protobuf.h",
-    "include/grpc++/support/config.h",
-    "include/grpc++/support/config_protobuf.h",
+    "include/grpc/grpc_security.h",
+    "include/grpc/census.h",
+    "src/core/lib/channel/channel_args.h",
+    "src/core/lib/channel/channel_stack.h",
+    "src/core/lib/channel/channel_stack_builder.h",
+    "src/core/lib/channel/compress_filter.h",
+    "src/core/lib/channel/connected_channel.h",
+    "src/core/lib/channel/context.h",
+    "src/core/lib/channel/http_client_filter.h",
+    "src/core/lib/channel/http_server_filter.h",
+    "src/core/lib/compression/algorithm_metadata.h",
+    "src/core/lib/compression/message_compress.h",
+    "src/core/lib/debug/trace.h",
+    "src/core/lib/http/format_request.h",
+    "src/core/lib/http/httpcli.h",
+    "src/core/lib/http/parser.h",
+    "src/core/lib/iomgr/closure.h",
+    "src/core/lib/iomgr/endpoint.h",
+    "src/core/lib/iomgr/endpoint_pair.h",
+    "src/core/lib/iomgr/error.h",
+    "src/core/lib/iomgr/ev_epoll_linux.h",
+    "src/core/lib/iomgr/ev_poll_and_epoll_posix.h",
+    "src/core/lib/iomgr/ev_poll_posix.h",
+    "src/core/lib/iomgr/ev_posix.h",
+    "src/core/lib/iomgr/exec_ctx.h",
+    "src/core/lib/iomgr/executor.h",
+    "src/core/lib/iomgr/iocp_windows.h",
+    "src/core/lib/iomgr/iomgr.h",
+    "src/core/lib/iomgr/iomgr_internal.h",
+    "src/core/lib/iomgr/iomgr_posix.h",
+    "src/core/lib/iomgr/load_file.h",
+    "src/core/lib/iomgr/network_status_tracker.h",
+    "src/core/lib/iomgr/polling_entity.h",
+    "src/core/lib/iomgr/pollset.h",
+    "src/core/lib/iomgr/pollset_set.h",
+    "src/core/lib/iomgr/pollset_set_windows.h",
+    "src/core/lib/iomgr/pollset_windows.h",
+    "src/core/lib/iomgr/resolve_address.h",
+    "src/core/lib/iomgr/sockaddr.h",
+    "src/core/lib/iomgr/sockaddr_posix.h",
+    "src/core/lib/iomgr/sockaddr_utils.h",
+    "src/core/lib/iomgr/sockaddr_windows.h",
+    "src/core/lib/iomgr/socket_utils_posix.h",
+    "src/core/lib/iomgr/socket_windows.h",
+    "src/core/lib/iomgr/tcp_client.h",
+    "src/core/lib/iomgr/tcp_posix.h",
+    "src/core/lib/iomgr/tcp_server.h",
+    "src/core/lib/iomgr/tcp_windows.h",
+    "src/core/lib/iomgr/time_averaged_stats.h",
+    "src/core/lib/iomgr/timer.h",
+    "src/core/lib/iomgr/timer_heap.h",
+    "src/core/lib/iomgr/udp_server.h",
+    "src/core/lib/iomgr/unix_sockets_posix.h",
+    "src/core/lib/iomgr/wakeup_fd_pipe.h",
+    "src/core/lib/iomgr/wakeup_fd_posix.h",
+    "src/core/lib/iomgr/workqueue.h",
+    "src/core/lib/iomgr/workqueue_posix.h",
+    "src/core/lib/iomgr/workqueue_windows.h",
+    "src/core/lib/json/json.h",
+    "src/core/lib/json/json_common.h",
+    "src/core/lib/json/json_reader.h",
+    "src/core/lib/json/json_writer.h",
+    "src/core/lib/surface/api_trace.h",
+    "src/core/lib/surface/call.h",
+    "src/core/lib/surface/call_test_only.h",
+    "src/core/lib/surface/channel.h",
+    "src/core/lib/surface/channel_init.h",
+    "src/core/lib/surface/channel_stack_type.h",
+    "src/core/lib/surface/completion_queue.h",
+    "src/core/lib/surface/event_string.h",
+    "src/core/lib/surface/init.h",
+    "src/core/lib/surface/lame_client.h",
+    "src/core/lib/surface/server.h",
+    "src/core/lib/transport/byte_stream.h",
+    "src/core/lib/transport/connectivity_state.h",
+    "src/core/lib/transport/metadata.h",
+    "src/core/lib/transport/metadata_batch.h",
+    "src/core/lib/transport/static_metadata.h",
+    "src/core/lib/transport/transport.h",
+    "src/core/lib/transport/transport_impl.h",
+    "src/core/ext/transport/chttp2/transport/bin_decoder.h",
+    "src/core/ext/transport/chttp2/transport/bin_encoder.h",
+    "src/core/ext/transport/chttp2/transport/chttp2_transport.h",
+    "src/core/ext/transport/chttp2/transport/frame.h",
+    "src/core/ext/transport/chttp2/transport/frame_data.h",
+    "src/core/ext/transport/chttp2/transport/frame_goaway.h",
+    "src/core/ext/transport/chttp2/transport/frame_ping.h",
+    "src/core/ext/transport/chttp2/transport/frame_rst_stream.h",
+    "src/core/ext/transport/chttp2/transport/frame_settings.h",
+    "src/core/ext/transport/chttp2/transport/frame_window_update.h",
+    "src/core/ext/transport/chttp2/transport/hpack_encoder.h",
+    "src/core/ext/transport/chttp2/transport/hpack_parser.h",
+    "src/core/ext/transport/chttp2/transport/hpack_table.h",
+    "src/core/ext/transport/chttp2/transport/http2_errors.h",
+    "src/core/ext/transport/chttp2/transport/huffsyms.h",
+    "src/core/ext/transport/chttp2/transport/incoming_metadata.h",
+    "src/core/ext/transport/chttp2/transport/internal.h",
+    "src/core/ext/transport/chttp2/transport/status_conversion.h",
+    "src/core/ext/transport/chttp2/transport/stream_map.h",
+    "src/core/ext/transport/chttp2/transport/timeout_encoding.h",
+    "src/core/ext/transport/chttp2/transport/varint.h",
+    "src/core/ext/transport/chttp2/alpn/alpn.h",
+    "src/core/lib/security/context/security_context.h",
+    "src/core/lib/security/credentials/composite/composite_credentials.h",
+    "src/core/lib/security/credentials/credentials.h",
+    "src/core/lib/security/credentials/fake/fake_credentials.h",
+    "src/core/lib/security/credentials/google_default/google_default_credentials.h",
+    "src/core/lib/security/credentials/iam/iam_credentials.h",
+    "src/core/lib/security/credentials/jwt/json_token.h",
+    "src/core/lib/security/credentials/jwt/jwt_credentials.h",
+    "src/core/lib/security/credentials/jwt/jwt_verifier.h",
+    "src/core/lib/security/credentials/oauth2/oauth2_credentials.h",
+    "src/core/lib/security/credentials/plugin/plugin_credentials.h",
+    "src/core/lib/security/credentials/ssl/ssl_credentials.h",
+    "src/core/lib/security/transport/auth_filters.h",
+    "src/core/lib/security/transport/handshake.h",
+    "src/core/lib/security/transport/secure_endpoint.h",
+    "src/core/lib/security/transport/security_connector.h",
+    "src/core/lib/security/transport/tsi_error.h",
+    "src/core/lib/security/util/b64.h",
+    "src/core/lib/security/util/json_util.h",
+    "src/core/lib/tsi/fake_transport_security.h",
+    "src/core/lib/tsi/ssl_transport_security.h",
+    "src/core/lib/tsi/ssl_types.h",
+    "src/core/lib/tsi/transport_security.h",
+    "src/core/lib/tsi/transport_security_interface.h",
+    "src/core/ext/client_config/client_channel.h",
+    "src/core/ext/client_config/client_channel_factory.h",
+    "src/core/ext/client_config/client_config.h",
+    "src/core/ext/client_config/connector.h",
+    "src/core/ext/client_config/initial_connect_string.h",
+    "src/core/ext/client_config/lb_policy.h",
+    "src/core/ext/client_config/lb_policy_factory.h",
+    "src/core/ext/client_config/lb_policy_registry.h",
+    "src/core/ext/client_config/parse_address.h",
+    "src/core/ext/client_config/resolver.h",
+    "src/core/ext/client_config/resolver_factory.h",
+    "src/core/ext/client_config/resolver_registry.h",
+    "src/core/ext/client_config/subchannel.h",
+    "src/core/ext/client_config/subchannel_call_holder.h",
+    "src/core/ext/client_config/subchannel_index.h",
+    "src/core/ext/client_config/uri_parser.h",
+    "src/core/ext/lb_policy/grpclb/load_balancer_api.h",
+    "src/core/ext/lb_policy/grpclb/proto/grpc/lb/v1/load_balancer.pb.h",
+    "src/core/ext/load_reporting/load_reporting.h",
+    "src/core/ext/load_reporting/load_reporting_filter.h",
+    "src/core/ext/census/aggregation.h",
+    "src/core/ext/census/census_interface.h",
+    "src/core/ext/census/census_rpc_stats.h",
+    "src/core/ext/census/gen/census.pb.h",
+    "src/core/ext/census/grpc_filter.h",
+    "src/core/ext/census/mlog.h",
+    "src/core/ext/census/rpc_metric_id.h",
   ],
   includes = [
     "include",
     ".",
   ],
   deps = [
-    "//external:protobuf_clib",
-    ":gpr",
-    ":grpc_unsecure",
+    ":gpr_objc",
+    "//external:libssl_objc",
+    "//external:nanopb",
   ],
+  sdk_dylibs = ["libz"],
 )
 
-cc_library(
-  name = "grpc_plugin_support",
+
+
+cc_binary(
+  name = "grpc_cpp_plugin",
   srcs = [
-    "src/compiler/config.h",
-    "src/compiler/cpp_generator.h",
-    "src/compiler/cpp_generator_helpers.h",
-    "src/compiler/csharp_generator.h",
-    "src/compiler/csharp_generator_helpers.h",
-    "src/compiler/generator_helpers.h",
-    "src/compiler/node_generator.h",
-    "src/compiler/node_generator_helpers.h",
-    "src/compiler/objective_c_generator.h",
-    "src/compiler/objective_c_generator_helpers.h",
-    "src/compiler/python_generator.h",
-    "src/compiler/ruby_generator.h",
-    "src/compiler/ruby_generator_helpers-inl.h",
-    "src/compiler/ruby_generator_map-inl.h",
-    "src/compiler/ruby_generator_string-inl.h",
-    "src/compiler/cpp_generator.cc",
-    "src/compiler/csharp_generator.cc",
-    "src/compiler/node_generator.cc",
-    "src/compiler/objective_c_generator.cc",
-    "src/compiler/python_generator.cc",
-    "src/compiler/ruby_generator.cc",
+    "src/compiler/cpp_plugin.cc",
   ],
-  hdrs = [
-    "include/grpc++/support/config.h",
-    "include/grpc++/support/config_protobuf.h",
-    "include/grpc++/impl/codegen/config.h",
-    "include/grpc++/impl/codegen/config_protobuf.h",
+  deps = [
+    "//external:protobuf_compiler",
+    ":grpc_plugin_support",
   ],
-  includes = [
-    "include",
-    ".",
+)
+
+
+cc_binary(
+  name = "grpc_csharp_plugin",
+  srcs = [
+    "src/compiler/csharp_plugin.cc",
   ],
   deps = [
     "//external:protobuf_compiler",
+    ":grpc_plugin_support",
   ],
 )
 
+
 cc_binary(
-  name = "grpc_cpp_plugin",
+  name = "grpc_node_plugin",
   srcs = [
-    "src/compiler/cpp_plugin.cc",
+    "src/compiler/node_plugin.cc",
+  ],
+  deps = [
+    "//external:protobuf_compiler",
+    ":grpc_plugin_support",
+  ],
+)
+
+
+cc_binary(
+  name = "grpc_objective_c_plugin",
+  srcs = [
+    "src/compiler/objective_c_plugin.cc",
+  ],
+  deps = [
+    "//external:protobuf_compiler",
+    ":grpc_plugin_support",
+  ],
+)
+
+
+cc_binary(
+  name = "grpc_python_plugin",
+  srcs = [
+    "src/compiler/python_plugin.cc",
+  ],
+  deps = [
+    "//external:protobuf_compiler",
+    ":grpc_plugin_support",
+  ],
+)
+
+
+cc_binary(
+  name = "grpc_ruby_plugin",
+  srcs = [
+    "src/compiler/ruby_plugin.cc",
   ],
   deps = [
     "//external:protobuf_compiler",
     ":grpc_plugin_support",
   ],
 )
+
+
+
+
+
+
+
+
+objc_path = "src/objective-c"
+
+rx_library_path = objc_path + "/RxLibrary"
+
+objc_library(
+  name = "rx_library",
+  hdrs = glob([
+    rx_library_path + "/*.h",
+    rx_library_path + "/transformations/*.h",
+  ]),
+  srcs = glob([
+    rx_library_path + "/*.m",
+    rx_library_path + "/transformations/*.m",
+  ]),
+  includes = [objc_path],
+  deps = [
+    ":rx_library_private",
+  ],
+)
+
+objc_library(
+  name = "rx_library_private",
+  hdrs = glob([rx_library_path + "/private/*.h"]),
+  srcs = glob([rx_library_path + "/private/*.m"]),
+  visibility = ["//visibility:private"],
+)
+
+objc_client_path = objc_path + "/GRPCClient"
+
+objc_library(
+  name = "grpc_client",
+  hdrs = glob([
+    objc_client_path + "/*.h",
+    objc_client_path + "/private/*.h",
+  ]),
+  srcs = glob([
+    objc_client_path + "/*.m",
+    objc_client_path + "/private/*.m",
+  ]),
+  includes = [objc_path],
+  bundles = [":gRPCCertificates"],
+  deps = [
+    ":grpc_objc",
+    ":rx_library",
+  ],
+)
+
+objc_bundle_library(
+    # The choice of name is signicant here, since it determines the bundle name.
+    name = "gRPCCertificates",
+    resources = ["etc/roots.pem"],
+)
+
+proto_objc_rpc_path = objc_path + "/ProtoRPC"
+
+objc_library(
+  name = "proto_objc_rpc",
+  hdrs = glob([
+    proto_objc_rpc_path + "/*.h",
+  ]),
+  srcs = glob([
+    proto_objc_rpc_path + "/*.m",
+  ]),
+  includes = [objc_path],
+  deps = [
+    ":grpc_client",
+    ":rx_library",
+    "//external:protobuf_objc",
+  ],
+)
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 16a9fb5774..3b2112a026 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -124,7 +124,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
 
   native.new_git_repository(
     name = "grpc",
-    commit = "39650266",
+    commit = "d7ff4ff40071d2b486a052183e3e9f9382afb745",
     init_submodules = True,
     remote = "https://github.com/grpc/grpc.git",
     build_file = path_prefix + "grpc.BUILD",
-- 
GitLab


From 2269152197182ad4b9413d9948e6a248bf109204 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Tue, 23 Aug 2016 20:26:32 -0800
Subject: [PATCH 084/223] Prevent non-termination when iterating over an
 unknown shape. Change: 131133261

---
 tensorflow/python/framework/tensor_shape.py      | 7 +++++++
 tensorflow/python/framework/tensor_shape_test.py | 7 +++++++
 2 files changed, 14 insertions(+)

diff --git a/tensorflow/python/framework/tensor_shape.py b/tensorflow/python/framework/tensor_shape.py
index 6d3fdb6d80..dcc31f6701 100644
--- a/tensorflow/python/framework/tensor_shape.py
+++ b/tensorflow/python/framework/tensor_shape.py
@@ -487,6 +487,13 @@ class TensorShape(object):
   # Python 3 wants __bool__, Python 2.7 wants __nonzero__
   __nonzero__ = __bool__
 
+  def __iter__(self):
+    """Returns `self.dims` if the rank is known, otherwise raises ValueError."""
+    if self._dims is None:
+      raise ValueError("Cannot iterate over a shape with unknown rank.")
+    else:
+      return iter(self._dims)
+
   def __getitem__(self, key):
     """Returns the value of a dimension or a shape, depending on the key.
 
diff --git a/tensorflow/python/framework/tensor_shape_test.py b/tensorflow/python/framework/tensor_shape_test.py
index 502be1df7d..01bcd2e2f2 100644
--- a/tensorflow/python/framework/tensor_shape_test.py
+++ b/tensorflow/python/framework/tensor_shape_test.py
@@ -187,6 +187,9 @@ class ShapeTest(test_util.TensorFlowTestCase):
       len(s)
     self.assertFalse(s)
     self.assertIs(None, s.dims)
+    with self.assertRaises(ValueError):
+      for _ in tensor_shape.TensorShape(None):
+        pass
 
   def testFullyDefinedShape(self):
     s = tensor_shape.TensorShape([tensor_shape.Dimension(
@@ -205,6 +208,8 @@ class ShapeTest(test_util.TensorFlowTestCase):
     self.assertEqual([3, 4, 7], s.as_list())
     s.assert_is_compatible_with([3, 4, 7])
     s.assert_same_rank([6, 3, 7])
+    for d1, d2 in zip(s, [3, 4, 7]):
+      assert d1.value == d2
 
   def testPartiallyDefinedShape(self):
     s = tensor_shape.TensorShape([tensor_shape.Dimension(
@@ -219,6 +224,8 @@ class ShapeTest(test_util.TensorFlowTestCase):
     self.assertEqual(tensor_shape.Dimension(None).value, s[1].value)
     self.assertEqual(tensor_shape.Dimension(7), s[2])
     s.assert_same_rank([6, 3, 7])
+    for d1, d2 in zip(s, [3, None, 7]):
+      assert d1.value == d2
 
   def testMergeFullShapes(self):
     self.assertEqual([3, 4, 7],
-- 
GitLab


From 08ce1a4d2fa4472e6361fe5ba095f307c4ce7889 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Tue, 23 Aug 2016 20:29:27 -0800
Subject: [PATCH 085/223] Add option for passing in hidden ops to Python code
 generation as a file. Change: 131133377

---
 tensorflow/python/BUILD                       | 239 ++----------------
 tensorflow/python/framework/python_op_gen.cc  |  16 +-
 tensorflow/python/framework/python_op_gen.h   |   5 +-
 .../python/framework/python_op_gen_main.cc    |  68 ++++-
 tensorflow/python/ops/hidden_ops.txt          | 224 ++++++++++++++++
 tensorflow/tensorflow.bzl                     |  37 ++-
 6 files changed, 344 insertions(+), 245 deletions(-)
 create mode 100644 tensorflow/python/ops/hidden_ops.txt

diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index fa10426ca2..56fd552330 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -311,9 +311,7 @@ py_test(
 tf_gen_op_wrapper_py(
     name = "functional_ops",
     out = "ops/gen_functional_ops.py",
-    hidden = [
-        "SymbolicGradient",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
 )
 
 py_library(
@@ -513,51 +511,19 @@ py_test(
 
 tf_gen_op_wrapper_py(
     name = "array_ops",
-    hidden = [
-        "BroadcastGradientArgs",
-        "ConcatOffset",
-        "Concat",
-        "Const",
-        "EditDistance",
-        "MirrorPad",
-        "MirrorPadGrad",
-        "OneHot",
-        "Pack",
-        "Pad",
-        "Placeholder",
-        "RefIdentity",
-        "Split",
-        "Slice",
-        "TileGrad",  # Exported through array_grad instead of array_ops.
-        "ZerosLike",  # TODO(josh11b): Use this instead of the Python version.
-        "Unpack",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_py(
     name = "candidate_sampling_ops",
-    hidden = [
-        "AllCandidateSampler",
-        "ComputeAccidentalHits",
-        "FixedUnigramCandidateSampler",
-        "LearnedUnigramCandidateSampler",
-        "LogUniformCandidateSampler",
-        "ThreadUnsafeUnigramCandidateSampler",
-        "UniformCandidateSampler",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_py(
     name = "control_flow_ops",
-    hidden = [
-        "Switch",
-        "Merge",
-        "RefMerge",
-        "Exit",
-        "RefExit",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
     deps = [
         "//tensorflow/core:control_flow_ops_op_lib",
@@ -567,263 +533,98 @@ tf_gen_op_wrapper_py(
 
 tf_gen_op_wrapper_py(
     name = "ctc_ops",
-    hidden = [
-        "CTCLoss",
-        "CTCGreedyDecoder",
-        "CTCBeamSearchDecoder",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_py(
     name = "data_flow_ops",
-    hidden = [
-        "Barrier",
-        "BarrierClose",
-        "BarrierIncompleteSize",
-        "BarrierInsertMany",
-        "BarrierReadySize",
-        "BarrierTakeMany",
-        "PriorityQueue",
-        "FIFOQueue",
-        "HashTable",
-        "InitializeTable",
-        "InitializeTableFromTextFile",
-        "LookupTableExport",
-        "LookupTableFind",
-        "LookupTableImport",
-        "LookupTableInsert",
-        "LookupTableSize",
-        "MutableHashTable",
-        "MutableHashTableOfTensors",
-        "Mutex",
-        "MutexAcquire",
-        "MutexRelease",
-        "PaddingFIFOQueue",
-        "QueueClose",
-        "QueueDequeue",
-        "QueueDequeueMany",
-        "QueueDequeueUpTo",
-        "QueueEnqueue",
-        "QueueEnqueueMany",
-        "QueueSize",
-        "RandomShuffleQueue",
-        "Stack",
-        "StackPop",
-        "StackPush",
-        "StackClose",
-        "TensorArray",
-        "TensorArrayClose",
-        "TensorArrayConcat",
-        "TensorArrayGrad",
-        "TensorArrayRead",
-        "TensorArrayPack",
-        "TensorArraySize",
-        "TensorArraySplit",
-        "TensorArrayUnpack",
-        "TensorArrayWrite",
-        "GetSessionHandle",
-        "GetSessionTensor",
-        "DeleteSessionTensor",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_py(
     name = "image_ops",
-    hidden = [
-        "RandomCrop",
-        "ResizeBilinearGrad",
-        "ResizeNearestNeighborGrad",
-        "AdjustContrastv2",
-        "ScaleImageGrad",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_py(
     name = "io_ops",
-    hidden = [
-        "FixedLengthRecordReader",
-        "IdentityReader",
-        "ReaderClose",
-        "ReaderEnqueueWork",
-        "ReaderNumRecordsProduced",
-        "ReaderNumWorkUnitsCompleted",
-        "ReaderRead",
-        "ReaderReadUpTo",
-        "ReaderReset",
-        "ReaderRestoreState",
-        "ReaderSerializeState",
-        "ReaderWorkQueueLength",
-        "Restore",
-        "RestoreSlice",
-        "Save",
-        "SaveSlices",
-        "ShardedFilename",
-        "ShardedFilespec",
-        "TextLineReader",
-        "TFRecordReader",
-        "WholeFileReader",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_py(
     name = "linalg_ops",
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_py(
     name = "logging_ops",
-    hidden = [
-        "Assert",
-        "AudioSummary",
-        "HistogramAccumulatorSummary",
-        "HistogramSummary",
-        "ImageSummary",
-        "MergeSummary",
-        "Print",
-        "ScalarSummary",
-        "TensorSummary",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_py(
     name = "math_ops",
-    hidden = [
-        "Abs",
-        "AddN",
-        "All",
-        "Any",
-        "BatchMatMul",
-        "Complex",
-        "Max",
-        "Mean",
-        "Min",
-        "Pow",
-        "Prod",
-        "Range",
-        "SparseMatMul",
-        "Sum",
-        "MatMul",
-        "Sigmoid",
-        "Tanh",
-        "SigmoidGrad",
-        "TanhGrad",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_py(
     name = "nn_ops",
-    hidden = [
-        "AvgPoolGrad",  # "*Grad" accessible through nn_grad instead of nn_ops.
-        "BatchNormWithGlobalNormalization",
-        "BatchNormWithGlobalNormalizationGrad",
-        "SoftmaxCrossEntropyWithLogits",
-        "SparseSoftmaxCrossEntropyWithLogits",
-        "LRNGrad",
-        "MaxPoolGrad",
-        "MaxPoolGradWithArgmax",
-        "ReluGrad",
-        "Relu6Grad",
-        "EluGrad",
-        "SoftplusGrad",
-        "SoftsignGrad",
-        "TopK",
-        "TopKV2",
-        "BiasAdd",
-        "BiasAddV1",
-        "Relu6",
-        "AvgPool",
-        "MaxPool",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_py(
     name = "parsing_ops",
-    hidden = [
-        "ParseExample",
-        "ParseSingleSequenceExample",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_py(
     name = "random_ops",
-    hidden = [
-        "RandomGamma",
-        "RandomUniform",
-        "RandomUniformInt",
-        "RandomShuffle",
-        "RandomStandardNormal",
-        "ParameterizedTruncatedNormal",
-        "TruncatedNormal",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_py(
     name = "script_ops",
-    hidden = [
-        "PyFunc",
-        "PyFuncStateless",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_py(
     name = "state_ops",
-    hidden = [
-        "Variable",
-        "TemporaryVariable",
-        "DestroyTemporaryVariable",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_py(
     name = "sparse_ops",
-    hidden = [
-        "DeserializeManySparse",
-        "SerializeManySparse",
-        "SerializeSparse",
-        "SparseAdd",
-        "SparseAddGrad",
-        "SparseConcat",
-        "SparseSplit",
-        "SparseSelectLastK",
-        "SparseReorder",
-        "SparseReshape",
-        "SparseToDense",
-        "SparseTensorDenseAdd",
-        "SparseTensorDenseMatMul",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_py(
     name = "string_ops",
-    hidden = [
-        "StringSplit",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
 tf_gen_op_wrapper_py(
     name = "user_ops",
-    hidden = [
-        "Fact",
-    ],
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = False,
 )
 
 tf_gen_op_wrapper_py(
     name = "training_ops",
     out = "training/gen_training_ops.py",
+    hidden_file = "ops/hidden_ops.txt",
     require_shape_functions = True,
 )
 
diff --git a/tensorflow/python/framework/python_op_gen.cc b/tensorflow/python/framework/python_op_gen.cc
index 708170d9c1..675594034e 100644
--- a/tensorflow/python/framework/python_op_gen.cc
+++ b/tensorflow/python/framework/python_op_gen.cc
@@ -646,7 +646,7 @@ void GenerateLowerCaseOpName(const string& str, string* result) {
 
 }  // namespace
 
-string GetPythonOps(const OpList& ops, const string& hidden_ops,
+string GetPythonOps(const OpList& ops, const std::vector<string>& hidden_ops,
                     bool require_shapes) {
   string result;
   // Header
@@ -668,15 +668,13 @@ from tensorflow.python.framework import op_def_library
 
 )");
 
-  std::vector<string> hidden_vec = str_util::Split(hidden_ops, ',');
-
   // We'll make a copy of ops that filters out descriptions.
   OpList cleaned_ops;
   auto out = cleaned_ops.mutable_op();
   out->Reserve(ops.op_size());
   for (const auto& op_def : ops.op()) {
     bool is_hidden = false;
-    for (const string& hidden : hidden_vec) {
+    for (const string& hidden : hidden_ops) {
       if (op_def.name() == hidden) {
         is_hidden = true;
         break;
@@ -726,22 +724,16 @@ _op_def_lib = _InitOpDefLibrary()
   return result;
 }
 
-void PrintPythonOps(const OpList& ops, const string& hidden_ops,
+void PrintPythonOps(const OpList& ops, const std::vector<string>& hidden_ops,
                     bool require_shapes) {
   printf("%s", GetPythonOps(ops, hidden_ops, require_shapes).c_str());
 }
 
-string GetAllPythonOps(const char* hidden, bool require_shapes) {
-  OpList ops;
-  OpRegistry::Global()->Export(false, &ops);
-  return GetPythonOps(ops, hidden, require_shapes);
-}
-
 string GetPythonWrappers(const char* op_wrapper_buf, size_t op_wrapper_len) {
   string op_list_str(op_wrapper_buf, op_wrapper_len);
   OpList ops;
   ops.ParseFromString(op_list_str);
-  return GetPythonOps(ops, "", false);
+  return GetPythonOps(ops, {}, false);
 }
 
 }  // namespace tensorflow
diff --git a/tensorflow/python/framework/python_op_gen.h b/tensorflow/python/framework/python_op_gen.h
index a852af7a74..424244fcc5 100644
--- a/tensorflow/python/framework/python_op_gen.h
+++ b/tensorflow/python/framework/python_op_gen.h
@@ -17,6 +17,7 @@ limitations under the License.
 #define TENSORFLOW_PYTHON_FRAMEWORK_PYTHON_OP_GEN_H_
 
 #include <string>
+#include <vector>
 #include "tensorflow/core/framework/op_def.pb.h"
 #include "tensorflow/core/platform/types.h"
 
@@ -26,9 +27,9 @@ namespace tensorflow {
 // list of Op names that should get a leading _ in the output.
 // The Print* version prints the output to stdout, Get* version returns the
 // output as a string.
-void PrintPythonOps(const OpList& ops, const string& hidden_ops,
+void PrintPythonOps(const OpList& ops, const std::vector<string>& hidden_ops,
                     bool require_shapes);
-string GetPythonOps(const OpList& ops, const string& hidden_ops,
+string GetPythonOps(const OpList& ops, const std::vector<string>& hidden_ops,
                     bool require_shapes);
 
 // Get the python wrappers for a list of ops in a OpList.
diff --git a/tensorflow/python/framework/python_op_gen_main.cc b/tensorflow/python/framework/python_op_gen_main.cc
index fd68996bf9..ca99f5fdc4 100644
--- a/tensorflow/python/framework/python_op_gen_main.cc
+++ b/tensorflow/python/framework/python_op_gen_main.cc
@@ -15,18 +15,73 @@ limitations under the License.
 
 #include "tensorflow/python/framework/python_op_gen.h"
 
+#include <memory>
+#include <string>
+#include <vector>
+
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/op_def.pb.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/io/inputbuffer.h"
+#include "tensorflow/core/lib/strings/scanner.h"
+#include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/init_main.h"
 #include "tensorflow/core/platform/logging.h"
 
 namespace tensorflow {
 namespace {
 
-void PrintAllPythonOps(const char* hidden, bool require_shapes) {
+Status ReadHiddenOpsFromFile(const string& filename,
+                             std::vector<string>* hidden_ops) {
+  std::unique_ptr<RandomAccessFile> file;
+  TF_CHECK_OK(Env::Default()->NewRandomAccessFile(filename, &file));
+  std::unique_ptr<io::InputBuffer> input_buffer(
+      new io::InputBuffer(file.get(), 256 << 10));
+  string line_contents;
+  Status s = input_buffer->ReadLine(&line_contents);
+  while (s.ok()) {
+    // The parser assumes that the op name is the first string on each
+    // line with no preceding whitespace, and ignores lines that do
+    // not start with an op name as a comment.
+    strings::Scanner scanner{StringPiece(line_contents)};
+    StringPiece op_name;
+    if (scanner.One(strings::Scanner::LETTER_DIGIT_DOT)
+            .Any(strings::Scanner::LETTER_DIGIT_DASH_DOT_SLASH_UNDERSCORE)
+            .GetResult(nullptr, &op_name)) {
+      hidden_ops->emplace_back(op_name.ToString());
+    }
+    s = input_buffer->ReadLine(&line_contents);
+  }
+  if (!errors::IsOutOfRange(s)) return s;
+  return Status::OK();
+}
+
+// The argument parsing is deliberately simplistic to support our only
+// known use cases:
+//
+// 1. Read all op names from a file.
+// 2. Read all op names from the arg as a comma-delimited list.
+//
+// Expected command-line argument syntax:
+// ARG ::= '@' FILENAME
+//       |  OP_NAME [',' OP_NAME]*
+Status ParseHiddenOpsCommandLine(const char* arg,
+                                 std::vector<string>* hidden_ops) {
+  std::vector<string> op_names = str_util::Split(arg, ',');
+  if (op_names.size() == 1 && op_names[0].substr(0, 1) == "@") {
+    const string filename = op_names[0].substr(1);
+    return tensorflow::ReadHiddenOpsFromFile(filename, hidden_ops);
+  } else {
+    *hidden_ops = std::move(op_names);
+  }
+  return Status::OK();
+}
+
+void PrintAllPythonOps(const std::vector<string>& hidden_ops,
+                       bool require_shapes) {
   OpList ops;
   OpRegistry::Global()->Export(false, &ops);
-  PrintPythonOps(ops, hidden, require_shapes);
+  PrintPythonOps(ops, hidden_ops, require_shapes);
 }
 
 }  // namespace
@@ -34,10 +89,15 @@ void PrintAllPythonOps(const char* hidden, bool require_shapes) {
 
 int main(int argc, char* argv[]) {
   tensorflow::port::InitMain(argv[0], &argc, &argv);
+  // Usage:
+  //   gen_main [ @FILENAME | OpName[,OpName]* ] (0 | 1)
   if (argc == 2) {
-    tensorflow::PrintAllPythonOps("", std::string(argv[1]) == "1");
+    tensorflow::PrintAllPythonOps({}, tensorflow::string(argv[1]) == "1");
   } else if (argc == 3) {
-    tensorflow::PrintAllPythonOps(argv[1], std::string(argv[2]) == "1");
+    std::vector<tensorflow::string> hidden_ops;
+    TF_CHECK_OK(tensorflow::ParseHiddenOpsCommandLine(argv[1], &hidden_ops));
+    tensorflow::PrintAllPythonOps(hidden_ops,
+                                  tensorflow::string(argv[2]) == "1");
   } else {
     return -1;
   }
diff --git a/tensorflow/python/ops/hidden_ops.txt b/tensorflow/python/ops/hidden_ops.txt
new file mode 100644
index 0000000000..d1d163f5b6
--- /dev/null
+++ b/tensorflow/python/ops/hidden_ops.txt
@@ -0,0 +1,224 @@
+# array_ops
+BroadcastGradientArgs
+ConcatOffset
+Concat
+Const
+EditDistance
+MirrorPad
+MirrorPadGrad
+OneHot
+Pack
+Pad
+Placeholder
+RefIdentity
+Split
+Slice
+TileGrad  # Exported through array_grad instead of array_ops.
+ZerosLike  # TODO(josh11b): Use this instead of the Python version.
+Unpack
+
+# candidate_sampling_ops
+AllCandidateSampler
+ComputeAccidentalHits
+FixedUnigramCandidateSampler
+LearnedUnigramCandidateSampler
+LogUniformCandidateSampler
+ThreadUnsafeUnigramCandidateSampler
+UniformCandidateSampler
+
+# control_flow_ops
+Switch
+Merge
+RefMerge
+Exit
+RefExit
+
+# ctc_ops
+CTCLoss
+CTCGreedyDecoder
+CTCBeamSearchDecoder
+
+# data_flow_ops
+Barrier
+BarrierClose
+BarrierIncompleteSize
+BarrierInsertMany
+BarrierReadySize
+BarrierTakeMany
+PriorityQueue
+FIFOQueue
+HashTable
+InitializeTable
+InitializeTableFromTextFile
+LookupTableExport
+LookupTableFind
+LookupTableImport
+LookupTableInsert
+LookupTableSize
+MutableHashTable
+MutableHashTableOfTensors
+Mutex
+MutexAcquire
+MutexRelease
+PaddingFIFOQueue
+QueueClose
+QueueDequeue
+QueueDequeueMany
+QueueDequeueUpTo
+QueueEnqueue
+QueueEnqueueMany
+QueueSize
+RandomShuffleQueue
+Stack
+StackPop
+StackPush
+StackClose
+TensorArray
+TensorArrayClose
+TensorArrayConcat
+TensorArrayGrad
+TensorArrayRead
+TensorArrayPack
+TensorArraySize
+TensorArraySplit
+TensorArrayUnpack
+TensorArrayWrite
+GetSessionHandle
+GetSessionTensor
+DeleteSessionTensor
+
+# functional_ops
+SymbolicGradient
+
+# image_ops
+RandomCrop
+ResizeBilinearGrad
+ResizeNearestNeighborGrad
+AdjustContrastv2
+ScaleImageGrad
+
+# io_ops
+FixedLengthRecordReader
+IdentityReader
+ReaderClose
+ReaderEnqueueWork
+ReaderNumRecordsProduced
+ReaderNumWorkUnitsCompleted
+ReaderRead
+ReaderReadUpTo
+ReaderReset
+ReaderRestoreState
+ReaderSerializeState
+ReaderWorkQueueLength
+Restore
+RestoreSlice
+Save
+SaveSlices
+ShardedFilename
+ShardedFilespec
+TextLineReader
+TFRecordReader
+WholeFileReader
+
+# linalg_ops
+# (None)
+
+# logging_ops
+Assert
+AudioSummary
+HistogramAccumulatorSummary
+HistogramSummary
+ImageSummary
+MergeSummary
+Print
+ScalarSummary
+TensorSummary
+
+# math_ops
+Abs
+AddN
+All
+Any
+BatchMatMul
+Complex
+Max
+Mean
+Min
+Pow
+Prod
+Range
+SparseMatMul
+Sum
+MatMul
+Sigmoid
+Tanh
+SigmoidGrad
+TanhGrad
+
+# nn_ops
+AvgPoolGrad  # "*Grad" accessible through nn_grad instead of nn_ops.
+BatchNormWithGlobalNormalization
+BatchNormWithGlobalNormalizationGrad
+SoftmaxCrossEntropyWithLogits
+SparseSoftmaxCrossEntropyWithLogits
+LRNGrad
+MaxPoolGrad
+MaxPoolGradWithArgmax
+ReluGrad
+Relu6Grad
+EluGrad
+SoftplusGrad
+SoftsignGrad
+TopK
+TopKV2
+BiasAdd
+BiasAddV1
+Relu6
+AvgPool
+MaxPool
+
+# parsing_ops
+ParseExample
+ParseSingleSequenceExample
+
+# random_ops
+RandomGamma
+RandomUniform
+RandomUniformInt
+RandomShuffle
+RandomStandardNormal
+ParameterizedTruncatedNormal
+TruncatedNormal
+
+# script_ops
+PyFunc
+PyFuncStateless
+
+# state_ops
+Variable
+TemporaryVariable
+DestroyTemporaryVariable
+
+# sparse_ops
+DeserializeManySparse
+SerializeManySparse
+SerializeSparse
+SparseAdd
+SparseAddGrad
+SparseConcat
+SparseSplit
+SparseSelectLastK
+SparseReorder
+SparseReshape
+SparseToDense
+SparseTensorDenseAdd
+SparseTensorDenseMatMul
+
+# string_ops
+StringSplit
+
+# user_ops
+Fact
+
+# training_ops
+# (None)
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index 8776b96f4f..898a8b7309 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -216,8 +216,8 @@ def tf_gen_op_wrappers_cc(name,
                     alwayslink=1,)
 
 # Invoke this rule in .../tensorflow/python to build the wrapper library.
-def tf_gen_op_wrapper_py(name, out=None, hidden=[], visibility=None, deps=[],
-                         require_shape_functions=False):
+def tf_gen_op_wrapper_py(name, out=None, hidden=None, visibility=None, deps=[],
+                         require_shape_functions=False, hidden_file=None):
   # Construct a cc_binary containing the specified ops.
   tool_name = "gen_" + name + "_py_wrappers_cc"
   if not deps:
@@ -236,12 +236,33 @@ def tf_gen_op_wrapper_py(name, out=None, hidden=[], visibility=None, deps=[],
   if not out:
     out = "ops/gen_" + name + ".py"
 
-  native.genrule(
-      name=name + "_pygenrule",
-      outs=[out],
-      tools=[tool_name],
-      cmd=("$(location " + tool_name + ") " + ",".join(hidden)
-           + " " + ("1" if require_shape_functions else "0") + " > $@"))
+  if hidden:
+    # `hidden` is a list of op names to be hidden in the generated module.
+    native.genrule(
+        name=name + "_pygenrule",
+        outs=[out],
+        tools=[tool_name],
+        cmd=("$(location " + tool_name + ") " + ",".join(hidden)
+             + " " + ("1" if require_shape_functions else "0") + " > $@"))
+  elif hidden_file:
+    # `hidden_file` is file containing a list of op names to be hidden in the
+    # generated module.
+    native.genrule(
+        name=name + "_pygenrule",
+        outs=[out],
+        srcs=[hidden_file],
+        tools=[tool_name],
+        cmd=("$(location " + tool_name + ") @$(location "
+             + hidden_file + ") " + ("1" if require_shape_functions else "0")
+             + " > $@"))
+  else:
+    # No ops should be hidden in the generated module.
+    native.genrule(
+        name=name + "_pygenrule",
+        outs=[out],
+        tools=[tool_name],
+        cmd=("$(location " + tool_name + ") "
+             + ("1" if require_shape_functions else "0") + " > $@"))
 
   # Make a py_library out of the generated python file.
   native.py_library(name=name,
-- 
GitLab


From 35d2eeadb4ea5d104b1ce786f37d9252fe31924b Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Tue, 23 Aug 2016 21:57:03 -0700
Subject: [PATCH 086/223] Update tf_op_files.txt to add tile_6.cc

To fix makefile
---
 tensorflow/contrib/makefile/tf_op_files.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt
index d96f0e3af9..4f5c3176de 100644
--- a/tensorflow/contrib/makefile/tf_op_files.txt
+++ b/tensorflow/contrib/makefile/tf_op_files.txt
@@ -12,6 +12,7 @@ tensorflow/core/kernels/tile_ops_cpu_impl_2.cc
 tensorflow/core/kernels/tile_ops_cpu_impl_3.cc
 tensorflow/core/kernels/tile_ops_cpu_impl_4.cc
 tensorflow/core/kernels/tile_ops_cpu_impl_5.cc
+tensorflow/core/kernels/tile_ops_cpu_impl_6.cc
 tensorflow/core/kernels/strided_slice_op_inst_6.cc
 tensorflow/core/kernels/strided_slice_op_inst_5.cc
 tensorflow/core/kernels/strided_slice_op_inst_4.cc
-- 
GitLab


From 8a3107801d15bf8af36221ff5bca0b94bf44d6d3 Mon Sep 17 00:00:00 2001
From: Derek Murray <derek.murray@gmail.com>
Date: Tue, 23 Aug 2016 21:58:57 -0700
Subject: [PATCH 087/223] Disable tf_stream_executor in the CMake build.
 (#4000)

Temporary workaround for issue #3996.
---
 tensorflow/contrib/cmake/CMakeLists.txt        | 3 ++-
 tensorflow/contrib/cmake/tf_core_kernels.cmake | 2 ++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt
index e5a0790ff4..4fe5960d3b 100644
--- a/tensorflow/contrib/cmake/CMakeLists.txt
+++ b/tensorflow/contrib/cmake/CMakeLists.txt
@@ -51,7 +51,8 @@ include(highwayhash)
 
 # Let's get to work!
 include(tf_core_framework.cmake)
-include(tf_stream_executor.cmake)
+# NOTE: Disabled until issue #3996 is fixed.
+# include(tf_stream_executor.cmake)
 include(tf_core_cpu.cmake)
 include(tf_models.cmake)
 include(tf_core_ops.cmake)
diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake
index 2fff5c2dd3..8f911e3cb1 100644
--- a/tensorflow/contrib/cmake/tf_core_kernels.cmake
+++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake
@@ -13,6 +13,8 @@ file(GLOB_RECURSE tf_core_kernels_exclude_srcs
    "${tensorflow_source_dir}/tensorflow/core/kernels/*testutil.cc"
    "${tensorflow_source_dir}/tensorflow/core/kernels/*main.cc"
    "${tensorflow_source_dir}/tensorflow/core/kernels/*.cu.cc"
+   "${tensorflow_source_dir}/tensorflow/core/kernels/debug_ops.h"
+   "${tensorflow_source_dir}/tensorflow/core/kernels/debug_ops.cc"
 )
 
 list(REMOVE_ITEM tf_core_kernels_srcs ${tf_core_kernels_exclude_srcs}) 
-- 
GitLab


From 3017e51d428cf7506e5dea1a0d34ac3e4d69fcf8 Mon Sep 17 00:00:00 2001
From: Rohan Jain <rohanj@google.com>
Date: Tue, 23 Aug 2016 21:10:14 -0800
Subject: [PATCH 088/223] Creating a FileIO object and an iterator interface
 for file reading.

Also extended the BufferedInputStream interface to add ReadLineAsString that reads the file line by line (along with the \n at the end).
Change: 131135539
---
 .../core/lib/io/buffered_inputstream.cc       |  16 ++-
 tensorflow/core/lib/io/buffered_inputstream.h |   9 ++
 tensorflow/python/lib/io/file_io.i            |  26 ++++
 tensorflow/python/lib/io/file_io.py           | 120 ++++++++++++++++--
 tensorflow/python/lib/io/file_io_test.py      |  88 ++++++++++---
 tensorflow/python/platform/base.i             |   6 +
 6 files changed, 233 insertions(+), 32 deletions(-)

diff --git a/tensorflow/core/lib/io/buffered_inputstream.cc b/tensorflow/core/lib/io/buffered_inputstream.cc
index 2d131546f1..2a1cd0ca21 100644
--- a/tensorflow/core/lib/io/buffered_inputstream.cc
+++ b/tensorflow/core/lib/io/buffered_inputstream.cc
@@ -47,7 +47,7 @@ Status BufferedInputStream::FillBuffer() {
   return s;
 }
 
-Status BufferedInputStream::ReadLine(string* result) {
+Status BufferedInputStream::ReadLineHelper(string* result, bool include_eol) {
   result->clear();
   Status s;
   while (true) {
@@ -60,7 +60,9 @@ Status BufferedInputStream::ReadLine(string* result) {
     }
     char c = buf_[pos_++];
     if (c == '\n') {
-      // We don't append the '\n' to *result
+      if (include_eol) {
+        *result += c;
+      }
       return Status::OK();
     }
     // We don't append '\r' to *result
@@ -131,5 +133,15 @@ int64 BufferedInputStream::Tell() const {
   return input_stream_->Tell() - (limit_ - pos_);
 }
 
+Status BufferedInputStream::ReadLine(string* result) {
+  return ReadLineHelper(result, false);
+}
+
+string BufferedInputStream::ReadLineAsString() {
+  string result;
+  ReadLineHelper(&result, true);
+  return result;
+}
+
 }  // namespace io
 }  // namespace tensorflow
diff --git a/tensorflow/core/lib/io/buffered_inputstream.h b/tensorflow/core/lib/io/buffered_inputstream.h
index 8beaf34a72..013c2a8022 100644
--- a/tensorflow/core/lib/io/buffered_inputstream.h
+++ b/tensorflow/core/lib/io/buffered_inputstream.h
@@ -56,8 +56,17 @@ class BufferedInputStream : public InputStreamInterface {
   // some other non-OK status.
   Status ReadLine(string* result);
 
+  // Returns one text line of data until end-of-file or a '\n' is read. The '\n'
+  // is included in the result.
+  // This method is a substitute for ReadLine() when called from Python which is
+  // the expectation in the python File::readline() API.
+  // Also, '\0's are treated like any other character within the line and given
+  // no special treatment.
+  string ReadLineAsString();
+
  private:
   Status FillBuffer();
+  Status ReadLineHelper(string* result, bool include_eol);
 
   InputStreamInterface* input_stream_;  // not owned.
   size_t size_;                         // buffer size.
diff --git a/tensorflow/python/lib/io/file_io.i b/tensorflow/python/lib/io/file_io.i
index 4e1c2aba69..132894cf7a 100644
--- a/tensorflow/python/lib/io/file_io.i
+++ b/tensorflow/python/lib/io/file_io.i
@@ -21,6 +21,9 @@ limitations under the License.
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
+#include "tensorflow/core/lib/io/buffered_inputstream.h"
+#include "tensorflow/core/lib/io/inputstream_interface.h"
+#include "tensorflow/core/lib/io/random_inputstream.h"
 #include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/lib/io/match.h"
 #include "tensorflow/core/platform/env.h"
@@ -155,6 +158,20 @@ void Stat(const string& filename, FileStatistics* stats,
     Set_TF_Status_from_Status(out_status, status);
   }
 }
+
+tensorflow::io::BufferedInputStream* CreateBufferedInputStream(
+    const string& filename, size_t buffer_size) {
+  std::unique_ptr<tensorflow::RandomAccessFile> file;
+  if (!tensorflow::Env::Default()->NewRandomAccessFile(filename, &file).ok()) {
+    return nullptr;
+  }
+  std::unique_ptr<tensorflow::io::RandomAccessInputStream> input_stream(
+      new tensorflow::io::RandomAccessInputStream(file.release()));
+  std::unique_ptr<tensorflow::io::BufferedInputStream> buffered_input_stream(
+      new tensorflow::io::BufferedInputStream(input_stream.release(),
+                                              buffer_size));
+  return buffered_input_stream.release();
+}
 %}
 
 // Wrap the above functions.
@@ -174,6 +191,15 @@ void DeleteRecursively(const string& dirname, TF_Status* out_status);
 bool IsDirectory(const string& dirname, TF_Status* out_status);
 void Stat(const string& filename, tensorflow::FileStatistics* stats,
           TF_Status* out_status);
+tensorflow::io::BufferedInputStream* CreateBufferedInputStream(
+    const string& filename, size_t buffer_size);
+
+%ignoreall
+%unignore tensorflow::io::BufferedInputStream;
+%unignore tensorflow::io::BufferedInputStream::ReadLineAsString;
+%include "tensorflow/core/lib/io/inputstream_interface.h"
+%include "tensorflow/core/lib/io/buffered_inputstream.h"
+%unignoreall
 
 %include "tensorflow/core/lib/io/path.h"
 %include "tensorflow/core/platform/file_statistics.h"
diff --git a/tensorflow/python/lib/io/file_io.py b/tensorflow/python/lib/io/file_io.py
index bca92aeaa9..f5b138c9da 100644
--- a/tensorflow/python/lib/io/file_io.py
+++ b/tensorflow/python/lib/io/file_io.py
@@ -28,6 +28,109 @@ from tensorflow.python.framework import errors
 from tensorflow.python.util import compat
 
 
+class FileIO(object):
+  """FileIO class that exposes methods to read / write to / from files.
+
+  The constructor takes the following arguments:
+  name: name of the file
+  mode: one of 'r', 'w', 'a', 'r+', 'w+', 'a+'.
+
+  Can be used as an iterator to iterate over lines in the file.
+
+  The default buffer size used for the BufferedInputStream used for reading
+  the file line by line is 1024 * 512 bytes.
+  """
+
+  def __init__(self, name, mode):
+    self.__name = name
+    self.__mode = mode
+    self._read_buf = None
+    if mode not in ("r", "w", "a", "r+", "w+", "a+"):
+      raise errors.InvalidArgumentError(
+          None, None, "mode is not 'r' or 'w' or 'a' or 'r+' or 'w+' or 'a+'")
+    self._read_check_passed = mode in ("r", "r+", "a+", "w+")
+    self._write_check_passed = mode in ("a", "w", "r+", "a+", "w+")
+
+  @property
+  def name(self):
+    """Returns the file name."""
+    return self.__name
+
+  @property
+  def mode(self):
+    """Returns the mode in which the file was opened."""
+    return self.__mode
+
+  def _prereadline_check(self):
+    if not self._read_buf:
+      if not self._read_check_passed:
+        raise errors.PermissionDeniedError(None, None,
+                                           "File isn't open for reading")
+      self._read_buf = pywrap_tensorflow.CreateBufferedInputStream(
+          compat.as_bytes(self.__name), 1024 * 512)
+      if not self._read_buf:
+        raise errors.InternalError(None, None,
+                                   "Could not open file for streaming")
+
+  def size(self):
+    """Returns the size of the file."""
+    return stat(self.__name).length
+
+  def write(self, file_content):
+    """Writes file_content to the file."""
+    if not self._write_check_passed:
+      raise errors.PermissionDeniedError(None, None,
+                                         "File isn't open for writing")
+    with errors.raise_exception_on_not_ok_status() as status:
+      pywrap_tensorflow.WriteStringToFile(
+          compat.as_bytes(self.__name), compat.as_bytes(file_content), status)
+
+  def read(self):
+    """Returns the contents of a file as a string."""
+    if not self._read_check_passed:
+      raise errors.PermissionDeniedError(None, None,
+                                         "File isn't open for reading")
+    with errors.raise_exception_on_not_ok_status() as status:
+      return pywrap_tensorflow.ReadFileToString(
+          compat.as_bytes(self.__name), status)
+
+  def readline(self):
+    r"""Reads the next line from the file. Leaves the '\n' at the end."""
+    self._prereadline_check()
+    return self._read_buf.ReadLineAsString()
+
+  def readlines(self):
+    """Returns all lines from the file in a list."""
+    self._prereadline_check()
+    lines = []
+    while True:
+      s = self.readline()
+      if not s:
+        break
+      lines.append(s)
+    return lines
+
+  def __enter__(self):
+    """Make usable with "with" statement."""
+    return self
+
+  def __exit__(self, unused_type, unused_value, unused_traceback):
+    """Make usable with "with" statement."""
+    self._read_buf = None
+
+  def __iter__(self):
+    return self
+
+  def next(self):
+    retval = self.readline()
+    if not retval:
+      raise StopIteration()
+    return retval
+
+  def __next__(self):
+    return self.next()
+
+
 def file_exists(filename):
   """Determines whether a path exists or not.
 
@@ -67,8 +170,8 @@ def read_file_to_string(filename):
     errors.OpError: Raises variety of errors that are subtypes e.g.
     NotFoundError etc.
   """
-  with errors.raise_exception_on_not_ok_status() as status:
-    return pywrap_tensorflow.ReadFileToString(compat.as_bytes(filename), status)
+  f = FileIO(filename, mode="r")
+  return f.read()
 
 
 def write_string_to_file(filename, file_content):
@@ -81,9 +184,8 @@ def write_string_to_file(filename, file_content):
   Raises:
     errors.OpError: If there are errors during the operation.
   """
-  with errors.raise_exception_on_not_ok_status() as status:
-    pywrap_tensorflow.WriteStringToFile(
-        compat.as_bytes(filename), compat.as_bytes(file_content), status)
+  f = FileIO(filename, mode="w")
+  f.write(file_content)
 
 
 def get_matching_files(filename):
@@ -133,9 +235,9 @@ def recursive_create_dir(dirname):
     errors.OpError: If the operation fails.
   """
   with errors.raise_exception_on_not_ok_status() as status:
-    dirs = dirname.split('/')
+    dirs = dirname.split("/")
     for i in range(len(dirs)):
-      partial_dir = '/'.join(dirs[0:i + 1])
+      partial_dir = "/".join(dirs[0:i + 1])
       if partial_dir and not file_exists(partial_dir):
         pywrap_tensorflow.CreateDir(compat.as_bytes(partial_dir), status)
 
@@ -219,8 +321,8 @@ def list_directory(dirname):
     errors.NotFoundError if directory doesn't exist
   """
   if not is_directory(dirname):
-    raise errors.NotFoundError(None, None, 'Could not find directory')
-  file_list = get_matching_files(os.path.join(compat.as_str_any(dirname), '*'))
+    raise errors.NotFoundError(None, None, "Could not find directory")
+  file_list = get_matching_files(os.path.join(compat.as_str_any(dirname), "*"))
   return [compat.as_bytes(pywrap_tensorflow.Basename(compat.as_bytes(filename)))
           for filename in file_list]
 
diff --git a/tensorflow/python/lib/io/file_io_test.py b/tensorflow/python/lib/io/file_io_test.py
index dc79d7142e..475299ad0e 100644
--- a/tensorflow/python/lib/io/file_io_test.py
+++ b/tensorflow/python/lib/io/file_io_test.py
@@ -41,16 +41,28 @@ class FileIoTest(tf.test.TestCase):
     with self.assertRaises(errors.NotFoundError):
       _ = file_io.read_file_to_string(file_path)
 
-  def testFileWrite(self):
+  def testWriteToString(self):
     file_path = os.path.join(self._base_dir, "temp_file")
     file_io.write_string_to_file(file_path, "testing")
     self.assertTrue(file_io.file_exists(file_path))
     file_contents = file_io.read_file_to_string(file_path)
     self.assertEqual(b"testing", file_contents)
 
+  def testFileWriteBadMode(self):
+    file_path = os.path.join(self._base_dir, "temp_file")
+    with self.assertRaises(errors.PermissionDeniedError):
+      file_io.FileIO(file_path, mode="r").write("testing")
+
+  def testFileReadBadMode(self):
+    file_path = os.path.join(self._base_dir, "temp_file")
+    file_io.FileIO(file_path, mode="w").write("testing")
+    self.assertTrue(file_io.file_exists(file_path))
+    with self.assertRaises(errors.PermissionDeniedError):
+      file_io.FileIO(file_path, mode="w").read()
+
   def testFileDelete(self):
     file_path = os.path.join(self._base_dir, "temp_file")
-    file_io.write_string_to_file(file_path, "testing")
+    file_io.FileIO(file_path, mode="w").write("testing")
     file_io.delete_file(file_path)
     self.assertFalse(file_io.file_exists(file_path))
 
@@ -65,7 +77,7 @@ class FileIoTest(tf.test.TestCase):
     files = ["file1.txt", "file2.txt", "file3.txt"]
     for name in files:
       file_path = os.path.join(dir_path, name)
-      file_io.write_string_to_file(file_path, "testing")
+      file_io.FileIO(file_path, mode="w").write("testing")
     expected_match = [os.path.join(dir_path, name) for name in files]
     self.assertItemsEqual(
         file_io.get_matching_files(os.path.join(dir_path, "file*.txt")),
@@ -77,14 +89,14 @@ class FileIoTest(tf.test.TestCase):
     dir_path = os.path.join(self._base_dir, "temp_dir/temp_dir1/temp_dir2")
     file_io.recursive_create_dir(dir_path)
     file_path = os.path.join(dir_path, "temp_file")
-    file_io.write_string_to_file(file_path, "testing")
+    file_io.FileIO(file_path, mode="w").write("testing")
     self.assertTrue(file_io.file_exists(file_path))
     file_io.delete_recursively(os.path.join(self._base_dir, "temp_dir"))
     self.assertFalse(file_io.file_exists(file_path))
 
   def testCopy(self):
     file_path = os.path.join(self._base_dir, "temp_file")
-    file_io.write_string_to_file(file_path, "testing")
+    file_io.FileIO(file_path, mode="w").write("testing")
     copy_path = os.path.join(self._base_dir, "copy_file")
     file_io.copy(file_path, copy_path)
     self.assertTrue(file_io.file_exists(copy_path))
@@ -92,24 +104,24 @@ class FileIoTest(tf.test.TestCase):
 
   def testCopyOverwrite(self):
     file_path = os.path.join(self._base_dir, "temp_file")
-    file_io.write_string_to_file(file_path, "testing")
+    file_io.FileIO(file_path, mode="w").write("testing")
     copy_path = os.path.join(self._base_dir, "copy_file")
-    file_io.write_string_to_file(copy_path, "copy")
+    file_io.FileIO(copy_path, mode="w").write("copy")
     file_io.copy(file_path, copy_path, overwrite=True)
     self.assertTrue(file_io.file_exists(copy_path))
     self.assertEqual(b"testing", file_io.read_file_to_string(file_path))
 
   def testCopyOverwriteFalse(self):
     file_path = os.path.join(self._base_dir, "temp_file")
-    file_io.write_string_to_file(file_path, "testing")
+    file_io.FileIO(file_path, mode="w").write("testing")
     copy_path = os.path.join(self._base_dir, "copy_file")
-    file_io.write_string_to_file(copy_path, "copy")
+    file_io.FileIO(copy_path, mode="w").write("copy")
     with self.assertRaises(errors.AlreadyExistsError):
       file_io.copy(file_path, copy_path, overwrite=False)
 
   def testRename(self):
     file_path = os.path.join(self._base_dir, "temp_file")
-    file_io.write_string_to_file(file_path, "testing")
+    file_io.FileIO(file_path, mode="w").write("testing")
     rename_path = os.path.join(self._base_dir, "rename_file")
     file_io.rename(file_path, rename_path)
     self.assertTrue(file_io.file_exists(rename_path))
@@ -117,18 +129,18 @@ class FileIoTest(tf.test.TestCase):
 
   def testRenameOverwrite(self):
     file_path = os.path.join(self._base_dir, "temp_file")
-    file_io.write_string_to_file(file_path, "testing")
+    file_io.FileIO(file_path, mode="w").write("testing")
     rename_path = os.path.join(self._base_dir, "rename_file")
-    file_io.write_string_to_file(rename_path, "rename")
+    file_io.FileIO(rename_path, mode="w").write("rename")
     file_io.rename(file_path, rename_path, overwrite=True)
     self.assertTrue(file_io.file_exists(rename_path))
     self.assertFalse(file_io.file_exists(file_path))
 
   def testRenameOverwriteFalse(self):
     file_path = os.path.join(self._base_dir, "temp_file")
-    file_io.write_string_to_file(file_path, "testing")
+    file_io.FileIO(file_path, mode="w").write("testing")
     rename_path = os.path.join(self._base_dir, "rename_file")
-    file_io.write_string_to_file(rename_path, "rename")
+    file_io.FileIO(rename_path, mode="w").write("rename")
     with self.assertRaises(errors.AlreadyExistsError):
       file_io.rename(file_path, rename_path, overwrite=False)
     self.assertTrue(file_io.file_exists(rename_path))
@@ -147,7 +159,7 @@ class FileIoTest(tf.test.TestCase):
     file_io.create_dir(dir_path)
     self.assertTrue(file_io.is_directory(dir_path))
     file_path = os.path.join(dir_path, "test_file")
-    file_io.write_string_to_file(file_path, "test")
+    file_io.FileIO(file_path, mode="w").write("test")
     # False for a file.
     self.assertFalse(file_io.is_directory(file_path))
 
@@ -157,11 +169,11 @@ class FileIoTest(tf.test.TestCase):
     files = [b"file1.txt", b"file2.txt", b"file3.txt"]
     for name in files:
       file_path = os.path.join(dir_path, compat.as_str_any(name))
-      file_io.write_string_to_file(file_path, "testing")
+      file_io.FileIO(file_path, mode="w").write("testing")
     subdir_path = os.path.join(dir_path, "sub_dir")
     file_io.create_dir(subdir_path)
     subdir_file_path = os.path.join(subdir_path, "file4.txt")
-    file_io.write_string_to_file(subdir_file_path, "testing")
+    file_io.FileIO(subdir_file_path, mode="w").write("testing")
     dir_list = file_io.list_directory(dir_path)
     self.assertItemsEqual(files + [b"sub_dir"], dir_list)
 
@@ -176,12 +188,14 @@ class FileIoTest(tf.test.TestCase):
     # subdir1_1 -> file: file3.txt
     # subdir1_2 -> dir: subdir2
     file_io.create_dir(dir_path)
-    file_io.write_string_to_file(os.path.join(dir_path, "file1.txt"), "testing")
+    file_io.FileIO(
+        os.path.join(dir_path, "file1.txt"), mode="w").write("testing")
     sub_dirs1 = ["subdir1_1", "subdir1_2", "subdir1_3"]
     for name in sub_dirs1:
       file_io.create_dir(os.path.join(dir_path, name))
-    file_io.write_string_to_file(
-        os.path.join(dir_path, "subdir1_1/file2.txt"), "testing")
+    file_io.FileIO(
+        os.path.join(dir_path, "subdir1_1/file2.txt"),
+        mode="w").write("testing")
     file_io.create_dir(os.path.join(dir_path, "subdir1_2/subdir2"))
 
   def testWalkInOrder(self):
@@ -256,7 +270,7 @@ class FileIoTest(tf.test.TestCase):
 
   def testStat(self):
     file_path = os.path.join(self._base_dir, "temp_file")
-    file_io.write_string_to_file(file_path, "testing")
+    file_io.FileIO(file_path, mode="w").write("testing")
     file_statistics = file_io.stat(file_path)
     os_statistics = os.stat(file_path)
     self.assertEquals(7, file_statistics.length)
@@ -264,5 +278,37 @@ class FileIoTest(tf.test.TestCase):
         int(os_statistics.st_mtime), int(file_statistics.mtime_nsec / 1e9))
     self.assertEqual(33188, file_statistics.mode)
 
+  def testReadLine(self):
+    file_path = os.path.join(self._base_dir, "temp_file")
+    f = file_io.FileIO(file_path, mode="r+")
+    f.write("testing1\ntesting2\ntesting3\n\ntesting5")
+    self.assertEqual(36, f.size())
+    self.assertEqual(b"testing1\n", f.readline())
+    self.assertEqual(b"testing2\n", f.readline())
+    self.assertEqual(b"testing3\n", f.readline())
+    self.assertEqual(b"\n", f.readline())
+    self.assertEqual(b"testing5", f.readline())
+    self.assertEqual(b"", f.readline())
+
+  def testReadingIterator(self):
+    file_path = os.path.join(self._base_dir, "temp_file")
+    f = file_io.FileIO(file_path, mode="r+")
+    data = ["testing1\n", "testing2\n", "testing3\n", "\n", "testing5"]
+    f.write("".join(data))
+    actual_data = []
+    for line in f:
+      actual_data.append(line)
+    self.assertSequenceEqual(actual_data,
+                             [compat.as_bytes(item) for item in data])
+
+  def testReadlines(self):
+    file_path = os.path.join(self._base_dir, "temp_file")
+    f = file_io.FileIO(file_path, mode="r+")
+    data = ["testing1\n", "testing2\n", "testing3\n", "\n", "testing5"]
+    f.write("".join(data))
+    lines = f.readlines()
+    self.assertSequenceEqual(lines, [compat.as_bytes(item) for item in data])
+
+
 if __name__ == "__main__":
   tf.test.main()
diff --git a/tensorflow/python/platform/base.i b/tensorflow/python/platform/base.i
index de4828130e..a083913684 100644
--- a/tensorflow/python/platform/base.i
+++ b/tensorflow/python/platform/base.i
@@ -213,3 +213,9 @@ _COPY_TYPEMAPS(unsigned int, mode_t);
 %define %ignoreall %ignore ""; %enddef
 %define %unignore %rename("%s") %enddef
 %define %unignoreall %rename("%s") ""; %enddef
+
+#if SWIG_VERSION < 0x030000
+// Define some C++11 keywords safe to ignore so older SWIG does not choke.
+%define final %enddef
+%define override %enddef
+#endif
-- 
GitLab


From d9f97399c1461c46704e9436319a343de7d98d28 Mon Sep 17 00:00:00 2001
From: Ian Langmore <langmore@google.com>
Date: Tue, 23 Aug 2016 22:15:20 -0800
Subject: [PATCH 089/223] Monte Carlo Ops for bayesflow.

* Entropy
* Importance sampling
* Expectation
* elbo-ratio (ELBO and KL)
* Renyi Ratio  (Renyi-ELBO and Renyi divergence)
Change: 131139115
---
 tensorflow/contrib/bayesflow/BUILD            |  25 +-
 tensorflow/contrib/bayesflow/__init__.py      |   2 +
 .../python/kernel_tests/entropy_test.py       | 338 ++++++++++++++
 .../python/kernel_tests/monte_carlo_test.py   | 179 ++++++++
 .../contrib/bayesflow/python/ops/entropy.py   | 422 ++++++++++++++++++
 .../bayesflow/python/ops/monte_carlo.py       | 292 ++++++++++++
 .../python/ops/variational_inference.py       |   2 +-
 .../python/framework/gen_docs_combined.py     |   8 +
 8 files changed, 1266 insertions(+), 2 deletions(-)
 create mode 100644 tensorflow/contrib/bayesflow/python/kernel_tests/entropy_test.py
 create mode 100644 tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py
 create mode 100644 tensorflow/contrib/bayesflow/python/ops/entropy.py
 create mode 100644 tensorflow/contrib/bayesflow/python/ops/monte_carlo.py

diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD
index 354c2283e4..7ba2ac4515 100644
--- a/tensorflow/contrib/bayesflow/BUILD
+++ b/tensorflow/contrib/bayesflow/BUILD
@@ -1,5 +1,6 @@
 # Description:
-#   Contains ops for statistical distributions (with pdf, cdf, sample, etc...).
+#   Contains ops for working with statistical distributions,
+#   particularly useful for Bayesian inference.
 #   APIs here are meant to evolve over time.
 
 licenses(["notice"])  # Apache 2.0
@@ -16,6 +17,28 @@ py_library(
     srcs_version = "PY2AND3",
 )
 
+cuda_py_test(
+    name = "entropy_test",
+    size = "small",
+    srcs = ["python/kernel_tests/entropy_test.py"],
+    additional_deps = [
+        ":bayesflow_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
+cuda_py_test(
+    name = "monte_carlo_test",
+    size = "small",
+    srcs = ["python/kernel_tests/monte_carlo_test.py"],
+    additional_deps = [
+        ":bayesflow_py",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:platform_test",
+    ],
+)
+
 cuda_py_test(
     name = "stochastic_graph_test",
     size = "small",
diff --git a/tensorflow/contrib/bayesflow/__init__.py b/tensorflow/contrib/bayesflow/__init__.py
index 365ef42836..ee473d8d7f 100644
--- a/tensorflow/contrib/bayesflow/__init__.py
+++ b/tensorflow/contrib/bayesflow/__init__.py
@@ -21,6 +21,8 @@ from __future__ import division
 from __future__ import print_function
 
 # pylint: disable=unused-import,wildcard-import,line-too-long
+from tensorflow.contrib.bayesflow.python.ops import entropy
+from tensorflow.contrib.bayesflow.python.ops import monte_carlo
 from tensorflow.contrib.bayesflow.python.ops import stochastic_gradient_estimators
 from tensorflow.contrib.bayesflow.python.ops import stochastic_graph
 from tensorflow.contrib.bayesflow.python.ops import variational_inference
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/entropy_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/entropy_test.py
new file mode 100644
index 0000000000..b3f2f275c1
--- /dev/null
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/entropy_test.py
@@ -0,0 +1,338 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for Monte Carlo Ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+distributions = tf.contrib.distributions
+layers = tf.contrib.layers
+entropy = tf.contrib.bayesflow.entropy
+
+
+class NormalNoEntropy(distributions.Normal):  # pylint: disable=no-init
+  """Normal distribution without a `.entropy` method."""
+
+  def entropy(self):
+    return NotImplementedError('Entropy removed by gremlins')
+
+
+def get_train_op(scalar_loss, optimizer='SGD', learning_rate=1.0, decay=0.0):
+  global_step = tf.Variable(0)
+
+  def decay_fn(rate, t):
+    return rate * (1 + tf.to_float(t))**(-decay)
+
+  train_op = layers.optimize_loss(
+      scalar_loss,
+      global_step,
+      learning_rate,
+      optimizer,
+      learning_rate_decay_fn=decay_fn)
+  return train_op
+
+
+def _assert_monotonic_decreasing(array, atol=1e-5):
+  array = np.asarray(array)
+  _assert_monotonic_increasing(-array, atol=atol)
+
+
+def _assert_monotonic_increasing(array, atol=1e-5):
+  array = np.asarray(array)
+  diff = np.diff(array.ravel())
+  np.testing.assert_array_less(-1 * atol, diff)
+
+
+class ElboRatioTest(tf.test.TestCase):
+  """Show sampling converges to true KL values."""
+
+  def setUp(self):
+    self._rng = np.random.RandomState(0)
+
+  def test_convergence_to_kl_using_sample_form_on_3dim_normal(self):
+    # Test that the sample mean KL is the same as analytic when we use samples
+    # to estimate every part of the KL divergence ratio.
+    vector_shape = (2, 3)
+    n_samples = 5000
+
+    with self.test_session():
+      q = distributions.MultivariateNormalDiag(
+          mu=self._rng.rand(*vector_shape),
+          diag_stdev=self._rng.rand(*vector_shape))
+      p = distributions.MultivariateNormalDiag(
+          mu=self._rng.rand(*vector_shape),
+          diag_stdev=self._rng.rand(*vector_shape))
+
+      # In this case, the log_ratio is the KL.
+      sample_kl = -1 * entropy.elbo_ratio(
+          log_p=p.log_prob,
+          q=q,
+          n=n_samples,
+          form=entropy.ELBOForms.sample,
+          seed=42)
+      actual_kl = distributions.kl(q, p)
+
+      # Relative tolerance (rtol) chosen 2 times as large as minimim needed to
+      # pass.
+      self.assertEqual((2,), sample_kl.get_shape())
+      self.assertAllClose(actual_kl.eval(), sample_kl.eval(), rtol=0.03)
+
+  def test_convergence_to_kl_using_analytic_entropy_form_on_3dim_normal(self):
+    # Test that the sample mean KL is the same as analytic when we use an
+    # analytic entropy combined with sampled cross-entropy.
+    n_samples = 5000
+
+    vector_shape = (2, 3)
+    with self.test_session():
+      q = distributions.MultivariateNormalDiag(
+          mu=self._rng.rand(*vector_shape),
+          diag_stdev=self._rng.rand(*vector_shape))
+      p = distributions.MultivariateNormalDiag(
+          mu=self._rng.rand(*vector_shape),
+          diag_stdev=self._rng.rand(*vector_shape))
+
+      # In this case, the log_ratio is the KL.
+      sample_kl = -1 * entropy.elbo_ratio(
+          log_p=p.log_prob,
+          q=q,
+          n=n_samples,
+          form=entropy.ELBOForms.analytic_entropy,
+          seed=42)
+      actual_kl = distributions.kl(q, p)
+
+      # Relative tolerance (rtol) chosen 2 times as large as minimim needed to
+      # pass.
+      self.assertEqual((2,), sample_kl.get_shape())
+      self.assertAllClose(actual_kl.eval(), sample_kl.eval(), rtol=0.05)
+
+  def test_sample_kl_zero_when_p_and_q_are_the_same_distribution(self):
+    n_samples = 50
+
+    vector_shape = (2, 3)
+    with self.test_session():
+      q = distributions.MultivariateNormalDiag(
+          mu=self._rng.rand(*vector_shape),
+          diag_stdev=self._rng.rand(*vector_shape))
+
+      # In this case, the log_ratio is the KL.
+      sample_kl = -1 * entropy.elbo_ratio(
+          log_p=q.log_prob,
+          q=q,
+          n=n_samples,
+          form=entropy.ELBOForms.sample,
+          seed=42)
+
+      self.assertEqual((2,), sample_kl.get_shape())
+      self.assertAllClose(np.zeros(2), sample_kl.eval())
+
+
+class EntropyShannonTest(tf.test.TestCase):
+
+  def test_normal_entropy_default_form_uses_exact_entropy(self):
+    with self.test_session():
+      dist = distributions.Normal(mu=1.11, sigma=2.22)
+      mc_entropy = entropy.entropy_shannon(dist, n=11)
+      exact_entropy = dist.entropy()
+      self.assertEqual(exact_entropy.get_shape(), mc_entropy.get_shape())
+      self.assertAllClose(exact_entropy.eval(), mc_entropy.eval())
+
+  def test_normal_entropy_analytic_form_uses_exact_entropy(self):
+    with self.test_session():
+      dist = distributions.Normal(mu=1.11, sigma=2.22)
+      mc_entropy = entropy.entropy_shannon(
+          dist, form=entropy.ELBOForms.analytic_entropy)
+      exact_entropy = dist.entropy()
+      self.assertEqual(exact_entropy.get_shape(), mc_entropy.get_shape())
+      self.assertAllClose(exact_entropy.eval(), mc_entropy.eval())
+
+  def test_normal_entropy_sample_form_gets_approximate_answer(self):
+    # Tested by showing we get a good answer that is not exact.
+    with self.test_session():
+      dist = distributions.Normal(mu=1.11, sigma=2.22)
+      mc_entropy = entropy.entropy_shannon(
+          dist, n=1000, form=entropy.ELBOForms.sample, seed=0)
+      exact_entropy = dist.entropy()
+
+      self.assertEqual(exact_entropy.get_shape(), mc_entropy.get_shape())
+
+      # Relative tolerance (rtol) chosen 2 times as large as minimim needed to
+      # pass.
+      self.assertAllClose(exact_entropy.eval(), mc_entropy.eval(), rtol=0.01)
+
+      # Make sure there is some error, proving we used samples
+      self.assertLess(0.0001, tf.abs(exact_entropy - mc_entropy).eval())
+
+  def test_default_entropy_falls_back_on_sample_if_analytic_not_available(self):
+    # Tested by showing we get a good answer that is not exact.
+    with self.test_session():
+      # NormalNoEntropy is like a Normal, but does not have .entropy method, so
+      # we are forced to fall back on sample entropy.
+      dist_no_entropy = NormalNoEntropy(mu=1.11, sigma=2.22)
+      dist_yes_entropy = distributions.Normal(mu=1.11, sigma=2.22)
+
+      mc_entropy = entropy.entropy_shannon(
+          dist_no_entropy, n=1000, form=entropy.ELBOForms.sample, seed=0)
+      exact_entropy = dist_yes_entropy.entropy()
+
+      self.assertEqual(exact_entropy.get_shape(), mc_entropy.get_shape())
+
+      # Relative tolerance (rtol) chosen 2 times as large as minimim needed to
+      # pass.
+      self.assertAllClose(exact_entropy.eval(), mc_entropy.eval(), rtol=0.01)
+
+      # Make sure there is some error, proving we used samples
+      self.assertLess(0.0001, tf.abs(exact_entropy - mc_entropy).eval())
+
+
+class RenyiRatioTest(tf.test.TestCase):
+  """Show renyi_ratio is minimized when the distributions match."""
+
+  def setUp(self):
+    self._rng = np.random.RandomState(0)
+
+  def test_fitting_two_dimensional_normal_n_equals_1000(self):
+    # Minmizing Renyi divergence should allow us to make one normal match
+    # another one exactly.
+    n = 1000
+    mu_true = np.array([1.0, -1.0], dtype=np.float64)
+    chol_true = np.array([[2.0, 0.0], [0.5, 1.0]], dtype=np.float64)
+    with self.test_session() as sess:
+      target = distributions.MultivariateNormalCholesky(mu_true, chol_true)
+
+      # Set up q distribution by defining mean/covariance as Variables
+      mu = tf.Variable(np.zeros(mu_true.shape), dtype=mu_true.dtype, name='mu')
+      mat = tf.Variable(
+          np.zeros(chol_true.shape), dtype=chol_true.dtype, name='mat')
+      chol = distributions.batch_matrix_diag_transform(
+          mat, transform=tf.nn.softplus)
+      q = distributions.MultivariateNormalCholesky(mu, chol)
+      for alpha in [0.25, 0.75]:
+
+        negative_renyi_divergence = entropy.renyi_ratio(
+            log_p=target.log_prob, q=q, n=n, alpha=alpha, seed=0)
+        train_op = get_train_op(
+            tf.reduce_mean(-negative_renyi_divergence),
+            optimizer='SGD',
+            learning_rate=0.5,
+            decay=0.1)
+
+        tf.initialize_all_variables().run()
+        renyis = []
+        for step in range(1000):
+          sess.run(train_op)
+          if step in [1, 5, 100]:
+            renyis.append(negative_renyi_divergence.eval())
+
+        # This optimization should maximize the renyi divergence.
+        _assert_monotonic_increasing(renyis, atol=0)
+
+        # Relative tolerance (rtol) chosen 2 times as large as minimim needed to
+        # pass.
+        self.assertAllClose(target.mu.eval(), q.mu.eval(), rtol=0.06)
+        self.assertAllClose(target.sigma.eval(), q.sigma.eval(), rtol=0.02)
+
+  def test_divergence_between_identical_distributions_is_zero(self):
+    n = 1000
+    vector_shape = (2, 3)
+    with self.test_session():
+      q = distributions.MultivariateNormalDiag(
+          mu=self._rng.rand(*vector_shape),
+          diag_stdev=self._rng.rand(*vector_shape))
+      for alpha in [0.25, 0.75]:
+
+        negative_renyi_divergence = entropy.renyi_ratio(
+            log_p=q.log_prob, q=q, n=n, alpha=alpha, seed=0)
+
+        self.assertEqual((2,), negative_renyi_divergence.get_shape())
+        self.assertAllClose(np.zeros(2), negative_renyi_divergence.eval())
+
+
+class RenyiAlphaTest(tf.test.TestCase):
+
+  def test_with_three_alphas(self):
+    with self.test_session():
+      for dtype in (tf.float32, tf.float64):
+        alpha_min = tf.constant(0.0, dtype=dtype)
+        alpha_max = 0.5
+        decay_time = 3
+
+        alpha_0 = entropy.renyi_alpha(
+            0, decay_time, alpha_min=alpha_min, alpha_max=alpha_max)
+        alpha_1 = entropy.renyi_alpha(
+            1, decay_time, alpha_min=alpha_min, alpha_max=alpha_max)
+        alpha_2 = entropy.renyi_alpha(
+            2, decay_time, alpha_min=alpha_min, alpha_max=alpha_max)
+        alpha_3 = entropy.renyi_alpha(
+            3, decay_time, alpha_min=alpha_min, alpha_max=alpha_max)
+
+        # Alpha should start at alpha_max.
+        self.assertAllClose(alpha_max, alpha_0.eval(), atol=1e-5)
+        # Alpha should finish at alpha_min.
+        self.assertAllClose(alpha_min.eval(), alpha_3.eval(), atol=1e-5)
+        # In between, alpha should be monotonically decreasing.
+        _assert_monotonic_decreasing(
+            [alpha_0.eval(), alpha_1.eval(), alpha_2.eval(), alpha_3.eval()])
+
+  def test_non_scalar_input_raises(self):
+    with self.test_session():
+      # Good values here
+      step = 0
+      alpha_min = 0.0
+      alpha_max = 0.5
+      decay_time = 3
+
+      # Use one bad value inside each check.
+      # The "bad" value is always the non-scalar one.
+      with self.assertRaisesRegexp(ValueError, 'must be scalar'):
+        entropy.renyi_alpha(
+            [step], decay_time, alpha_min=alpha_min, alpha_max=alpha_max).eval()
+
+      with self.assertRaisesRegexp(ValueError, 'must be scalar'):
+        entropy.renyi_alpha(
+            step, [decay_time], alpha_min=alpha_min, alpha_max=alpha_max).eval()
+
+      with self.assertRaisesRegexp(ValueError, 'must be scalar'):
+        entropy.renyi_alpha(
+            step, decay_time, alpha_min=[alpha_min], alpha_max=alpha_max).eval()
+
+      with self.assertRaisesRegexp(ValueError, 'must be scalar'):
+        entropy.renyi_alpha(
+            step, decay_time, alpha_min=alpha_min, alpha_max=[alpha_max]).eval()
+
+  def test_input_with_wrong_sign_raises(self):
+    with self.test_session():
+      # Good values here
+      step = 0
+      alpha_min = 0.0
+      alpha_max = 0.5
+      decay_time = 3
+
+      # Use one bad value inside each check.
+      # The "bad" value is always the non-scalar one.
+      with self.assertRaisesOpError('decay_time must be positive'):
+        entropy.renyi_alpha(
+            step, 0.0, alpha_min=alpha_min, alpha_max=alpha_max).eval()
+
+      with self.assertRaisesOpError('step must be non-negative'):
+        entropy.renyi_alpha(
+            -1, decay_time, alpha_min=alpha_min, alpha_max=alpha_max).eval()
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py
new file mode 100644
index 0000000000..2dd06d2d4b
--- /dev/null
+++ b/tensorflow/contrib/bayesflow/python/kernel_tests/monte_carlo_test.py
@@ -0,0 +1,179 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for Monte Carlo Ops."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+
+distributions = tf.contrib.distributions
+layers = tf.contrib.layers
+monte_carlo = tf.contrib.bayesflow.monte_carlo
+
+
+class ExpectationImportanceSampleTest(tf.test.TestCase):
+
+  def test_normal_integral_mean_and_var_correctly_estimated(self):
+    n = int(1e6)
+    with self.test_session():
+      mu_p = tf.constant([-1.0, 1.0], dtype=tf.float64)
+      mu_q = tf.constant([0.0, 0.0], dtype=tf.float64)
+      sigma_p = tf.constant([0.5, 0.5], dtype=tf.float64)
+      sigma_q = tf.constant([1.0, 1.0], dtype=tf.float64)
+      p = distributions.Normal(mu=mu_p, sigma=sigma_p)
+      q = distributions.Normal(mu=mu_q, sigma=sigma_q)
+
+      # Compute E_p[X].
+      e_x = monte_carlo.expectation_importance_sampler(
+          f=lambda x: x, log_p=p.log_prob, sampling_dist_q=q, n=n, seed=42)
+
+      # Compute E_p[X^2].
+      e_x2 = monte_carlo.expectation_importance_sampler(
+          f=tf.square,
+          log_p=p.log_prob,
+          sampling_dist_q=q,
+          n=n,
+          seed=42)
+
+      stdev = tf.sqrt(e_x2 - tf.square(e_x))
+
+      # Relative tolerance (rtol) chosen 2 times as large as minimim needed to
+      # pass.
+      # Convergence of mean is +- 0.003 if n = 100M
+      # Convergence of std is +- 0.00001 if n = 100M
+      self.assertEqual(p.get_batch_shape(), e_x.get_shape())
+      self.assertAllClose(p.mean().eval(), e_x.eval(), rtol=0.01)
+      self.assertAllClose(p.std().eval(), stdev.eval(), rtol=0.02)
+
+  def test_multivariate_normal_prob_positive_product_of_components(self):
+    # Test that importance sampling can correctly estimate the probability that
+    # the product of components in a MultivariateNormal are > 0.
+    n = 1000
+    with self.test_session():
+      p = distributions.MultivariateNormalDiag(
+          mu=[0.0, 0.0], diag_stdev=[1.0, 1.0])
+      q = distributions.MultivariateNormalDiag(
+          mu=[0.5, 0.5], diag_stdev=[3., 3.])
+
+      # Compute E_p[X_1 * X_2 > 0], with X_i the ith component of X ~ p(x).
+      # Should equal 1/2 because p is a spherical Gaussian centered at (0, 0).
+      def indicator(x):
+        x1_times_x2 = tf.reduce_prod(x, reduction_indices=[-1])
+        return 0.5 * (tf.sign(x1_times_x2) + 1.0)
+
+      prob = monte_carlo.expectation_importance_sampler(
+          f=indicator, log_p=p.log_prob, sampling_dist_q=q, n=n, seed=42)
+
+      # Relative tolerance (rtol) chosen 2 times as large as minimim needed to
+      # pass.
+      # Convergence is +- 0.004 if n = 100k.
+      self.assertEqual(p.get_batch_shape(), prob.get_shape())
+      self.assertAllClose(0.5, prob.eval(), rtol=0.05)
+
+
+class ExpectationImportanceSampleLogspaceTest(tf.test.TestCase):
+
+  def test_normal_distribution_second_moment_estimated_correctly(self):
+    # Test the importance sampled estimate against an analytical result.
+    n = int(1e6)
+    with self.test_session():
+      mu_p = tf.constant([0.0, 0.0], dtype=tf.float64)
+      mu_q = tf.constant([-1.0, 1.0], dtype=tf.float64)
+      sigma_p = tf.constant([1.0, 2 / 3.], dtype=tf.float64)
+      sigma_q = tf.constant([1.0, 1.0], dtype=tf.float64)
+      p = distributions.Normal(mu=mu_p, sigma=sigma_p)
+      q = distributions.Normal(mu=mu_q, sigma=sigma_q)
+
+      # Compute E_p[X^2].
+      # Should equal [1, (2/3)^2]
+      log_e_x2 = monte_carlo.expectation_importance_sampler_logspace(
+          log_f=lambda x: tf.log(tf.square(x)),
+          log_p=p.log_prob,
+          sampling_dist_q=q,
+          n=n,
+          seed=42)
+      e_x2 = tf.exp(log_e_x2)
+
+      # Relative tolerance (rtol) chosen 2 times as large as minimim needed to
+      # pass.
+      self.assertEqual(p.get_batch_shape(), e_x2.get_shape())
+      self.assertAllClose([1., (2 / 3.)**2], e_x2.eval(), rtol=0.02)
+
+
+class ExpectationTest(tf.test.TestCase):
+
+  def test_mc_estimate_of_normal_mean_and_variance_is_correct_vs_analytic(self):
+    n = 10000
+    with self.test_session():
+      p = distributions.Normal(mu=[1.0, -1.0], sigma=[0.3, 0.5])
+      # Compute E_p[X] and E_p[X^2].
+      z = p.sample_n(n=n)
+      e_x = monte_carlo.expectation(lambda x: x, p, z=z, seed=42)
+      e_x2 = monte_carlo.expectation(tf.square, p, z=z, seed=0)
+      var = e_x2 - tf.square(e_x)
+
+      self.assertEqual(p.get_batch_shape(), e_x.get_shape())
+      self.assertEqual(p.get_batch_shape(), e_x2.get_shape())
+
+      # Relative tolerance (rtol) chosen 2 times as large as minimim needed to
+      # pass.
+      self.assertAllClose(p.mean().eval(), e_x.eval(), rtol=0.01)
+      self.assertAllClose(p.variance().eval(), var.eval(), rtol=0.02)
+
+
+class GetSamplesTest(tf.test.TestCase):
+  """Test the private method 'get_samples'."""
+
+  def test_raises_if_both_z_and_n_are_none(self):
+    with self.test_session():
+      dist = distributions.Normal(mu=0., sigma=1.)
+      z = None
+      n = None
+      seed = None
+      with self.assertRaisesRegexp(ValueError, 'exactly one'):
+        monte_carlo._get_samples(dist, z, n, seed)
+
+  def test_raises_if_both_z_and_n_are_not_none(self):
+    with self.test_session():
+      dist = distributions.Normal(mu=0., sigma=1.)
+      z = dist.sample_n(n=1)
+      n = 1
+      seed = None
+      with self.assertRaisesRegexp(ValueError, 'exactly one'):
+        monte_carlo._get_samples(dist, z, n, seed)
+
+  def test_returns_n_samples_if_n_provided(self):
+    with self.test_session():
+      dist = distributions.Normal(mu=0., sigma=1.)
+      z = None
+      n = 10
+      seed = None
+      z = monte_carlo._get_samples(dist, z, n, seed)
+      self.assertEqual((10,), z.get_shape())
+
+  def test_returns_z_if_z_provided(self):
+    with self.test_session():
+      dist = distributions.Normal(mu=0., sigma=1.)
+      z = dist.sample_n(n=10)
+      n = None
+      seed = None
+      z = monte_carlo._get_samples(dist, z, n, seed)
+      self.assertEqual((10,), z.get_shape())
+
+
+if __name__ == '__main__':
+  tf.test.main()
diff --git a/tensorflow/contrib/bayesflow/python/ops/entropy.py b/tensorflow/contrib/bayesflow/python/ops/entropy.py
new file mode 100644
index 0000000000..209b269c00
--- /dev/null
+++ b/tensorflow/contrib/bayesflow/python/ops/entropy.py
@@ -0,0 +1,422 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Entropy Ops.
+
+## Background
+
+Common Shannon entropy, the Evidence Lower BOund (ELBO), KL divergence, and more
+all have information theoretic use and interpretations.  They are also often
+used in variational inference.  This library brings together `Ops` for
+estimating them, e.g. using Monte Carlo expectations.
+
+## Examples
+
+Example of fitting a variational posterior with the ELBO.
+
+```
+# We start by assuming knowledge of the log of a joint density p(z, x) over
+# latent variable z and fixed measurement x.  Since x is fixed, the Python
+# function does not take x as an argument.
+def log_joint(z):
+  theta = tf.Variable(0.)  # Trainable variable that helps define log_joint.
+  ...
+
+# Next, define a Normal distribution with trainable parameters.
+q = distributions.Normal(mu=tf.Variable(0.), sigma=tf.Variable(1.))
+
+# Now, define a loss function (negative ELBO) that, when minimized, will adjust
+# mu, sigma, and theta, increasing the ELBO, which we hope will both reduce the
+# KL divergence between q(z) and p(z | x), and increase p(x).  Note that we
+# cannot guarantee both, but in general we expect both to happen.
+elbo = entropy.elbo_ratio(log_p, q, n=10)
+loss = -elbo
+
+# Minimize the loss
+train_op = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
+tf.initialize_all_variables().run()
+for step in range(100):
+  train_op.run()
+```
+
+## Ops
+
+@@elbo_ratio
+@@entropy_shannon
+@@renyi_ratio
+@@renyi_alpha
+
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+
+from tensorflow.contrib.bayesflow.python.ops import monte_carlo
+from tensorflow.contrib.bayesflow.python.ops import variational_inference
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import check_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.platform import tf_logging as logging
+
+# Make utility functions from monte_carlo available.
+# pylint: disable=protected-access
+_get_samples = monte_carlo._get_samples
+_logspace_mean = monte_carlo._logspace_mean
+_sample_mean = monte_carlo._sample_mean
+
+# pylint: enable=protected-access
+
+__all__ = [
+    'elbo_ratio',
+    'entropy_shannon',
+    'renyi_ratio',
+    'renyi_alpha',
+]
+
+
+ELBOForms = variational_inference.ELBOForms  # pylint: disable=invalid-name
+
+
+def elbo_ratio(log_p,
+               q,
+               z=None,
+               n=None,
+               seed=None,
+               form=None,
+               name='elbo_ratio'):
+  r"""Estimate of the ratio appearing in the `ELBO` and `KL` divergence.
+
+  With `p(z) := exp{log_p(z)}`, this `Op` returns an approximation of
+
+  ```
+  E_q[ Log[p(Z) / q(Z)] ]
+  ```
+
+  The term `E_q[ Log[p(Z)] ]` is always computed as a sample mean.
+  The term `E_q[ Log[q(z)] ]` can be computed with samples, or an exact formula
+  if `q.entropy()` is defined.  This is controlled with the kwarg `form`.
+
+  This log-ratio appears in different contexts:
+
+  #### `KL[q || p]`
+
+  If `log_p(z) = Log[p(z)]` for distribution `p`, this `Op` approximates
+  the negative Kullback-Leibler divergence.
+
+  ```
+  elbo_ratio(log_p, q, n=100) = -1 * KL[q || p],
+  KL[q || p] = E[ Log[q(Z)] - Log[p(Z)] ]
+  ```
+
+  Note that if `p` is a `Distribution`, then `distributions.kl(q, p)` may be
+  defined and available as an exact result.
+
+  #### ELBO
+
+  If `log_p(z) = Log[p(z, x)]` is the log joint of a distribution `p`, this is
+  the Evidence Lower BOund (ELBO):
+
+  ```
+  ELBO ~= E[ Log[p(Z, x)] - Log[q(Z)] ]
+        = Log[p(x)] - KL[q || p]
+       <= Log[p(x)]
+  ```
+
+  User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+  Args:
+    log_p:  Callable mapping samples from `q` to `Tensors` with
+      shape broadcastable to `q.batch_shape`.
+      For example, `log_p` works "just like" `q.log_prob`.
+    q:  `tf.contrib.distributions.BaseDistribution`.
+    z:  `Tensor` of samples from `q`, produced by `q.sample_n`.
+    n:  Integer `Tensor`.  Number of samples to generate if `z` is not provided.
+    seed:  Python integer to seed the random number generator.
+    form:  Either `ELBOForms.analytic_entropy` (use formula for entropy of `q`)
+      or `ELBOForms.sample` (sample estimate of entropy), or `ELBOForms.default`
+      (attempt analytic entropy, fallback on sample).
+      Default value is `ELBOForms.default`.
+    name:  A name to give this `Op`.
+
+  Returns:
+    Scalar `Tensor` holding sample mean KL divergence.  `shape` is the batch
+      shape of `q`, and `dtype` is the same as `q`.
+
+  Raises:
+    ValueError:  If `form` is not handled by this function.
+  """
+  form = ELBOForms.default if form is None else form
+
+  with ops.name_scope(name, values=[n, z]):
+    z = _get_samples(q, z, n, seed)
+
+    entropy = entropy_shannon(q, z=z, form=form)
+
+    # If log_p(z) = Log[p(z)], cross entropy = -E_q[log(p(Z))]
+    negative_cross_entropy = _sample_mean(log_p(z))
+
+    return entropy + negative_cross_entropy
+
+
+def entropy_shannon(p,
+                    z=None,
+                    n=None,
+                    seed=None,
+                    form=None,
+                    name='entropy_shannon'):
+  r"""Monte Carlo or deterministic computation of Shannon's entropy.
+
+  Depending on the kwarg `form`, this `Op` returns either the analytic entropy
+  of the distribution `p`, or the sampled entropy:
+
+  ```
+  -n^{-1} sum_{i=1}^n p.log_prob(z_i),  where z_i ~ p,
+      \approx - E_p[ Log[p(Z)] ]
+      = Entropy[p]
+  ```
+
+  User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+  Args:
+    p:  `tf.contrib.distributions.BaseDistribution`
+    z:  `Tensor` of samples from `p`, produced by `p.sample_n(n)` for some `n`.
+    n:  Integer `Tensor`.  Number of samples to generate if `z` is not provided.
+    seed:  Python integer to seed the random number generator.
+    form:  Either `ELBOForms.analytic_entropy` (use formula for entropy of `q`)
+      or `ELBOForms.sample` (sample estimate of entropy), or `ELBOForms.default`
+      (attempt analytic entropy, fallback on sample).
+      Default value is `ELBOForms.default`.
+    name:  A name to give this `Op`.
+
+  Returns:
+    A `Tensor` with same `dtype` as `p`, and shape equal to `p.batch_shape`.
+
+  Raises:
+    ValueError:  If `form` not handled by this function.
+    ValueError:  If `form` is `ELBOForms.analytic_entropy` and `n` was provided.
+  """
+  form = ELBOForms.default if form is None else form
+
+  if n is not None and form == ELBOForms.analytic_entropy:
+    raise ValueError('If form == ELBOForms.analytic_entropy, n must be None.')
+
+  with ops.name_scope(name, values=[n, z]):
+    # Entropy: -E_p[log(p(Z))].
+    entropy = None
+
+    # Try analytic path
+    if form in [ELBOForms.default, ELBOForms.analytic_entropy]:
+      try:
+        entropy = p.entropy()
+        logging.info('Using analytic entropy(p:%s)', p)
+      except NotImplementedError as e:
+        if form == ELBOForms.analytic_entropy:
+          raise e
+    elif form != ELBOForms.sample:
+      raise ValueError('ELBOForm not handled by this function: %s' % form)
+
+    # Sample path
+    if entropy is None:
+      logging.info('Using sampled entropy(p:%s)', p)
+      entropy = -1. * monte_carlo.expectation(
+          p.log_prob, p, z=z, n=n, seed=seed)
+
+    return entropy
+
+
+def renyi_ratio(log_p, q, alpha, z=None, n=None, seed=None, name='renyi_ratio'):
+  r"""Monte Carlo estimate of the ratio appearing in Renyi divergence.
+
+  This can be used to compute the Renyi (alpha) divergence, or a log evidence
+  approximation based on Renyi divergence.
+
+  #### Definition
+
+  With `z_i` iid samples from `q`, and `exp{log_p(z)} = p(z)`, this `Op` returns
+  the (biased for finite `n`) estimate:
+
+  ```
+  (1 - alpha)^{-1} Log[ n^{-1} sum_{i=1}^n ( p(z_i) / q(z_i) )^{1 - alpha},
+  \approx (1 - alpha)^{-1} Log[ E_q[ (p(Z) / q(Z))^{1 - alpha} ]  ]
+  ```
+
+  This ratio appears in different contexts:
+
+  #### Renyi divergence
+
+  If `log_p(z) = Log[p(z)]` is the log prob of a distribution, and
+  `alpha > 0`, `alpha != 1`, this `Op` approximates `-1` times Renyi divergence:
+
+  ```
+  # Choose reasonably high n to limit bias, see below.
+  renyi_ratio(log_p, q, alpha, n=100)
+                  \approx -1 * D_alpha[q || p],  where
+  D_alpha[q || p] := (1 - alpha)^{-1} Log E_q[(p(Z) / q(Z))^{1 - alpha}]
+  ```
+
+  The Renyi (or "alpha") divergence is non-negative and equal to zero iff
+  `q = p`.  Various limits of `alpha` lead to different special case results:
+
+  ```
+  alpha       D_alpha[q || p]
+  -----       ---------------
+  --> 0       Log[ int_{q > 0} p(z) dz ]
+  = 0.5,      -2 Log[1 - Hel^2[q || p]],  (\propto squared Hellinger distance)
+  --> 1       KL[q || p]
+  = 2         Log[ 1 + chi^2[q || p] ],   (\propto squared Chi-2 divergence)
+  --> infty   Log[ max_z{q(z) / p(z)} ],  (min description length principle).
+  ```
+
+  See "Renyi Divergence Variational Inference", by Li and Turner.
+
+  #### Log evidence approximation
+
+  If `log_p(z) = Log[p(z, x)]` is the log of the joint distribution `p`, this is
+  an alternative to the ELBO common in variational inference.
+
+  ```
+  L_alpha(q, p) = Log[p(x)] - D_alpha[q || p]
+  ```
+
+  If `q` and `p` have the same support, and `0 < a <= b < 1`, one can show
+  `ELBO <= D_b <= D_a <= Log[p(x)]`.  Thus, this `Op` allows a smooth
+  interpolation between the ELBO and the true evidence.
+
+  #### Stability notes
+
+  Note that when `1 - alpha` is not small, the ratio `(p(z) / q(z))^{1 - alpha}`
+  is subject to underflow/overflow issues.  For that reason, it is evaluated in
+  log-space after centering.  Nonetheless, infinite/NaN results may occur.  For
+  that reason, one may wish to shrink `alpha` gradually.  See the `Op`
+  `renyi_alpha`.  Using `float64` will also help.
+
+
+  #### Bias for finite sample size
+
+  Due to nonlinearity of the logarithm, for random variables `{X_1,...,X_n}`,
+  `E[ Log[sum_{i=1}^n X_i] ] != Log[ E[sum_{i=1}^n X_i] ]`.  As a result, this
+  estimate is biased for finite `n`.  For `alpha < 1`, it is non-decreasing
+  with `n` (in expectation).  For example, if `n = 1`, this estimator yields the
+  same result as `elbo_ratio`, and as `n` increases the expected value
+  of the estimator increases.
+
+  #### Call signature
+
+  User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+  Args:
+    log_p:  Callable mapping samples from `q` to `Tensors` with
+      shape broadcastable to `q.batch_shape`.
+      For example, `log_p` works "just like" `q.log_prob`.
+    q: `tf.contrib.distributions.BaseDistribution`.
+       `float64` `dtype` recommended.
+       `log_p` and `q` should be supported on the same set.
+    alpha:  `Tensor` with shape `q.batch_shape` and values not equal to 1.
+    z:  `Tensor` of samples from `q`, produced by `q.sample_n`.
+    n:  Integer `Tensor`.  The number of samples to use if `z` is not provided.
+      Note that this can be highly biased for small `n`, see docstring.
+    seed:  Python integer to seed the random number generator.
+    name:  A name to give this `Op`.
+
+  Returns:
+    renyi_result:  The scaled log of sample mean.  `Tensor` with `shape` equal
+      to batch shape of `q`, and `dtype` = `q.dtype`.
+  """
+  with ops.name_scope(name, values=[alpha, n, z]):
+    z = _get_samples(q, z, n, seed)
+
+    # Evaluate sample mean in logspace.  Note that _logspace_mean will compute
+    # (among other things) the mean of q.log_prob(z), which could also be
+    # obtained with q.entropy().  However, DON'T use analytic entropy, because
+    # that increases variance, and could result in NaN/Inf values of a sensitive
+    # term.
+
+    # log_values
+    # = (1 - alpha) * ( Log p - Log q )
+    log_values = (1. - alpha) * (log_p(z) - q.log_prob(z))
+
+    # log_mean_values
+    # = Log[ E[ values ] ]
+    # = Log[ E[ (p / q)^{1-alpha} ] ]
+    log_mean_values = _logspace_mean(log_values)
+
+    return log_mean_values / (1. - alpha)
+
+
+def renyi_alpha(step,
+                decay_time,
+                alpha_min,
+                alpha_max=0.99999,
+                name='renyi_alpha'):
+  r"""Exponentially decaying `Tensor` appropriate for Renyi ratios.
+
+  When minimizing the Renyi divergence for `0 <= alpha < 1` (or maximizing the
+  Renyi equivalent of elbo) in high dimensions, it is not uncommon to experience
+  `NaN` and `inf` values when `alpha` is far from `1`.
+
+  For that reason, it is often desirable to start the optimization with `alpha`
+  very close to 1, and reduce it to a final `alpha_min` according to some
+  schedule.  The user may even want to optimize using `elbo_ratio` for
+  some fixed time before switching to Renyi based methods.
+
+  This `Op` returns an `alpha` decaying exponentially with step:
+
+  ```
+  s(step) = (exp{step / decay_time} - 1) / (e - 1)
+  t(s) = max(0, min(s, 1)),  (smooth growth from 0 to 1)
+  alpha(t) = (1 - t) alpha_min + t alpha_max
+  ```
+
+  Args:
+    step:  Non-negative scalar `Tensor`.  Typically the global step or an
+      offset version thereof.
+    decay_time:  Postive scalar `Tensor`.
+    alpha_min:  `float` or `double` `Tensor`.
+      The minimal, final value of `alpha`, achieved when `step >= decay_time`
+    alpha_max:  `Tensor` of same `dtype` as `alpha_min`.
+      The maximal, beginning value of `alpha`, achieved when `step == 0`
+    name:  A name to give this `Op`.
+
+  Returns:
+    alpha:  A `Tensor` of same `dtype` as `alpha_min`.
+  """
+  with ops.name_scope(name, values=[step, decay_time, alpha_min, alpha_max]):
+    alpha_min = ops.convert_to_tensor(alpha_min, name='alpha_min')
+    dtype = alpha_min.dtype
+
+    alpha_max = ops.convert_to_tensor(alpha_max, dtype=dtype, name='alpha_max')
+    decay_time = math_ops.cast(decay_time, dtype)
+    step = math_ops.cast(step, dtype)
+
+    check_scalars = [
+        check_ops.assert_rank(step, 0, message='step must be scalar'),
+        check_ops.assert_rank(
+            decay_time, 0, message='decay_time must be scalar'),
+        check_ops.assert_rank(alpha_min, 0, message='alpha_min must be scalar'),
+        check_ops.assert_rank(alpha_max, 0, message='alpha_max must be scalar'),
+    ]
+    check_sign = [
+        check_ops.assert_non_negative(
+            step, message='step must be non-negative'),
+        check_ops.assert_positive(
+            decay_time, message='decay_time must be positive'),
+    ]
+
+    with ops.control_dependencies(check_scalars + check_sign):
+      theta = (math_ops.exp(step / decay_time) - 1.) / (math.e - 1.)
+      theta = math_ops.minimum(math_ops.maximum(theta, 0.), 1.)
+      return alpha_max * (1. - theta) + alpha_min * theta
diff --git a/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py b/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py
new file mode 100644
index 0000000000..ce58f82efa
--- /dev/null
+++ b/tensorflow/contrib/bayesflow/python/ops/monte_carlo.py
@@ -0,0 +1,292 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+r"""Monte Carlo integration and helpers.
+
+## Background
+
+Monte Carlo integration refers to the practice of estimating an expectation with
+a sample mean.  For example, given random variable `Z in R^k` with density `p`,
+the expectation of function `f` can be approximated like:
+
+```
+E_p[f(Z)] = \int f(z) p(z) dz
+          ~ S_n
+          := n^{-1} \sum_{i=1}^n f(z_i),  z_i iid samples from p.
+```
+
+If `E_p[|f(Z)|] < infinity`, then `S_n --> E_p[f(Z)]` by the strong law of large
+numbers.  If `E_p[f(Z)^2] < infinity`, then `S_n` is asymptotically normal with
+variance `Var[f(Z)] / n`.
+
+Practicioners of Bayesian statistics often find themselves wanting to estimate
+`E_p[f(Z)]` when the distribution `p` is known only up to a constant.  For
+example, the joint distribution `p(z, x)` may be known, but the evidence
+`p(x) = \int p(z, x) dz` may be intractable.  In that case, a parameterized
+distribution family `q_lambda(z)` may be chosen, and the optimal `lambda` is the
+one minimizing the KL divergence between `q_lambda(z)` and
+`p(z | x)`.  We only know `p(z, x)`, but that is sufficient to find `lambda`.
+
+
+## Log-space evaluation and subtracting the maximum.
+
+Care must be taken when the random variable lives in a high dimensional space.
+For example, the naive importance sample estimate `E_q[f(Z) p(Z) / q(Z)]`
+involves the ratio of two terms `p(Z) / q(Z)`, each of which must have tails
+dropping off faster than `O(|z|^{-(k + 1)})` in order to have finite integral.
+This ratio would often be zero or infinity up to numerical precision.
+
+For that reason, we write
+
+```
+Log E_q[ f(Z) p(Z) / q(Z) ]
+   = Log E_q[ exp{Log[f(Z)] + Log[p(Z)] - Log[q(Z)] - C} ] + C,  where
+C := Max[ Log[f(Z)] + Log[p(Z)] - Log[q(Z)] ].
+```
+
+The maximum value of the exponentiated term will be 0.0, and the the expecation
+can be evaluated in a stable manner.
+
+## Ops
+
+@@expectation
+@@expectation_importance_sampler
+@@expectation_importance_sampler_logspace
+
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn
+
+__all__ = [
+    'expectation',
+    'expectation_importance_sampler',
+    'expectation_importance_sampler_logspace',
+]
+
+
+def expectation_importance_sampler(f,
+                                   log_p,
+                                   sampling_dist_q,
+                                   z=None,
+                                   n=None,
+                                   seed=None,
+                                   name='expectation_importance_sampler'):
+  r"""Monte Carlo estimate of `E_p[f(Z)] = E_q[f(Z) p(Z) / q(Z)]`.
+
+  With `p(z) := exp{log_p(z)}`, this `Op` returns
+
+  ```
+  n^{-1} sum_{i=1}^n [ f(z_i) p(z_i) / q(z_i) ],  z_i ~ q,
+  \approx E_q[ f(Z) p(Z) / q(Z) ]
+  =       E_p[f(Z)]
+  ```
+
+  This integral is done in log-space with max-subtraction to better handle the
+  often extreme values that `f(z) p(z) / q(z)` can take on.
+
+  If `f >= 0`, it is up to 2x more efficient to exponentiate the result of
+  `expectation_importance_sampler_logspace` applied to `Log[f]`.
+
+  User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+  Args:
+    f: Callable mapping samples from `sampling_dist_q` to `Tensors` with shape
+      broadcastable to `q.batch_shape`.
+      For example, `f` works "just like" `q.log_prob`.
+    log_p:  Callable mapping samples from `sampling_dist_q` to `Tensors` with
+      shape broadcastable to `q.batch_shape`.
+      For example, `log_p` works "just like" `sampling_dist_q.log_prob`.
+    sampling_dist_q:  The sampling distribution.
+      `tf.contrib.distributions.BaseDistribution`.
+      `float64` `dtype` recommended.
+      `log_p` and `q` should be supported on the same set.
+    z:  `Tensor` of samples from `q`, produced by `q.sample_n`.
+    n:  Integer `Tensor`.  Number of samples to generate if `z` is not provided.
+    seed:  Python integer to seed the random number generator.
+    name:  A name to give this `Op`.
+
+  Returns:
+    The importance sampling estimate.  `Tensor` with `shape` equal
+      to batch shape of `q`, and `dtype` = `q.dtype`.
+  """
+  q = sampling_dist_q
+  with ops.name_scope(name, values=[z, n]):
+    z = _get_samples(q, z, n, seed)
+
+    log_p_z = log_p(z)
+    q_log_prob_z = q.log_prob(z)
+
+    def _importance_sampler_positive_f(log_f_z):
+      # Same as expectation_importance_sampler_logspace, but using Tensors
+      # rather than samples and functions.  Allows us to sample once.
+      log_values = log_f_z + log_p_z - q_log_prob_z
+      return _logspace_mean(log_values)
+
+    # With f_plus(z) = max(0, f(z)), f_minus(z) = max(0, -f(z)),
+    # E_p[f(Z)] = E_p[f_plus(Z)] - E_p[f_minus(Z)]
+    #           = E_p[f_plus(Z) + 1] - E_p[f_minus(Z) + 1]
+    # Without incurring bias, 1 is added to each to prevent zeros in logspace.
+    # The logarithm is approximately linear around 1 + epsilon, so this is good
+    # for small values of 'z' as well.
+    f_z = f(z)
+    log_f_plus_z = math_ops.log(nn.relu(f_z) + 1.)
+    log_f_minus_z = math_ops.log(nn.relu(-1. * f_z) + 1.)
+
+    log_f_plus_integral = _importance_sampler_positive_f(log_f_plus_z)
+    log_f_minus_integral = _importance_sampler_positive_f(log_f_minus_z)
+
+  return math_ops.exp(log_f_plus_integral) - math_ops.exp(log_f_minus_integral)
+
+
+def expectation_importance_sampler_logspace(
+    log_f,
+    log_p,
+    sampling_dist_q,
+    z=None,
+    n=None,
+    seed=None,
+    name='expectation_importance_sampler_logspace'):
+  r"""Importance sampling with a positive function, in log-space.
+
+  With `p(z) := exp{log_p(z)}`, and `f(z) = exp{log_f(z)}`, this `Op`
+  returns
+
+  ```
+  Log[ n^{-1} sum_{i=1}^n [ f(z_i) p(z_i) / q(z_i) ] ],  z_i ~ q,
+  \approx Log[ E_q[ f(Z) p(Z) / q(Z) ] ]
+  =       Log[E_p[f(Z)]]
+  ```
+
+  This integral is done in log-space with max-subtraction to better handle the
+  often extreme values that `f(z) p(z) / q(z)` can take on.
+
+  In contrast to `expectation_importance_sampler`, this `Op` returns values in
+  log-space.
+
+
+  User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+  Args:
+    log_f: Callable mapping samples from `sampling_dist_q` to `Tensors` with
+      shape broadcastable to `q.batch_shape`.
+      For example, `log_f` works "just like" `sampling_dist_q.log_prob`.
+    log_p:  Callable mapping samples from `sampling_dist_q` to `Tensors` with
+      shape broadcastable to `q.batch_shape`.
+      For example, `log_p` works "just like" `q.log_prob`.
+    sampling_dist_q:  The sampling distribution.
+      `tf.contrib.distributions.BaseDistribution`.
+      `float64` `dtype` recommended.
+      `log_p` and `q` should be supported on the same set.
+    z:  `Tensor` of samples from `q`, produced by `q.sample_n`.
+    n:  Integer `Tensor`.  Number of samples to generate if `z` is not provided.
+    seed:  Python integer to seed the random number generator.
+    name:  A name to give this `Op`.
+
+  Returns:
+    Logarithm of the importance sampling estimate.  `Tensor` with `shape` equal
+      to batch shape of `q`, and `dtype` = `q.dtype`.
+  """
+  q = sampling_dist_q
+  with ops.name_scope(name, values=[z, n]):
+    z = _get_samples(q, z, n, seed)
+    log_values = log_f(z) + log_p(z) - q.log_prob(z)
+    return _logspace_mean(log_values)
+
+
+def _logspace_mean(log_values):
+  """Evaluate `Log[E[values]]` in a stable manner.
+
+  Args:
+    log_values:  `Tensor` holding `Log[values]`.
+
+  Returns:
+    `Tensor` of same `dtype` as `log_values`, reduced across dim 0.
+      `Log[Mean[values]]`.
+  """
+  # center = Max[Log[values]],  with stop-gradient
+  # The center hopefully keep the exponentiated term small.  It is cancelled
+  # from the final result, so putting stop gradient on it will not change the
+  # final result.  We put stop gradient on to eliminate unnecessary computation.
+  center = array_ops.stop_gradient(_sample_max(log_values))
+
+  # centered_values = exp{Log[values] - E[Log[values]]}
+  centered_values = math_ops.exp(log_values - center)
+
+  # log_mean_of_values = Log[ E[centered_values] ] + center
+  #                    = Log[ E[exp{log_values - E[log_values]}] ] + center
+  #                    = Log[E[values]] - E[log_values] + center
+  #                    = Log[E[values]]
+  log_mean_of_values = math_ops.log(_sample_mean(centered_values)) + center
+
+  return log_mean_of_values
+
+
+def expectation(f, p, z=None, n=None, seed=None, name='expectation'):
+  r"""Monte Carlo estimate of an expectation:  `E_p[f(Z)]` with sample mean.
+
+  This `Op` returns
+
+  ```
+  n^{-1} sum_{i=1}^n f(z_i),  where z_i ~ p
+  \approx E_p[f(Z)]
+  ```
+
+  User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+  Args:
+    f: Callable mapping samples from `sampling_dist_q` to `Tensors` with
+      shape broadcastable to `q.batch_shape`.
+      For example, `f` works "just like" `sampling_dist_q.log_prob`.
+    p:  `tf.contrib.distributions.BaseDistribution`.
+    z:  `Tensor` of samples from `p`, produced by `p.sample_n`.
+    n:  Integer `Tensor`.  Number of samples to generate if `z` is not provided.
+    seed:  Python integer to seed the random number generator.
+    name:  A name to give this `Op`.
+
+  Returns:
+    A `Tensor` with same `dtype` as `p`, and shape equal to `p.batch_shape`.
+  """
+  with ops.name_scope(name, values=[n, z]):
+    z = _get_samples(p, z, n, seed)
+    return _sample_mean(f(z))
+
+
+def _sample_mean(values):
+  """Mean over sample indices.  In this module this is always [0]."""
+  return math_ops.reduce_mean(values, reduction_indices=[0])
+
+
+def _sample_max(values):
+  """Max over sample indices.  In this module this is always [0]."""
+  return math_ops.reduce_max(values, reduction_indices=[0])
+
+
+def _get_samples(dist, z, n, seed):
+  """Check args and return samples."""
+  with ops.name_scope('get_samples', values=[z, n]):
+    if (n is None) == (z is None):
+      raise ValueError(
+          'Must specify exactly one of arguments "n" and "z".  Found: '
+          'n = %s, z = %s' % (n, z))
+    if n is not None:
+      return dist.sample_n(n=n, seed=seed)
+    else:
+      return ops.convert_to_tensor(z, name='z')
diff --git a/tensorflow/contrib/bayesflow/python/ops/variational_inference.py b/tensorflow/contrib/bayesflow/python/ops/variational_inference.py
index f276d8b829..784c79492c 100644
--- a/tensorflow/contrib/bayesflow/python/ops/variational_inference.py
+++ b/tensorflow/contrib/bayesflow/python/ops/variational_inference.py
@@ -24,7 +24,7 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib import distributions
-from tensorflow.contrib.bayesflow import stochastic_graph as sg
+from tensorflow.contrib.bayesflow.python.ops import stochastic_graph as sg
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import tf_logging as logging
diff --git a/tensorflow/python/framework/gen_docs_combined.py b/tensorflow/python/framework/gen_docs_combined.py
index 172441e2e9..42cf4ffe37 100644
--- a/tensorflow/python/framework/gen_docs_combined.py
+++ b/tensorflow/python/framework/gen_docs_combined.py
@@ -55,6 +55,8 @@ def module_names():
       "tf.python_io",
       "tf.summary",
       "tf.test",
+      "tf.contrib.bayesflow.entropy",
+      "tf.contrib.bayesflow.monte_carlo",
       "tf.contrib.bayesflow.stochastic_graph",
       "tf.contrib.bayesflow.variational_inference",
       "tf.contrib.copy_graph",
@@ -190,6 +192,12 @@ def all_libraries(module_to_name, members, documented):
               prefix=PREFIX_TEXT),
       library("summary", "Summary Operations", tf.summary),
       library("test", "Testing", tf.test),
+      library("contrib.bayesflow.entropy",
+              "BayesFlow Entropy (contrib)",
+              tf.contrib.bayesflow.entropy),
+      library("contrib.bayesflow.monte_carlo",
+              "BayesFlow Monte Carlo (contrib)",
+              tf.contrib.bayesflow.monte_carlo),
       library("contrib.bayesflow.stochastic_graph",
               "BayesFlow Stochastic Graph (contrib)",
               tf.contrib.bayesflow.stochastic_graph),
-- 
GitLab


From 64c26e4ff1e673ee19d4235fba832513bbd99e60 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 22:34:26 -0800
Subject: [PATCH 090/223] Update generated Python Op docs. Change: 131140006

---
 .../python/contrib.bayesflow.entropy.md       | 304 ++++++++++++++++++
 .../python/contrib.bayesflow.monte_carlo.md   | 179 +++++++++++
 ...ntrib.bayesflow.monte_carlo.expectation.md |  29 ++
 ...tf.contrib.bayesflow.entropy.elbo_ratio.md |  68 ++++
 ...f.contrib.bayesflow.entropy.renyi_alpha.md |  38 +++
 ...te_carlo.expectation_importance_sampler.md |  43 +++
 ...f.contrib.bayesflow.entropy.renyi_ratio.md | 103 ++++++
 ...ntrib.bayesflow.entropy.entropy_shannon.md |  38 +++
 ...expectation_importance_sampler_logspace.md |  45 +++
 tensorflow/g3doc/api_docs/python/index.md     |  11 +
 10 files changed, 858 insertions(+)
 create mode 100644 tensorflow/g3doc/api_docs/python/contrib.bayesflow.entropy.md
 create mode 100644 tensorflow/g3doc/api_docs/python/contrib.bayesflow.monte_carlo.md
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.bayesflow.monte_carlo.expectation.md
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.bayesflow.entropy.elbo_ratio.md
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diff --git a/tensorflow/g3doc/api_docs/python/contrib.bayesflow.entropy.md b/tensorflow/g3doc/api_docs/python/contrib.bayesflow.entropy.md
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+<!-- This file is machine generated: DO NOT EDIT! -->
+
+# BayesFlow Entropy (contrib)
+[TOC]
+
+Entropy Ops.
+
+## Background
+
+Common Shannon entropy, the Evidence Lower BOund (ELBO), KL divergence, and more
+all have information theoretic use and interpretations.  They are also often
+used in variational inference.  This library brings together `Ops` for
+estimating them, e.g. using Monte Carlo expectations.
+
+## Examples
+
+Example of fitting a variational posterior with the ELBO.
+
+```
+# We start by assuming knowledge of the log of a joint density p(z, x) over
+# latent variable z and fixed measurement x.  Since x is fixed, the Python
+# function does not take x as an argument.
+def log_joint(z):
+  theta = tf.Variable(0.)  # Trainable variable that helps define log_joint.
+  ...
+
+# Next, define a Normal distribution with trainable parameters.
+q = distributions.Normal(mu=tf.Variable(0.), sigma=tf.Variable(1.))
+
+# Now, define a loss function (negative ELBO) that, when minimized, will adjust
+# mu, sigma, and theta, increasing the ELBO, which we hope will both reduce the
+# KL divergence between q(z) and p(z | x), and increase p(x).  Note that we
+# cannot guarantee both, but in general we expect both to happen.
+elbo = entropy.elbo_ratio(log_p, q, n=10)
+loss = -elbo
+
+# Minimize the loss
+train_op = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
+tf.initialize_all_variables().run()
+for step in range(100):
+  train_op.run()
+```
+
+## Ops
+
+- - -
+
+### `tf.contrib.bayesflow.entropy.elbo_ratio(log_p, q, z=None, n=None, seed=None, form=None, name='elbo_ratio')` {#elbo_ratio}
+
+Estimate of the ratio appearing in the `ELBO` and `KL` divergence.
+
+With `p(z) := exp{log_p(z)}`, this `Op` returns an approximation of
+
+```
+E_q[ Log[p(Z) / q(Z)] ]
+```
+
+The term `E_q[ Log[p(Z)] ]` is always computed as a sample mean.
+The term `E_q[ Log[q(z)] ]` can be computed with samples, or an exact formula
+if `q.entropy()` is defined.  This is controlled with the kwarg `form`.
+
+This log-ratio appears in different contexts:
+
+#### `KL[q || p]`
+
+If `log_p(z) = Log[p(z)]` for distribution `p`, this `Op` approximates
+the negative Kullback-Leibler divergence.
+
+```
+elbo_ratio(log_p, q, n=100) = -1 * KL[q || p],
+KL[q || p] = E[ Log[q(Z)] - Log[p(Z)] ]
+```
+
+Note that if `p` is a `Distribution`, then `distributions.kl(q, p)` may be
+defined and available as an exact result.
+
+#### ELBO
+
+If `log_p(z) = Log[p(z, x)]` is the log joint of a distribution `p`, this is
+the Evidence Lower BOund (ELBO):
+
+```
+ELBO ~= E[ Log[p(Z, x)] - Log[q(Z)] ]
+      = Log[p(x)] - KL[q || p]
+     <= Log[p(x)]
+```
+
+User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+##### Args:
+
+
+*  <b>`log_p`</b>: Callable mapping samples from `q` to `Tensors` with
+    shape broadcastable to `q.batch_shape`.
+    For example, `log_p` works "just like" `q.log_prob`.
+*  <b>`q`</b>: `tf.contrib.distributions.BaseDistribution`.
+*  <b>`z`</b>: `Tensor` of samples from `q`, produced by `q.sample_n`.
+*  <b>`n`</b>: Integer `Tensor`.  Number of samples to generate if `z` is not provided.
+*  <b>`seed`</b>: Python integer to seed the random number generator.
+*  <b>`form`</b>: Either `ELBOForms.analytic_entropy` (use formula for entropy of `q`)
+    or `ELBOForms.sample` (sample estimate of entropy), or `ELBOForms.default`
+    (attempt analytic entropy, fallback on sample).
+    Default value is `ELBOForms.default`.
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+  Scalar `Tensor` holding sample mean KL divergence.  `shape` is the batch
+    shape of `q`, and `dtype` is the same as `q`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `form` is not handled by this function.
+
+
+- - -
+
+### `tf.contrib.bayesflow.entropy.entropy_shannon(p, z=None, n=None, seed=None, form=None, name='entropy_shannon')` {#entropy_shannon}
+
+Monte Carlo or deterministic computation of Shannon's entropy.
+
+Depending on the kwarg `form`, this `Op` returns either the analytic entropy
+of the distribution `p`, or the sampled entropy:
+
+```
+-n^{-1} sum_{i=1}^n p.log_prob(z_i),  where z_i ~ p,
+    \approx - E_p[ Log[p(Z)] ]
+    = Entropy[p]
+```
+
+User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+##### Args:
+
+
+*  <b>`p`</b>: `tf.contrib.distributions.BaseDistribution`
+*  <b>`z`</b>: `Tensor` of samples from `p`, produced by `p.sample_n(n)` for some `n`.
+*  <b>`n`</b>: Integer `Tensor`.  Number of samples to generate if `z` is not provided.
+*  <b>`seed`</b>: Python integer to seed the random number generator.
+*  <b>`form`</b>: Either `ELBOForms.analytic_entropy` (use formula for entropy of `q`)
+    or `ELBOForms.sample` (sample estimate of entropy), or `ELBOForms.default`
+    (attempt analytic entropy, fallback on sample).
+    Default value is `ELBOForms.default`.
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+  A `Tensor` with same `dtype` as `p`, and shape equal to `p.batch_shape`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `form` not handled by this function.
+*  <b>`ValueError`</b>: If `form` is `ELBOForms.analytic_entropy` and `n` was provided.
+
+
+- - -
+
+### `tf.contrib.bayesflow.entropy.renyi_ratio(log_p, q, alpha, z=None, n=None, seed=None, name='renyi_ratio')` {#renyi_ratio}
+
+Monte Carlo estimate of the ratio appearing in Renyi divergence.
+
+This can be used to compute the Renyi (alpha) divergence, or a log evidence
+approximation based on Renyi divergence.
+
+#### Definition
+
+With `z_i` iid samples from `q`, and `exp{log_p(z)} = p(z)`, this `Op` returns
+the (biased for finite `n`) estimate:
+
+```
+(1 - alpha)^{-1} Log[ n^{-1} sum_{i=1}^n ( p(z_i) / q(z_i) )^{1 - alpha},
+\approx (1 - alpha)^{-1} Log[ E_q[ (p(Z) / q(Z))^{1 - alpha} ]  ]
+```
+
+This ratio appears in different contexts:
+
+#### Renyi divergence
+
+If `log_p(z) = Log[p(z)]` is the log prob of a distribution, and
+`alpha > 0`, `alpha != 1`, this `Op` approximates `-1` times Renyi divergence:
+
+```
+# Choose reasonably high n to limit bias, see below.
+renyi_ratio(log_p, q, alpha, n=100)
+                \approx -1 * D_alpha[q || p],  where
+D_alpha[q || p] := (1 - alpha)^{-1} Log E_q[(p(Z) / q(Z))^{1 - alpha}]
+```
+
+The Renyi (or "alpha") divergence is non-negative and equal to zero iff
+`q = p`.  Various limits of `alpha` lead to different special case results:
+
+```
+alpha       D_alpha[q || p]
+-----       ---------------
+--> 0       Log[ int_{q > 0} p(z) dz ]
+= 0.5,      -2 Log[1 - Hel^2[q || p]],  (\propto squared Hellinger distance)
+--> 1       KL[q || p]
+= 2         Log[ 1 + chi^2[q || p] ],   (\propto squared Chi-2 divergence)
+--> infty   Log[ max_z{q(z) / p(z)} ],  (min description length principle).
+```
+
+See "Renyi Divergence Variational Inference", by Li and Turner.
+
+#### Log evidence approximation
+
+If `log_p(z) = Log[p(z, x)]` is the log of the joint distribution `p`, this is
+an alternative to the ELBO common in variational inference.
+
+```
+L_alpha(q, p) = Log[p(x)] - D_alpha[q || p]
+```
+
+If `q` and `p` have the same support, and `0 < a <= b < 1`, one can show
+`ELBO <= D_b <= D_a <= Log[p(x)]`.  Thus, this `Op` allows a smooth
+interpolation between the ELBO and the true evidence.
+
+#### Stability notes
+
+Note that when `1 - alpha` is not small, the ratio `(p(z) / q(z))^{1 - alpha}`
+is subject to underflow/overflow issues.  For that reason, it is evaluated in
+log-space after centering.  Nonetheless, infinite/NaN results may occur.  For
+that reason, one may wish to shrink `alpha` gradually.  See the `Op`
+`renyi_alpha`.  Using `float64` will also help.
+
+
+#### Bias for finite sample size
+
+Due to nonlinearity of the logarithm, for random variables `{X_1,...,X_n}`,
+`E[ Log[sum_{i=1}^n X_i] ] != Log[ E[sum_{i=1}^n X_i] ]`.  As a result, this
+estimate is biased for finite `n`.  For `alpha < 1`, it is non-decreasing
+with `n` (in expectation).  For example, if `n = 1`, this estimator yields the
+same result as `elbo_ratio`, and as `n` increases the expected value
+of the estimator increases.
+
+#### Call signature
+
+User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+##### Args:
+
+
+*  <b>`log_p`</b>: Callable mapping samples from `q` to `Tensors` with
+    shape broadcastable to `q.batch_shape`.
+    For example, `log_p` works "just like" `q.log_prob`.
+*  <b>`q`</b>: `tf.contrib.distributions.BaseDistribution`.
+     `float64` `dtype` recommended.
+     `log_p` and `q` should be supported on the same set.
+*  <b>`alpha`</b>: `Tensor` with shape `q.batch_shape` and values not equal to 1.
+*  <b>`z`</b>: `Tensor` of samples from `q`, produced by `q.sample_n`.
+*  <b>`n`</b>: Integer `Tensor`.  The number of samples to use if `z` is not provided.
+    Note that this can be highly biased for small `n`, see docstring.
+*  <b>`seed`</b>: Python integer to seed the random number generator.
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+
+*  <b>`renyi_result`</b>: The scaled log of sample mean.  `Tensor` with `shape` equal
+    to batch shape of `q`, and `dtype` = `q.dtype`.
+
+
+- - -
+
+### `tf.contrib.bayesflow.entropy.renyi_alpha(step, decay_time, alpha_min, alpha_max=0.99999, name='renyi_alpha')` {#renyi_alpha}
+
+Exponentially decaying `Tensor` appropriate for Renyi ratios.
+
+When minimizing the Renyi divergence for `0 <= alpha < 1` (or maximizing the
+Renyi equivalent of elbo) in high dimensions, it is not uncommon to experience
+`NaN` and `inf` values when `alpha` is far from `1`.
+
+For that reason, it is often desirable to start the optimization with `alpha`
+very close to 1, and reduce it to a final `alpha_min` according to some
+schedule.  The user may even want to optimize using `elbo_ratio` for
+some fixed time before switching to Renyi based methods.
+
+This `Op` returns an `alpha` decaying exponentially with step:
+
+```
+s(step) = (exp{step / decay_time} - 1) / (e - 1)
+t(s) = max(0, min(s, 1)),  (smooth growth from 0 to 1)
+alpha(t) = (1 - t) alpha_min + t alpha_max
+```
+
+##### Args:
+
+
+*  <b>`step`</b>: Non-negative scalar `Tensor`.  Typically the global step or an
+    offset version thereof.
+*  <b>`decay_time`</b>: Postive scalar `Tensor`.
+*  <b>`alpha_min`</b>: `float` or `double` `Tensor`.
+    The minimal, final value of `alpha`, achieved when `step >= decay_time`
+*  <b>`alpha_max`</b>: `Tensor` of same `dtype` as `alpha_min`.
+    The maximal, beginning value of `alpha`, achieved when `step == 0`
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+
+*  <b>`alpha`</b>: A `Tensor` of same `dtype` as `alpha_min`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/contrib.bayesflow.monte_carlo.md b/tensorflow/g3doc/api_docs/python/contrib.bayesflow.monte_carlo.md
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+<!-- This file is machine generated: DO NOT EDIT! -->
+
+# BayesFlow Monte Carlo (contrib)
+[TOC]
+
+Monte Carlo integration and helpers.
+
+## Background
+
+Monte Carlo integration refers to the practice of estimating an expectation with
+a sample mean.  For example, given random variable `Z in R^k` with density `p`,
+the expectation of function `f` can be approximated like:
+
+```
+E_p[f(Z)] = \int f(z) p(z) dz
+          ~ S_n
+          := n^{-1} \sum_{i=1}^n f(z_i),  z_i iid samples from p.
+```
+
+If `E_p[|f(Z)|] < infinity`, then `S_n --> E_p[f(Z)]` by the strong law of large
+numbers.  If `E_p[f(Z)^2] < infinity`, then `S_n` is asymptotically normal with
+variance `Var[f(Z)] / n`.
+
+Practicioners of Bayesian statistics often find themselves wanting to estimate
+`E_p[f(Z)]` when the distribution `p` is known only up to a constant.  For
+example, the joint distribution `p(z, x)` may be known, but the evidence
+`p(x) = \int p(z, x) dz` may be intractable.  In that case, a parameterized
+distribution family `q_lambda(z)` may be chosen, and the optimal `lambda` is the
+one minimizing the KL divergence between `q_lambda(z)` and
+`p(z | x)`.  We only know `p(z, x)`, but that is sufficient to find `lambda`.
+
+
+## Log-space evaluation and subtracting the maximum.
+
+Care must be taken when the random variable lives in a high dimensional space.
+For example, the naive importance sample estimate `E_q[f(Z) p(Z) / q(Z)]`
+involves the ratio of two terms `p(Z) / q(Z)`, each of which must have tails
+dropping off faster than `O(|z|^{-(k + 1)})` in order to have finite integral.
+This ratio would often be zero or infinity up to numerical precision.
+
+For that reason, we write
+
+```
+Log E_q[ f(Z) p(Z) / q(Z) ]
+   = Log E_q[ exp{Log[f(Z)] + Log[p(Z)] - Log[q(Z)] - C} ] + C,  where
+C := Max[ Log[f(Z)] + Log[p(Z)] - Log[q(Z)] ].
+```
+
+The maximum value of the exponentiated term will be 0.0, and the the expecation
+can be evaluated in a stable manner.
+
+## Ops
+
+- - -
+
+### `tf.contrib.bayesflow.monte_carlo.expectation(f, p, z=None, n=None, seed=None, name='expectation')` {#expectation}
+
+Monte Carlo estimate of an expectation:  `E_p[f(Z)]` with sample mean.
+
+This `Op` returns
+
+```
+n^{-1} sum_{i=1}^n f(z_i),  where z_i ~ p
+\approx E_p[f(Z)]
+```
+
+User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+##### Args:
+
+
+*  <b>`f`</b>: Callable mapping samples from `sampling_dist_q` to `Tensors` with
+    shape broadcastable to `q.batch_shape`.
+    For example, `f` works "just like" `sampling_dist_q.log_prob`.
+*  <b>`p`</b>: `tf.contrib.distributions.BaseDistribution`.
+*  <b>`z`</b>: `Tensor` of samples from `p`, produced by `p.sample_n`.
+*  <b>`n`</b>: Integer `Tensor`.  Number of samples to generate if `z` is not provided.
+*  <b>`seed`</b>: Python integer to seed the random number generator.
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+  A `Tensor` with same `dtype` as `p`, and shape equal to `p.batch_shape`.
+
+
+- - -
+
+### `tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler(f, log_p, sampling_dist_q, z=None, n=None, seed=None, name='expectation_importance_sampler')` {#expectation_importance_sampler}
+
+Monte Carlo estimate of `E_p[f(Z)] = E_q[f(Z) p(Z) / q(Z)]`.
+
+With `p(z) := exp{log_p(z)}`, this `Op` returns
+
+```
+n^{-1} sum_{i=1}^n [ f(z_i) p(z_i) / q(z_i) ],  z_i ~ q,
+\approx E_q[ f(Z) p(Z) / q(Z) ]
+=       E_p[f(Z)]
+```
+
+This integral is done in log-space with max-subtraction to better handle the
+often extreme values that `f(z) p(z) / q(z)` can take on.
+
+If `f >= 0`, it is up to 2x more efficient to exponentiate the result of
+`expectation_importance_sampler_logspace` applied to `Log[f]`.
+
+User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+##### Args:
+
+
+*  <b>`f`</b>: Callable mapping samples from `sampling_dist_q` to `Tensors` with shape
+    broadcastable to `q.batch_shape`.
+    For example, `f` works "just like" `q.log_prob`.
+*  <b>`log_p`</b>: Callable mapping samples from `sampling_dist_q` to `Tensors` with
+    shape broadcastable to `q.batch_shape`.
+    For example, `log_p` works "just like" `sampling_dist_q.log_prob`.
+*  <b>`sampling_dist_q`</b>: The sampling distribution.
+    `tf.contrib.distributions.BaseDistribution`.
+    `float64` `dtype` recommended.
+    `log_p` and `q` should be supported on the same set.
+*  <b>`z`</b>: `Tensor` of samples from `q`, produced by `q.sample_n`.
+*  <b>`n`</b>: Integer `Tensor`.  Number of samples to generate if `z` is not provided.
+*  <b>`seed`</b>: Python integer to seed the random number generator.
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+  The importance sampling estimate.  `Tensor` with `shape` equal
+    to batch shape of `q`, and `dtype` = `q.dtype`.
+
+
+- - -
+
+### `tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler_logspace(log_f, log_p, sampling_dist_q, z=None, n=None, seed=None, name='expectation_importance_sampler_logspace')` {#expectation_importance_sampler_logspace}
+
+Importance sampling with a positive function, in log-space.
+
+With `p(z) := exp{log_p(z)}`, and `f(z) = exp{log_f(z)}`, this `Op`
+returns
+
+```
+Log[ n^{-1} sum_{i=1}^n [ f(z_i) p(z_i) / q(z_i) ] ],  z_i ~ q,
+\approx Log[ E_q[ f(Z) p(Z) / q(Z) ] ]
+=       Log[E_p[f(Z)]]
+```
+
+This integral is done in log-space with max-subtraction to better handle the
+often extreme values that `f(z) p(z) / q(z)` can take on.
+
+In contrast to `expectation_importance_sampler`, this `Op` returns values in
+log-space.
+
+
+User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+##### Args:
+
+
+*  <b>`log_f`</b>: Callable mapping samples from `sampling_dist_q` to `Tensors` with
+    shape broadcastable to `q.batch_shape`.
+    For example, `log_f` works "just like" `sampling_dist_q.log_prob`.
+*  <b>`log_p`</b>: Callable mapping samples from `sampling_dist_q` to `Tensors` with
+    shape broadcastable to `q.batch_shape`.
+    For example, `log_p` works "just like" `q.log_prob`.
+*  <b>`sampling_dist_q`</b>: The sampling distribution.
+    `tf.contrib.distributions.BaseDistribution`.
+    `float64` `dtype` recommended.
+    `log_p` and `q` should be supported on the same set.
+*  <b>`z`</b>: `Tensor` of samples from `q`, produced by `q.sample_n`.
+*  <b>`n`</b>: Integer `Tensor`.  Number of samples to generate if `z` is not provided.
+*  <b>`seed`</b>: Python integer to seed the random number generator.
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+  Logarithm of the importance sampling estimate.  `Tensor` with `shape` equal
+    to batch shape of `q`, and `dtype` = `q.dtype`.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.bayesflow.monte_carlo.expectation.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.bayesflow.monte_carlo.expectation.md
new file mode 100644
index 0000000000..f34649987b
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+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.bayesflow.monte_carlo.expectation.md
@@ -0,0 +1,29 @@
+### `tf.contrib.bayesflow.monte_carlo.expectation(f, p, z=None, n=None, seed=None, name='expectation')` {#expectation}
+
+Monte Carlo estimate of an expectation:  `E_p[f(Z)]` with sample mean.
+
+This `Op` returns
+
+```
+n^{-1} sum_{i=1}^n f(z_i),  where z_i ~ p
+\approx E_p[f(Z)]
+```
+
+User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+##### Args:
+
+
+*  <b>`f`</b>: Callable mapping samples from `sampling_dist_q` to `Tensors` with
+    shape broadcastable to `q.batch_shape`.
+    For example, `f` works "just like" `sampling_dist_q.log_prob`.
+*  <b>`p`</b>: `tf.contrib.distributions.BaseDistribution`.
+*  <b>`z`</b>: `Tensor` of samples from `p`, produced by `p.sample_n`.
+*  <b>`n`</b>: Integer `Tensor`.  Number of samples to generate if `z` is not provided.
+*  <b>`seed`</b>: Python integer to seed the random number generator.
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+  A `Tensor` with same `dtype` as `p`, and shape equal to `p.batch_shape`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.bayesflow.entropy.elbo_ratio.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.bayesflow.entropy.elbo_ratio.md
new file mode 100644
index 0000000000..b62875b66c
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.bayesflow.entropy.elbo_ratio.md
@@ -0,0 +1,68 @@
+### `tf.contrib.bayesflow.entropy.elbo_ratio(log_p, q, z=None, n=None, seed=None, form=None, name='elbo_ratio')` {#elbo_ratio}
+
+Estimate of the ratio appearing in the `ELBO` and `KL` divergence.
+
+With `p(z) := exp{log_p(z)}`, this `Op` returns an approximation of
+
+```
+E_q[ Log[p(Z) / q(Z)] ]
+```
+
+The term `E_q[ Log[p(Z)] ]` is always computed as a sample mean.
+The term `E_q[ Log[q(z)] ]` can be computed with samples, or an exact formula
+if `q.entropy()` is defined.  This is controlled with the kwarg `form`.
+
+This log-ratio appears in different contexts:
+
+#### `KL[q || p]`
+
+If `log_p(z) = Log[p(z)]` for distribution `p`, this `Op` approximates
+the negative Kullback-Leibler divergence.
+
+```
+elbo_ratio(log_p, q, n=100) = -1 * KL[q || p],
+KL[q || p] = E[ Log[q(Z)] - Log[p(Z)] ]
+```
+
+Note that if `p` is a `Distribution`, then `distributions.kl(q, p)` may be
+defined and available as an exact result.
+
+#### ELBO
+
+If `log_p(z) = Log[p(z, x)]` is the log joint of a distribution `p`, this is
+the Evidence Lower BOund (ELBO):
+
+```
+ELBO ~= E[ Log[p(Z, x)] - Log[q(Z)] ]
+      = Log[p(x)] - KL[q || p]
+     <= Log[p(x)]
+```
+
+User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+##### Args:
+
+
+*  <b>`log_p`</b>: Callable mapping samples from `q` to `Tensors` with
+    shape broadcastable to `q.batch_shape`.
+    For example, `log_p` works "just like" `q.log_prob`.
+*  <b>`q`</b>: `tf.contrib.distributions.BaseDistribution`.
+*  <b>`z`</b>: `Tensor` of samples from `q`, produced by `q.sample_n`.
+*  <b>`n`</b>: Integer `Tensor`.  Number of samples to generate if `z` is not provided.
+*  <b>`seed`</b>: Python integer to seed the random number generator.
+*  <b>`form`</b>: Either `ELBOForms.analytic_entropy` (use formula for entropy of `q`)
+    or `ELBOForms.sample` (sample estimate of entropy), or `ELBOForms.default`
+    (attempt analytic entropy, fallback on sample).
+    Default value is `ELBOForms.default`.
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+  Scalar `Tensor` holding sample mean KL divergence.  `shape` is the batch
+    shape of `q`, and `dtype` is the same as `q`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `form` is not handled by this function.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.bayesflow.entropy.renyi_alpha.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.bayesflow.entropy.renyi_alpha.md
new file mode 100644
index 0000000000..c8834f7182
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.bayesflow.entropy.renyi_alpha.md
@@ -0,0 +1,38 @@
+### `tf.contrib.bayesflow.entropy.renyi_alpha(step, decay_time, alpha_min, alpha_max=0.99999, name='renyi_alpha')` {#renyi_alpha}
+
+Exponentially decaying `Tensor` appropriate for Renyi ratios.
+
+When minimizing the Renyi divergence for `0 <= alpha < 1` (or maximizing the
+Renyi equivalent of elbo) in high dimensions, it is not uncommon to experience
+`NaN` and `inf` values when `alpha` is far from `1`.
+
+For that reason, it is often desirable to start the optimization with `alpha`
+very close to 1, and reduce it to a final `alpha_min` according to some
+schedule.  The user may even want to optimize using `elbo_ratio` for
+some fixed time before switching to Renyi based methods.
+
+This `Op` returns an `alpha` decaying exponentially with step:
+
+```
+s(step) = (exp{step / decay_time} - 1) / (e - 1)
+t(s) = max(0, min(s, 1)),  (smooth growth from 0 to 1)
+alpha(t) = (1 - t) alpha_min + t alpha_max
+```
+
+##### Args:
+
+
+*  <b>`step`</b>: Non-negative scalar `Tensor`.  Typically the global step or an
+    offset version thereof.
+*  <b>`decay_time`</b>: Postive scalar `Tensor`.
+*  <b>`alpha_min`</b>: `float` or `double` `Tensor`.
+    The minimal, final value of `alpha`, achieved when `step >= decay_time`
+*  <b>`alpha_max`</b>: `Tensor` of same `dtype` as `alpha_min`.
+    The maximal, beginning value of `alpha`, achieved when `step == 0`
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+
+*  <b>`alpha`</b>: A `Tensor` of same `dtype` as `alpha_min`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler.md
new file mode 100644
index 0000000000..23c689c019
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler.md
@@ -0,0 +1,43 @@
+### `tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler(f, log_p, sampling_dist_q, z=None, n=None, seed=None, name='expectation_importance_sampler')` {#expectation_importance_sampler}
+
+Monte Carlo estimate of `E_p[f(Z)] = E_q[f(Z) p(Z) / q(Z)]`.
+
+With `p(z) := exp{log_p(z)}`, this `Op` returns
+
+```
+n^{-1} sum_{i=1}^n [ f(z_i) p(z_i) / q(z_i) ],  z_i ~ q,
+\approx E_q[ f(Z) p(Z) / q(Z) ]
+=       E_p[f(Z)]
+```
+
+This integral is done in log-space with max-subtraction to better handle the
+often extreme values that `f(z) p(z) / q(z)` can take on.
+
+If `f >= 0`, it is up to 2x more efficient to exponentiate the result of
+`expectation_importance_sampler_logspace` applied to `Log[f]`.
+
+User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+##### Args:
+
+
+*  <b>`f`</b>: Callable mapping samples from `sampling_dist_q` to `Tensors` with shape
+    broadcastable to `q.batch_shape`.
+    For example, `f` works "just like" `q.log_prob`.
+*  <b>`log_p`</b>: Callable mapping samples from `sampling_dist_q` to `Tensors` with
+    shape broadcastable to `q.batch_shape`.
+    For example, `log_p` works "just like" `sampling_dist_q.log_prob`.
+*  <b>`sampling_dist_q`</b>: The sampling distribution.
+    `tf.contrib.distributions.BaseDistribution`.
+    `float64` `dtype` recommended.
+    `log_p` and `q` should be supported on the same set.
+*  <b>`z`</b>: `Tensor` of samples from `q`, produced by `q.sample_n`.
+*  <b>`n`</b>: Integer `Tensor`.  Number of samples to generate if `z` is not provided.
+*  <b>`seed`</b>: Python integer to seed the random number generator.
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+  The importance sampling estimate.  `Tensor` with `shape` equal
+    to batch shape of `q`, and `dtype` = `q.dtype`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.bayesflow.entropy.renyi_ratio.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.bayesflow.entropy.renyi_ratio.md
new file mode 100644
index 0000000000..cf51e6cd09
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.bayesflow.entropy.renyi_ratio.md
@@ -0,0 +1,103 @@
+### `tf.contrib.bayesflow.entropy.renyi_ratio(log_p, q, alpha, z=None, n=None, seed=None, name='renyi_ratio')` {#renyi_ratio}
+
+Monte Carlo estimate of the ratio appearing in Renyi divergence.
+
+This can be used to compute the Renyi (alpha) divergence, or a log evidence
+approximation based on Renyi divergence.
+
+#### Definition
+
+With `z_i` iid samples from `q`, and `exp{log_p(z)} = p(z)`, this `Op` returns
+the (biased for finite `n`) estimate:
+
+```
+(1 - alpha)^{-1} Log[ n^{-1} sum_{i=1}^n ( p(z_i) / q(z_i) )^{1 - alpha},
+\approx (1 - alpha)^{-1} Log[ E_q[ (p(Z) / q(Z))^{1 - alpha} ]  ]
+```
+
+This ratio appears in different contexts:
+
+#### Renyi divergence
+
+If `log_p(z) = Log[p(z)]` is the log prob of a distribution, and
+`alpha > 0`, `alpha != 1`, this `Op` approximates `-1` times Renyi divergence:
+
+```
+# Choose reasonably high n to limit bias, see below.
+renyi_ratio(log_p, q, alpha, n=100)
+                \approx -1 * D_alpha[q || p],  where
+D_alpha[q || p] := (1 - alpha)^{-1} Log E_q[(p(Z) / q(Z))^{1 - alpha}]
+```
+
+The Renyi (or "alpha") divergence is non-negative and equal to zero iff
+`q = p`.  Various limits of `alpha` lead to different special case results:
+
+```
+alpha       D_alpha[q || p]
+-----       ---------------
+--> 0       Log[ int_{q > 0} p(z) dz ]
+= 0.5,      -2 Log[1 - Hel^2[q || p]],  (\propto squared Hellinger distance)
+--> 1       KL[q || p]
+= 2         Log[ 1 + chi^2[q || p] ],   (\propto squared Chi-2 divergence)
+--> infty   Log[ max_z{q(z) / p(z)} ],  (min description length principle).
+```
+
+See "Renyi Divergence Variational Inference", by Li and Turner.
+
+#### Log evidence approximation
+
+If `log_p(z) = Log[p(z, x)]` is the log of the joint distribution `p`, this is
+an alternative to the ELBO common in variational inference.
+
+```
+L_alpha(q, p) = Log[p(x)] - D_alpha[q || p]
+```
+
+If `q` and `p` have the same support, and `0 < a <= b < 1`, one can show
+`ELBO <= D_b <= D_a <= Log[p(x)]`.  Thus, this `Op` allows a smooth
+interpolation between the ELBO and the true evidence.
+
+#### Stability notes
+
+Note that when `1 - alpha` is not small, the ratio `(p(z) / q(z))^{1 - alpha}`
+is subject to underflow/overflow issues.  For that reason, it is evaluated in
+log-space after centering.  Nonetheless, infinite/NaN results may occur.  For
+that reason, one may wish to shrink `alpha` gradually.  See the `Op`
+`renyi_alpha`.  Using `float64` will also help.
+
+
+#### Bias for finite sample size
+
+Due to nonlinearity of the logarithm, for random variables `{X_1,...,X_n}`,
+`E[ Log[sum_{i=1}^n X_i] ] != Log[ E[sum_{i=1}^n X_i] ]`.  As a result, this
+estimate is biased for finite `n`.  For `alpha < 1`, it is non-decreasing
+with `n` (in expectation).  For example, if `n = 1`, this estimator yields the
+same result as `elbo_ratio`, and as `n` increases the expected value
+of the estimator increases.
+
+#### Call signature
+
+User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+##### Args:
+
+
+*  <b>`log_p`</b>: Callable mapping samples from `q` to `Tensors` with
+    shape broadcastable to `q.batch_shape`.
+    For example, `log_p` works "just like" `q.log_prob`.
+*  <b>`q`</b>: `tf.contrib.distributions.BaseDistribution`.
+     `float64` `dtype` recommended.
+     `log_p` and `q` should be supported on the same set.
+*  <b>`alpha`</b>: `Tensor` with shape `q.batch_shape` and values not equal to 1.
+*  <b>`z`</b>: `Tensor` of samples from `q`, produced by `q.sample_n`.
+*  <b>`n`</b>: Integer `Tensor`.  The number of samples to use if `z` is not provided.
+    Note that this can be highly biased for small `n`, see docstring.
+*  <b>`seed`</b>: Python integer to seed the random number generator.
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+
+*  <b>`renyi_result`</b>: The scaled log of sample mean.  `Tensor` with `shape` equal
+    to batch shape of `q`, and `dtype` = `q.dtype`.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.bayesflow.entropy.entropy_shannon.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.bayesflow.entropy.entropy_shannon.md
new file mode 100644
index 0000000000..bf37868e33
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.bayesflow.entropy.entropy_shannon.md
@@ -0,0 +1,38 @@
+### `tf.contrib.bayesflow.entropy.entropy_shannon(p, z=None, n=None, seed=None, form=None, name='entropy_shannon')` {#entropy_shannon}
+
+Monte Carlo or deterministic computation of Shannon's entropy.
+
+Depending on the kwarg `form`, this `Op` returns either the analytic entropy
+of the distribution `p`, or the sampled entropy:
+
+```
+-n^{-1} sum_{i=1}^n p.log_prob(z_i),  where z_i ~ p,
+    \approx - E_p[ Log[p(Z)] ]
+    = Entropy[p]
+```
+
+User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+##### Args:
+
+
+*  <b>`p`</b>: `tf.contrib.distributions.BaseDistribution`
+*  <b>`z`</b>: `Tensor` of samples from `p`, produced by `p.sample_n(n)` for some `n`.
+*  <b>`n`</b>: Integer `Tensor`.  Number of samples to generate if `z` is not provided.
+*  <b>`seed`</b>: Python integer to seed the random number generator.
+*  <b>`form`</b>: Either `ELBOForms.analytic_entropy` (use formula for entropy of `q`)
+    or `ELBOForms.sample` (sample estimate of entropy), or `ELBOForms.default`
+    (attempt analytic entropy, fallback on sample).
+    Default value is `ELBOForms.default`.
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+  A `Tensor` with same `dtype` as `p`, and shape equal to `p.batch_shape`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `form` not handled by this function.
+*  <b>`ValueError`</b>: If `form` is `ELBOForms.analytic_entropy` and `n` was provided.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler_logspace.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler_logspace.md
new file mode 100644
index 0000000000..dff7940d4b
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler_logspace.md
@@ -0,0 +1,45 @@
+### `tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler_logspace(log_f, log_p, sampling_dist_q, z=None, n=None, seed=None, name='expectation_importance_sampler_logspace')` {#expectation_importance_sampler_logspace}
+
+Importance sampling with a positive function, in log-space.
+
+With `p(z) := exp{log_p(z)}`, and `f(z) = exp{log_f(z)}`, this `Op`
+returns
+
+```
+Log[ n^{-1} sum_{i=1}^n [ f(z_i) p(z_i) / q(z_i) ] ],  z_i ~ q,
+\approx Log[ E_q[ f(Z) p(Z) / q(Z) ] ]
+=       Log[E_p[f(Z)]]
+```
+
+This integral is done in log-space with max-subtraction to better handle the
+often extreme values that `f(z) p(z) / q(z)` can take on.
+
+In contrast to `expectation_importance_sampler`, this `Op` returns values in
+log-space.
+
+
+User supplies either `Tensor` of samples `z`, or number of samples to draw `n`
+
+##### Args:
+
+
+*  <b>`log_f`</b>: Callable mapping samples from `sampling_dist_q` to `Tensors` with
+    shape broadcastable to `q.batch_shape`.
+    For example, `log_f` works "just like" `sampling_dist_q.log_prob`.
+*  <b>`log_p`</b>: Callable mapping samples from `sampling_dist_q` to `Tensors` with
+    shape broadcastable to `q.batch_shape`.
+    For example, `log_p` works "just like" `q.log_prob`.
+*  <b>`sampling_dist_q`</b>: The sampling distribution.
+    `tf.contrib.distributions.BaseDistribution`.
+    `float64` `dtype` recommended.
+    `log_p` and `q` should be supported on the same set.
+*  <b>`z`</b>: `Tensor` of samples from `q`, produced by `q.sample_n`.
+*  <b>`n`</b>: Integer `Tensor`.  Number of samples to generate if `z` is not provided.
+*  <b>`seed`</b>: Python integer to seed the random number generator.
+*  <b>`name`</b>: A name to give this `Op`.
+
+##### Returns:
+
+  Logarithm of the importance sampling estimate.  `Tensor` with `shape` equal
+    to batch shape of `q`, and `dtype` = `q.dtype`.
+
diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md
index 18d77fed93..f22463d6b9 100644
--- a/tensorflow/g3doc/api_docs/python/index.md
+++ b/tensorflow/g3doc/api_docs/python/index.md
@@ -593,6 +593,17 @@
   * [`is_built_with_cuda`](../../api_docs/python/test.md#is_built_with_cuda)
   * [`main`](../../api_docs/python/test.md#main)
 
+* **[BayesFlow Entropy (contrib)](../../api_docs/python/contrib.bayesflow.entropy.md)**:
+  * [`elbo_ratio`](../../api_docs/python/contrib.bayesflow.entropy.md#elbo_ratio)
+  * [`entropy_shannon`](../../api_docs/python/contrib.bayesflow.entropy.md#entropy_shannon)
+  * [`renyi_alpha`](../../api_docs/python/contrib.bayesflow.entropy.md#renyi_alpha)
+  * [`renyi_ratio`](../../api_docs/python/contrib.bayesflow.entropy.md#renyi_ratio)
+
+* **[BayesFlow Monte Carlo (contrib)](../../api_docs/python/contrib.bayesflow.monte_carlo.md)**:
+  * [`expectation`](../../api_docs/python/contrib.bayesflow.monte_carlo.md#expectation)
+  * [`expectation_importance_sampler`](../../api_docs/python/contrib.bayesflow.monte_carlo.md#expectation_importance_sampler)
+  * [`expectation_importance_sampler_logspace`](../../api_docs/python/contrib.bayesflow.monte_carlo.md#expectation_importance_sampler_logspace)
+
 * **[BayesFlow Stochastic Graph (contrib)](../../api_docs/python/contrib.bayesflow.stochastic_graph.md)**:
   * [`DistributionTensor`](../../api_docs/python/contrib.bayesflow.stochastic_graph.md#DistributionTensor)
   * [`get_current_value_type`](../../api_docs/python/contrib.bayesflow.stochastic_graph.md#get_current_value_type)
-- 
GitLab


From 28c9bc3ab1bbcba5056470519bd1118f2722e05b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Tue, 23 Aug 2016 23:27:35 -0800
Subject: [PATCH 091/223] Slightly simplify base64 decoding function. Change:
 131142953

---
 tensorflow/core/lib/strings/base64.cc | 67 +++++++++------------------
 1 file changed, 22 insertions(+), 45 deletions(-)

diff --git a/tensorflow/core/lib/strings/base64.cc b/tensorflow/core/lib/strings/base64.cc
index 5eaa35f2b7..7f0b8987a5 100644
--- a/tensorflow/core/lib/strings/base64.cc
+++ b/tensorflow/core/lib/strings/base64.cc
@@ -15,6 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/lib/strings/base64.h"
 
+#include <cstring>
 #include <memory>
 #include "tensorflow/core/lib/core/errors.h"
 
@@ -57,38 +58,14 @@ inline uint32 Convert(char x) {
   return static_cast<uint32>(z);
 }
 
-Status DecodeOneChar(const char* codes, char* result) {
-  const uint32 packed = (Convert(codes[0]) << 2) |
-                        (Convert(codes[1]) >> 4);
+Status DecodeThreeChars(const char* codes, char* result) {
+  const uint32 packed = (Convert(codes[0]) << 18) | (Convert(codes[1]) << 12) |
+                        (Convert(codes[2]) << 6) | (Convert(codes[3]));
   // Convert() return value has upper 25 bits set if input is invalid.
   // Therefore `packed` has high bits set iff at least one of code is invalid.
   if (TF_PREDICT_FALSE((packed & 0xFF000000) != 0)) {
     return errors::InvalidArgument("Invalid character found in base64.");
   }
-  *result = static_cast<char>(packed);
-  return Status::OK();
-}
-
-Status DecodeTwoChars(const char* codes, char* result) {
-  const uint32 packed = (Convert(codes[0]) << 10) |
-                        (Convert(codes[1]) << 4) |
-                        (Convert(codes[2]) >> 2);
-  if (TF_PREDICT_FALSE((packed & 0xFF000000) != 0)) {
-    return errors::InvalidArgument("Invalid character found in base64.");
-  }
-  result[0] = static_cast<char>(packed >> 8);
-  result[1] = static_cast<char>(packed);
-  return Status::OK();
-}
-
-Status DecodeThreeChars(const char* codes, char* result) {
-  const uint32 packed = (Convert(codes[0]) << 18) |
-                        (Convert(codes[1]) << 12) |
-                        (Convert(codes[2]) << 6) |
-                        (Convert(codes[3]));
-  if (TF_PREDICT_FALSE((packed & 0xFF000000) != 0)) {
-    return errors::InvalidArgument("Invalid character found in base64.");
-  }
   result[0] = static_cast<char>(packed >> 16);
   result[1] = static_cast<char>(packed >> 8);
   result[2] = static_cast<char>(packed);
@@ -106,7 +83,10 @@ Status Base64Decode(StringPiece data, string* decoded) {
     return Status::OK();
   }
 
-  // max_decoded_size may overestimate by up to 3 bytes.
+  // This decoding procedure will write 3 * ceil(data.size() / 4) bytes to be
+  // output buffer, then truncate if necessary. Therefore we must overestimate
+  // and allocate sufficient amount. Currently max_decoded_size may overestimate
+  // by up to 3 bytes.
   const size_t max_decoded_size = 3 * (data.size() / 4) + 3;
   std::unique_ptr<char[]> buffer(new char[max_decoded_size]);
   char* current = buffer.get();
@@ -135,25 +115,22 @@ Status Base64Decode(StringPiece data, string* decoded) {
     }
   }
 
-  switch (end - b64) {
-    case 4:
-      TF_RETURN_IF_ERROR(DecodeThreeChars(b64, current));
-      current += 3;
-      break;
-    case 3:
-      TF_RETURN_IF_ERROR(DecodeTwoChars(b64, current));
-      current += 2;
-      break;
-    case 2:
-      TF_RETURN_IF_ERROR(DecodeOneChar(b64, current));
-      current += 1;
-      break;
-    default:  // case 1
-      // We may check this condition early by checking data.size() % 4 == 1.
-      return errors::InvalidArgument(
-          "Base64 string length cannot be 1 modulo 4.");
+  const int remain = end - b64;
+  if (TF_PREDICT_FALSE(remain == 1)) {
+    // We may check this condition early by checking data.size() % 4 == 1.
+    return errors::InvalidArgument(
+        "Base64 string length cannot be 1 modulo 4.");
   }
 
+  // A valid base64 character will replace paddings, if any.
+  char tail[4] = {kBase64UrlSafeChars[0], kBase64UrlSafeChars[0],
+                  kBase64UrlSafeChars[0], kBase64UrlSafeChars[0]};
+  // Copy tail of the input into the array, then decode.
+  std::memcpy(tail, b64, remain * sizeof(*b64));
+  TF_RETURN_IF_ERROR(DecodeThreeChars(tail, current));
+  // We know how many parsed characters are valid.
+  current += remain - 1;
+
   decoded->assign(buffer.get(), current - buffer.get());
   return Status::OK();
 }
-- 
GitLab


From 2c6989db76b5cbd0cbac37f3f7567e8edb2c1799 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 24 Aug 2016 03:39:45 -0800
Subject: [PATCH 092/223] Mark internal control flow ops as not fetchable.
 Change: 131158417

---
 tensorflow/python/ops/control_flow_ops.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 15a780d767..aab4042f34 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -1355,6 +1355,8 @@ def cond(pred, fn1, fn2, name=None):
     pivot_1 = array_ops.identity(p_1, name="switch_t")
     pivot_2 = array_ops.identity(p_2, name="switch_f")
     pred = array_ops.identity(pred, name="pred_id")
+    for tensor in [p_1, p_2, pivot_1, pivot_2, pred]:
+      tensor.op.graph.prevent_fetching(tensor.op)
 
     # Build the graph for the true branch in a new context.
     context_t = CondContext(pred, pivot_1, branch=1)
-- 
GitLab


From d607d8ad3eac64496a62f90d0392cfdce7aad72e Mon Sep 17 00:00:00 2001
From: Phil Stahlfeld <philstahlfeld@google.com>
Date: Wed, 24 Aug 2016 08:29:48 -0800
Subject: [PATCH 093/223] Remove deprecated
 TensorFlow(Linear)?(Regressor|Classifier) classes. Change: 131180869

---
 tensorflow/contrib/learn/__init__.py          |  4 --
 .../learn/python/learn/estimators/__init__.py |  4 --
 .../learn/python/learn/estimators/linear.py   | 17 ------
 .../learn/python/learn/tests/base_test.py     | 57 +++++++------------
 .../learn/python/learn/tests/io_test.py       | 12 ++--
 .../python/learn/tests/multioutput_test.py    |  6 +-
 .../python/learn/tests/regression_test.py     |  2 +-
 .../learn/python/learn/tests/saver_test.py    |  7 +--
 .../examples/skflow/iris_save_restore.py      |  4 +-
 tensorflow/examples/skflow/mnist.py           |  8 +--
 tensorflow/examples/skflow/mnist_weights.py   |  8 +--
 .../skflow/out_of_core_data_classification.py |  4 +-
 12 files changed, 45 insertions(+), 88 deletions(-)

diff --git a/tensorflow/contrib/learn/__init__.py b/tensorflow/contrib/learn/__init__.py
index e2a0aab657..84c5a35671 100644
--- a/tensorflow/contrib/learn/__init__.py
+++ b/tensorflow/contrib/learn/__init__.py
@@ -23,7 +23,6 @@ Train and evaluate TensorFlow models.
 @@BaseEstimator
 @@Estimator
 @@ModeKeys
-@@TensorFlowClassifier
 @@DNNClassifier
 @@DNNRegressor
 @@TensorFlowDNNClassifier
@@ -31,11 +30,8 @@ Train and evaluate TensorFlow models.
 @@TensorFlowEstimator
 @@LinearClassifier
 @@LinearRegressor
-@@TensorFlowLinearClassifier
-@@TensorFlowLinearRegressor
 @@TensorFlowRNNClassifier
 @@TensorFlowRNNRegressor
-@@TensorFlowRegressor
 
 ## Graph actions
 
diff --git a/tensorflow/contrib/learn/python/learn/estimators/__init__.py b/tensorflow/contrib/learn/python/learn/estimators/__init__.py
index 87046218ee..384f2ba53b 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/__init__.py
@@ -37,10 +37,6 @@ from tensorflow.contrib.learn.python.learn.estimators.estimator import infer_rea
 from tensorflow.contrib.learn.python.learn.estimators.estimator import ModeKeys
 from tensorflow.contrib.learn.python.learn.estimators.linear import LinearClassifier
 from tensorflow.contrib.learn.python.learn.estimators.linear import LinearRegressor
-from tensorflow.contrib.learn.python.learn.estimators.linear import TensorFlowClassifier
-from tensorflow.contrib.learn.python.learn.estimators.linear import TensorFlowLinearClassifier
-from tensorflow.contrib.learn.python.learn.estimators.linear import TensorFlowLinearRegressor
-from tensorflow.contrib.learn.python.learn.estimators.linear import TensorFlowRegressor
 from tensorflow.contrib.learn.python.learn.estimators.logistic_regressor import LogisticRegressor
 from tensorflow.contrib.learn.python.learn.estimators.random_forest import TensorForestEstimator
 from tensorflow.contrib.learn.python.learn.estimators.random_forest import TensorForestLossMonitor
diff --git a/tensorflow/contrib/learn/python/learn/estimators/linear.py b/tensorflow/contrib/learn/python/learn/estimators/linear.py
index 197a4c2ea2..9e1c20c671 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/linear.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/linear.py
@@ -21,9 +21,7 @@ from __future__ import print_function
 
 from tensorflow.contrib import layers
 from tensorflow.contrib.framework.python.ops import variables as contrib_variables
-from tensorflow.contrib.learn.python.learn.estimators import _sklearn
 from tensorflow.contrib.learn.python.learn.estimators import dnn_linear_combined
-from tensorflow.contrib.learn.python.learn.estimators.base import DeprecatedMixin
 from tensorflow.contrib.linear_optimizer.python import sdca_optimizer
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import logging_ops
@@ -315,18 +313,3 @@ class LinearRegressor(dnn_linear_combined.DNNLinearCombinedRegressor):
   @property
   def bias_(self):
     return self.linear_bias_
-
-
-# TensorFlowLinearRegressor and TensorFlowLinearClassifier are deprecated.
-class TensorFlowLinearRegressor(DeprecatedMixin, LinearRegressor,
-                                _sklearn.RegressorMixin):
-  pass
-
-
-class TensorFlowLinearClassifier(DeprecatedMixin, LinearClassifier,
-                                 _sklearn.ClassifierMixin):
-  pass
-
-
-TensorFlowRegressor = TensorFlowLinearRegressor
-TensorFlowClassifier = TensorFlowLinearClassifier
diff --git a/tensorflow/contrib/learn/python/learn/tests/base_test.py b/tensorflow/contrib/learn/python/learn/tests/base_test.py
index 3e0f9df6d8..421fb4b9f4 100644
--- a/tensorflow/contrib/learn/python/learn/tests/base_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/base_test.py
@@ -41,37 +41,26 @@ class BaseTest(tf.test.TestCase):
     x = np.random.rand(1000)
     y = 2 * x + 3
     feature_columns = learn.infer_real_valued_columns_from_input(x)
-    regressor = learn.TensorFlowLinearRegressor(feature_columns=feature_columns)
-    regressor.fit(x, y)
+    regressor = learn.LinearRegressor(feature_columns=feature_columns)
+    regressor.fit(x, y, max_steps=100)
     score = mean_squared_error(y, regressor.predict(x))
     self.assertLess(score, 1.0, "Failed with score = {0}".format(score))
 
   def testIris(self):
     iris = datasets.load_iris()
-    classifier = learn.TensorFlowLinearClassifier(
+    classifier = learn.LinearClassifier(
         feature_columns=learn.infer_real_valued_columns_from_input(iris.data),
         n_classes=3)
-    classifier.fit(iris.data, [x for x in iris.target])
+    classifier.fit(iris.data, [x for x in iris.target], max_steps=100)
     score = accuracy_score(iris.target, classifier.predict(iris.data))
     self.assertGreater(score, 0.7, "Failed with score = {0}".format(score))
 
-  def testIrisClassWeight(self):
-    iris = datasets.load_iris()
-    # Note, class_weight are not supported anymore :( Use weight_column.
-    with self.assertRaises(ValueError):
-      classifier = learn.TensorFlowLinearClassifier(
-          feature_columns=learn.infer_real_valued_columns_from_input(iris.data),
-          n_classes=3, class_weight=[0.1, 0.8, 0.1])
-      classifier.fit(iris.data, iris.target)
-      score = accuracy_score(iris.target, classifier.predict(iris.data))
-      self.assertLess(score, 0.7, "Failed with score = {0}".format(score))
-
   def testIrisAllVariables(self):
     iris = datasets.load_iris()
-    classifier = learn.TensorFlowLinearClassifier(
+    classifier = learn.LinearClassifier(
         feature_columns=learn.infer_real_valued_columns_from_input(iris.data),
         n_classes=3)
-    classifier.fit(iris.data, [x for x in iris.target])
+    classifier.fit(iris.data, [x for x in iris.target], max_steps=100)
     self.assertEqual(
         classifier.get_variable_names(),
         ["centered_bias_weight",
@@ -89,23 +78,20 @@ class BaseTest(tf.test.TestCase):
   def testIrisSummaries(self):
     iris = datasets.load_iris()
     output_dir = tempfile.mkdtemp() + "learn_tests/"
-    classifier = learn.TensorFlowLinearClassifier(
+    classifier = learn.LinearClassifier(
         feature_columns=learn.infer_real_valued_columns_from_input(iris.data),
         n_classes=3, model_dir=output_dir)
-    classifier.fit(iris.data, iris.target)
+    classifier.fit(iris.data, iris.target, max_steps=100)
     score = accuracy_score(iris.target, classifier.predict(iris.data))
     self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))
     # TODO(ipolosukhin): Check that summaries are correclty written.
 
   def testIrisContinueTraining(self):
     iris = datasets.load_iris()
-    classifier = learn.TensorFlowLinearClassifier(
+    classifier = learn.LinearClassifier(
         feature_columns=learn.infer_real_valued_columns_from_input(iris.data),
-        n_classes=3,
-        learning_rate=0.01,
-        continue_training=True,
-        steps=250)
-    classifier.fit(iris.data, iris.target)
+        n_classes=3)
+    classifier.fit(iris.data, iris.target, steps=100)
     score1 = accuracy_score(iris.target, classifier.predict(iris.data))
     classifier.fit(iris.data, iris.target, steps=500)
     score2 = accuracy_score(iris.target, classifier.predict(iris.data))
@@ -130,10 +116,10 @@ class BaseTest(tf.test.TestCase):
         for y in iris.target:
           yield y
 
-    classifier = learn.TensorFlowLinearClassifier(
+    classifier = learn.LinearClassifier(
         feature_columns=learn.infer_real_valued_columns_from_input(iris.data),
-        n_classes=3, steps=100)
-    classifier.fit(iris_data(), iris_target())
+        n_classes=3)
+    classifier.fit(iris_data(), iris_target(), max_steps=500)
     score1 = accuracy_score(iris.target, classifier.predict(iris.data))
     score2 = accuracy_score(iris.target,
                             classifier.predict(iris_predict_data()))
@@ -147,22 +133,19 @@ class BaseTest(tf.test.TestCase):
     if log_loss:
       random.seed(42)
       iris = datasets.load_iris()
-      classifier = learn.TensorFlowClassifier(
+      classifier = learn.LinearClassifier(
           feature_columns=learn.infer_real_valued_columns_from_input(iris.data),
-          n_classes=3, steps=250)
-      classifier.fit(iris.data, iris.target)
+          n_classes=3)
+      classifier.fit(iris.data, iris.target, max_steps=250)
       score = log_loss(iris.target, classifier.predict_proba(iris.data))
       self.assertLess(score, 0.8, "Failed with score = {0}".format(score))
 
   def testBoston(self):
     random.seed(42)
     boston = datasets.load_boston()
-    regressor = learn.TensorFlowLinearRegressor(
-        feature_columns=learn.infer_real_valued_columns_from_input(boston.data),
-        batch_size=boston.data.shape[0],
-        steps=500,
-        learning_rate=0.001)
-    regressor.fit(boston.data, boston.target)
+    regressor = learn.LinearRegressor(
+        feature_columns=learn.infer_real_valued_columns_from_input(boston.data))
+    regressor.fit(boston.data, boston.target, max_steps=500)
     score = mean_squared_error(boston.target, regressor.predict(boston.data))
     self.assertLess(score, 150, "Failed with score = {0}".format(score))
 
diff --git a/tensorflow/contrib/learn/python/learn/tests/io_test.py b/tensorflow/contrib/learn/python/learn/tests/io_test.py
index 0806728e3d..80732337f9 100644
--- a/tensorflow/contrib/learn/python/learn/tests/io_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/io_test.py
@@ -41,10 +41,10 @@ class IOTest(tf.test.TestCase):
       iris = datasets.load_iris()
       data = pd.DataFrame(iris.data)
       labels = pd.DataFrame(iris.target)
-      classifier = learn.TensorFlowLinearClassifier(
+      classifier = learn.LinearClassifier(
           feature_columns=learn.infer_real_valued_columns_from_input(data),
           n_classes=3)
-      classifier.fit(data, labels)
+      classifier.fit(data, labels, steps=100)
       score = accuracy_score(labels[0], classifier.predict(data))
       self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))
     else:
@@ -57,10 +57,10 @@ class IOTest(tf.test.TestCase):
       iris = datasets.load_iris()
       data = pd.DataFrame(iris.data)
       labels = pd.Series(iris.target)
-      classifier = learn.TensorFlowLinearClassifier(
+      classifier = learn.LinearClassifier(
           feature_columns=learn.infer_real_valued_columns_from_input(data),
           n_classes=3)
-      classifier.fit(data, labels)
+      classifier.fit(data, labels, steps=100)
       score = accuracy_score(labels, classifier.predict(data))
       self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))
 
@@ -110,10 +110,10 @@ class IOTest(tf.test.TestCase):
       data = dd.from_pandas(data, npartitions=2)
       labels = pd.DataFrame(iris.target)
       labels = dd.from_pandas(labels, npartitions=2)
-      classifier = learn.TensorFlowLinearClassifier(
+      classifier = learn.LinearClassifier(
           feature_columns=learn.infer_real_valued_columns_from_input(data),
           n_classes=3)
-      classifier.fit(data, labels)
+      classifier.fit(data, labels, steps=100)
       predictions = data.map_partitions(classifier.predict).compute()
       score = accuracy_score(labels.compute(), predictions)
       self.assertGreater(score, 0.5, "Failed with score = {0}".format(score))
diff --git a/tensorflow/contrib/learn/python/learn/tests/multioutput_test.py b/tensorflow/contrib/learn/python/learn/tests/multioutput_test.py
index d632b3735e..a51f746890 100644
--- a/tensorflow/contrib/learn/python/learn/tests/multioutput_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/multioutput_test.py
@@ -35,10 +35,10 @@ class MultiOutputTest(tf.test.TestCase):
     rng = np.random.RandomState(1)
     x = np.sort(200 * rng.rand(100, 1) - 100, axis=0)
     y = np.array([np.pi * np.sin(x).ravel(), np.pi * np.cos(x).ravel()]).T
-    regressor = learn.TensorFlowLinearRegressor(
+    regressor = learn.LinearRegressor(
         feature_columns=learn.infer_real_valued_columns_from_input(x),
-        learning_rate=0.01, target_dimension=2)
-    regressor.fit(x, y)
+        target_dimension=2)
+    regressor.fit(x, y, steps=100)
     score = mean_squared_error(regressor.predict(x), y)
     self.assertLess(score, 10, "Failed with score = {0}".format(score))
 
diff --git a/tensorflow/contrib/learn/python/learn/tests/regression_test.py b/tensorflow/contrib/learn/python/learn/tests/regression_test.py
index a1ee9a1a31..252dd73e9e 100644
--- a/tensorflow/contrib/learn/python/learn/tests/regression_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/regression_test.py
@@ -37,7 +37,7 @@ class RegressionTest(tf.test.TestCase):
     weights = 10 * rng.randn(n_weights)
     y = np.dot(x, weights)
     y += rng.randn(len(x)) * 0.05 + rng.normal(bias, 0.01)
-    regressor = learn.TensorFlowLinearRegressor(
+    regressor = learn.LinearRegressor(
         feature_columns=learn.infer_real_valued_columns_from_input(x),
         optimizer="SGD")
     regressor.fit(x, y, steps=200)
diff --git a/tensorflow/contrib/learn/python/learn/tests/saver_test.py b/tensorflow/contrib/learn/python/learn/tests/saver_test.py
index 00c80935a7..2c939712bb 100644
--- a/tensorflow/contrib/learn/python/learn/tests/saver_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/saver_test.py
@@ -35,10 +35,9 @@ class SaverTest(tf.test.TestCase):
     iris = datasets.load_iris()
     cont_features = [
         tf.contrib.layers.real_valued_column('', dimension=4)]
-    classifier = learn.TensorFlowLinearClassifier(
-        feature_columns=cont_features, n_classes=3)
-    classifier.fit(iris.data, iris.target)
-    classifier.save(path)
+    classifier = learn.LinearClassifier(
+        feature_columns=cont_features, n_classes=3, model_dir=path)
+    classifier.fit(iris.data, iris.target, steps=200)
     # TODO(ipolosukhin): Remove or restore.
     # new_classifier = learn.TensorFlowEstimator.restore(path)
     # self.assertEqual(type(new_classifier), type(classifier))
diff --git a/tensorflow/examples/skflow/iris_save_restore.py b/tensorflow/examples/skflow/iris_save_restore.py
index 4836183e9d..8955f92b3a 100644
--- a/tensorflow/examples/skflow/iris_save_restore.py
+++ b/tensorflow/examples/skflow/iris_save_restore.py
@@ -29,10 +29,10 @@ iris = datasets.load_iris()
 x_train, x_test, y_train, y_test = cross_validation.train_test_split(
     iris.data, iris.target, test_size=0.2, random_state=42)
 
-classifier = learn.TensorFlowLinearClassifier(
+classifier = learn.LinearClassifier(
     feature_columns=learn.infer_real_valued_columns_from_input(x_train),
     n_classes=3)
-classifier.fit(x_train, y_train)
+classifier.fit(x_train, y_train, steps=200)
 score = metrics.accuracy_score(y_test, classifier.predict(x_test))
 print('Accuracy: {0:f}'.format(score))
 
diff --git a/tensorflow/examples/skflow/mnist.py b/tensorflow/examples/skflow/mnist.py
index 3b11708a27..782d091561 100644
--- a/tensorflow/examples/skflow/mnist.py
+++ b/tensorflow/examples/skflow/mnist.py
@@ -33,10 +33,10 @@ mnist = learn.datasets.load_dataset('mnist')
 ### Linear classifier.
 
 feature_columns = learn.infer_real_valued_columns_from_input(mnist.train.images)
-classifier = learn.TensorFlowLinearClassifier(
-    feature_columns=feature_columns, n_classes=10, batch_size=100, steps=1000,
-    learning_rate=0.01)
-classifier.fit(mnist.train.images, mnist.train.labels)
+classifier = learn.LinearClassifier(
+    feature_columns=feature_columns, n_classes=10)
+classifier.fit(mnist.train.images, mnist.train.labels, batch_size=100,
+               steps=1000)
 score = metrics.accuracy_score(
     mnist.test.labels, classifier.predict(mnist.test.images))
 print('Accuracy: {0:f}'.format(score))
diff --git a/tensorflow/examples/skflow/mnist_weights.py b/tensorflow/examples/skflow/mnist_weights.py
index c4e98f7bf1..aa06f4adb0 100644
--- a/tensorflow/examples/skflow/mnist_weights.py
+++ b/tensorflow/examples/skflow/mnist_weights.py
@@ -32,10 +32,10 @@ mnist = learn.datasets.load_dataset('mnist')
 ### Linear classifier.
 
 feature_columns = learn.infer_real_valued_columns_from_input(mnist.train.images)
-classifier = learn.TensorFlowLinearClassifier(
-    feature_columns=feature_columns, n_classes=10, batch_size=100, steps=1000,
-    learning_rate=0.01)
-classifier.fit(mnist.train.images, mnist.train.labels)
+classifier = learn.LinearClassifier(
+    feature_columns=feature_columns, n_classes=10)
+classifier.fit(mnist.train.images, mnist.train.labels, batch_size=100,
+               steps=1000)
 score = metrics.accuracy_score(
     mnist.test.labels, classifier.predict(mnist.test.images))
 print('Accuracy: {0:f}'.format(score))
diff --git a/tensorflow/examples/skflow/out_of_core_data_classification.py b/tensorflow/examples/skflow/out_of_core_data_classification.py
index 5ed6033cc0..760b4367d3 100644
--- a/tensorflow/examples/skflow/out_of_core_data_classification.py
+++ b/tensorflow/examples/skflow/out_of_core_data_classification.py
@@ -50,12 +50,12 @@ x_train, y_train, x_test, y_test = [
     for data in [x_train, y_train, x_test, y_test]]
 
 # Initialize a TensorFlow linear classifier
-classifier = learn.TensorFlowLinearClassifier(
+classifier = learn.LinearClassifier(
     feature_columns=learn.infer_real_valued_columns_from_input(x_train),
     n_classes=3)
 
 # Fit the model using training set.
-classifier.fit(x_train, y_train)
+classifier.fit(x_train, y_train, steps=200)
 # Make predictions on each partitions of testing data
 predictions = x_test.map_partitions(classifier.predict).compute()
 # Calculate accuracy
-- 
GitLab


From 66d1c0cbb9367d8a59c53ca8fef45b1546e26d9e Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Wed, 24 Aug 2016 08:42:08 -0800
Subject: [PATCH 094/223] Remove unnecessary compiler option from the
 grpc++_unsecure target.

This is no longer necessary because we manually delete the C99 source
files from this target that appear in the upstream gRPC BUILD file.

The gRPC issue for this problem is: https://github.com/grpc/grpc/issues/7851
Change: 131182216
---
 grpc.BUILD | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/grpc.BUILD b/grpc.BUILD
index 7febe623d3..2579536b17 100644
--- a/grpc.BUILD
+++ b/grpc.BUILD
@@ -1763,9 +1763,6 @@ cc_library(
     ":gpr",
     ":grpc_unsecure",
   ],
-  copts = [
-    "-std=gnu99",
-  ],
 )
 
 
-- 
GitLab


From 6e95ddd1eaa585acf555c7bca5cdb6384425e47e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 24 Aug 2016 08:45:49 -0800
Subject: [PATCH 095/223] GCS File system optimization.

Current implementation allocates new buffer every time it reads buffer size of bytes from the file.
Buffer can be re-allocated many times for the same file.

After this change buffer gets re-used and grows only when needed.
Change: 131182639
---
 .../core/platform/cloud/gcs_file_system.cc    | 12 +++--
 .../platform/cloud/gcs_file_system_test.cc    | 45 +++++++++++++++++--
 2 files changed, 50 insertions(+), 7 deletions(-)

diff --git a/tensorflow/core/platform/cloud/gcs_file_system.cc b/tensorflow/core/platform/cloud/gcs_file_system.cc
index 61aba930f5..5abb954c7b 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system.cc
@@ -107,13 +107,18 @@ class GcsRandomAccessFile : public RandomAccessFile {
     }
 
     // Update the buffer content based on the new requested range.
-    auto buffer_size = n + read_ahead_bytes_;
-    buffer_.reset(new char[buffer_size]);
+    const size_t desired_buffer_size = n + read_ahead_bytes_;
+    if (n > buffer_size_ || desired_buffer_size > 2 * buffer_size_) {
+      // Re-allocate only if buffer size increased significantly.
+      buffer_.reset(new char[desired_buffer_size]);
+      buffer_size_ = desired_buffer_size;
+    }
+
     buffer_start_offset_ = offset;
     buffer_content_size_ = 0;
     StringPiece buffer_content;
     TF_RETURN_IF_ERROR(
-        ReadFromGCS(offset, buffer_size, &buffer_content, buffer_.get()));
+        ReadFromGCS(offset, buffer_size_, &buffer_content, buffer_.get()));
     buffer_content_size_ = buffer_content.size();
 
     // Set the results.
@@ -159,6 +164,7 @@ class GcsRandomAccessFile : public RandomAccessFile {
   // The buffer-related members need to be mutable, because they are modified
   // by the const Read() method.
   mutable std::unique_ptr<char[]> buffer_;
+  mutable size_t buffer_size_ = 0;
   // The original file offset of the first byte in the buffer.
   mutable size_t buffer_start_offset_ = 0;
   mutable size_t buffer_content_size_ = 0;
diff --git a/tensorflow/core/platform/cloud/gcs_file_system_test.cc b/tensorflow/core/platform/cloud/gcs_file_system_test.cc
index 8b0604b7ee..70dd5744f8 100644
--- a/tensorflow/core/platform/cloud/gcs_file_system_test.cc
+++ b/tensorflow/core/platform/cloud/gcs_file_system_test.cc
@@ -83,6 +83,43 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_NoReadAhead) {
   EXPECT_EQ("6789", result);
 }
 
+TEST(GcsFileSystemTest, NewRandomAccessFile_NoReadAhead_differentN) {
+  std::vector<HttpRequest*> requests(
+      {new FakeHttpRequest(
+           "Uri: https://bucket.storage.googleapis.com/random_access.txt\n"
+           "Auth Token: fake_token\n"
+           "Range: 0-2\n",
+           "012"),
+       new FakeHttpRequest(
+           "Uri: https://bucket.storage.googleapis.com/random_access.txt\n"
+           "Auth Token: fake_token\n"
+           "Range: 3-12\n",
+           "3456789")});
+  GcsFileSystem fs(std::unique_ptr<AuthProvider>(new FakeAuthProvider),
+                   std::unique_ptr<HttpRequest::Factory>(
+                       new FakeHttpRequestFactory(&requests)),
+                   0 /* read ahead bytes */);
+
+  std::unique_ptr<RandomAccessFile> file;
+  TF_EXPECT_OK(fs.NewRandomAccessFile("gs://bucket/random_access.txt", &file));
+
+  char small_scratch[3];
+  StringPiece result;
+
+  // Read the first chunk.
+  TF_EXPECT_OK(file->Read(0, sizeof(small_scratch), &result, small_scratch));
+  EXPECT_EQ("012", result);
+
+  // Read the second chunk that is larger. Requires allocation of new buffer.
+  char large_scratch[10];
+
+  EXPECT_EQ(errors::Code::OUT_OF_RANGE,
+            file->Read(sizeof(small_scratch), sizeof(large_scratch), &result,
+                       large_scratch)
+                .code());
+  EXPECT_EQ("3456789", result);
+}
+
 TEST(GcsFileSystemTest, NewRandomAccessFile_WithReadAhead) {
   std::vector<HttpRequest*> requests(
       {new FakeHttpRequest(
@@ -93,7 +130,7 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_WithReadAhead) {
        new FakeHttpRequest(
            "Uri: https://bucket.storage.googleapis.com/random_access.txt\n"
            "Auth Token: fake_token\n"
-           "Range: 6-15\n",
+           "Range: 6-14\n",
            "6789abcd"),
        new FakeHttpRequest(
            "Uri: https://bucket.storage.googleapis.com/random_access.txt\n"
@@ -125,18 +162,18 @@ TEST(GcsFileSystemTest, NewRandomAccessFile_WithReadAhead) {
   EXPECT_EQ("4567", result);
 
   // The chunk is only partially cached -- the request will be made to
-  // reload the cache. 5 + 5 = 10 bytes will be requested.
+  // reload the cache. 9 bytes will be requested (same as initial buffer size).
   TF_EXPECT_OK(file->Read(6, 5, &result, scratch));
   EXPECT_EQ("6789a", result);
 
   // The range can only be partially satisfied. An attempt to fill the cache
-  // with 10 + 5 = 15 bytes will be made.
+  // with 10 + 5 = 15 bytes will be made (buffer is resized for this request).
   EXPECT_EQ(errors::Code::OUT_OF_RANGE,
             file->Read(6, 10, &result, scratch).code());
   EXPECT_EQ("6789abcd", result);
 
   // The range cannot be satisfied. An attempt to fill the cache
-  // with 10 + 5 = 15 bytes will be made.
+  // with 15 bytes will be made (same as current buffer size).
   EXPECT_EQ(errors::Code::OUT_OF_RANGE,
             file->Read(15, 10, &result, scratch).code());
   EXPECT_TRUE(result.empty());
-- 
GitLab


From 80dbd5b19a93c4e3bbc40db9aa39d5a2ee3984f1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 24 Aug 2016 09:05:03 -0800
Subject: [PATCH 096/223] Update generated Python Op docs. Change: 131184964

---
 .../g3doc/api_docs/python/contrib.learn.md    | 1018 +----------------
 ...ontrib.learn.TensorFlowLinearClassifier.md |  247 ----
 .../tf.contrib.learn.TensorFlowClassifier.md  |  247 ----
 .../tf.contrib.learn.TensorFlowRegressor.md   |  247 ----
 ...contrib.learn.TensorFlowLinearRegressor.md |  247 ----
 tensorflow/g3doc/api_docs/python/index.md     |    4 -
 6 files changed, 5 insertions(+), 2005 deletions(-)
 delete mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowLinearClassifier.md
 delete mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowClassifier.md
 delete mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.TensorFlowRegressor.md
 delete mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowLinearRegressor.md

diff --git a/tensorflow/g3doc/api_docs/python/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md
index 684badf83a..c6ffd5fa13 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.learn.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md
@@ -528,258 +528,6 @@ The following standard keys are defined:
 * `EVAL`: evaluation mode.
 * `INFER`: inference mode.
 
-- - -
-
-### `class tf.contrib.learn.TensorFlowClassifier` {#TensorFlowClassifier}
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.__init__(*args, **kwargs)` {#TensorFlowClassifier.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.bias_` {#TensorFlowClassifier.bias_}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.config` {#TensorFlowClassifier.config}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.dnn_bias_` {#TensorFlowClassifier.dnn_bias_}
-
-Returns bias of deep neural network part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.dnn_weights_` {#TensorFlowClassifier.dnn_weights_}
-
-Returns weights of deep neural network part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#TensorFlowClassifier.evaluate}
-
-See `Evaluable`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If at least one of `x` or `y` is provided, and at least one of
-      `input_fn` or `feed_fn` is provided.
-      Or if `metrics` is not `None` or `dict`.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowClassifier.export}
-
-Exports inference graph into given dir.
-
-##### Args:
-
-
-*  <b>`export_dir`</b>: A string containing a directory to write the exported graph
-    and checkpoints.
-*  <b>`signature_fn`</b>: Function that returns a default signature and a named
-    signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
-    for features and `Tensor` or `dict` of `Tensor`s for predictions.
-*  <b>`input_fn`</b>: Function that given `Tensor` of `Example` strings, parses it
-    into features that are then passed to the model.
-*  <b>`default_batch_size`</b>: Default batch size of the `Example` placeholder.
-*  <b>`exports_to_keep`</b>: Number of exports to keep.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.fit(x, y, steps=None, batch_size=None, monitors=None, logdir=None)` {#TensorFlowClassifier.fit}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.get_params(deep=True)` {#TensorFlowClassifier.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-
-    If `True`, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.get_variable_names()` {#TensorFlowClassifier.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.get_variable_value(name)` {#TensorFlowClassifier.get_variable_value}
-
-Returns value of the variable given by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.linear_bias_` {#TensorFlowClassifier.linear_bias_}
-
-Returns bias of the linear part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.linear_weights_` {#TensorFlowClassifier.linear_weights_}
-
-Returns weights per feature of the linear part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.model_dir` {#TensorFlowClassifier.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None)` {#TensorFlowClassifier.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: Matrix of shape [n_samples, n_features...]. Can be iterator that
-     returns arrays of features. The training input samples for fitting the
-     model. If set, `input_fn` must be `None`.
-*  <b>`y`</b>: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-     iterator that returns array of targets. The training target values
-     (class labels in classification, real numbers in regression). If set,
-     `input_fn` must be `None`.
-*  <b>`input_fn`</b>: Input function. If set, `x`, `y`, and `batch_size` must be
-    `None`.
-*  <b>`steps`</b>: Number of steps for which to train model. If `None`, train forever.
-*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to first
-    dimension of `x`. Must be `None` if `input_fn` is provided.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-    inside the training loop.
-
-##### Returns:
-
-  `self`, for chaining.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If at least one of `x` and `y` is provided, and `input_fn` is
-      provided.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.predict(x=None, input_fn=None, batch_size=None, outputs=None, axis=1)` {#TensorFlowClassifier.predict}
-
-Predict class or regression for `x`.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.predict_proba(x=None, input_fn=None, batch_size=None, outputs=None)` {#TensorFlowClassifier.predict_proba}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.save(path)` {#TensorFlowClassifier.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.set_params(**params)` {#TensorFlowClassifier.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Args:
-
-
-*  <b>`**params`</b>: Parameters.
-
-##### Returns:
-
-  self
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If params contain invalid names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.weights_` {#TensorFlowClassifier.weights_}
-
-
-
-
-
 - - -
 
 ### `class tf.contrib.learn.DNNClassifier` {#DNNClassifier}
@@ -3070,520 +2818,16 @@ component of a nested object.
 
 - - -
 
-### `class tf.contrib.learn.TensorFlowLinearClassifier` {#TensorFlowLinearClassifier}
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.__init__(*args, **kwargs)` {#TensorFlowLinearClassifier.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.bias_` {#TensorFlowLinearClassifier.bias_}
-
-
-
+### `class tf.contrib.learn.TensorFlowRNNClassifier` {#TensorFlowRNNClassifier}
 
+TensorFlow RNN Classifier model.
 - - -
 
-#### `tf.contrib.learn.TensorFlowLinearClassifier.config` {#TensorFlowLinearClassifier.config}
-
-
-
-
-- - -
+#### `tf.contrib.learn.TensorFlowRNNClassifier.__init__(rnn_size, n_classes, cell_type='gru', num_layers=1, input_op_fn=null_input_op_fn, initial_state=None, bidirectional=False, sequence_length=None, attn_length=None, attn_size=None, attn_vec_size=None, batch_size=32, steps=50, optimizer='Adagrad', learning_rate=0.1, class_weight=None, clip_gradients=5.0, continue_training=False, config=None, verbose=1)` {#TensorFlowRNNClassifier.__init__}
 
-#### `tf.contrib.learn.TensorFlowLinearClassifier.dnn_bias_` {#TensorFlowLinearClassifier.dnn_bias_}
+Initializes a TensorFlowRNNClassifier instance.
 
-Returns bias of deep neural network part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.dnn_weights_` {#TensorFlowLinearClassifier.dnn_weights_}
-
-Returns weights of deep neural network part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#TensorFlowLinearClassifier.evaluate}
-
-See `Evaluable`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If at least one of `x` or `y` is provided, and at least one of
-      `input_fn` or `feed_fn` is provided.
-      Or if `metrics` is not `None` or `dict`.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowLinearClassifier.export}
-
-Exports inference graph into given dir.
-
-##### Args:
-
-
-*  <b>`export_dir`</b>: A string containing a directory to write the exported graph
-    and checkpoints.
-*  <b>`signature_fn`</b>: Function that returns a default signature and a named
-    signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
-    for features and `Tensor` or `dict` of `Tensor`s for predictions.
-*  <b>`input_fn`</b>: Function that given `Tensor` of `Example` strings, parses it
-    into features that are then passed to the model.
-*  <b>`default_batch_size`</b>: Default batch size of the `Example` placeholder.
-*  <b>`exports_to_keep`</b>: Number of exports to keep.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.fit(x, y, steps=None, batch_size=None, monitors=None, logdir=None)` {#TensorFlowLinearClassifier.fit}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.get_params(deep=True)` {#TensorFlowLinearClassifier.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-
-    If `True`, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.get_variable_names()` {#TensorFlowLinearClassifier.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.get_variable_value(name)` {#TensorFlowLinearClassifier.get_variable_value}
-
-Returns value of the variable given by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.linear_bias_` {#TensorFlowLinearClassifier.linear_bias_}
-
-Returns bias of the linear part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.linear_weights_` {#TensorFlowLinearClassifier.linear_weights_}
-
-Returns weights per feature of the linear part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.model_dir` {#TensorFlowLinearClassifier.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None)` {#TensorFlowLinearClassifier.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: Matrix of shape [n_samples, n_features...]. Can be iterator that
-     returns arrays of features. The training input samples for fitting the
-     model. If set, `input_fn` must be `None`.
-*  <b>`y`</b>: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-     iterator that returns array of targets. The training target values
-     (class labels in classification, real numbers in regression). If set,
-     `input_fn` must be `None`.
-*  <b>`input_fn`</b>: Input function. If set, `x`, `y`, and `batch_size` must be
-    `None`.
-*  <b>`steps`</b>: Number of steps for which to train model. If `None`, train forever.
-*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to first
-    dimension of `x`. Must be `None` if `input_fn` is provided.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-    inside the training loop.
-
-##### Returns:
-
-  `self`, for chaining.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If at least one of `x` and `y` is provided, and `input_fn` is
-      provided.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.predict(x=None, input_fn=None, batch_size=None, outputs=None, axis=1)` {#TensorFlowLinearClassifier.predict}
-
-Predict class or regression for `x`.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.predict_proba(x=None, input_fn=None, batch_size=None, outputs=None)` {#TensorFlowLinearClassifier.predict_proba}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.save(path)` {#TensorFlowLinearClassifier.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.set_params(**params)` {#TensorFlowLinearClassifier.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Args:
-
-
-*  <b>`**params`</b>: Parameters.
-
-##### Returns:
-
-  self
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If params contain invalid names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.weights_` {#TensorFlowLinearClassifier.weights_}
-
-
-
-
-
-- - -
-
-### `class tf.contrib.learn.TensorFlowLinearRegressor` {#TensorFlowLinearRegressor}
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.__init__(*args, **kwargs)` {#TensorFlowLinearRegressor.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.bias_` {#TensorFlowLinearRegressor.bias_}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.config` {#TensorFlowLinearRegressor.config}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.dnn_bias_` {#TensorFlowLinearRegressor.dnn_bias_}
-
-Returns bias of deep neural network part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.dnn_weights_` {#TensorFlowLinearRegressor.dnn_weights_}
-
-Returns weights of deep neural network part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#TensorFlowLinearRegressor.evaluate}
-
-See `Evaluable`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If at least one of `x` or `y` is provided, and at least one of
-      `input_fn` or `feed_fn` is provided.
-      Or if `metrics` is not `None` or `dict`.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowLinearRegressor.export}
-
-Exports inference graph into given dir.
-
-##### Args:
-
-
-*  <b>`export_dir`</b>: A string containing a directory to write the exported graph
-    and checkpoints.
-*  <b>`signature_fn`</b>: Function that returns a default signature and a named
-    signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
-    for features and `Tensor` or `dict` of `Tensor`s for predictions.
-*  <b>`input_fn`</b>: Function that given `Tensor` of `Example` strings, parses it
-    into features that are then passed to the model.
-*  <b>`default_batch_size`</b>: Default batch size of the `Example` placeholder.
-*  <b>`exports_to_keep`</b>: Number of exports to keep.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.fit(x, y, steps=None, batch_size=None, monitors=None, logdir=None)` {#TensorFlowLinearRegressor.fit}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.get_params(deep=True)` {#TensorFlowLinearRegressor.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-
-    If `True`, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.get_variable_names()` {#TensorFlowLinearRegressor.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.get_variable_value(name)` {#TensorFlowLinearRegressor.get_variable_value}
-
-Returns value of the variable given by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.linear_bias_` {#TensorFlowLinearRegressor.linear_bias_}
-
-Returns bias of the linear part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.linear_weights_` {#TensorFlowLinearRegressor.linear_weights_}
-
-Returns weights per feature of the linear part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.model_dir` {#TensorFlowLinearRegressor.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None)` {#TensorFlowLinearRegressor.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: Matrix of shape [n_samples, n_features...]. Can be iterator that
-     returns arrays of features. The training input samples for fitting the
-     model. If set, `input_fn` must be `None`.
-*  <b>`y`</b>: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-     iterator that returns array of targets. The training target values
-     (class labels in classification, real numbers in regression). If set,
-     `input_fn` must be `None`.
-*  <b>`input_fn`</b>: Input function. If set, `x`, `y`, and `batch_size` must be
-    `None`.
-*  <b>`steps`</b>: Number of steps for which to train model. If `None`, train forever.
-*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to first
-    dimension of `x`. Must be `None` if `input_fn` is provided.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-    inside the training loop.
-
-##### Returns:
-
-  `self`, for chaining.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If at least one of `x` and `y` is provided, and `input_fn` is
-      provided.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.predict(x=None, input_fn=None, batch_size=None, outputs=None, axis=1)` {#TensorFlowLinearRegressor.predict}
-
-Predict class or regression for `x`.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.predict_proba(x=None, input_fn=None, batch_size=None, outputs=None)` {#TensorFlowLinearRegressor.predict_proba}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.save(path)` {#TensorFlowLinearRegressor.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.set_params(**params)` {#TensorFlowLinearRegressor.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Args:
-
-
-*  <b>`**params`</b>: Parameters.
-
-##### Returns:
-
-  self
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If params contain invalid names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.weights_` {#TensorFlowLinearRegressor.weights_}
-
-
-
-
-
-- - -
-
-### `class tf.contrib.learn.TensorFlowRNNClassifier` {#TensorFlowRNNClassifier}
-
-TensorFlow RNN Classifier model.
-- - -
-
-#### `tf.contrib.learn.TensorFlowRNNClassifier.__init__(rnn_size, n_classes, cell_type='gru', num_layers=1, input_op_fn=null_input_op_fn, initial_state=None, bidirectional=False, sequence_length=None, attn_length=None, attn_size=None, attn_vec_size=None, batch_size=32, steps=50, optimizer='Adagrad', learning_rate=0.1, class_weight=None, clip_gradients=5.0, continue_training=False, config=None, verbose=1)` {#TensorFlowRNNClassifier.__init__}
-
-Initializes a TensorFlowRNNClassifier instance.
-
-##### Args:
+##### Args:
 
 
 *  <b>`rnn_size`</b>: The size for rnn cell, e.g. size of your word embeddings.
@@ -4307,258 +3551,6 @@ Returns weights of the rnn layer.
 
 
 
-- - -
-
-### `class tf.contrib.learn.TensorFlowRegressor` {#TensorFlowRegressor}
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.__init__(*args, **kwargs)` {#TensorFlowRegressor.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.bias_` {#TensorFlowRegressor.bias_}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.config` {#TensorFlowRegressor.config}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.dnn_bias_` {#TensorFlowRegressor.dnn_bias_}
-
-Returns bias of deep neural network part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.dnn_weights_` {#TensorFlowRegressor.dnn_weights_}
-
-Returns weights of deep neural network part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#TensorFlowRegressor.evaluate}
-
-See `Evaluable`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If at least one of `x` or `y` is provided, and at least one of
-      `input_fn` or `feed_fn` is provided.
-      Or if `metrics` is not `None` or `dict`.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowRegressor.export}
-
-Exports inference graph into given dir.
-
-##### Args:
-
-
-*  <b>`export_dir`</b>: A string containing a directory to write the exported graph
-    and checkpoints.
-*  <b>`signature_fn`</b>: Function that returns a default signature and a named
-    signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
-    for features and `Tensor` or `dict` of `Tensor`s for predictions.
-*  <b>`input_fn`</b>: Function that given `Tensor` of `Example` strings, parses it
-    into features that are then passed to the model.
-*  <b>`default_batch_size`</b>: Default batch size of the `Example` placeholder.
-*  <b>`exports_to_keep`</b>: Number of exports to keep.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.fit(x, y, steps=None, batch_size=None, monitors=None, logdir=None)` {#TensorFlowRegressor.fit}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.get_params(deep=True)` {#TensorFlowRegressor.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-
-    If `True`, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.get_variable_names()` {#TensorFlowRegressor.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.get_variable_value(name)` {#TensorFlowRegressor.get_variable_value}
-
-Returns value of the variable given by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.linear_bias_` {#TensorFlowRegressor.linear_bias_}
-
-Returns bias of the linear part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.linear_weights_` {#TensorFlowRegressor.linear_weights_}
-
-Returns weights per feature of the linear part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.model_dir` {#TensorFlowRegressor.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None)` {#TensorFlowRegressor.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: Matrix of shape [n_samples, n_features...]. Can be iterator that
-     returns arrays of features. The training input samples for fitting the
-     model. If set, `input_fn` must be `None`.
-*  <b>`y`</b>: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-     iterator that returns array of targets. The training target values
-     (class labels in classification, real numbers in regression). If set,
-     `input_fn` must be `None`.
-*  <b>`input_fn`</b>: Input function. If set, `x`, `y`, and `batch_size` must be
-    `None`.
-*  <b>`steps`</b>: Number of steps for which to train model. If `None`, train forever.
-*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to first
-    dimension of `x`. Must be `None` if `input_fn` is provided.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-    inside the training loop.
-
-##### Returns:
-
-  `self`, for chaining.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If at least one of `x` and `y` is provided, and `input_fn` is
-      provided.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.predict(x=None, input_fn=None, batch_size=None, outputs=None, axis=1)` {#TensorFlowRegressor.predict}
-
-Predict class or regression for `x`.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.predict_proba(x=None, input_fn=None, batch_size=None, outputs=None)` {#TensorFlowRegressor.predict_proba}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.save(path)` {#TensorFlowRegressor.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.set_params(**params)` {#TensorFlowRegressor.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Args:
-
-
-*  <b>`**params`</b>: Parameters.
-
-##### Returns:
-
-  self
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If params contain invalid names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.weights_` {#TensorFlowRegressor.weights_}
-
-
-
-
-
 
 ## Graph actions
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowLinearClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowLinearClassifier.md
deleted file mode 100644
index 775ccf8fd5..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.TensorFlowLinearClassifier.md
+++ /dev/null
@@ -1,247 +0,0 @@
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.__init__(*args, **kwargs)` {#TensorFlowLinearClassifier.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.bias_` {#TensorFlowLinearClassifier.bias_}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.config` {#TensorFlowLinearClassifier.config}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.dnn_bias_` {#TensorFlowLinearClassifier.dnn_bias_}
-
-Returns bias of deep neural network part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.dnn_weights_` {#TensorFlowLinearClassifier.dnn_weights_}
-
-Returns weights of deep neural network part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#TensorFlowLinearClassifier.evaluate}
-
-See `Evaluable`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If at least one of `x` or `y` is provided, and at least one of
-      `input_fn` or `feed_fn` is provided.
-      Or if `metrics` is not `None` or `dict`.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowLinearClassifier.export}
-
-Exports inference graph into given dir.
-
-##### Args:
-
-
-*  <b>`export_dir`</b>: A string containing a directory to write the exported graph
-    and checkpoints.
-*  <b>`signature_fn`</b>: Function that returns a default signature and a named
-    signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
-    for features and `Tensor` or `dict` of `Tensor`s for predictions.
-*  <b>`input_fn`</b>: Function that given `Tensor` of `Example` strings, parses it
-    into features that are then passed to the model.
-*  <b>`default_batch_size`</b>: Default batch size of the `Example` placeholder.
-*  <b>`exports_to_keep`</b>: Number of exports to keep.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.fit(x, y, steps=None, batch_size=None, monitors=None, logdir=None)` {#TensorFlowLinearClassifier.fit}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.get_params(deep=True)` {#TensorFlowLinearClassifier.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-
-    If `True`, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.get_variable_names()` {#TensorFlowLinearClassifier.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.get_variable_value(name)` {#TensorFlowLinearClassifier.get_variable_value}
-
-Returns value of the variable given by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.linear_bias_` {#TensorFlowLinearClassifier.linear_bias_}
-
-Returns bias of the linear part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.linear_weights_` {#TensorFlowLinearClassifier.linear_weights_}
-
-Returns weights per feature of the linear part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.model_dir` {#TensorFlowLinearClassifier.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None)` {#TensorFlowLinearClassifier.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: Matrix of shape [n_samples, n_features...]. Can be iterator that
-     returns arrays of features. The training input samples for fitting the
-     model. If set, `input_fn` must be `None`.
-*  <b>`y`</b>: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-     iterator that returns array of targets. The training target values
-     (class labels in classification, real numbers in regression). If set,
-     `input_fn` must be `None`.
-*  <b>`input_fn`</b>: Input function. If set, `x`, `y`, and `batch_size` must be
-    `None`.
-*  <b>`steps`</b>: Number of steps for which to train model. If `None`, train forever.
-*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to first
-    dimension of `x`. Must be `None` if `input_fn` is provided.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-    inside the training loop.
-
-##### Returns:
-
-  `self`, for chaining.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If at least one of `x` and `y` is provided, and `input_fn` is
-      provided.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.predict(x=None, input_fn=None, batch_size=None, outputs=None, axis=1)` {#TensorFlowLinearClassifier.predict}
-
-Predict class or regression for `x`.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.predict_proba(x=None, input_fn=None, batch_size=None, outputs=None)` {#TensorFlowLinearClassifier.predict_proba}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.save(path)` {#TensorFlowLinearClassifier.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.set_params(**params)` {#TensorFlowLinearClassifier.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Args:
-
-
-*  <b>`**params`</b>: Parameters.
-
-##### Returns:
-
-  self
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If params contain invalid names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearClassifier.weights_` {#TensorFlowLinearClassifier.weights_}
-
-
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowClassifier.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowClassifier.md
deleted file mode 100644
index 8d70087e97..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.learn.TensorFlowClassifier.md
+++ /dev/null
@@ -1,247 +0,0 @@
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.__init__(*args, **kwargs)` {#TensorFlowClassifier.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.bias_` {#TensorFlowClassifier.bias_}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.config` {#TensorFlowClassifier.config}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.dnn_bias_` {#TensorFlowClassifier.dnn_bias_}
-
-Returns bias of deep neural network part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.dnn_weights_` {#TensorFlowClassifier.dnn_weights_}
-
-Returns weights of deep neural network part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#TensorFlowClassifier.evaluate}
-
-See `Evaluable`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If at least one of `x` or `y` is provided, and at least one of
-      `input_fn` or `feed_fn` is provided.
-      Or if `metrics` is not `None` or `dict`.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowClassifier.export}
-
-Exports inference graph into given dir.
-
-##### Args:
-
-
-*  <b>`export_dir`</b>: A string containing a directory to write the exported graph
-    and checkpoints.
-*  <b>`signature_fn`</b>: Function that returns a default signature and a named
-    signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
-    for features and `Tensor` or `dict` of `Tensor`s for predictions.
-*  <b>`input_fn`</b>: Function that given `Tensor` of `Example` strings, parses it
-    into features that are then passed to the model.
-*  <b>`default_batch_size`</b>: Default batch size of the `Example` placeholder.
-*  <b>`exports_to_keep`</b>: Number of exports to keep.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.fit(x, y, steps=None, batch_size=None, monitors=None, logdir=None)` {#TensorFlowClassifier.fit}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.get_params(deep=True)` {#TensorFlowClassifier.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-
-    If `True`, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.get_variable_names()` {#TensorFlowClassifier.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.get_variable_value(name)` {#TensorFlowClassifier.get_variable_value}
-
-Returns value of the variable given by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.linear_bias_` {#TensorFlowClassifier.linear_bias_}
-
-Returns bias of the linear part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.linear_weights_` {#TensorFlowClassifier.linear_weights_}
-
-Returns weights per feature of the linear part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.model_dir` {#TensorFlowClassifier.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None)` {#TensorFlowClassifier.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: Matrix of shape [n_samples, n_features...]. Can be iterator that
-     returns arrays of features. The training input samples for fitting the
-     model. If set, `input_fn` must be `None`.
-*  <b>`y`</b>: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-     iterator that returns array of targets. The training target values
-     (class labels in classification, real numbers in regression). If set,
-     `input_fn` must be `None`.
-*  <b>`input_fn`</b>: Input function. If set, `x`, `y`, and `batch_size` must be
-    `None`.
-*  <b>`steps`</b>: Number of steps for which to train model. If `None`, train forever.
-*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to first
-    dimension of `x`. Must be `None` if `input_fn` is provided.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-    inside the training loop.
-
-##### Returns:
-
-  `self`, for chaining.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If at least one of `x` and `y` is provided, and `input_fn` is
-      provided.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.predict(x=None, input_fn=None, batch_size=None, outputs=None, axis=1)` {#TensorFlowClassifier.predict}
-
-Predict class or regression for `x`.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.predict_proba(x=None, input_fn=None, batch_size=None, outputs=None)` {#TensorFlowClassifier.predict_proba}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.save(path)` {#TensorFlowClassifier.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.set_params(**params)` {#TensorFlowClassifier.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Args:
-
-
-*  <b>`**params`</b>: Parameters.
-
-##### Returns:
-
-  self
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If params contain invalid names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowClassifier.weights_` {#TensorFlowClassifier.weights_}
-
-
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.TensorFlowRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.TensorFlowRegressor.md
deleted file mode 100644
index 2471d73521..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.learn.TensorFlowRegressor.md
+++ /dev/null
@@ -1,247 +0,0 @@
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.__init__(*args, **kwargs)` {#TensorFlowRegressor.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.bias_` {#TensorFlowRegressor.bias_}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.config` {#TensorFlowRegressor.config}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.dnn_bias_` {#TensorFlowRegressor.dnn_bias_}
-
-Returns bias of deep neural network part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.dnn_weights_` {#TensorFlowRegressor.dnn_weights_}
-
-Returns weights of deep neural network part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#TensorFlowRegressor.evaluate}
-
-See `Evaluable`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If at least one of `x` or `y` is provided, and at least one of
-      `input_fn` or `feed_fn` is provided.
-      Or if `metrics` is not `None` or `dict`.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowRegressor.export}
-
-Exports inference graph into given dir.
-
-##### Args:
-
-
-*  <b>`export_dir`</b>: A string containing a directory to write the exported graph
-    and checkpoints.
-*  <b>`signature_fn`</b>: Function that returns a default signature and a named
-    signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
-    for features and `Tensor` or `dict` of `Tensor`s for predictions.
-*  <b>`input_fn`</b>: Function that given `Tensor` of `Example` strings, parses it
-    into features that are then passed to the model.
-*  <b>`default_batch_size`</b>: Default batch size of the `Example` placeholder.
-*  <b>`exports_to_keep`</b>: Number of exports to keep.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.fit(x, y, steps=None, batch_size=None, monitors=None, logdir=None)` {#TensorFlowRegressor.fit}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.get_params(deep=True)` {#TensorFlowRegressor.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-
-    If `True`, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.get_variable_names()` {#TensorFlowRegressor.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.get_variable_value(name)` {#TensorFlowRegressor.get_variable_value}
-
-Returns value of the variable given by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.linear_bias_` {#TensorFlowRegressor.linear_bias_}
-
-Returns bias of the linear part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.linear_weights_` {#TensorFlowRegressor.linear_weights_}
-
-Returns weights per feature of the linear part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.model_dir` {#TensorFlowRegressor.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None)` {#TensorFlowRegressor.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: Matrix of shape [n_samples, n_features...]. Can be iterator that
-     returns arrays of features. The training input samples for fitting the
-     model. If set, `input_fn` must be `None`.
-*  <b>`y`</b>: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-     iterator that returns array of targets. The training target values
-     (class labels in classification, real numbers in regression). If set,
-     `input_fn` must be `None`.
-*  <b>`input_fn`</b>: Input function. If set, `x`, `y`, and `batch_size` must be
-    `None`.
-*  <b>`steps`</b>: Number of steps for which to train model. If `None`, train forever.
-*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to first
-    dimension of `x`. Must be `None` if `input_fn` is provided.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-    inside the training loop.
-
-##### Returns:
-
-  `self`, for chaining.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If at least one of `x` and `y` is provided, and `input_fn` is
-      provided.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.predict(x=None, input_fn=None, batch_size=None, outputs=None, axis=1)` {#TensorFlowRegressor.predict}
-
-Predict class or regression for `x`.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.predict_proba(x=None, input_fn=None, batch_size=None, outputs=None)` {#TensorFlowRegressor.predict_proba}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.save(path)` {#TensorFlowRegressor.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.set_params(**params)` {#TensorFlowRegressor.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Args:
-
-
-*  <b>`**params`</b>: Parameters.
-
-##### Returns:
-
-  self
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If params contain invalid names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowRegressor.weights_` {#TensorFlowRegressor.weights_}
-
-
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowLinearRegressor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowLinearRegressor.md
deleted file mode 100644
index 338f1126c9..0000000000
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.learn.TensorFlowLinearRegressor.md
+++ /dev/null
@@ -1,247 +0,0 @@
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.__init__(*args, **kwargs)` {#TensorFlowLinearRegressor.__init__}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.bias_` {#TensorFlowLinearRegressor.bias_}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.config` {#TensorFlowLinearRegressor.config}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.dnn_bias_` {#TensorFlowLinearRegressor.dnn_bias_}
-
-Returns bias of deep neural network part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.dnn_weights_` {#TensorFlowLinearRegressor.dnn_weights_}
-
-Returns weights of deep neural network part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)` {#TensorFlowLinearRegressor.evaluate}
-
-See `Evaluable`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If at least one of `x` or `y` is provided, and at least one of
-      `input_fn` or `feed_fn` is provided.
-      Or if `metrics` is not `None` or `dict`.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.export(export_dir, signature_fn=None, input_fn=None, default_batch_size=1, exports_to_keep=None)` {#TensorFlowLinearRegressor.export}
-
-Exports inference graph into given dir.
-
-##### Args:
-
-
-*  <b>`export_dir`</b>: A string containing a directory to write the exported graph
-    and checkpoints.
-*  <b>`signature_fn`</b>: Function that returns a default signature and a named
-    signature map, given `Tensor` of `Example` strings, `dict` of `Tensor`s
-    for features and `Tensor` or `dict` of `Tensor`s for predictions.
-*  <b>`input_fn`</b>: Function that given `Tensor` of `Example` strings, parses it
-    into features that are then passed to the model.
-*  <b>`default_batch_size`</b>: Default batch size of the `Example` placeholder.
-*  <b>`exports_to_keep`</b>: Number of exports to keep.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.fit(x, y, steps=None, batch_size=None, monitors=None, logdir=None)` {#TensorFlowLinearRegressor.fit}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.get_params(deep=True)` {#TensorFlowLinearRegressor.get_params}
-
-Get parameters for this estimator.
-
-##### Args:
-
-
-*  <b>`deep`</b>: boolean, optional
-
-    If `True`, will return the parameters for this estimator and
-    contained subobjects that are estimators.
-
-##### Returns:
-
-  params : mapping of string to any
-  Parameter names mapped to their values.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.get_variable_names()` {#TensorFlowLinearRegressor.get_variable_names}
-
-Returns list of all variable names in this model.
-
-##### Returns:
-
-  List of names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.get_variable_value(name)` {#TensorFlowLinearRegressor.get_variable_value}
-
-Returns value of the variable given by name.
-
-##### Args:
-
-
-*  <b>`name`</b>: string, name of the tensor.
-
-##### Returns:
-
-  Numpy array - value of the tensor.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.linear_bias_` {#TensorFlowLinearRegressor.linear_bias_}
-
-Returns bias of the linear part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.linear_weights_` {#TensorFlowLinearRegressor.linear_weights_}
-
-Returns weights per feature of the linear part.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.model_dir` {#TensorFlowLinearRegressor.model_dir}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.partial_fit(x=None, y=None, input_fn=None, steps=1, batch_size=None, monitors=None)` {#TensorFlowLinearRegressor.partial_fit}
-
-Incremental fit on a batch of samples.
-
-This method is expected to be called several times consecutively
-on different or the same chunks of the dataset. This either can
-implement iterative training or out-of-core/online training.
-
-This is especially useful when the whole dataset is too big to
-fit in memory at the same time. Or when model is taking long time
-to converge, and you want to split up training into subparts.
-
-##### Args:
-
-
-*  <b>`x`</b>: Matrix of shape [n_samples, n_features...]. Can be iterator that
-     returns arrays of features. The training input samples for fitting the
-     model. If set, `input_fn` must be `None`.
-*  <b>`y`</b>: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
-     iterator that returns array of targets. The training target values
-     (class labels in classification, real numbers in regression). If set,
-     `input_fn` must be `None`.
-*  <b>`input_fn`</b>: Input function. If set, `x`, `y`, and `batch_size` must be
-    `None`.
-*  <b>`steps`</b>: Number of steps for which to train model. If `None`, train forever.
-*  <b>`batch_size`</b>: minibatch size to use on the input, defaults to first
-    dimension of `x`. Must be `None` if `input_fn` is provided.
-*  <b>`monitors`</b>: List of `BaseMonitor` subclass instances. Used for callbacks
-    inside the training loop.
-
-##### Returns:
-
-  `self`, for chaining.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If at least one of `x` and `y` is provided, and `input_fn` is
-      provided.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.predict(x=None, input_fn=None, batch_size=None, outputs=None, axis=1)` {#TensorFlowLinearRegressor.predict}
-
-Predict class or regression for `x`.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.predict_proba(x=None, input_fn=None, batch_size=None, outputs=None)` {#TensorFlowLinearRegressor.predict_proba}
-
-
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.save(path)` {#TensorFlowLinearRegressor.save}
-
-Saves checkpoints and graph to given path.
-
-##### Args:
-
-
-*  <b>`path`</b>: Folder to save model to.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.set_params(**params)` {#TensorFlowLinearRegressor.set_params}
-
-Set the parameters of this estimator.
-
-The method works on simple estimators as well as on nested objects
-(such as pipelines). The former have parameters of the form
-``<component>__<parameter>`` so that it's possible to update each
-component of a nested object.
-
-##### Args:
-
-
-*  <b>`**params`</b>: Parameters.
-
-##### Returns:
-
-  self
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: If params contain invalid names.
-
-
-- - -
-
-#### `tf.contrib.learn.TensorFlowLinearRegressor.weights_` {#TensorFlowLinearRegressor.weights_}
-
-
-
-
diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md
index f22463d6b9..74ae9b0557 100644
--- a/tensorflow/g3doc/api_docs/python/index.md
+++ b/tensorflow/g3doc/api_docs/python/index.md
@@ -815,13 +815,9 @@
   * [`run_feeds`](../../api_docs/python/contrib.learn.md#run_feeds)
   * [`run_n`](../../api_docs/python/contrib.learn.md#run_n)
   * [`RunConfig`](../../api_docs/python/contrib.learn.md#RunConfig)
-  * [`TensorFlowClassifier`](../../api_docs/python/contrib.learn.md#TensorFlowClassifier)
   * [`TensorFlowDNNClassifier`](../../api_docs/python/contrib.learn.md#TensorFlowDNNClassifier)
   * [`TensorFlowDNNRegressor`](../../api_docs/python/contrib.learn.md#TensorFlowDNNRegressor)
   * [`TensorFlowEstimator`](../../api_docs/python/contrib.learn.md#TensorFlowEstimator)
-  * [`TensorFlowLinearClassifier`](../../api_docs/python/contrib.learn.md#TensorFlowLinearClassifier)
-  * [`TensorFlowLinearRegressor`](../../api_docs/python/contrib.learn.md#TensorFlowLinearRegressor)
-  * [`TensorFlowRegressor`](../../api_docs/python/contrib.learn.md#TensorFlowRegressor)
   * [`TensorFlowRNNClassifier`](../../api_docs/python/contrib.learn.md#TensorFlowRNNClassifier)
   * [`TensorFlowRNNRegressor`](../../api_docs/python/contrib.learn.md#TensorFlowRNNRegressor)
   * [`train`](../../api_docs/python/contrib.learn.md#train)
-- 
GitLab


From c7be5d889aeb26ed8987918e9956371fb4722f9a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 24 Aug 2016 09:53:15 -0800
Subject: [PATCH 097/223] Add utility functions related to cpu info to CpuUtils
 Change: 131191358

---
 tensorflow/core/BUILD                         |  2 +
 .../hexagon/profile_utils/cpu_utils.cc        | 86 +++++++++++++++++++
 .../hexagon/profile_utils/cpu_utils.h         | 23 +++++
 .../hexagon/profile_utils/cpu_utils_test.cc   | 25 ++++++
 4 files changed, 136 insertions(+)
 create mode 100644 tensorflow/core/platform/hexagon/profile_utils/cpu_utils.cc

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 91d414edb5..b0e89c9fb9 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -859,6 +859,8 @@ cc_library(
             "lib/**/*.cc",
             "platform/*.h",
             "platform/*.cc",
+            "platform/hexagon/**/*.h",
+            "platform/hexagon/**/*.cc",
         ] + tf_additional_lib_srcs(),
         exclude = [
             "**/*test*",
diff --git a/tensorflow/core/platform/hexagon/profile_utils/cpu_utils.cc b/tensorflow/core/platform/hexagon/profile_utils/cpu_utils.cc
new file mode 100644
index 0000000000..b3224edee1
--- /dev/null
+++ b/tensorflow/core/platform/hexagon/profile_utils/cpu_utils.cc
@@ -0,0 +1,86 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/core/platform/hexagon/profile_utils/cpu_utils.h"
+
+#include "tensorflow/core/platform/logging.h"
+
+namespace tensorflow {
+namespace profile_utils {
+
+namespace {
+
+const class StaticVariableInitializer {
+ public:
+  StaticVariableInitializer() {
+    CpuUtils::GetCpuFrequency();
+    CpuUtils::GetClockPerMicroSec();
+    CpuUtils::GetMicroSecPerClock();
+  }
+} STATIC_VARIABLE_INITIALIZER;
+
+}  // anonymous namespace for initializer
+
+/* static */ constexpr int64 CpuUtils::INVALID_FREQUENCY;
+
+/* static */ int64 CpuUtils::GetCpuFrequency() {
+  static const int64 cpu_frequency = GetCpuFrequencyImpl();
+  return cpu_frequency;
+}
+
+/* static */ int CpuUtils::GetClockPerMicroSec() {
+  static const int clock_per_micro_sec =
+      static_cast<int>(GetCpuFrequency() / (1000LL * 1000LL));
+  return clock_per_micro_sec;
+}
+
+/* static */ double CpuUtils::GetMicroSecPerClock() {
+  static const double micro_sec_per_clock =
+      (1000.0 * 1000.0) / static_cast<double>(GetCpuFrequency());
+  return micro_sec_per_clock;
+}
+
+/* static */ int64 CpuUtils::GetCpuFrequencyImpl() {
+#if defined(__linux__)
+  double bogomips;
+  FILE* fp = popen("grep '^bogomips' /proc/cpuinfo | head -1", "r");
+  const int retval_of_bogomips = fscanf(fp, "bogomips : %lf", &bogomips);
+  pclose(fp);
+  const double freq_ghz = bogomips / 1000.0 / 2.0;
+  if (retval_of_bogomips != 1 || freq_ghz < 0.01) {
+    LOG(WARNING) << "Failed to get CPU frequency: " << freq_ghz << " Hz";
+    return INVALID_FREQUENCY;
+  }
+  return static_cast<int64>(freq_ghz * 1000.0 * 1000.0 * 1000.0);
+#elif defined(__APPLE__)
+  int64 freq_hz;
+  FILE* fp =
+      popen("sysctl hw | grep hw.cpufrequency_max: | cut -d' ' -f 2", "r");
+  fscanf(fp, "%lld", &freq_hz);
+  pclose(fp);
+  if (freq_hz < 1e6) {
+    LOG(WARNING) << "Failed to get CPU frequency: " << freq_ghz << " Hz";
+    return INVALID_FREQUENCY;
+  }
+  return freq_hz;
+#else
+  // TODO(satok): Support other OS if needed
+  // Return INVALID_FREQUENCY on unsupported OS
+  return INVALID_FREQUENCY;
+#endif
+}
+
+}  // namespace profile_utils
+}  // namespace tensorflow
diff --git a/tensorflow/core/platform/hexagon/profile_utils/cpu_utils.h b/tensorflow/core/platform/hexagon/profile_utils/cpu_utils.h
index d1b174e829..8e08f17fc6 100644
--- a/tensorflow/core/platform/hexagon/profile_utils/cpu_utils.h
+++ b/tensorflow/core/platform/hexagon/profile_utils/cpu_utils.h
@@ -30,6 +30,9 @@ namespace profile_utils {
 
 class CpuUtils {
  public:
+  // Constant for invalid frequency.
+  // This value is returned when the furequency is not obtained somehow.
+  static constexpr int64 INVALID_FREQUENCY = -1;
   static constexpr uint64 DUMMY_CYCLE_CLOCK = 1;
 
   // Return cpu count obtained by rdtsc. This function is designed to
@@ -78,7 +81,27 @@ class CpuUtils {
 #endif
   }
 
+  // Return cpu frequency. As this method caches the cpu frequency internally,
+  // there is no overhead except function call to call this method.
+  static int64 GetCpuFrequency();
+
+  // Return cached cpu count per each micro second.
+  // As this method caches the cpu frequency internally,
+  // there is no overhead except function call to call this method.
+  static int GetClockPerMicroSec();
+
+  // Return micro secound per each clock
+  // As this method caches the cpu frequency internally,
+  // there is no overhead except function call to call this method.
+  static double GetMicroSecPerClock();
+
  private:
+  // Return cpu frequency.
+  // CAVEAT: as this method calls system call and parse the mssage,
+  // this call may be slow. This is why this class caches the value by
+  // StaticVariableInitializer.
+  static int64 GetCpuFrequencyImpl();
+
   TF_DISALLOW_COPY_AND_ASSIGN(CpuUtils);
 };
 
diff --git a/tensorflow/core/platform/hexagon/profile_utils/cpu_utils_test.cc b/tensorflow/core/platform/hexagon/profile_utils/cpu_utils_test.cc
index b0a2ade37f..0db1903695 100644
--- a/tensorflow/core/platform/hexagon/profile_utils/cpu_utils_test.cc
+++ b/tensorflow/core/platform/hexagon/profile_utils/cpu_utils_test.cc
@@ -42,5 +42,30 @@ TEST(CpuUtils, CheckGetCurrentCycleClock) {
   }
 }
 
+TEST(CpuUtils, CheckCpuFrequency) {
+  const int64 cpu_frequency = CpuUtils::GetCpuFrequency();
+  CHECK_GT(cpu_frequency, 0);
+  CHECK_NE(cpu_frequency, CpuUtils::INVALID_FREQUENCY);
+  if (DBG) {
+    LOG(INFO) << "Cpu frequency = " << cpu_frequency;
+  }
+}
+
+TEST(CpuUtils, CheckClockPerMicroSec) {
+  const int clock_per_micro_sec = CpuUtils::GetClockPerMicroSec();
+  CHECK_GT(clock_per_micro_sec, 0);
+  if (DBG) {
+    LOG(INFO) << "Clock per micro sec = " << clock_per_micro_sec;
+  }
+}
+
+TEST(CpuUtils, CheckMicroSecPerClock) {
+  const double micro_sec_per_clock = CpuUtils::GetMicroSecPerClock();
+  CHECK_GT(micro_sec_per_clock, 0.0);
+  if (DBG) {
+    LOG(INFO) << "Micro sec per clock = " << micro_sec_per_clock;
+  }
+}
+
 }  // namespace profile_utils
 }  // namespace tensorflow
-- 
GitLab


From cd58382cb1d8f8e34410a663d5b8630fb4661fd9 Mon Sep 17 00:00:00 2001
From: Justine Tunney <jart@google.com>
Date: Wed, 24 Aug 2016 10:49:36 -0800
Subject: [PATCH 098/223] Make image dashboard sizing slightly nicer

This change causes the image size to start out at 300x300. Then once we
fetch all the image metadata for a row, the heights will be scaled down
based on the aspect ratio and the row height will change accordingly.

When the images are reloaded sixty seconds later, the page contents will
not reflow. However the images will disappear while they're loading.
That will be fixed next.
Change: 131198628
---
 .../tf-image-dashboard/tf-image-loader.html        | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/tensorflow/tensorboard/components/tf-image-dashboard/tf-image-loader.html b/tensorflow/tensorboard/components/tf-image-dashboard/tf-image-loader.html
index d0adf35901..f0635b6de2 100644
--- a/tensorflow/tensorboard/components/tf-image-dashboard/tf-image-loader.html
+++ b/tensorflow/tensorboard/components/tf-image-dashboard/tf-image-loader.html
@@ -13,16 +13,15 @@ future for loading older images.
     display: block;
   }
   img {
-    width: 100%;
-    height: 100%;
     image-rendering: pixelated;
     border: 1px solid #555;
   }
   </style>
   <template>
-    <template is="dom-if" if="[[imageUrl]]">
-      <img src="[[imageUrl]]" on-error="reload">
-    </template>
+    <img src="[[imageUrl]]"
+         width="300"
+         height="[[height]]"
+         on-error="reload">
   </template>
   <script>
     Polymer({
@@ -32,6 +31,10 @@ future for loading older images.
         tag: String,
         imagesGenerator: Function,
         imageUrl: String,
+        height: {
+          type: Number,
+          value: 300
+        }
       },
       reload: function() {
         var _this = this;
@@ -39,6 +42,7 @@ future for loading older images.
         this.imagesGenerator(this.tag, this.run).then(function(metadatas) {
           var last_metadata = _.last(metadatas);
           _this.imageUrl = last_metadata.url;
+          _this.height = 300 / last_metadata.width * last_metadata.height;
         });
       },
       ready: function() {
-- 
GitLab


From dd6fce9c575fc648e5ef6873da5aa48075a2fdbb Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Wed, 24 Aug 2016 11:07:46 -0800
Subject: [PATCH 099/223] Fix typo-caused issue on mac in cpu_utils.cc Change:
 131200816

---
 tensorflow/core/platform/hexagon/profile_utils/cpu_utils.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/platform/hexagon/profile_utils/cpu_utils.cc b/tensorflow/core/platform/hexagon/profile_utils/cpu_utils.cc
index b3224edee1..d94d9411f1 100644
--- a/tensorflow/core/platform/hexagon/profile_utils/cpu_utils.cc
+++ b/tensorflow/core/platform/hexagon/profile_utils/cpu_utils.cc
@@ -71,7 +71,7 @@ const class StaticVariableInitializer {
   fscanf(fp, "%lld", &freq_hz);
   pclose(fp);
   if (freq_hz < 1e6) {
-    LOG(WARNING) << "Failed to get CPU frequency: " << freq_ghz << " Hz";
+    LOG(WARNING) << "Failed to get CPU frequency: " << freq_hz << " Hz";
     return INVALID_FREQUENCY;
   }
   return freq_hz;
-- 
GitLab


From fd69b50c84dc18a15a396488b8936e4b5cebc0d8 Mon Sep 17 00:00:00 2001
From: Yuefeng Zhou <yuefengz@google.com>
Date: Wed, 24 Aug 2016 11:10:14 -0800
Subject: [PATCH 100/223] Make transpose op support half type on CPU. Change:
 131201072

---
 tensorflow/core/kernels/transpose_functor_cpu.cc    | 1 +
 tensorflow/python/kernel_tests/transpose_op_test.py | 7 +++++++
 2 files changed, 8 insertions(+)

diff --git a/tensorflow/core/kernels/transpose_functor_cpu.cc b/tensorflow/core/kernels/transpose_functor_cpu.cc
index 07de6e0b6a..f8c87e7e2e 100644
--- a/tensorflow/core/kernels/transpose_functor_cpu.cc
+++ b/tensorflow/core/kernels/transpose_functor_cpu.cc
@@ -80,6 +80,7 @@ Status DoTranspose<Device>(const Device& d, const Tensor& in,
       break;
 
     case DT_BFLOAT16:
+    case DT_HALF:
     case DT_INT16:
     case DT_QINT16:
     case DT_QUINT16:
diff --git a/tensorflow/python/kernel_tests/transpose_op_test.py b/tensorflow/python/kernel_tests/transpose_op_test.py
index 4b47cb9fa2..ec8a41f59c 100644
--- a/tensorflow/python/kernel_tests/transpose_op_test.py
+++ b/tensorflow/python/kernel_tests/transpose_op_test.py
@@ -122,6 +122,13 @@ class TransposeTest(tf.test.TestCase):
     self._compareCpu(np.arange(0, 8).reshape([2, 4]).astype(np.float32),
                      np.array([1, 0]).astype(np.int32))
 
+  def testHalf(self):
+    self._compare(np.arange(0, 21).reshape([3, 7]).astype(np.float16))
+    self._compare(
+        np.arange(0, 210).reshape([2, 3, 5, 7]).astype(np.float16))
+    self._compare(
+        np.arange(0, 16).reshape([1, 2, 1, 2, 1, 2, 1, 2]).astype(np.float16))
+
   def testFloat(self):
     self._compare_cpu_gpu(np.arange(0, 21).reshape([3, 7]).astype(np.float32))
     self._compare_cpu_gpu(
-- 
GitLab


From 46231cf242c19d74af75370eefd9e9b7c504c08a Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Wed, 24 Aug 2016 11:35:42 -0800
Subject: [PATCH 101/223] Move host_info.h internal, move public functions into
 cpu_info.h and make public.

Hostname() is not public, but kLittleEndian arguably is (some code
might need to know what the endianness is, like decode_raw_op,
so it probably should be public).  Similarly, NumSchedulableCPUs
is probably something a lot of code wants to know.  It is also
already used widely and seems hard to remove in short notice,
which suggests something about its utility.
Change: 131203680
---
 .../factorization/kernels/clustering_ops.cc   |  2 +-
 .../contrib/ffmpeg/default/ffmpeg_lib.cc      |  2 +-
 tensorflow/core/BUILD                         |  6 ++--
 .../core/common_runtime/direct_session.cc     |  2 +-
 .../kernel_benchmark_testlib.cc               |  2 +-
 .../core/common_runtime/local_device.cc       |  2 +-
 .../core/common_runtime/process_util.cc       |  2 +-
 tensorflow/core/kernels/cast_op.h             |  2 +-
 tensorflow/core/kernels/decode_raw_op.cc      |  2 +-
 tensorflow/core/lib/core/coding.cc            |  2 +-
 tensorflow/core/lib/core/raw_coding.h         |  2 +-
 tensorflow/core/lib/gtl/inlined_vector.h      |  2 +-
 tensorflow/core/lib/png/png_io.cc             |  2 +-
 tensorflow/core/lib/wav/wav_io.cc             |  2 +-
 tensorflow/core/platform/cpu_info.h           | 34 +++++++++++++++++++
 tensorflow/core/platform/host_info.h          | 17 ----------
 16 files changed, 51 insertions(+), 32 deletions(-)
 create mode 100644 tensorflow/core/platform/cpu_info.h

diff --git a/tensorflow/contrib/factorization/kernels/clustering_ops.cc b/tensorflow/contrib/factorization/kernels/clustering_ops.cc
index 5f680ceadb..2ee366d922 100644
--- a/tensorflow/contrib/factorization/kernels/clustering_ops.cc
+++ b/tensorflow/contrib/factorization/kernels/clustering_ops.cc
@@ -31,7 +31,7 @@
 #include "tensorflow/core/lib/gtl/top_n.h"
 #include "tensorflow/core/lib/random/philox_random.h"
 #include "tensorflow/core/lib/random/simple_philox.h"
-#include "tensorflow/core/platform/host_info.h"
+#include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/platform/logging.h"
 
 namespace tensorflow {
diff --git a/tensorflow/contrib/ffmpeg/default/ffmpeg_lib.cc b/tensorflow/contrib/ffmpeg/default/ffmpeg_lib.cc
index 9db453f0dd..a34c64d328 100644
--- a/tensorflow/contrib/ffmpeg/default/ffmpeg_lib.cc
+++ b/tensorflow/contrib/ffmpeg/default/ffmpeg_lib.cc
@@ -26,8 +26,8 @@
 
 #include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/lib/strings/str_util.h"
+#include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/platform/env.h"
-#include "tensorflow/core/platform/host_info.h"
 
 using tensorflow::strings::StrCat;
 
diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index b0e89c9fb9..7f0f876afa 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -167,11 +167,11 @@ cc_library(
         "lib/strings/str_util.h",  # TODO(josh11b): make internal
         "lib/strings/strcat.h",
         "lib/strings/stringprintf.h",
+        "platform/cpu_info.h",
         "platform/env.h",
         "platform/file_system.h",
         "platform/fingerprint.h",
         "platform/hexagon/profile_utils/cpu_utils.h",
-        "platform/host_info.h",  # TODO(josh11b): make internal
         "platform/init_main.h",
         "platform/logging.h",
         "platform/macros.h",
@@ -529,6 +529,7 @@ cc_library(
         ":framework",
         ":framework_internal",
         ":lib",
+        ":lib_internal",
         ":proto_text",
         ":protos_all_cc",
         ":shape_inference_testutil",
@@ -903,6 +904,7 @@ cc_library(
         "lib/wav/wav_io.h",
         "platform/demangle.h",
         "platform/denormal.h",
+        "platform/host_info.h",
         "platform/platform.h",
         "platform/tensor_coding.h",
         "platform/tracing.h",
@@ -1041,6 +1043,7 @@ filegroup(
         "lib/strings/numbers.h",
         "lib/strings/str_util.h",
         "lib/strings/strcat.h",
+        "platform/cpu_info.h",
         "platform/default/dynamic_annotations.h",
         "platform/default/integral_types.h",
         "platform/default/logging.h",
@@ -1051,7 +1054,6 @@ filegroup(
         "platform/file_statistics.h",
         "platform/file_system.h",
         "platform/fingerprint.h",
-        "platform/host_info.h",
         "platform/logging.h",
         "platform/macros.h",
         "platform/mem.h",
diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc
index c8786c90d3..f64ab7c4ff 100644
--- a/tensorflow/core/common_runtime/direct_session.cc
+++ b/tensorflow/core/common_runtime/direct_session.cc
@@ -52,7 +52,7 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/numbers.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/lib/strings/strcat.h"
-#include "tensorflow/core/platform/host_info.h"
+#include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/mutex.h"
 #include "tensorflow/core/platform/types.h"
diff --git a/tensorflow/core/common_runtime/kernel_benchmark_testlib.cc b/tensorflow/core/common_runtime/kernel_benchmark_testlib.cc
index 7d0aefa70f..c568896de7 100644
--- a/tensorflow/core/common_runtime/kernel_benchmark_testlib.cc
+++ b/tensorflow/core/common_runtime/kernel_benchmark_testlib.cc
@@ -26,7 +26,7 @@ limitations under the License.
 #include "tensorflow/core/lib/core/notification.h"
 #include "tensorflow/core/lib/core/threadpool.h"
 #include "tensorflow/core/lib/strings/str_util.h"
-#include "tensorflow/core/platform/host_info.h"
+#include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/test_benchmark.h"
 #include "tensorflow/core/platform/types.h"
diff --git a/tensorflow/core/common_runtime/local_device.cc b/tensorflow/core/common_runtime/local_device.cc
index bee63fc61f..bbd04e2dbb 100644
--- a/tensorflow/core/common_runtime/local_device.cc
+++ b/tensorflow/core/common_runtime/local_device.cc
@@ -19,7 +19,7 @@ limitations under the License.
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/common_runtime/eigen_thread_pool.h"
 #include "tensorflow/core/lib/core/threadpool.h"
-#include "tensorflow/core/platform/host_info.h"
+#include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/types.h"
 #include "tensorflow/core/public/session_options.h"
diff --git a/tensorflow/core/common_runtime/process_util.cc b/tensorflow/core/common_runtime/process_util.cc
index 9c26434281..d738fef7be 100644
--- a/tensorflow/core/common_runtime/process_util.cc
+++ b/tensorflow/core/common_runtime/process_util.cc
@@ -18,7 +18,7 @@ limitations under the License.
 #include <string.h>
 
 #include "tensorflow/core/lib/core/threadpool.h"
-#include "tensorflow/core/platform/host_info.h"
+#include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/tracing.h"
 #include "tensorflow/core/platform/types.h"
diff --git a/tensorflow/core/kernels/cast_op.h b/tensorflow/core/kernels/cast_op.h
index 0ed1964653..7bd08cfda6 100644
--- a/tensorflow/core/kernels/cast_op.h
+++ b/tensorflow/core/kernels/cast_op.h
@@ -19,7 +19,7 @@ limitations under the License.
 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
 #include "tensorflow/core/framework/bfloat16.h"
 #include "tensorflow/core/framework/tensor_types.h"
-#include "tensorflow/core/platform/host_info.h"
+#include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/platform/types.h"
 
 namespace tensorflow {
diff --git a/tensorflow/core/kernels/decode_raw_op.cc b/tensorflow/core/kernels/decode_raw_op.cc
index 2ad2c94b61..d3bd9913e0 100644
--- a/tensorflow/core/kernels/decode_raw_op.cc
+++ b/tensorflow/core/kernels/decode_raw_op.cc
@@ -21,7 +21,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/core/errors.h"
-#include "tensorflow/core/platform/host_info.h"
+#include "tensorflow/core/platform/cpu_info.h"
 
 namespace tensorflow {
 
diff --git a/tensorflow/core/lib/core/coding.cc b/tensorflow/core/lib/core/coding.cc
index fb698cf53c..21fb548053 100644
--- a/tensorflow/core/lib/core/coding.cc
+++ b/tensorflow/core/lib/core/coding.cc
@@ -15,7 +15,7 @@ limitations under the License.
 
 #include "tensorflow/core/lib/core/coding.h"
 
-#include "tensorflow/core/platform/host_info.h"
+#include "tensorflow/core/platform/cpu_info.h"
 
 namespace tensorflow {
 namespace core {
diff --git a/tensorflow/core/lib/core/raw_coding.h b/tensorflow/core/lib/core/raw_coding.h
index 6cab6b8680..bbfd33d303 100644
--- a/tensorflow/core/lib/core/raw_coding.h
+++ b/tensorflow/core/lib/core/raw_coding.h
@@ -17,7 +17,7 @@ limitations under the License.
 #define TENSORFLOW_LIB_CORE_RAW_CODING_H_
 
 #include <string.h>
-#include "tensorflow/core/platform/host_info.h"
+#include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/platform/types.h"
 
 namespace tensorflow {
diff --git a/tensorflow/core/lib/gtl/inlined_vector.h b/tensorflow/core/lib/gtl/inlined_vector.h
index 14b670530f..21af965da2 100644
--- a/tensorflow/core/lib/gtl/inlined_vector.h
+++ b/tensorflow/core/lib/gtl/inlined_vector.h
@@ -42,7 +42,7 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/core/lib/gtl/manual_constructor.h"
-#include "tensorflow/core/platform/host_info.h"
+#include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/types.h"
 
diff --git a/tensorflow/core/lib/png/png_io.cc b/tensorflow/core/lib/png/png_io.cc
index ada783f8a4..cbee7a416e 100644
--- a/tensorflow/core/lib/png/png_io.cc
+++ b/tensorflow/core/lib/png/png_io.cc
@@ -25,7 +25,7 @@ limitations under the License.
 
 #include "tensorflow/core/lib/core/casts.h"
 #include "tensorflow/core/lib/png/png_io.h"
-#include "tensorflow/core/platform/host_info.h"  // endian
+#include "tensorflow/core/platform/cpu_info.h"  // endian
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/png.h"
 
diff --git a/tensorflow/core/lib/wav/wav_io.cc b/tensorflow/core/lib/wav/wav_io.cc
index 825d4f5957..31c81b7dde 100644
--- a/tensorflow/core/lib/wav/wav_io.cc
+++ b/tensorflow/core/lib/wav/wav_io.cc
@@ -23,7 +23,7 @@ limitations under the License.
 #include "tensorflow/core/lib/core/coding.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/wav/wav_io.h"
-#include "tensorflow/core/platform/host_info.h"
+#include "tensorflow/core/platform/cpu_info.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/macros.h"
 
diff --git a/tensorflow/core/platform/cpu_info.h b/tensorflow/core/platform/cpu_info.h
new file mode 100644
index 0000000000..706dc4dcc5
--- /dev/null
+++ b/tensorflow/core/platform/cpu_info.h
@@ -0,0 +1,34 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_PLATFORM_CPU_INFO_H_
+#define TENSORFLOW_PLATFORM_CPU_INFO_H_
+
+namespace tensorflow {
+namespace port {
+
+// TODO(jeff,sanjay): Make portable
+static const bool kLittleEndian = true;
+
+// Returns an estimate of the number of schedulable CPUs for this
+// process.  Usually, it's constant throughout the lifetime of a
+// process, but it might change if the underlying cluster management
+// software can change it dynamically.
+int NumSchedulableCPUs();
+
+}  // namespace port
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_PLATFORM_CPU_INFO_H_
diff --git a/tensorflow/core/platform/host_info.h b/tensorflow/core/platform/host_info.h
index df51afb63f..6124c95923 100644
--- a/tensorflow/core/platform/host_info.h
+++ b/tensorflow/core/platform/host_info.h
@@ -21,26 +21,9 @@ limitations under the License.
 namespace tensorflow {
 namespace port {
 
-// TODO(jeff,sanjay): Make portable
-static const bool kLittleEndian = true;
-
-// TODO(jeff,sanjay): Find appropriate places for all the code below.
-// Possible places for any particular item below:
-//  (a) Here, so it gets reimplemented on every platform
-//  (b) Env
-//  (c) config.h (auto-generated by autotools?)
-//  (d) macros.h
-//  ...
-
 // Return the hostname of the machine on which this process is running
 string Hostname();
 
-// Returns an estimate of the number of schedulable CPUs for this
-// process.  Usually, it's constant throughout the lifetime of a
-// process, but it might change if the underlying cluster management
-// software can change it dynamically.
-int NumSchedulableCPUs();
-
 }  // namespace port
 }  // namespace tensorflow
 
-- 
GitLab


From 698cf5f1bc98e28990d4627bc1b1deb84b49b6c1 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 24 Aug 2016 12:04:00 -0800
Subject: [PATCH 102/223] - Change float to double which stabilizes the duality
 gap - Fix primal loss computation by using previous weights (this is to have
 a consistent duality gap). Change: 131206587

---
 .../contrib/linear_optimizer/kernels/sdca_ops.cc  | 15 ++++++++++++---
 .../linear_optimizer/python/ops/sdca_ops.py       |  9 +++++----
 2 files changed, 17 insertions(+), 7 deletions(-)

diff --git a/tensorflow/contrib/linear_optimizer/kernels/sdca_ops.cc b/tensorflow/contrib/linear_optimizer/kernels/sdca_ops.cc
index c83e37e814..10c6d6478e 100644
--- a/tensorflow/contrib/linear_optimizer/kernels/sdca_ops.cc
+++ b/tensorflow/contrib/linear_optimizer/kernels/sdca_ops.cc
@@ -64,7 +64,8 @@ using UnalignedInt64Vector = TTypes<const int64>::UnalignedConstVec;
 struct ExampleStatistics {
   // feature_weights dot feature_values for the example.
   double wx = 0;
-
+  // dot product using the previous weights
+  double prev_wx = 0;
   // sum of squared feature values occurring in the example divided by
   // L2 * sum(example_weights).
   double normalized_squared_norm = 0;
@@ -279,7 +280,7 @@ const ExampleStatistics Example::ComputeWxAndWeightedExampleNorm(
   result.normalized_squared_norm =
       squared_norm_ / regularization.symmetric_l2();
 
-  // Compute the w \dot x.
+  // Compute the w \dot x and prev_w \dot x.
 
   // Sparse features contribution.
   for (size_t j = 0; j < sparse_features_.size(); ++j) {
@@ -294,6 +295,9 @@ const ExampleStatistics Example::ComputeWxAndWeightedExampleNorm(
       const double feature_weight =
           sparse_weights.nominals(feature_index) +
           sparse_weights.deltas(feature_index) * num_partitions;
+      result.prev_wx +=
+          feature_value *
+          regularization.Shrink(sparse_weights.nominals(feature_index));
       result.wx += feature_value * regularization.Shrink(feature_weight);
     }
   }
@@ -306,9 +310,14 @@ const ExampleStatistics Example::ComputeWxAndWeightedExampleNorm(
     const Eigen::Tensor<float, 1, Eigen::RowMajor> feature_weights =
         dense_weights.nominals +
         dense_weights.deltas * dense_weights.deltas.constant(num_partitions);
+    const Eigen::Tensor<float, 0, Eigen::RowMajor> prev_prediction =
+        (dense_vector.row() *
+         regularization.EigenShrink(dense_weights.nominals))
+            .sum();
     const Eigen::Tensor<float, 0, Eigen::RowMajor> prediction =
         (dense_vector.row() * regularization.EigenShrink(feature_weights))
             .sum();
+    result.prev_wx += prev_prediction();
     result.wx += prediction();
   }
 
@@ -699,7 +708,7 @@ class DistributedSdcaLargeBatchSolver : public OpKernel {
         // Update example data.
         example_state_data(example_index, 0) = new_dual;
         example_state_data(example_index, 1) = loss_updater_->ComputePrimalLoss(
-            example_statistics.wx, example_label, example_weight);
+            example_statistics.prev_wx, example_label, example_weight);
         example_state_data(example_index, 2) =
             loss_updater_->ComputeDualLoss(dual, example_label, example_weight);
         example_state_data(example_index, 3) = example_weight;
diff --git a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py
index 5d81b3af09..6f35472f85 100644
--- a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py
+++ b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py
@@ -150,7 +150,7 @@ class _ShardedMutableHashTable(lookup_ops.LookupInterface):
 
     return control_flow_ops.group(*return_values)
 
-  def values_reduce_sum(self, name=None):
+  def values_reduce_sum(self, dtype=dtypes.float64, name=None):
     """Computes reduce_sum reducing dimension 0 across all values in all shards.
 
     Args:
@@ -165,7 +165,8 @@ class _ShardedMutableHashTable(lookup_ops.LookupInterface):
     sums = []
     for table_shard in self._table_shards:
       _, exported_values = table_shard.export(name=name)
-      sums.append(math_ops.reduce_sum(exported_values, 0))
+      sums.append(math_ops.reduce_sum(
+          math_ops.cast(exported_values, dtype), 0))
     return math_ops.add_n(sums)
 
 
@@ -594,9 +595,9 @@ class SdcaModel(object):
     example_weights = summed_values[3]
     # TODO(andreasst): what about handle examples_weights == 0?
     return (
-        primal_loss + dual_loss + math_ops.to_float(self._l1_loss()) +
+        primal_loss + dual_loss + math_ops.to_double(self._l1_loss()) +
         (2.0 *
-         math_ops.to_float(self._l2_loss(self._symmetric_l2_regularization())))
+         math_ops.to_double(self._l2_loss(self._symmetric_l2_regularization())))
     ) / example_weights
 
   def unregularized_loss(self, examples):
-- 
GitLab


From 379d571b630440e51ac727a01ef081026f7d1a90 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 24 Aug 2016 12:28:59 -0800
Subject: [PATCH 103/223] Add name scopes to slim's train and create_train_op
 to remove dangling and duplicated nodes in TensorBoard. Change: 131209068

---
 .../contrib/slim/python/slim/learning.py      | 65 ++++++++++---------
 1 file changed, 36 insertions(+), 29 deletions(-)

diff --git a/tensorflow/contrib/slim/python/slim/learning.py b/tensorflow/contrib/slim/python/slim/learning.py
index c4374dc9a1..4e48f9c6fd 100644
--- a/tensorflow/contrib/slim/python/slim/learning.py
+++ b/tensorflow/contrib/slim/python/slim/learning.py
@@ -448,24 +448,29 @@ def create_train_op(
 
   # Scale gradients.
   if gradient_multipliers:
-    grads = multiply_gradients(grads, gradient_multipliers)
+    with ops.name_scope('multiply_grads'):
+      grads = multiply_gradients(grads, gradient_multipliers)
 
   # Clip gradients.
   if clip_gradient_norm > 0:
-    grads = clip_gradient_norms(grads, clip_gradient_norm)
+    with ops.name_scope('clip_grads'):
+      grads = clip_gradient_norms(grads, clip_gradient_norm)
 
   # Summarize gradients.
   if summarize_gradients:
-    add_gradients_summaries(grads)
+    with ops.name_scope('summarize_grads'):
+      add_gradients_summaries(grads)
 
   # Create gradient updates.
   grad_updates = optimizer.apply_gradients(grads, global_step=global_step)
 
-  # Make sure total_loss is valid.
-  total_loss = array_ops.check_numerics(total_loss, 'LossTensor is inf or nan')
+  with ops.name_scope('train_op'):
+    # Make sure total_loss is valid.
+    total_loss = array_ops.check_numerics(total_loss,
+                                          'LossTensor is inf or nan')
 
-  # Ensure the train_tensor computes grad_updates.
-  return control_flow_ops.with_dependencies([grad_updates], total_loss)
+    # Ensure the train_tensor computes grad_updates.
+    return control_flow_ops.with_dependencies([grad_updates], total_loss)
 
 
 def _wait_for_step(sess, global_step, step):
@@ -672,11 +677,17 @@ def train(train_op,
       global_step = variables.get_or_create_global_step()
     saver = saver or tf_saver.Saver()
 
-    if init_op == _USE_DEFAULT:
-      init_op = tf_variables.initialize_all_variables()
+    with ops.name_scope('init_ops'):
+      if init_op == _USE_DEFAULT:
+        init_op = tf_variables.initialize_all_variables()
 
-    if ready_op == _USE_DEFAULT:
-      ready_op = tf_variables.report_uninitialized_variables()
+      if ready_op == _USE_DEFAULT:
+        ready_op = tf_variables.report_uninitialized_variables()
+
+      if local_init_op == _USE_DEFAULT:
+        local_init_op = control_flow_ops.group(
+            tf_variables.initialize_local_variables(),
+            data_flow_ops.initialize_all_tables())
 
     if summary_op == _USE_DEFAULT:
       summary_op = logging_ops.merge_all_summaries()
@@ -684,11 +695,6 @@ def train(train_op,
     if summary_writer == _USE_DEFAULT:
       summary_writer = supervisor.Supervisor.USE_DEFAULT
 
-    if local_init_op == _USE_DEFAULT:
-      local_init_op = control_flow_ops.group(
-          tf_variables.initialize_local_variables(),
-          data_flow_ops.initialize_all_tables())
-
     cleanup_op = None
 
     if is_chief and sync_optimizer:
@@ -705,19 +711,20 @@ def train(train_op,
       cleanup_op = sync_optimizer.get_clean_up_op()
 
     if train_step_kwargs == _USE_DEFAULT:
-      train_step_kwargs = {}
-
-      if number_of_steps:
-        should_stop_op = math_ops.greater_equal(global_step, number_of_steps)
-      else:
-        should_stop_op = constant_op.constant(False)
-      train_step_kwargs['should_stop'] = should_stop_op
-      train_step_kwargs['should_log'] = math_ops.equal(
-          math_ops.mod(global_step, log_every_n_steps), 0)
-      if is_chief and trace_every_n_steps is not None:
-        train_step_kwargs['should_trace'] = math_ops.equal(
-            math_ops.mod(global_step, trace_every_n_steps), 0)
-        train_step_kwargs['logdir'] = logdir
+      with ops.name_scope('train_step'):
+        train_step_kwargs = {}
+
+        if number_of_steps:
+          should_stop_op = math_ops.greater_equal(global_step, number_of_steps)
+        else:
+          should_stop_op = constant_op.constant(False)
+        train_step_kwargs['should_stop'] = should_stop_op
+        train_step_kwargs['should_log'] = math_ops.equal(
+            math_ops.mod(global_step, log_every_n_steps), 0)
+        if is_chief and trace_every_n_steps is not None:
+          train_step_kwargs['should_trace'] = math_ops.equal(
+              math_ops.mod(global_step, trace_every_n_steps), 0)
+          train_step_kwargs['logdir'] = logdir
 
   sv = supervisor.Supervisor(
       graph=graph,
-- 
GitLab


From 61ab47cebb2731cb16f5d4f5b32651c20766bb06 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 24 Aug 2016 12:31:32 -0800
Subject: [PATCH 104/223] In C++ shape inference, change ShapeHandle and
 DimensionHandle into classes that hide the implicit casts to and from a
 pointer. Change: 131209340

---
 tensorflow/core/framework/shape_inference.cc  |   6 +-
 tensorflow/core/framework/shape_inference.h   |  53 ++-
 .../core/framework/shape_inference_test.cc    | 405 +++++++++---------
 .../framework/shape_inference_testutil.cc     |  44 +-
 .../core/framework/shape_inference_testutil.h |  81 ++--
 .../shape_inference_testutil_test.cc          |  17 +-
 tensorflow/core/graph/shape_inferer.cc        |   2 +-
 tensorflow/core/ops/array_ops.cc              |   2 +-
 tensorflow/core/ops/math_ops.cc               |  19 +-
 .../python/framework/cpp_shape_inference.cc   |   4 +-
 10 files changed, 352 insertions(+), 281 deletions(-)

diff --git a/tensorflow/core/framework/shape_inference.cc b/tensorflow/core/framework/shape_inference.cc
index 90b3a6a688..6118bf1288 100644
--- a/tensorflow/core/framework/shape_inference.cc
+++ b/tensorflow/core/framework/shape_inference.cc
@@ -224,7 +224,7 @@ Status InferenceContext::WithValue(DimensionHandle dim, int64 value,
 
 Status InferenceContext::Merge(DimensionHandle d0, DimensionHandle d1,
                                DimensionHandle* out) {
-  if (d0 == d1 || !ValueKnown(d1)) {
+  if (d0.SameHandle(d1) || !ValueKnown(d1)) {
     *out = d0;
     return Status::OK();
   } else if (!ValueKnown(d0)) {
@@ -266,7 +266,7 @@ Status InferenceContext::MergePrefix(ShapeHandle s, ShapeHandle prefix,
 
 Status InferenceContext::Merge(ShapeHandle s0, ShapeHandle s1,
                                ShapeHandle* out) {
-  if (s0 == s1 || !RankKnown(s1)) {
+  if (s0.SameHandle(s1) || !RankKnown(s1)) {
     *out = s0;
     return Status::OK();
   } else if (!RankKnown(s0)) {
@@ -286,7 +286,7 @@ Status InferenceContext::Merge(ShapeHandle s0, ShapeHandle s1,
   for (int i = 0; i < rank; ++i) {
     auto d0 = Dim(s0, i);
     auto d1 = Dim(s1, i);
-    if (d0 == d1) continue;
+    if (d0.SameHandle(d1)) continue;
 
     auto v0 = Value(d0);
     auto v1 = Value(d1);
diff --git a/tensorflow/core/framework/shape_inference.h b/tensorflow/core/framework/shape_inference.h
index 6f561f467a..f65dac1686 100644
--- a/tensorflow/core/framework/shape_inference.h
+++ b/tensorflow/core/framework/shape_inference.h
@@ -28,6 +28,7 @@ limitations under the License.
 namespace tensorflow {
 namespace shape_inference {
 
+struct DimensionOrConstant;
 class InferenceContext;
 
 // Dimension values are accessed through InferenceContext.
@@ -43,7 +44,26 @@ class Dimension {
   TF_DISALLOW_COPY_AND_ASSIGN(Dimension);
 };
 
-typedef const Dimension* DimensionHandle;
+class DimensionHandle {
+ public:
+  DimensionHandle() {}
+
+ private:
+  DimensionHandle(const Dimension* dim) { ptr_ = dim; }
+
+  const Dimension* operator->() { return ptr_; }
+  bool IsSet() const { return ptr_ != nullptr; }
+  bool SameHandle(DimensionHandle d) const { return ptr_ == d.ptr_; }
+
+  const Dimension* ptr_ = nullptr;
+
+  friend class DimensionOrConstant;
+  friend class InferenceContext;
+  friend class ShapeInferenceTest;
+  friend class ShapeInferenceTestutil;
+
+  // Intentionally copyable.
+};
 
 // Shape rank and dimensions are accessed through InferenceContext.
 class Shape {
@@ -56,9 +76,29 @@ class Shape {
   const std::vector<DimensionHandle> dims_;
 
   friend class InferenceContext;
+
   TF_DISALLOW_COPY_AND_ASSIGN(Shape);
 };
 
+class ShapeHandle {
+ public:
+  ShapeHandle() {}
+
+ private:
+  ShapeHandle(const Shape* shape) { ptr_ = shape; }
+  const Shape* operator->() { return ptr_; }
+  bool IsSet() const { return ptr_ != nullptr; }
+  bool SameHandle(ShapeHandle s) const { return ptr_ == s.ptr_; }
+
+  const Shape* ptr_ = nullptr;
+
+  friend class InferenceContext;
+  friend class ShapeInferenceTest;
+  friend class ShapeInferenceTestutil;
+
+  // Intentionally copyable.
+};
+
 // Struct used to allow functions to take DimensionHandle or a dimension value.
 // Not meant to be constructed directly.
 struct DimensionOrConstant {
@@ -77,8 +117,6 @@ struct DimensionOrConstant {
   DimensionOrConstant();
 };
 
-typedef const Shape* ShapeHandle;
-
 // Note: This is experimental support for op shape inference in C++.  Shape
 // inference functions are not ready to be implemented yet.
 //
@@ -173,7 +211,7 @@ class InferenceContext {
   int32 Rank(ShapeHandle s) { return s->rank_; }
   bool RankKnown(ShapeHandle s) { return Rank(s) != kUnknownRank; }
   inline int64 Value(DimensionOrConstant d) {
-    return d.dim ? d.dim->value_ : d.val;
+    return d.dim.IsSet() ? d.dim->value_ : d.val;
   }
   inline bool ValueKnown(DimensionOrConstant d) {
     return Value(d) != kUnknownDim;
@@ -285,7 +323,7 @@ class InferenceContext {
   // Returns a new dimension of the given size.  The returned value is owned by
   // this context.
   inline DimensionHandle MakeDim(DimensionOrConstant d) {
-    if (d.dim) {
+    if (d.dim.IsSet()) {
       return d.dim;
     } else {
       all_dims_.push_back(new Dimension(d.val));
@@ -448,11 +486,10 @@ inline Shape::Shape(const std::vector<DimensionHandle>& dims)
 
 inline DimensionOrConstant::DimensionOrConstant(DimensionHandle dim)
     : dim(dim) {
-  DCHECK(dim != nullptr) << "Internal error: Got nullptr for Dimension.";
+  DCHECK(dim.IsSet()) << "Internal error: Got nullptr for Dimension.";
 }
 
-inline DimensionOrConstant::DimensionOrConstant(int64 val)
-    : dim(nullptr), val(val) {
+inline DimensionOrConstant::DimensionOrConstant(int64 val) : val(val) {
   DCHECK(val >= 0 || val == InferenceContext::kUnknownDim)
       << "Dimension must be non-negative or equal to "
          "InferenceContext::kUnknownDim but got"
diff --git a/tensorflow/core/framework/shape_inference_test.cc b/tensorflow/core/framework/shape_inference_test.cc
index 7b6c9407c4..fe5fc45b8a 100644
--- a/tensorflow/core/framework/shape_inference_test.cc
+++ b/tensorflow/core/framework/shape_inference_test.cc
@@ -24,6 +24,17 @@ limitations under the License.
 namespace tensorflow {
 namespace shape_inference {
 
+class ShapeInferenceTest : public ::testing::Test {
+ protected:
+  // These give access to private functions of DimensionHandle and ShapeHandle.
+  bool SameHandle(DimensionHandle a, DimensionHandle b) {
+    return a.SameHandle(b);
+  }
+  bool SameHandle(ShapeHandle a, ShapeHandle b) { return a.SameHandle(b); }
+  bool IsSet(DimensionHandle d) { return d.IsSet(); }
+  bool IsSet(ShapeHandle s) { return s.IsSet(); }
+};
+
 static OpDef MakeOpDef(int num_inputs, int num_outputs) {
   OpRegistrationData op_reg_data;
   OpDefBuilder b("dummy");
@@ -37,7 +48,7 @@ static OpDef MakeOpDef(int num_inputs, int num_outputs) {
   return op_reg_data.op_def;
 }
 
-TEST(ShapeInferenceTest, DimensionOrConstant) {
+TEST_F(ShapeInferenceTest, DimensionOrConstant) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(1, 1), {"?"}, {});
   EXPECT_EQ(InferenceContext::kUnknownDim,
@@ -50,7 +61,7 @@ TEST(ShapeInferenceTest, DimensionOrConstant) {
 #endif
 }
 
-TEST(ShapeInferenceTest, RankAndDimInspection) {
+TEST_F(ShapeInferenceTest, RankAndDimInspection) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(3, 2), {"?", "[1,?,3]", "[]"}, {});
   EXPECT_EQ(3, c.num_inputs());
@@ -70,17 +81,17 @@ TEST(ShapeInferenceTest, RankAndDimInspection) {
   EXPECT_EQ(3, c.Rank(in1));
   auto d = c.Dim(in1, 0);
   EXPECT_EQ(1, c.Value(d));
-  EXPECT_TRUE(d == c.Dim(in1, -3));
+  EXPECT_TRUE(SameHandle(d, c.Dim(in1, -3)));
   EXPECT_TRUE(c.ValueKnown(d));
   EXPECT_EQ("1", c.DebugString(d));
   d = c.Dim(in1, 1);
   EXPECT_EQ(InferenceContext::kUnknownDim, c.Value(d));
   EXPECT_FALSE(c.ValueKnown(d));
-  EXPECT_TRUE(d == c.Dim(in1, -2));
+  EXPECT_TRUE(SameHandle(d, c.Dim(in1, -2)));
   EXPECT_EQ("?", c.DebugString(d));
   d = c.Dim(in1, 2);
   EXPECT_EQ(3, c.Value(d));
-  EXPECT_TRUE(d == c.Dim(in1, -1));
+  EXPECT_TRUE(SameHandle(d, c.Dim(in1, -1)));
   EXPECT_TRUE(c.ValueKnown(d));
   EXPECT_EQ("3", c.DebugString(d));
 
@@ -90,27 +101,27 @@ TEST(ShapeInferenceTest, RankAndDimInspection) {
   EXPECT_EQ(0, c.Rank(in2));
 }
 
-TEST(ShapeInferenceTest, NumElements) {
+TEST_F(ShapeInferenceTest, NumElements) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(3, 2), {"?", "[1,?,3]", "[5,4,3,2]"}, {});
 
   EXPECT_EQ("?", c.DebugString(c.NumElements(c.input(0))));
   EXPECT_EQ("?", c.DebugString(c.NumElements(c.input(1))));
 
-  // Different pointers (not the same unknown value).
-  EXPECT_TRUE(c.Dim(c.input(1), 1) != c.NumElements(c.input(1)));
+  // Different handles (not the same unknown value).
+  EXPECT_FALSE(SameHandle(c.Dim(c.input(1), 1), c.NumElements(c.input(1))));
 
   EXPECT_EQ("120", c.DebugString(c.NumElements(c.input(2))));
 }
 
-TEST(ShapeInferenceTest, WithRank) {
+TEST_F(ShapeInferenceTest, WithRank) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(2, 2), {"?", "[1,?,3]"}, {});
 
   auto in0 = c.input(0);
   auto in1 = c.input(1);
-  ShapeHandle s1 = nullptr;
-  ShapeHandle s2 = nullptr;
+  ShapeHandle s1;
+  ShapeHandle s2;
 
   // WithRank on a shape with unknown dimensionality always succeeds.
   EXPECT_TRUE(c.WithRank(in0, 1, &s1).ok());
@@ -118,12 +129,12 @@ TEST(ShapeInferenceTest, WithRank) {
 
   EXPECT_TRUE(c.WithRank(in0, 2, &s2).ok());
   EXPECT_EQ("[?,?]", c.DebugString(s2));
-  EXPECT_TRUE(s1 != s2);                      // different pointers
-  EXPECT_TRUE(c.Dim(s2, 0) != c.Dim(s2, 1));  // different pointers.
+  EXPECT_FALSE(SameHandle(s1, s2));
+  EXPECT_FALSE(SameHandle(c.Dim(s2, 0), c.Dim(s2, 1)));
 
   EXPECT_TRUE(c.WithRank(in0, 1, &s2).ok());
   EXPECT_EQ("[?]", c.DebugString(s2));
-  EXPECT_TRUE(s1 != s2);  // different pointers
+  EXPECT_FALSE(SameHandle(s1, s2));
 
   EXPECT_TRUE(c.WithRank(in0, 0, &s1).ok());
   EXPECT_EQ("[]", c.DebugString(s1));
@@ -132,93 +143,93 @@ TEST(ShapeInferenceTest, WithRank) {
   s1 = in1;
   EXPECT_EQ("Invalid argument: Shape must be rank 2 but is rank 3",
             c.WithRank(in1, 2, &s1).ToString());
-  EXPECT_TRUE(s1 == nullptr);
+  EXPECT_FALSE(IsSet(s1));
   EXPECT_TRUE(c.WithRank(in1, 3, &s1).ok());
-  EXPECT_TRUE(s1 == in1);  // same pointers
+  EXPECT_TRUE(SameHandle(s1, in1));
 
   // Inputs are unchanged.
   EXPECT_EQ("?", c.DebugString(in0));
   EXPECT_EQ("[1,?,3]", c.DebugString(in1));
 }
 
-TEST(ShapeInferenceTest, WithRankAtMost) {
+TEST_F(ShapeInferenceTest, WithRankAtMost) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(2, 2), {"?", "[1,?,3]"}, {});
 
   auto in0 = c.input(0);
   auto in1 = c.input(1);
-  ShapeHandle s1 = nullptr;
-  ShapeHandle s2 = nullptr;
+  ShapeHandle s1;
+  ShapeHandle s2;
 
   // WithRankAtMost on a shape with unknown dimensionality always succeeds.
   EXPECT_TRUE(c.WithRankAtMost(in0, 1, &s1).ok());
   EXPECT_EQ("?", c.DebugString(s1));
-  EXPECT_TRUE(in0 != s1);  // different pointers
+  EXPECT_FALSE(SameHandle(in0, s1));
 
   EXPECT_TRUE(c.WithRankAtMost(in0, 2, &s2).ok());
   EXPECT_EQ("?", c.DebugString(s2));
-  EXPECT_TRUE(s1 != s2);  // different pointers
+  EXPECT_FALSE(SameHandle(s1, s2));
 
   // WithRankAtMost on shape with known dimensionality.
   s1 = in1;
   EXPECT_EQ("Invalid argument: Shape must be at most rank 2 but is rank 3",
             c.WithRankAtMost(in1, 2, &s1).ToString());
-  EXPECT_TRUE(s1 == nullptr);
+  EXPECT_FALSE(IsSet(s1));
   EXPECT_TRUE(c.WithRankAtMost(in1, 3, &s1).ok());
-  EXPECT_TRUE(s1 == in1);  // same pointers
+  EXPECT_TRUE(SameHandle(s1, in1));
   EXPECT_TRUE(c.WithRankAtMost(in1, 4, &s1).ok());
-  EXPECT_TRUE(s1 == in1);  // same pointers
+  EXPECT_TRUE(SameHandle(s1, in1));
   EXPECT_TRUE(c.WithRankAtMost(in1, 5, &s1).ok());
-  EXPECT_TRUE(s1 == in1);  // same pointers
+  EXPECT_TRUE(SameHandle(s1, in1));
 
   // Inputs are unchanged.
   EXPECT_EQ("?", c.DebugString(in0));
   EXPECT_EQ("[1,?,3]", c.DebugString(in1));
 }
 
-TEST(ShapeInferenceTest, WithRankAtLeast) {
+TEST_F(ShapeInferenceTest, WithRankAtLeast) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(2, 2), {"?", "[1,?,3]"}, {});
 
   auto in0 = c.input(0);
   auto in1 = c.input(1);
-  ShapeHandle s1 = nullptr;
-  ShapeHandle s2 = nullptr;
+  ShapeHandle s1;
+  ShapeHandle s2;
 
   // WithRankAtLeast on a shape with unknown dimensionality always succeeds.
   EXPECT_TRUE(c.WithRankAtLeast(in0, 1, &s1).ok());
   EXPECT_EQ("?", c.DebugString(s1));
-  EXPECT_TRUE(in0 != s1);  // different pointers
+  EXPECT_FALSE(SameHandle(in0, s1));
 
   EXPECT_TRUE(c.WithRankAtLeast(in0, 2, &s2).ok());
   EXPECT_EQ("?", c.DebugString(s2));
-  EXPECT_TRUE(s1 != s2);  // different pointers
+  EXPECT_FALSE(SameHandle(s1, s2));
 
   // WithRankAtLeast on shape with known dimensionality.
   s1 = in1;
   EXPECT_EQ("Invalid argument: Shape must be at least rank 4 but is rank 3",
             c.WithRankAtLeast(in1, 4, &s1).ToString());
-  EXPECT_TRUE(s1 == nullptr);
+  EXPECT_FALSE(IsSet(s1));
   EXPECT_TRUE(c.WithRankAtLeast(in1, 3, &s1).ok());
-  EXPECT_TRUE(s1 == in1);  // same pointers
+  EXPECT_TRUE(SameHandle(s1, in1));
   EXPECT_TRUE(c.WithRankAtLeast(in1, 2, &s1).ok());
-  EXPECT_TRUE(s1 == in1);  // same pointers
+  EXPECT_TRUE(SameHandle(s1, in1));
   EXPECT_TRUE(c.WithRankAtLeast(in1, 0, &s1).ok());
-  EXPECT_TRUE(s1 == in1);  // same pointers
+  EXPECT_TRUE(SameHandle(s1, in1));
 
   // Inputs are unchanged.
   EXPECT_EQ("?", c.DebugString(in0));
   EXPECT_EQ("[1,?,3]", c.DebugString(in1));
 }
 
-TEST(ShapeInferenceTest, WithValue) {
+TEST_F(ShapeInferenceTest, WithValue) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(1, 2), {"[1,?]"}, {});
 
   auto d0 = c.Dim(c.input(0), 0);
   auto d1 = c.Dim(c.input(0), 1);
-  DimensionHandle out1 = nullptr;
-  DimensionHandle out2 = nullptr;
+  DimensionHandle out1;
+  DimensionHandle out2;
 
   // WithValue on a dimension with unknown value always succeeds.
   EXPECT_TRUE(c.WithValue(d1, 1, &out1).ok());
@@ -226,31 +237,31 @@ TEST(ShapeInferenceTest, WithValue) {
 
   EXPECT_TRUE(c.WithValue(d1, 2, &out2).ok());
   EXPECT_EQ(2, c.Value(out2));
-  EXPECT_TRUE(out1 != out2);  // different pointers
-  EXPECT_TRUE(out1 != d1);    // different pointers
+  EXPECT_FALSE(SameHandle(out1, out2));
+  EXPECT_FALSE(SameHandle(out1, d1));
 
   EXPECT_TRUE(c.WithValue(d1, 1, &out2).ok());
   EXPECT_EQ(1, c.Value(out2));
-  EXPECT_TRUE(out1 != out2);  // different pointers
+  EXPECT_FALSE(SameHandle(out1, out2));
 
   // WithValue on dimension with known size.
   out1 = d0;
   EXPECT_EQ("Invalid argument: Dimension must be 0 but is 1",
             c.WithValue(d0, 0, &out1).ToString());
-  EXPECT_TRUE(out1 == nullptr);
+  EXPECT_FALSE(IsSet(out1));
   out1 = d0;
   EXPECT_EQ("Invalid argument: Dimension must be 2 but is 1",
             c.WithValue(d0, 2, &out1).ToString());
-  EXPECT_TRUE(out1 == nullptr);
+  EXPECT_FALSE(IsSet(out1));
   EXPECT_TRUE(c.WithValue(d0, 1, &out1).ok());
-  EXPECT_TRUE(d0 == out1);  // same pointers
+  EXPECT_TRUE(SameHandle(d0, out1));
 
   // Inputs are unchanged.
   EXPECT_EQ("1", c.DebugString(d0));
   EXPECT_EQ("?", c.DebugString(d1));
 }
 
-TEST(ShapeInferenceTest, MergeDim) {
+TEST_F(ShapeInferenceTest, MergeDim) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(1, 2), {"[2,?,2,1,?]"}, {});
 
@@ -259,38 +270,38 @@ TEST(ShapeInferenceTest, MergeDim) {
   auto d2_b = c.Dim(c.input(0), 2);
   auto d1 = c.Dim(c.input(0), 3);
   auto d_unknown_b = c.Dim(c.input(0), 4);
-  DimensionHandle out = nullptr;
+  DimensionHandle out;
 
   // Merging anything with unknown returns the same pointer.
   EXPECT_TRUE(c.Merge(d2, d_unknown, &out).ok());
-  EXPECT_TRUE(d2 == out);
+  EXPECT_TRUE(SameHandle(d2, out));
   EXPECT_TRUE(c.Merge(d_unknown, d2, &out).ok());
-  EXPECT_TRUE(d2 == out);
+  EXPECT_TRUE(SameHandle(d2, out));
   EXPECT_TRUE(c.Merge(d_unknown, d_unknown_b, &out).ok());
-  EXPECT_TRUE(d_unknown == out);
+  EXPECT_TRUE(SameHandle(d_unknown, out));
 
   // Merging with self returns self.
   EXPECT_TRUE(c.Merge(d2, d2, &out).ok());
-  EXPECT_TRUE(d2 == out);
+  EXPECT_TRUE(SameHandle(d2, out));
   EXPECT_TRUE(c.Merge(d_unknown, d_unknown, &out).ok());
-  EXPECT_TRUE(d_unknown == out);
+  EXPECT_TRUE(SameHandle(d_unknown, out));
 
   // Merging equal values returns first one.
   EXPECT_TRUE(c.Merge(d2, d2_b, &out).ok());
-  EXPECT_TRUE(d2 == out);
+  EXPECT_TRUE(SameHandle(d2, out));
   EXPECT_TRUE(c.Merge(d2_b, d2, &out).ok());
-  EXPECT_TRUE(d2_b == out);
+  EXPECT_TRUE(SameHandle(d2_b, out));
 
   // Merging inequal values is an error.
   EXPECT_EQ("Invalid argument: Dimensions must be equal, but are 2 and 1",
             c.Merge(d2, d1, &out).ToString());
-  EXPECT_TRUE(out == nullptr);
+  EXPECT_FALSE(IsSet(out));
   EXPECT_EQ("Invalid argument: Dimensions must be equal, but are 1 and 2",
             c.Merge(d1, d2, &out).ToString());
-  EXPECT_TRUE(out == nullptr);
+  EXPECT_FALSE(IsSet(out));
 }
 
-TEST(ShapeInferenceTest, MergeShape) {
+TEST_F(ShapeInferenceTest, MergeShape) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(7, 2),
                      {"?", "[1,2]", "[?,2]", "[1,?]", "[1,3]", "?", "[1]"}, {});
@@ -302,54 +313,54 @@ TEST(ShapeInferenceTest, MergeShape) {
   auto s_1_3 = c.input(4);
   auto s_unknown_b = c.input(5);
   auto s_1 = c.input(6);
-  ShapeHandle out = nullptr;
+  ShapeHandle out;
 
   // Merging any shape with unknown returns the shape.
   EXPECT_TRUE(c.Merge(s_unknown, s_1_2, &out).ok());
-  EXPECT_TRUE(s_1_2 == out);
+  EXPECT_TRUE(SameHandle(s_1_2, out));
   EXPECT_TRUE(c.Merge(s_u_2, s_unknown, &out).ok());
-  EXPECT_TRUE(s_u_2 == out);
+  EXPECT_TRUE(SameHandle(s_u_2, out));
   EXPECT_TRUE(c.Merge(s_unknown, s_unknown_b, &out).ok());
-  EXPECT_TRUE(s_unknown == out);
+  EXPECT_TRUE(SameHandle(s_unknown, out));
 
   // Merging with self returns self.
   EXPECT_TRUE(c.Merge(s_1_2, s_1_2, &out).ok());
-  EXPECT_TRUE(out == s_1_2);
+  EXPECT_TRUE(SameHandle(out, s_1_2));
 
   // Merging where one of the inputs is the right answer - return that input.
-  out = nullptr;
+  out = ShapeHandle();
   EXPECT_TRUE(c.Merge(s_1_2, s_u_2, &out).ok());
-  EXPECT_TRUE(s_1_2 == out);
-  out = nullptr;
+  EXPECT_TRUE(SameHandle(s_1_2, out));
+  out = ShapeHandle();
   EXPECT_TRUE(c.Merge(s_u_2, s_1_2, &out).ok());
-  EXPECT_TRUE(s_1_2 == out);
+  EXPECT_TRUE(SameHandle(s_1_2, out));
 
   // Merging where neither input is the right answer.
   EXPECT_TRUE(c.Merge(s_u_2, s_1_u, &out).ok());
-  EXPECT_TRUE(out != s_u_2);
-  EXPECT_TRUE(out != s_1_u);
+  EXPECT_FALSE(SameHandle(out, s_u_2));
+  EXPECT_FALSE(SameHandle(out, s_1_u));
   EXPECT_EQ("[1,2]", c.DebugString(out));
-  EXPECT_TRUE(c.Dim(s_1_u, 0) == c.Dim(out, 0));  // same pointers
-  EXPECT_TRUE(c.Dim(s_u_2, 1) == c.Dim(out, 1));  // same pointers
+  EXPECT_TRUE(SameHandle(c.Dim(s_1_u, 0), c.Dim(out, 0)));
+  EXPECT_TRUE(SameHandle(c.Dim(s_u_2, 1), c.Dim(out, 1)));
 
   // Incompatible merges give errors and set out to nullptr.
   out = s_unknown;
   EXPECT_EQ(("Invalid argument: Dimension 1 in both shapes must be equal, but "
              "are 2 and 3"),
             c.Merge(s_u_2, s_1_3, &out).ToString());
-  EXPECT_TRUE(out == nullptr);
+  EXPECT_FALSE(IsSet(out));
   out = s_unknown;
   EXPECT_EQ(("Invalid argument: Dimension 1 in both shapes must be equal, but "
              "are 3 and 2"),
             c.Merge(s_1_3, s_u_2, &out).ToString());
-  EXPECT_TRUE(out == nullptr);
+  EXPECT_FALSE(IsSet(out));
   out = s_unknown;
   EXPECT_EQ("Invalid argument: Shapes must be equal rank, but are 1 and 2",
             c.Merge(s_1, s_1_2, &out).ToString());
-  EXPECT_TRUE(out == nullptr);
+  EXPECT_FALSE(IsSet(out));
 }
 
-TEST(ShapeInferenceTest, MergePrefix) {
+TEST_F(ShapeInferenceTest, MergePrefix) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(4, 2), {"?", "[?,2]", "[1,?,3]", "[2,4]"},
                      {});
@@ -359,43 +370,43 @@ TEST(ShapeInferenceTest, MergePrefix) {
   auto s_1_u_3 = c.input(2);
   auto s_2_4 = c.input(3);
 
-  ShapeHandle s_out = nullptr;
-  ShapeHandle s_prefix_out = nullptr;
+  ShapeHandle s_out;
+  ShapeHandle s_prefix_out;
 
   // Merging with unknown returns the inputs.
   EXPECT_TRUE(c.MergePrefix(s_unknown, s_u_2, &s_out, &s_prefix_out).ok());
-  EXPECT_TRUE(s_out == s_unknown);
-  EXPECT_TRUE(s_prefix_out == s_u_2);
+  EXPECT_TRUE(SameHandle(s_out, s_unknown));
+  EXPECT_TRUE(SameHandle(s_prefix_out, s_u_2));
   EXPECT_TRUE(c.MergePrefix(s_1_u_3, s_unknown, &s_out, &s_prefix_out).ok());
-  EXPECT_TRUE(s_out == s_1_u_3);
-  EXPECT_TRUE(s_prefix_out == s_unknown);
+  EXPECT_TRUE(SameHandle(s_out, s_1_u_3));
+  EXPECT_TRUE(SameHandle(s_prefix_out, s_unknown));
 
   EXPECT_TRUE(c.MergePrefix(s_1_u_3, s_u_2, &s_out, &s_prefix_out).ok());
-  EXPECT_TRUE(s_out != s_1_u_3);
+  EXPECT_FALSE(SameHandle(s_out, s_1_u_3));
   EXPECT_EQ("[1,2]", c.DebugString(s_prefix_out));
   EXPECT_EQ("[1,2,3]", c.DebugString(s_out));
-  EXPECT_TRUE(c.Dim(s_prefix_out, 0) == c.Dim(s_out, 0));
-  EXPECT_TRUE(c.Dim(s_out, 0) == c.Dim(s_1_u_3, 0));
-  EXPECT_TRUE(c.Dim(s_prefix_out, 1) == c.Dim(s_out, 1));
-  EXPECT_TRUE(c.Dim(s_prefix_out, 1) == c.Dim(s_u_2, 1));
+  EXPECT_TRUE(SameHandle(c.Dim(s_prefix_out, 0), c.Dim(s_out, 0)));
+  EXPECT_TRUE(SameHandle(c.Dim(s_out, 0), c.Dim(s_1_u_3, 0)));
+  EXPECT_TRUE(SameHandle(c.Dim(s_prefix_out, 1), c.Dim(s_out, 1)));
+  EXPECT_TRUE(SameHandle(c.Dim(s_prefix_out, 1), c.Dim(s_u_2, 1)));
 
   // Incompatible merges give errors and set outs to nullptr.
   s_out = s_unknown;
   s_prefix_out = s_unknown;
   EXPECT_EQ(("Invalid argument: Dimensions must be equal, but are 1 and 2"),
             c.MergePrefix(s_1_u_3, s_2_4, &s_out, &s_prefix_out).ToString());
-  EXPECT_TRUE(s_out == nullptr);
-  EXPECT_TRUE(s_prefix_out == nullptr);
+  EXPECT_FALSE(IsSet(s_out));
+  EXPECT_FALSE(IsSet(s_prefix_out));
 
   s_out = s_unknown;
   s_prefix_out = s_unknown;
   EXPECT_EQ(("Invalid argument: Shape must be at least rank 3 but is rank 2"),
             c.MergePrefix(s_2_4, s_1_u_3, &s_out, &s_prefix_out).ToString());
-  EXPECT_TRUE(s_out == nullptr);
-  EXPECT_TRUE(s_prefix_out == nullptr);
+  EXPECT_FALSE(IsSet(s_out));
+  EXPECT_FALSE(IsSet(s_prefix_out));
 }
 
-TEST(ShapeInferenceTest, Subshape) {
+TEST_F(ShapeInferenceTest, Subshape) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(2, 2), {"[1,2,3,?,5]", "?"}, {});
 
@@ -403,20 +414,20 @@ TEST(ShapeInferenceTest, Subshape) {
   ShapeHandle out;
   EXPECT_TRUE(c.Subshape(unknown, 0, &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
-  EXPECT_TRUE(out == unknown);
+  EXPECT_TRUE(SameHandle(out, unknown));
   EXPECT_TRUE(c.Subshape(unknown, 1, &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
-  EXPECT_TRUE(out != unknown);
+  EXPECT_FALSE(SameHandle(out, unknown));
   EXPECT_TRUE(c.Subshape(unknown, 200, &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
-  EXPECT_TRUE(out != unknown);
+  EXPECT_FALSE(SameHandle(out, unknown));
 
   const int kFullRank = 5;
   ShapeHandle out_arr[4];
   auto in0 = c.input(0);
   EXPECT_TRUE(c.Subshape(in0, 0, &out).ok());
   EXPECT_EQ("[1,2,3,?,5]", c.DebugString(out));
-  EXPECT_TRUE(out == in0);
+  EXPECT_TRUE(SameHandle(out, in0));
   EXPECT_EQ(kFullRank, c.Rank(out));
   for (int start = 0; start <= kFullRank + 1; ++start) {
     for (int end = start; end <= kFullRank + 1; ++end) {
@@ -438,8 +449,8 @@ TEST(ShapeInferenceTest, Subshape) {
             << "start: " << start << " end: " << end << " arr_idx: " << arr_idx
             << " in0: " << c.DebugString(in0) << " out: " << c.DebugString(out);
         for (int d = 0; d < c.Rank(out); ++d) {
-          EXPECT_TRUE(c.Dim(in0, start + d) == c.Dim(out, d)) << "arr_idx: "
-                                                              << arr_idx;
+          EXPECT_TRUE(SameHandle(c.Dim(in0, start + d), c.Dim(out, d)))
+              << "arr_idx: " << arr_idx;
         }
       }
     }
@@ -451,22 +462,22 @@ TEST(ShapeInferenceTest, Subshape) {
       "Invalid argument: Subshape must have computed start <= end, but is 5 "
       "and 2 (computed from start 6 and end -3 over shape with rank 5)",
       c.Subshape(in0, 6, -3, &out).ToString());
-  EXPECT_TRUE(out == nullptr);
+  EXPECT_FALSE(IsSet(out));
   out = unknown;
   EXPECT_EQ(
       "Invalid argument: Subshape start out of bounds: -50, for shape with "
       "rank 5",
       c.Subshape(in0, -50, 100, &out).ToString());
-  EXPECT_TRUE(out == nullptr);
+  EXPECT_FALSE(IsSet(out));
   out = unknown;
   EXPECT_EQ(
       "Invalid argument: Subshape end out of bounds: -50, for shape with rank "
       "5",
       c.Subshape(in0, 0, -50, &out).ToString());
-  EXPECT_TRUE(out == nullptr);
+  EXPECT_FALSE(IsSet(out));
 }
 
-TEST(ShapeInferenceTest, Concatenate) {
+TEST_F(ShapeInferenceTest, Concatenate) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(3, 2), {"[1,?,3]", "[4,5]", "?"}, {});
 
@@ -476,23 +487,23 @@ TEST(ShapeInferenceTest, Concatenate) {
   ShapeHandle out;
   EXPECT_TRUE(c.Concatenate(unknown, unknown, &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
-  EXPECT_TRUE(out != unknown);
+  EXPECT_FALSE(SameHandle(out, unknown));
   EXPECT_TRUE(c.Concatenate(unknown, in0, &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
-  EXPECT_TRUE(out != unknown);
+  EXPECT_FALSE(SameHandle(out, unknown));
 
   EXPECT_TRUE(c.Concatenate(in0, in1, &out).ok());
   EXPECT_EQ("[1,?,3,4,5]", c.DebugString(out));
   int out_i = 0;
   for (int i = 0; i < c.Rank(in0); ++i, ++out_i) {
-    EXPECT_TRUE(c.Dim(in0, i) == c.Dim(out, out_i));
+    EXPECT_TRUE(SameHandle(c.Dim(in0, i), c.Dim(out, out_i)));
   }
   for (int i = 0; i < c.Rank(in1); ++i, ++out_i) {
-    EXPECT_TRUE(c.Dim(in1, i) == c.Dim(out, out_i));
+    EXPECT_TRUE(SameHandle(c.Dim(in1, i), c.Dim(out, out_i)));
   }
 }
 
-TEST(ShapeInferenceTest, ReplaceDim) {
+TEST_F(ShapeInferenceTest, ReplaceDim) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(2, 0), {"[1,2,3]", "?"}, {});
 
@@ -517,13 +528,13 @@ TEST(ShapeInferenceTest, ReplaceDim) {
 
   // out of range indexing.
   EXPECT_FALSE(c.ReplaceDim(in, 3, c.Dim(in, 1), &replaced).ok());
-  EXPECT_TRUE(replaced == nullptr);
+  EXPECT_FALSE(IsSet(replaced));
   replaced = in;
   EXPECT_FALSE(c.ReplaceDim(in, -4, c.Dim(in, 1), &replaced).ok());
-  EXPECT_TRUE(replaced == nullptr);
+  EXPECT_FALSE(IsSet(replaced));
 }
 
-TEST(ShapeInferenceTest, MakeShape) {
+TEST_F(ShapeInferenceTest, MakeShape) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(1, 2), {"[1,2,3,?,5]"}, {});
 
@@ -536,18 +547,18 @@ TEST(ShapeInferenceTest, MakeShape) {
 
   auto s = c.MakeShape(dims);
   EXPECT_EQ("[5,?,3,2,1]", c.DebugString(s));
-  EXPECT_TRUE(c.Dim(s, 0) == c.Dim(in0, rank - 1));
+  EXPECT_TRUE(SameHandle(c.Dim(s, 0), c.Dim(in0, rank - 1)));
 
   auto s2 = c.MakeShape(dims);
-  EXPECT_TRUE(s != s2);  // different pointers
-  EXPECT_TRUE(c.Dim(s2, 0) == c.Dim(in0, rank - 1));
+  EXPECT_FALSE(SameHandle(s, s2));
+  EXPECT_TRUE(SameHandle(c.Dim(s2, 0), c.Dim(in0, rank - 1)));
 
   auto s3 = c.MakeShape({1, 2, dims[2]});
-  EXPECT_TRUE(s != s3);  // different pointers
+  EXPECT_FALSE(SameHandle(s, s3));
   EXPECT_EQ("[1,2,3]", c.DebugString(s3));
 }
 
-TEST(ShapeInferenceTest, UnknownShape) {
+TEST_F(ShapeInferenceTest, UnknownShape) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(0, 2), {}, {});
 
@@ -555,10 +566,10 @@ TEST(ShapeInferenceTest, UnknownShape) {
   auto u1 = c.UnknownShape();
   EXPECT_EQ("?", c.DebugString(u0));
   EXPECT_EQ("?", c.DebugString(u1));
-  EXPECT_TRUE(u0 != u1);  // different pointers
+  EXPECT_FALSE(SameHandle(u0, u1));
 }
 
-TEST(ShapeInferenceTest, Scalar) {
+TEST_F(ShapeInferenceTest, Scalar) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(0, 2), {}, {});
 
@@ -568,7 +579,7 @@ TEST(ShapeInferenceTest, Scalar) {
   EXPECT_EQ("[]", c.DebugString(s1));
 }
 
-TEST(ShapeInferenceTest, Vector) {
+TEST_F(ShapeInferenceTest, Vector) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(0, 2), {}, {});
 
@@ -580,32 +591,32 @@ TEST(ShapeInferenceTest, Vector) {
   auto d1 = c.UnknownDim();
   auto s2 = c.Vector(d1);
   EXPECT_EQ("[?]", c.DebugString(s2));
-  EXPECT_TRUE(d1 == c.Dim(s2, 0));
+  EXPECT_TRUE(SameHandle(d1, c.Dim(s2, 0)));
 }
 
-TEST(ShapeInferenceTest, Matrix) {
+TEST_F(ShapeInferenceTest, Matrix) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(0, 2), {}, {});
 
   auto s0 = c.Matrix(1, 2);
   EXPECT_EQ("[1,2]", c.DebugString(s0));
-  auto s1 = c.Matrix(static_cast<int64>(0), InferenceContext::kUnknownDim);
+  auto s1 = c.Matrix(0, InferenceContext::kUnknownDim);
   EXPECT_EQ("[0,?]", c.DebugString(s1));
 
   auto d1 = c.UnknownDim();
   auto d2 = c.UnknownDim();
   auto s2 = c.Matrix(d1, d2);
   EXPECT_EQ("[?,?]", c.DebugString(s2));
-  EXPECT_TRUE(d1 == c.Dim(s2, 0));
-  EXPECT_TRUE(d2 == c.Dim(s2, 1));
+  EXPECT_TRUE(SameHandle(d1, c.Dim(s2, 0)));
+  EXPECT_TRUE(SameHandle(d2, c.Dim(s2, 1)));
 
   auto s3 = c.Matrix(d1, 100);
   EXPECT_EQ("[?,100]", c.DebugString(s3));
-  EXPECT_TRUE(d1 == c.Dim(s2, 0));
+  EXPECT_TRUE(SameHandle(d1, c.Dim(s2, 0)));
 }
 
-TEST(ShapeInferenceTest, MakeShapeFromShapeTensor) {
-  auto create = [](Tensor* t) {
+TEST_F(ShapeInferenceTest, MakeShapeFromShapeTensor) {
+  auto create = [&](Tensor* t) {
     NodeDef def;
     InferenceContext c(&def, MakeOpDef(1, 0), {"?"}, {t});
     ShapeHandle out;
@@ -613,7 +624,7 @@ TEST(ShapeInferenceTest, MakeShapeFromShapeTensor) {
     if (s.ok()) {
       return c.DebugString(out);
     } else {
-      EXPECT_TRUE(out == nullptr);
+      EXPECT_FALSE(IsSet(out));
       return s.error_message();
     }
   };
@@ -649,7 +660,7 @@ TEST(ShapeInferenceTest, MakeShapeFromShapeTensor) {
   }
 }
 
-TEST(ShapeInferenceTest, MakeShapeFromShapeProto) {
+TEST_F(ShapeInferenceTest, MakeShapeFromShapeProto) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(0, 2), {}, {});
   TensorShapeProto proto;
@@ -662,7 +673,7 @@ TEST(ShapeInferenceTest, MakeShapeFromShapeProto) {
   proto.add_dim()->set_size(0);
   EXPECT_EQ("An unknown shape must not have any dimensions set.",
             c.MakeShapeFromShapeProto(proto, &out).error_message());
-  EXPECT_TRUE(out == nullptr);
+  EXPECT_FALSE(IsSet(out));
 
   // With known rank.
   proto.set_unknown_rank(false);
@@ -678,45 +689,45 @@ TEST(ShapeInferenceTest, MakeShapeFromShapeProto) {
   EXPECT_EQ(("Shape [0,?,1000,-2] has dimensions with values below -1 "
              "(where -1 means unknown)"),
             c.MakeShapeFromShapeProto(proto, &out).error_message());
-  EXPECT_TRUE(out == nullptr);
+  EXPECT_FALSE(IsSet(out));
 }
 
-TEST(ShapeInferenceTest, MakeDim) {
+TEST_F(ShapeInferenceTest, MakeDim) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(0, 2), {}, {});
 
-  auto* d0 = c.MakeDim(1);
-  auto* d1 = c.MakeDim(1);
-  auto* d2 = c.MakeDim(2);
+  auto d0 = c.MakeDim(1);
+  auto d1 = c.MakeDim(1);
+  auto d2 = c.MakeDim(2);
   EXPECT_EQ("1", c.DebugString(d0));
   EXPECT_EQ("1", c.DebugString(d1));
-  EXPECT_TRUE(d0 != d1);  // different pointers
+  EXPECT_FALSE(SameHandle(d0, d1));
   EXPECT_EQ("2", c.DebugString(d2));
 }
 
-TEST(ShapeInferenceTest, UnknownDim) {
+TEST_F(ShapeInferenceTest, UnknownDim) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(0, 2), {}, {});
 
-  auto* d0 = c.UnknownDim();
-  auto* d1 = c.UnknownDim();
+  auto d0 = c.UnknownDim();
+  auto d1 = c.UnknownDim();
   EXPECT_EQ("?", c.DebugString(d0));
   EXPECT_EQ("?", c.DebugString(d1));
-  EXPECT_TRUE(d0 != d1);  // different pointers
+  EXPECT_FALSE(SameHandle(d0, d1));
 }
 
-TEST(ShapeInferenceTest, UnknownShapeOfRank) {
+TEST_F(ShapeInferenceTest, UnknownShapeOfRank) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(0, 2), {}, {});
 
-  auto* unknown_shape_of_rank_3 = c.UnknownShapeOfRank(3);
+  auto unknown_shape_of_rank_3 = c.UnknownShapeOfRank(3);
   EXPECT_EQ("[?,?,?]", c.DebugString(unknown_shape_of_rank_3));
 
-  auto* unknown_shape_of_rank_0 = c.UnknownShapeOfRank(0);
+  auto unknown_shape_of_rank_0 = c.UnknownShapeOfRank(0);
   EXPECT_EQ("[]", c.DebugString(unknown_shape_of_rank_0));
 }
 
-TEST(ShapeInferenceTest, InputTensors) {
+TEST_F(ShapeInferenceTest, InputTensors) {
   const Tensor t1 = tensorflow::test::AsTensor<float>({10});
   const Tensor t2 = tensorflow::test::AsTensor<float>({20, 30});
   NodeDef def;
@@ -727,7 +738,7 @@ TEST(ShapeInferenceTest, InputTensors) {
   EXPECT_TRUE(c.input_tensor(2) == nullptr);
 }
 
-TEST(ShapeInferenceTest, MakeDimForScalarInput) {
+TEST_F(ShapeInferenceTest, MakeDimForScalarInput) {
   Tensor t1 = tensorflow::test::AsScalar<int32>(20);
   Tensor t2 = tensorflow::test::AsScalar<int32>(-1);
   NodeDef def;
@@ -752,7 +763,7 @@ TEST(ShapeInferenceTest, MakeDimForScalarInput) {
       c.MakeDimForScalarInput(1, &d).error_message());
 }
 
-TEST(ShapeInferenceTest, GetAttr) {
+TEST_F(ShapeInferenceTest, GetAttr) {
   OpRegistrationData op_reg_data;
   op_reg_data.op_def = MakeOpDef(0, 2);
   NodeDef def;
@@ -767,7 +778,7 @@ TEST(ShapeInferenceTest, GetAttr) {
   EXPECT_EQ("bar", value);
 }
 
-TEST(ShapeInferenceTest, Divide) {
+TEST_F(ShapeInferenceTest, Divide) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(1, 2), {"[6,?]"}, {});
 
@@ -779,13 +790,13 @@ TEST(ShapeInferenceTest, Divide) {
   DimensionHandle out;
   EXPECT_TRUE(c.Divide(d_unknown, 2, &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
-  EXPECT_TRUE(out != d_unknown);
+  EXPECT_FALSE(SameHandle(out, d_unknown));
 
   // Dividing anything by 1 returns the input.
   EXPECT_TRUE(c.Divide(d_unknown, 1, &out).ok());
-  EXPECT_TRUE(out == d_unknown);
+  EXPECT_TRUE(SameHandle(out, d_unknown));
   EXPECT_TRUE(c.Divide(d_6, 1, &out).ok());
-  EXPECT_TRUE(out == d_6);
+  EXPECT_TRUE(SameHandle(out, d_6));
 
   EXPECT_TRUE(c.Divide(d_6, 2, &out).ok());
   EXPECT_EQ("3", c.DebugString(out));
@@ -798,7 +809,7 @@ TEST(ShapeInferenceTest, Divide) {
             c.Divide(d_6, -1, &out).error_message());
 }
 
-TEST(ShapeInferenceTest, Add) {
+TEST_F(ShapeInferenceTest, Add) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(1, 2), {"[6,?,0]"}, {});
 
@@ -811,19 +822,19 @@ TEST(ShapeInferenceTest, Add) {
   DimensionHandle out;
   EXPECT_TRUE(c.Add(d_unknown, 1, &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
-  EXPECT_TRUE(out != d_unknown);
+  EXPECT_FALSE(SameHandle(out, d_unknown));
 
   // Adding 0 to anything gives input.
-  EXPECT_TRUE(c.Add(d_unknown, static_cast<int64>(0), &out).ok());
-  EXPECT_TRUE(out == d_unknown);
-  EXPECT_TRUE(c.Add(d_6, static_cast<int64>(0), &out).ok());
-  EXPECT_TRUE(out == d_6);
+  EXPECT_TRUE(c.Add(d_unknown, 0, &out).ok());
+  EXPECT_TRUE(SameHandle(out, d_unknown));
+  EXPECT_TRUE(c.Add(d_6, 0, &out).ok());
+  EXPECT_TRUE(SameHandle(out, d_6));
 
   // Adding dimension with value 0 to anything gives input.
   EXPECT_TRUE(c.Add(d_unknown, c.MakeDim(0ll), &out).ok());
-  EXPECT_TRUE(out == d_unknown);
+  EXPECT_TRUE(SameHandle(out, d_unknown));
   EXPECT_TRUE(c.Add(d_6, c.MakeDim(0ll), &out).ok());
-  EXPECT_TRUE(out == d_6);
+  EXPECT_TRUE(SameHandle(out, d_6));
 
   // Test addition.
   EXPECT_TRUE(c.Add(d_6, 2, &out).ok());
@@ -840,14 +851,14 @@ TEST(ShapeInferenceTest, Add) {
   EXPECT_TRUE(c.Add(d_6, c.UnknownDim(), &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
   EXPECT_TRUE(c.Add(d_0, d_6, &out).ok());
-  EXPECT_TRUE(out == d_6);
+  EXPECT_TRUE(SameHandle(out, d_6));
 
   EXPECT_EQ(
       "Dimension size overflow from adding 6 and 9223372036854775802",
       c.Add(d_6, std::numeric_limits<int64>::max() - 5, &out).error_message());
 }
 
-TEST(ShapeInferenceTest, Subtract) {
+TEST_F(ShapeInferenceTest, Subtract) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(1, 2), {"[6,?,0,5]"}, {});
 
@@ -861,19 +872,19 @@ TEST(ShapeInferenceTest, Subtract) {
   DimensionHandle out;
   EXPECT_TRUE(c.Subtract(d_unknown, 1, &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
-  EXPECT_TRUE(out != d_unknown);
+  EXPECT_FALSE(SameHandle(out, d_unknown));
 
   // Subtracting 0 from anything gives input.
   EXPECT_TRUE(c.Subtract(d_unknown, 0ll, &out).ok());
-  EXPECT_TRUE(out == d_unknown);
+  EXPECT_TRUE(SameHandle(out, d_unknown));
   EXPECT_TRUE(c.Subtract(d_6, 0ll, &out).ok());
-  EXPECT_TRUE(out == d_6);
+  EXPECT_TRUE(SameHandle(out, d_6));
 
   // Subtracting dimension with value 0 from anything gives input.
   EXPECT_TRUE(c.Subtract(d_unknown, c.MakeDim(0ll), &out).ok());
-  EXPECT_TRUE(out == d_unknown);
+  EXPECT_TRUE(SameHandle(out, d_unknown));
   EXPECT_TRUE(c.Subtract(d_6, c.MakeDim(0ll), &out).ok());
-  EXPECT_TRUE(out == d_6);
+  EXPECT_TRUE(SameHandle(out, d_6));
 
   // Test subtraction.
   EXPECT_TRUE(c.Subtract(d_6, 2, &out).ok());
@@ -889,13 +900,13 @@ TEST(ShapeInferenceTest, Subtract) {
   EXPECT_TRUE(c.Subtract(d_6, c.UnknownDim(), &out).ok());
   EXPECT_EQ("?", c.DebugString(out));
   EXPECT_TRUE(c.Subtract(d_6, d_0, &out).ok());
-  EXPECT_TRUE(out == d_6);
+  EXPECT_TRUE(SameHandle(out, d_6));
 
   EXPECT_EQ("Negative dimension size caused by subtracting 6 from 5",
             c.Subtract(d_5, d_6, &out).error_message());
 }
 
-TEST(ShapeInferenceTest, Multiply) {
+TEST_F(ShapeInferenceTest, Multiply) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(1, 2), {"[6,?,0,1]"}, {});
 
@@ -911,7 +922,7 @@ TEST(ShapeInferenceTest, Multiply) {
   EXPECT_EQ("?", c.DebugString(out));
 
   // Multiplying 0 to anything gives 0.
-  EXPECT_TRUE(c.Multiply(d_unknown, static_cast<int64>(0), &out).ok());
+  EXPECT_TRUE(c.Multiply(d_unknown, 0, &out).ok());
   EXPECT_EQ("0", c.DebugString(out));
   EXPECT_TRUE(c.Multiply(d_unknown, d_0, &out).ok());
   EXPECT_EQ("0", c.DebugString(out));
@@ -921,18 +932,18 @@ TEST(ShapeInferenceTest, Multiply) {
   // Multiplying 1 to anything gives the original.
   // (unknown -> unknown)
   EXPECT_TRUE(c.Multiply(d_unknown, 1, &out).ok());
-  EXPECT_EQ(d_unknown, out);
+  EXPECT_TRUE(SameHandle(d_unknown, out));
   EXPECT_TRUE(c.Multiply(d_unknown, d_1, &out).ok());
-  EXPECT_EQ(d_unknown, out);
+  EXPECT_TRUE(SameHandle(d_unknown, out));
   EXPECT_TRUE(c.Multiply(d_1, d_unknown, &out).ok());
-  EXPECT_EQ(d_unknown, out);
+  EXPECT_TRUE(SameHandle(d_unknown, out));
   // (known -> known)
   EXPECT_TRUE(c.Multiply(d_6, 1, &out).ok());
-  EXPECT_EQ(d_6, out);
+  EXPECT_TRUE(SameHandle(d_6, out));
   EXPECT_TRUE(c.Multiply(d_6, d_1, &out).ok());
-  EXPECT_EQ(d_6, out);
+  EXPECT_TRUE(SameHandle(d_6, out));
   EXPECT_TRUE(c.Multiply(d_1, d_6, &out).ok());
-  EXPECT_EQ(d_6, out);
+  EXPECT_TRUE(SameHandle(d_6, out));
 
   // Test multiplication.
   EXPECT_TRUE(c.Multiply(d_6, 2, &out).ok());
@@ -947,7 +958,7 @@ TEST(ShapeInferenceTest, Multiply) {
   EXPECT_EQ("?", c.DebugString(out));
 }
 
-TEST(ShapeInferenceTest, FullyDefined) {
+TEST_F(ShapeInferenceTest, FullyDefined) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(0, 2), {}, {});
 
@@ -960,7 +971,7 @@ TEST(ShapeInferenceTest, FullyDefined) {
   EXPECT_TRUE(c.FullyDefined(c.Scalar()));
 }
 
-TEST(ShapeInferenceTest, ValidateKnownDim) {
+TEST_F(ShapeInferenceTest, ValidateKnownDim) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(0, 2), {}, {});
 
@@ -968,7 +979,7 @@ TEST(ShapeInferenceTest, ValidateKnownDim) {
   EXPECT_TRUE(c.ValidateKnownDim(c.Dim(c.Matrix(1, 2), 0), "known").ok());
 }
 
-TEST(ShapeInferenceTest, Min) {
+TEST_F(ShapeInferenceTest, Min) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(1, 2), {"[1,2,?,0]"}, {});
 
@@ -981,9 +992,9 @@ TEST(ShapeInferenceTest, Min) {
   // Minimum involving zero and unknown returns zero.
   DimensionHandle out;
   EXPECT_TRUE(c.Min(d_0, d_unknown, &out).ok());
-  EXPECT_EQ(d_0, out);
+  EXPECT_TRUE(SameHandle(d_0, out));
   EXPECT_TRUE(c.Min(d_unknown, d_0, &out).ok());
-  EXPECT_EQ(d_0, out);
+  EXPECT_TRUE(SameHandle(d_0, out));
   EXPECT_TRUE(c.Min(c.MakeDim(0ll), d_unknown, &out).ok());
   EXPECT_EQ("0", c.DebugString(out));
   EXPECT_TRUE(c.Min(d_unknown, 0ll, &out).ok());
@@ -999,24 +1010,24 @@ TEST(ShapeInferenceTest, Min) {
 
   // Minimum with constant second arg.
   EXPECT_TRUE(c.Min(d_1, 1, &out).ok());
-  EXPECT_EQ(d_1, out);
+  EXPECT_TRUE(SameHandle(d_1, out));
   EXPECT_TRUE(c.Min(d_1, 3, &out).ok());
-  EXPECT_EQ(d_1, out);
+  EXPECT_TRUE(SameHandle(d_1, out));
   EXPECT_TRUE(c.Min(d_2, 1, &out).ok());
   EXPECT_EQ("1", c.DebugString(out));
 
   // Minimum with two dimensions.
   EXPECT_TRUE(c.Min(d_1, d_1, &out).ok());
-  EXPECT_EQ(d_1, out);
+  EXPECT_TRUE(SameHandle(d_1, out));
   EXPECT_TRUE(c.Min(d_1, d_2, &out).ok());
-  EXPECT_EQ(d_1, out);
+  EXPECT_TRUE(SameHandle(d_1, out));
   EXPECT_TRUE(c.Min(d_2, d_1, &out).ok());
-  EXPECT_EQ(d_1, out);
+  EXPECT_TRUE(SameHandle(d_1, out));
   EXPECT_TRUE(c.Min(d_2, d_2, &out).ok());
-  EXPECT_EQ(d_2, out);
+  EXPECT_TRUE(SameHandle(d_2, out));
 }
 
-TEST(ShapeInferenceTest, Max) {
+TEST_F(ShapeInferenceTest, Max) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(1, 2), {"[1,2,?]"}, {});
 
@@ -1036,24 +1047,24 @@ TEST(ShapeInferenceTest, Max) {
 
   // Maximum with constant second arg.
   EXPECT_TRUE(c.Max(d_1, 1, &out).ok());
-  EXPECT_EQ(d_1, out);
+  EXPECT_TRUE(SameHandle(d_1, out));
   EXPECT_TRUE(c.Max(d_2, 1, &out).ok());
-  EXPECT_EQ(d_2, out);
+  EXPECT_TRUE(SameHandle(d_2, out));
   EXPECT_TRUE(c.Max(d_2, 3, &out).ok());
   EXPECT_EQ("3", c.DebugString(out));
 
   // Maximum with two dimensions.
   EXPECT_TRUE(c.Max(d_1, d_1, &out).ok());
-  EXPECT_EQ(d_1, out);
+  EXPECT_TRUE(SameHandle(d_1, out));
   EXPECT_TRUE(c.Max(d_1, d_2, &out).ok());
-  EXPECT_EQ(d_2, out);
+  EXPECT_TRUE(SameHandle(d_2, out));
   EXPECT_TRUE(c.Max(d_2, d_1, &out).ok());
-  EXPECT_EQ(d_2, out);
+  EXPECT_TRUE(SameHandle(d_2, out));
   EXPECT_TRUE(c.Max(d_2, d_2, &out).ok());
-  EXPECT_EQ(d_2, out);
+  EXPECT_TRUE(SameHandle(d_2, out));
 }
 
-TEST(ShapeInferenceTest, ValidateSparseTensor_UnknownShapes) {
+TEST_F(ShapeInferenceTest, ValidateSparseTensor_UnknownShapes) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(3, 1), {"?", "?", "?"}, {});
   EXPECT_EQ(3, c.num_inputs());
@@ -1065,7 +1076,7 @@ TEST(ShapeInferenceTest, ValidateSparseTensor_UnknownShapes) {
   TF_EXPECT_OK(c.ValidateSparseTensor(indices, values, shape));
 }
 
-TEST(ShapeInferenceTest, ValidateSparseTensor_UnknownDims) {
+TEST_F(ShapeInferenceTest, ValidateSparseTensor_UnknownDims) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(3, 1), {"[?,?]", "[?]", "[?]"}, {});
   EXPECT_EQ(3, c.num_inputs());
@@ -1077,7 +1088,7 @@ TEST(ShapeInferenceTest, ValidateSparseTensor_UnknownDims) {
   TF_EXPECT_OK(c.ValidateSparseTensor(indices, values, shape));
 }
 
-TEST(ShapeInferenceTest, ValidateSparseTensor_InvalidIndicesRank) {
+TEST_F(ShapeInferenceTest, ValidateSparseTensor_InvalidIndicesRank) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(3, 1), {"[?]", "[?]", "[?]"}, {});
   EXPECT_EQ(3, c.num_inputs());
@@ -1090,7 +1101,7 @@ TEST(ShapeInferenceTest, ValidateSparseTensor_InvalidIndicesRank) {
             c.ValidateSparseTensor(indices, values, shape).code());
 }
 
-TEST(ShapeInferenceTest, ValidateSparseTensor_InvalidNumElements) {
+TEST_F(ShapeInferenceTest, ValidateSparseTensor_InvalidNumElements) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(3, 1), {"[5,3]", "[4]", "[3]"}, {});
   EXPECT_EQ(3, c.num_inputs());
@@ -1103,7 +1114,7 @@ TEST(ShapeInferenceTest, ValidateSparseTensor_InvalidNumElements) {
             c.ValidateSparseTensor(indices, values, shape).code());
 }
 
-TEST(ShapeInferenceTest, ValidateSparseTensor_InvalidRank) {
+TEST_F(ShapeInferenceTest, ValidateSparseTensor_InvalidRank) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(3, 1), {"[5,3]", "[5]", "[4]"}, {});
   EXPECT_EQ(3, c.num_inputs());
@@ -1116,7 +1127,7 @@ TEST(ShapeInferenceTest, ValidateSparseTensor_InvalidRank) {
             c.ValidateSparseTensor(indices, values, shape).code());
 }
 
-TEST(ShapeInferenceTest, ValidateSparseTensor_UnknownNumIndexElements) {
+TEST_F(ShapeInferenceTest, ValidateSparseTensor_UnknownNumIndexElements) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(3, 1), {"[?,3]", "[5]", "[3]"}, {});
   EXPECT_EQ(3, c.num_inputs());
@@ -1128,7 +1139,7 @@ TEST(ShapeInferenceTest, ValidateSparseTensor_UnknownNumIndexElements) {
   TF_EXPECT_OK(c.ValidateSparseTensor(indices, values, shape));
 }
 
-TEST(ShapeInferenceTest, ValidateSparseTensor_UnknownNumValueElements) {
+TEST_F(ShapeInferenceTest, ValidateSparseTensor_UnknownNumValueElements) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(3, 1), {"[5,3]", "[?]", "[3]"}, {});
   EXPECT_EQ(3, c.num_inputs());
@@ -1140,7 +1151,7 @@ TEST(ShapeInferenceTest, ValidateSparseTensor_UnknownNumValueElements) {
   TF_EXPECT_OK(c.ValidateSparseTensor(indices, values, shape));
 }
 
-TEST(ShapeInferenceTest, ValidateSparseTensor_UnknownIndexRank) {
+TEST_F(ShapeInferenceTest, ValidateSparseTensor_UnknownIndexRank) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(3, 1), {"[5,?]", "[5]", "[3]"}, {});
   EXPECT_EQ(3, c.num_inputs());
@@ -1152,7 +1163,7 @@ TEST(ShapeInferenceTest, ValidateSparseTensor_UnknownIndexRank) {
   TF_EXPECT_OK(c.ValidateSparseTensor(indices, values, shape));
 }
 
-TEST(ShapeInferenceTest, ValidateSparseTensor_UnknownShapeRank) {
+TEST_F(ShapeInferenceTest, ValidateSparseTensor_UnknownShapeRank) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(3, 1), {"[5,3]", "[5]", "[?]"}, {});
   EXPECT_EQ(3, c.num_inputs());
@@ -1164,7 +1175,7 @@ TEST(ShapeInferenceTest, ValidateSparseTensor_UnknownShapeRank) {
   TF_EXPECT_OK(c.ValidateSparseTensor(indices, values, shape));
 }
 
-TEST(ShapeInferenceTest, ValidateSparseTensor) {
+TEST_F(ShapeInferenceTest, ValidateSparseTensor) {
   NodeDef def;
   InferenceContext c(&def, MakeOpDef(3, 1), {"[5,3]", "[5]", "[3]"}, {});
   EXPECT_EQ(3, c.num_inputs());
diff --git a/tensorflow/core/framework/shape_inference_testutil.cc b/tensorflow/core/framework/shape_inference_testutil.cc
index 28d2197629..ea6711163c 100644
--- a/tensorflow/core/framework/shape_inference_testutil.cc
+++ b/tensorflow/core/framework/shape_inference_testutil.cc
@@ -14,8 +14,6 @@ limitations under the License.
 ==============================================================================*/
 #include "tensorflow/core/framework/shape_inference_testutil.h"
 
-#include <unordered_map>
-
 #include "tensorflow/core/framework/node_def_util.h"
 #include "tensorflow/core/framework/op.h"
 #include "tensorflow/core/framework/shape_inference.h"
@@ -24,13 +22,13 @@ limitations under the License.
 #include "tensorflow/core/lib/strings/str_util.h"
 
 namespace tensorflow {
+namespace shape_inference {
 
-using shape_inference::DimensionHandle;
-using shape_inference::ShapeHandle;
 using errors::Unknown;
 
-Status InferShapes(ShapeInferenceTestOp op, const string& ins,
-                   const string& expected_outs) {
+Status ShapeInferenceTestutil::InferShapes(ShapeInferenceTestOp op,
+                                           const string& ins,
+                                           const string& expected_outs) {
   const OpRegistrationData* op_reg_data;
   TF_RETURN_IF_ERROR(OpRegistry::Global()->LookUp(op.name, &op_reg_data));
 
@@ -48,16 +46,6 @@ Status InferShapes(ShapeInferenceTestOp op, const string& ins,
   TF_RETURN_IF_ERROR(op_reg_data->shape_inference_fn(&c));
   const int num_outputs = c.num_outputs();
 
-  std::unordered_map<DimensionHandle, std::pair<int, int>>
-      dim_to_input_and_dim_idx;
-  std::unordered_map<ShapeHandle, int> shape_to_input_idx;
-  for (int i = 0; i < c.num_inputs(); ++i) {
-    auto in = c.input(i);
-    shape_to_input_idx[in] = i;
-    for (int j = 0; j < c.Rank(in); ++j) {
-      dim_to_input_and_dim_idx[c.Dim(in, j)] = std::make_pair(i, j);
-    }
-  }
   if (expected_outs == "e") {
     return Unknown("Shape inference should have returned error");
   }
@@ -70,13 +58,19 @@ Status InferShapes(ShapeInferenceTestOp op, const string& ins,
   }
   for (int i = 0; i < num_outputs; ++i) {
     StringPiece expected(expected_outs_v[i]);
-    const shape_inference::Shape* out = c.output(i);
+    shape_inference::ShapeHandle out = c.output(i);
 
     string err_prefix = strings::StrCat("Output ", i);
     string err_suffix =
         strings::StrCat("; output shape was ", c.DebugString(out));
 
-    const int in_index = gtl::FindWithDefault(shape_to_input_idx, out, -1);
+    int in_index = -1;
+    for (int i = 0; i < c.num_inputs(); ++i) {
+      if (c.input(i).SameHandle(out)) {
+        in_index = i;
+      }
+    }
+
     if (expected.starts_with("in")) {
       if (in_index == -1) {
         return Unknown(err_prefix, " did not match any input shape",
@@ -121,8 +115,17 @@ Status InferShapes(ShapeInferenceTestOp op, const string& ins,
       err_prefix = strings::StrCat("Output dim ", i, ",", j);
       StringPiece expected_dim(expected_dims[j]);
       DimensionHandle out_dim = c.Dim(out, j);
-      std::pair<int, int> in_dim_idx = gtl::FindWithDefault(
-          dim_to_input_and_dim_idx, out_dim, std::make_pair(-1, -1));
+
+      std::pair<int, int> in_dim_idx(-1, -1);
+      for (int i = 0; i < c.num_inputs(); ++i) {
+        auto in = c.input(i);
+        for (int j = 0; j < c.Rank(in); ++j) {
+          if (c.Dim(in, j).SameHandle(out_dim)) {
+            in_dim_idx = std::make_pair(i, j);
+          }
+        }
+      }
+
       if (expected_dim == "?") {
         if (in_dim_idx.first != -1) {
           return Unknown(err_prefix,
@@ -167,4 +170,5 @@ Status InferShapes(ShapeInferenceTestOp op, const string& ins,
   return Status::OK();
 }
 
+}  // namespace shape_inference
 }  // namespace tensorflow
diff --git a/tensorflow/core/framework/shape_inference_testutil.h b/tensorflow/core/framework/shape_inference_testutil.h
index b4561496c5..b5d187405a 100644
--- a/tensorflow/core/framework/shape_inference_testutil.h
+++ b/tensorflow/core/framework/shape_inference_testutil.h
@@ -35,39 +35,56 @@ struct ShapeInferenceTestOp {
   std::vector<const Tensor*> input_tensors;
 };
 
-// Run shape inference for <op.name>, given inputs specified by <ins>
-// and returns an error if the inferred shape does not match expected_outs.
-//
-// <ins> is a semicolon separated list of shapes. Each shape is formatted
-// according to the formatting per
-// shape_inference::InferenceContext::InferenceContext.
-//
-// <expected_outs> is a semicolon separated list of shapes. Each shape is
-// formatted as one of:
-// * ? - an unknown shape, but not matching an input shape
-// * in0|in2|... - output shape must be the same as one of these input shapes.
-// * [1,?,d0_0|d0_1] - output shape is of known rank, with comma-separated
-//      dimension values.
-//      Each dimension value is one of:
-//      * a constant, which means that constant not equal to a specific input
-//      * ?, which means an unknown dim size not equal to a specific input
-//      * d0_0|d1_2, indicating that the dim size must be equal to one of
-//            the given input dimensions; the first number is the input # and
-//            the second is which dimension in that input it corresponds to.
-// <expected_outs> can be "e"; this is used to indicate that shape inference
-// should have failed.
-Status InferShapes(ShapeInferenceTestOp op, const string& ins,
-                   const string& expected_outs);
+namespace shape_inference {
 
-#define INFER_OK(op, i, o) EXPECT_EQ("", InferShapes(op, i, o).error_message())
-#define INFER_ERROR(error_substring, op, i)                              \
-  {                                                                      \
-    string error_message = InferShapes(op, i, "e").error_message();      \
-    const string& substring = error_substring;                           \
-    EXPECT_NE("", error_message);                                        \
-    EXPECT_TRUE(StringPiece(error_message).contains(substring))          \
-        << "Expected to see '" << substring << "' in '" << error_message \
-        << "'";                                                          \
+class ShapeInferenceTestutil {
+ public:
+  // Run shape inference for <op.name>, given inputs specified by <ins>
+  // and returns an error if the inferred shape does not match expected_outs.
+  //
+  // <ins> is a semicolon separated list of shapes. Each shape is formatted
+  // according to the formatting per
+  // shape_inference::InferenceContext::InferenceContext.
+  //
+  // <expected_outs> is a semicolon separated list of shapes. Each shape is
+  // formatted as one of:
+  // * ? - an unknown shape, but not matching an input shape
+  // * in0|in2|... - output shape must be the same as one of these input shapes.
+  // * [1,?,d0_0|d0_1] - output shape is of known rank, with comma-separated
+  //      dimension values.
+  //      Each dimension value is one of:
+  //      * a constant, which means that constant not equal to a specific input
+  //      * ?, which means an unknown dim size not equal to a specific input
+  //      * d0_0|d1_2, indicating that the dim size must be equal to one of
+  //            the given input dimensions; the first number is the input # and
+  //            the second is which dimension in that input it corresponds to.
+  // <expected_outs> can be "e"; this is used to indicate that shape inference
+  // should have failed.
+  static Status InferShapes(ShapeInferenceTestOp op, const string& ins,
+                            const string& expected_outs);
+
+ private:
+  ShapeInferenceTestutil() {}
+};
+
+}  // namespace shape_inference
+
+#define INFER_OK(op, i, o)                                                    \
+  EXPECT_EQ(                                                                  \
+      "", ::tensorflow::shape_inference::ShapeInferenceTestutil::InferShapes( \
+              op, i, o)                                                       \
+              .error_message())
+#define INFER_ERROR(error_substring, op, i)                                 \
+  {                                                                         \
+    string error_message =                                                  \
+        ::tensorflow::shape_inference::ShapeInferenceTestutil::InferShapes( \
+            op, i, "e")                                                     \
+            .error_message();                                               \
+    const string& substring = error_substring;                              \
+    EXPECT_NE("", error_message);                                           \
+    EXPECT_TRUE(StringPiece(error_message).contains(substring))             \
+        << "Expected to see '" << substring << "' in '" << error_message    \
+        << "'";                                                             \
   }
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/framework/shape_inference_testutil_test.cc b/tensorflow/core/framework/shape_inference_testutil_test.cc
index 4d6d5858b4..61a8f8c9c2 100644
--- a/tensorflow/core/framework/shape_inference_testutil_test.cc
+++ b/tensorflow/core/framework/shape_inference_testutil_test.cc
@@ -21,9 +21,7 @@ limitations under the License.
 #include "tensorflow/core/platform/test.h"
 
 namespace tensorflow {
-
-using shape_inference::InferenceContext;
-static constexpr auto kUnknownDim = InferenceContext::kUnknownDim;
+namespace shape_inference {
 
 namespace {
 
@@ -54,7 +52,8 @@ string RunInferShapes(const string& op_name, const string& ins,
                   .Attr("N", num_inputs)
                   .Finalize(&op.node_def));
   global_fn_ptr = &fn;
-  return InferShapes(op, ins, expected_outs).error_message();
+  return ShapeInferenceTestutil::InferShapes(op, ins, expected_outs)
+      .error_message();
 }
 
 }  // namespace
@@ -79,7 +78,7 @@ TEST(ShapeInferenceTestutilTest, Failures) {
     return Status::OK();
   };
   auto fn_output_u_2 = [](InferenceContext* c) {
-    c->set_output(0, c->Matrix(kUnknownDim, 2));
+    c->set_output(0, c->Matrix(InferenceContext::kUnknownDim, 2));
     return Status::OK();
   };
   const string& op = "OpOneOut";
@@ -88,9 +87,10 @@ TEST(ShapeInferenceTestutilTest, Failures) {
             RunInferShapes(op, "[1];[2];[1]", "e", fn_copy_input_0));
   EXPECT_EQ("Wrong number of expected outputs (2 vs 1)",
             RunInferShapes(op, "[1];[2];[1]", "[1];[2]", fn_copy_input_0));
-  EXPECT_EQ(
-      "Op type not registered 'NoSuchOp'",
-      InferShapes(ShapeInferenceTestOp("NoSuchOp"), "", "").error_message());
+  EXPECT_EQ("Op type not registered 'NoSuchOp'",
+            ShapeInferenceTestutil::InferShapes(
+                ShapeInferenceTestOp("NoSuchOp"), "", "")
+                .error_message());
 
   // Wrong shape error messages.
   EXPECT_EQ(
@@ -176,4 +176,5 @@ TEST(ShapeInferenceTestutilTest, Failures) {
             RunInferShapes(op, ins, "[d0_1,2,?,d0_0|d2_0]", fn));
 }
 
+}  // namespace shape_inference
 }  // namespace tensorflow
diff --git a/tensorflow/core/graph/shape_inferer.cc b/tensorflow/core/graph/shape_inferer.cc
index d27c83d8d5..49f0bf297f 100644
--- a/tensorflow/core/graph/shape_inferer.cc
+++ b/tensorflow/core/graph/shape_inferer.cc
@@ -31,7 +31,7 @@ Status ShapeInferer::AddNode(const Node* node) {
   // from 'input's InferenceContext, and store into a vector
   // indexed by 'node's input.
   std::vector<const Node*> input_nodes(node->num_inputs());
-  std::vector<const shape_inference::Shape*> input_shapes(node->num_inputs());
+  std::vector<shape_inference::ShapeHandle> input_shapes(node->num_inputs());
   for (const Edge* e : node->in_edges()) {
     if (e->IsControlEdge()) continue;
 
diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index 051e0c1302..65052e565c 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -1386,7 +1386,7 @@ REGISTER_OP("UniqueWithCounts")
     .Output("count: int32")
     .Attr("T: type")
     .SetShapeFn([](InferenceContext* c) {
-      auto* uniq = c->Vector(InferenceContext::kUnknownDim);
+      auto uniq = c->Vector(InferenceContext::kUnknownDim);
       c->set_output(0, uniq);
       c->set_output(1, c->input(0));
       c->set_output(2, uniq);
diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index 4d42ab4b47..02cf34182d 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -65,14 +65,15 @@ Status BroadcastBinaryOpShapeFn(InferenceContext* c) {
   // and
   // pad with 1 to make them the same length.
   std::vector<DimensionHandle> dims;
-  DimensionHandle dim_one = rank_x == rank_y ? nullptr : c->MakeDim(1);
+  DimensionHandle dim_one;
+  if (rank_x != rank_y) dim_one = c->MakeDim(1);
   for (int i = 0; i < rank_out; ++i) {
-    const auto* dim_x = i < (rank_out - rank_x)
-                            ? dim_one
-                            : c->Dim(shape_x, i - (rank_out - rank_x));
-    const auto* dim_y = i < (rank_out - rank_y)
-                            ? dim_one
-                            : c->Dim(shape_y, i - (rank_out - rank_y));
+    const auto dim_x = i < (rank_out - rank_x)
+                           ? dim_one
+                           : c->Dim(shape_x, i - (rank_out - rank_x));
+    const bool dim_y_is_one = (i < (rank_out - rank_y));
+    const auto dim_y =
+        dim_y_is_one ? dim_one : c->Dim(shape_y, i - (rank_out - rank_y));
     if (!c->ValueKnown(dim_x) || !c->ValueKnown(dim_y)) {
       // One or both dimensions is unknown.
       //
@@ -94,7 +95,7 @@ Status BroadcastBinaryOpShapeFn(InferenceContext* c) {
         dims.push_back(c->UnknownDim());
       }
     } else if (c->Value(dim_x) == 1 || c->Value(dim_y) == 1) {
-      if (c->Value(dim_x) == 1 && dim_y != dim_one) {
+      if (c->Value(dim_x) == 1 && !dim_y_is_one) {
         // We will broadcast dim_x to dim_y.
         dims.push_back(dim_y);
       } else {
@@ -833,7 +834,7 @@ REGISTER_OP("Select")
               DimensionHandle merged_dim;
               TF_RETURN_IF_ERROR(
                   c->Merge(c->Dim(data, 0), c->Dim(cond, 0), &merged_dim));
-              if (merged_dim != c->Dim(data, 0)) {
+              if (c->Value(merged_dim) != c->Value(c->Dim(data, 0))) {
                 // Merging used the cond dim.  Update data to refer to it.
                 std::vector<DimensionHandle> dims{merged_dim};
                 for (int i = 1; i < data_rank; ++i) {
diff --git a/tensorflow/python/framework/cpp_shape_inference.cc b/tensorflow/python/framework/cpp_shape_inference.cc
index 49ef244944..dcf9243fb5 100644
--- a/tensorflow/python/framework/cpp_shape_inference.cc
+++ b/tensorflow/python/framework/cpp_shape_inference.cc
@@ -64,12 +64,12 @@ Status RunCppShapeInferenceImpl(
   output_tensor_shape_protos->resize(c.num_outputs());
   TensorShapeProto out;
   for (int i = 0; i < c.num_outputs(); ++i) {
-    const shape_inference::Shape* s = c.output(i);
+    shape_inference::ShapeHandle s = c.output(i);
     out.Clear();
     if (c.RankKnown(s)) {
       const int32 rank = c.Rank(s);
       for (int i = 0; i < rank; ++i) {
-        const shape_inference::Dimension* d = c.Dim(s, i);
+        shape_inference::DimensionHandle d = c.Dim(s, i);
         auto* out_dim = out.add_dim();
         if (c.ValueKnown(d)) {
           out_dim->set_size(c.Value(d));
-- 
GitLab


From 4a2ea16d83c5f172aef88eead51132705c26c3e9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 24 Aug 2016 12:37:43 -0800
Subject: [PATCH 105/223] Make tests that rely on thread registration with the
 coordinator more robust by waiting for the threads to register before joining
 the coordinator.

Also make test that used StopInN more robust by using Events.
Change: 131210034
---
 .../python/training/coordinator_test.py       | 30 ++++++++++++++++---
 1 file changed, 26 insertions(+), 4 deletions(-)

diff --git a/tensorflow/python/training/coordinator_test.py b/tensorflow/python/training/coordinator_test.py
index 4e20e7c3d3..1e691d2a70 100644
--- a/tensorflow/python/training/coordinator_test.py
+++ b/tensorflow/python/training/coordinator_test.py
@@ -25,9 +25,10 @@ import time
 import tensorflow as tf
 
 
-def StopInN(coord, n_secs):
-  time.sleep(n_secs)
+def StopOnEvent(coord, wait_for_stop, set_when_stopped):
+  wait_for_stop.wait()
   coord.request_stop()
+  set_when_stopped.set()
 
 
 def RaiseInN(coord, n_secs, ex, report_exception):
@@ -53,6 +54,14 @@ def SleepABit(n_secs, coord=None):
   time.sleep(n_secs)
 
 
+def WaitForThreadsToRegister(coord, num_threads):
+  while True:
+    with coord._lock:
+      if len(coord._registered_threads) == num_threads:
+        break
+    time.sleep(0.001)
+
+
 class CoordinatorTest(tf.test.TestCase):
 
   def testStopAPI(self):
@@ -67,9 +76,15 @@ class CoordinatorTest(tf.test.TestCase):
     coord = tf.train.Coordinator()
     self.assertFalse(coord.should_stop())
     self.assertFalse(coord.wait_for_stop(0.1))
-    threading.Thread(target=StopInN, args=(coord, 0.02)).start()
+    wait_for_stop_ev = threading.Event()
+    has_stopped_ev = threading.Event()
+    t = threading.Thread(target=StopOnEvent,
+                         args=(coord, wait_for_stop_ev, has_stopped_ev))
+    t.start()
     self.assertFalse(coord.should_stop())
     self.assertFalse(coord.wait_for_stop(0.01))
+    wait_for_stop_ev.set()
+    has_stopped_ev.wait()
     self.assertTrue(coord.wait_for_stop(0.05))
     self.assertTrue(coord.should_stop())
 
@@ -93,6 +108,7 @@ class CoordinatorTest(tf.test.TestCase):
         threading.Thread(target=SleepABit, args=(0.01, coord))]
     for t in threads:
       t.start()
+    WaitForThreadsToRegister(coord, 3)
     coord.join()
     for t in threads:
       self.assertFalse(t.is_alive())
@@ -105,6 +121,7 @@ class CoordinatorTest(tf.test.TestCase):
         threading.Thread(target=SleepABit, args=(0.01, coord))]
     for t in threads:
       t.start()
+    WaitForThreadsToRegister(coord, 2)
     # threads[1] is not registered we must pass it in.
     coord.join(threads[1:1])
     for t in threads:
@@ -113,12 +130,17 @@ class CoordinatorTest(tf.test.TestCase):
   def testJoinGraceExpires(self):
     def TestWithGracePeriod(stop_grace_period):
       coord = tf.train.Coordinator()
+      wait_for_stop_ev = threading.Event()
+      has_stopped_ev = threading.Event()
       threads = [
-          threading.Thread(target=StopInN, args=(coord, 0.01)),
+          threading.Thread(target=StopOnEvent,
+                           args=(coord, wait_for_stop_ev, has_stopped_ev)),
           threading.Thread(target=SleepABit, args=(10.0,))]
       for t in threads:
         t.daemon = True
         t.start()
+      wait_for_stop_ev.set()
+      has_stopped_ev.wait()
       with self.assertRaisesRegexp(RuntimeError, "threads still running"):
         coord.join(threads, stop_grace_period_secs=stop_grace_period)
     TestWithGracePeriod(1e-10)
-- 
GitLab


From 48572a0836cbfc8beff3a34d22ff8947f9c4bfe8 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Wed, 24 Aug 2016 12:42:19 -0800
Subject: [PATCH 106/223] Relax assumption about default file permission in
 file_io_test.py Change: 131210593

---
 tensorflow/python/lib/io/file_io_test.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/lib/io/file_io_test.py b/tensorflow/python/lib/io/file_io_test.py
index 475299ad0e..a4cef8cb5a 100644
--- a/tensorflow/python/lib/io/file_io_test.py
+++ b/tensorflow/python/lib/io/file_io_test.py
@@ -276,7 +276,10 @@ class FileIoTest(tf.test.TestCase):
     self.assertEquals(7, file_statistics.length)
     self.assertEqual(
         int(os_statistics.st_mtime), int(file_statistics.mtime_nsec / 1e9))
-    self.assertEqual(33188, file_statistics.mode)
+
+    # 644 and 666 are the two possible default permissions of newly-created
+    # files.
+    self.assertTrue(file_statistics.mode in [0o100644, 0o100666])
 
   def testReadLine(self):
     file_path = os.path.join(self._base_dir, "temp_file")
-- 
GitLab


From d03f2545ecc3012e6c941a3a1e957f7d7f8d5040 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 24 Aug 2016 23:02:26 +0100
Subject: [PATCH 107/223] Add a new target including the C api. (#3830)

* Add a new target including the C api.

* Fix the dependencies order.
---
 tensorflow/BUILD | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 21f301285e..efd2adb3bd 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -182,6 +182,15 @@ cc_binary(
     ],
 )
 
+cc_binary(
+    name = "libtensorflow_c.so",
+    linkshared = 1,
+    deps = [
+        "//tensorflow/c:c_api",
+        "//tensorflow/core:tensorflow",
+    ],
+)
+
 cc_binary(
     name = "libtensorflow_cc.so",
     linkshared = 1,
-- 
GitLab


From fe8ee1f1c5bbaf3ea6ab188175263688b6da7fa3 Mon Sep 17 00:00:00 2001
From: Sangheum <sangheum.hwang@gmail.com>
Date: Thu, 25 Aug 2016 07:03:21 +0900
Subject: [PATCH 108/223] Fix TypeError in gradient clipping ops (#3707)

* Fix TypeError in gradient clipping ops

* Fix whitespace issue
---
 .../python/kernel_tests/clip_ops_test.py      | 40 +++++++++++++++++++
 tensorflow/python/ops/clip_ops.py             |  4 +-
 2 files changed, 42 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/kernel_tests/clip_ops_test.py b/tensorflow/python/kernel_tests/clip_ops_test.py
index 1f9e69713a..bb842a2998 100644
--- a/tensorflow/python/kernel_tests/clip_ops_test.py
+++ b/tensorflow/python/kernel_tests/clip_ops_test.py
@@ -57,8 +57,13 @@ class ClipTest(tf.test.TestCase):
       clip_norm = 4.0
       ans = tf.clip_by_norm(x, clip_norm)
       tf_ans = ans.eval()
+      
+      clip_tensor = tf.constant(4.0)
+      ans = tf.clip_by_norm(x, clip_norm)
+      tf_ans_tensor = ans.eval()
 
     self.assertAllClose(np_ans, tf_ans)
+    self.assertAllClose(np_ans, tf_ans_tensor)
 
   def testClipByNormNotClipped(self):
     # No norm clipping when clip_norm >= 5
@@ -148,6 +153,28 @@ class ClipTest(tf.test.TestCase):
     self.assertAllClose(np_ans_0, tf_ans_1)
     self.assertAllClose(np_ans_1, tf_ans_2)
 
+  def testClipByGlobalNormClippedTensor(self):
+    # Norm clipping when clip_norm < 5
+    with self.test_session():
+      x0 = tf.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
+      x1 = tf.constant([1.0, -2.0])
+      # Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
+      clip_norm = tf.constant(4.0)
+
+      # Answers are the original tensors scaled by 4.0/5.0
+      np_ans_0 = [[-1.6, 0.0, 0.0],
+                  [3.2, 0.0, 0.0]]
+      np_ans_1 = [0.8, -1.6]
+
+      ans, norm = tf.clip_by_global_norm((x0, x1), clip_norm)
+      tf_ans_1 = ans[0].eval()
+      tf_ans_2 = ans[1].eval()
+      tf_norm = norm.eval()
+
+    self.assertAllClose(tf_norm, 5.0)
+    self.assertAllClose(np_ans_0, tf_ans_1)
+    self.assertAllClose(np_ans_1, tf_ans_2)
+
   def testClipByGlobalNormSupportsNone(self):
     # Norm clipping when clip_norm < 5
     with self.test_session():
@@ -259,6 +286,19 @@ class ClipTest(tf.test.TestCase):
 
     self.assertAllClose(np_ans, tf_ans)
 
+  def testClipByAverageNormClippedTensor(self):
+    # Norm clipping when average clip_norm < 0.83333333
+    with self.test_session():
+      x = tf.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
+      # Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
+      np_ans = [[-2.88, 0.0, 0.0],
+                [3.84, 0.0, 0.0]]
+      clip_norm = tf.constant(0.8)
+      ans = tf.clip_by_average_norm(x, clip_norm)
+      tf_ans = ans.eval()
+
+    self.assertAllClose(np_ans, tf_ans)
+
   def testClipByAverageNormNotClipped(self):
     # No norm clipping when average clip_norm >= 0.83333333
     with self.test_session():
diff --git a/tensorflow/python/ops/clip_ops.py b/tensorflow/python/ops/clip_ops.py
index 9f2f972f7d..65e05626f0 100644
--- a/tensorflow/python/ops/clip_ops.py
+++ b/tensorflow/python/ops/clip_ops.py
@@ -206,7 +206,7 @@ def clip_by_global_norm(t_list, clip_norm, use_norm=None, name=None):
     # Calculate L2-norm, clip elements by ratio of clip_norm to L2-norm
     scale = clip_norm * math_ops.minimum(
         1.0 / use_norm,
-        constant_op.constant(1.0 / clip_norm, dtype=use_norm.dtype))
+        constant_op.constant(1.0, dtype=use_norm.dtype) / clip_norm)
 
     values = [
         ops.convert_to_tensor(
@@ -268,7 +268,7 @@ def clip_by_average_norm(t, clip_norm, name=None):
         math_ops.reduce_sum(t * t, math_ops.range(array_ops.rank(t))))
     tclip = array_ops.identity(
         t * clip_norm * math_ops.minimum(
-            l2norm_inv * n_element, constant_op.constant(1.0 / clip_norm)),
+            l2norm_inv * n_element, constant_op.constant(1.0) / clip_norm),
         name=name)
 
   return tclip
-- 
GitLab


From bef05b0b1af222d58ca3e49df29770f71dec3973 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 24 Aug 2016 13:58:30 -0800
Subject: [PATCH 109/223] Reduce the size of Node objects by using the class_
 field instead of is_host_send_ and is_host_recv_. Change: 131219758

---
 tensorflow/core/graph/graph.cc | 14 ++++---------
 tensorflow/core/graph/graph.h  | 38 +++++++++++++++++-----------------
 2 files changed, 23 insertions(+), 29 deletions(-)

diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc
index a47fd89975..95f1c06a2c 100644
--- a/tensorflow/core/graph/graph.cc
+++ b/tensorflow/core/graph/graph.cc
@@ -46,8 +46,6 @@ Node::Node()
     : id_(-1),
       cost_id_(-1),
       class_(NC_UNINITIALIZED),
-      is_host_send_(false),
-      is_host_recv_(false),
       props_(nullptr),
       assigned_device_name_() {}
 
@@ -89,8 +87,10 @@ void Node::Initialize(int id, int cost_id, Properties* props) {
   SET_CLASS(NC_NEXT_ITERATION, ts, "NextIteration", "RefNextIteration");
   SET_CLASS(NC_LOOP_COND, ts, "LoopCond", "");
   SET_CLASS(NC_CONTROL_TRIGGER, ts, "ControlTrigger", "");
-  SET_CLASS(NC_SEND, ts, "_Send", "_HostSend");
-  SET_CLASS(NC_RECV, ts, "_Recv", "_HostRecv");
+  SET_CLASS(NC_SEND, ts, "_Send", "");
+  SET_CLASS(NC_HOST_SEND, ts, "_HostSend", "");
+  SET_CLASS(NC_RECV, ts, "_Recv", "");
+  SET_CLASS(NC_HOST_RECV, ts, "_HostRecv", "");
   SET_CLASS(NC_CONSTANT, ts, "Const", "HostConst");
   SET_CLASS(NC_VARIABLE, ts, "Variable", "");
   SET_CLASS(NC_IDENTITY, ts, "Identity", "RefIdentity");
@@ -101,12 +101,6 @@ void Node::Initialize(int id, int cost_id, Properties* props) {
     class_ = NC_OTHER;  // Catch all
   }
 #undef SET_CLASS
-
-  if (ts == "_HostSend") {
-    is_host_send_ = true;
-  } else if (ts == "_HostRecv") {
-    is_host_recv_ = true;
-  }
 }
 
 void Node::Clear() {
diff --git a/tensorflow/core/graph/graph.h b/tensorflow/core/graph/graph.h
index 3dc80a07a4..5a3008b317 100644
--- a/tensorflow/core/graph/graph.h
+++ b/tensorflow/core/graph/graph.h
@@ -106,30 +106,30 @@ class Node {
   bool IsOp() const { return id() > 1; }
 
   // Node class helpers
-  bool IsSwitch() const { return (class_ == NC_SWITCH); }
-  bool IsMerge() const { return (class_ == NC_MERGE); }
-  bool IsEnter() const { return (class_ == NC_ENTER); }
-  bool IsExit() const { return (class_ == NC_EXIT); }
-  bool IsNextIteration() const { return (class_ == NC_NEXT_ITERATION); }
-  bool IsLoopCond() const { return (class_ == NC_LOOP_COND); }
-  bool IsControlTrigger() const { return (class_ == NC_CONTROL_TRIGGER); }
-  bool IsSend() const { return (class_ == NC_SEND); }
-  bool IsRecv() const { return (class_ == NC_RECV); }
-  bool IsConstant() const { return (class_ == NC_CONSTANT); }
-  bool IsVariable() const { return (class_ == NC_VARIABLE); }
-  bool IsIdentity() const { return (class_ == NC_IDENTITY); }
-  bool IsGetSessionHandle() const { return (class_ == NC_GET_SESSION_HANDLE); }
-  bool IsGetSessionTensor() const { return (class_ == NC_GET_SESSION_TENSOR); }
+  bool IsSwitch() const { return class_ == NC_SWITCH; }
+  bool IsMerge() const { return class_ == NC_MERGE; }
+  bool IsEnter() const { return class_ == NC_ENTER; }
+  bool IsExit() const { return class_ == NC_EXIT; }
+  bool IsNextIteration() const { return class_ == NC_NEXT_ITERATION; }
+  bool IsLoopCond() const { return class_ == NC_LOOP_COND; }
+  bool IsControlTrigger() const { return class_ == NC_CONTROL_TRIGGER; }
+  bool IsSend() const { return class_ == NC_SEND || class_ == NC_HOST_SEND; }
+  bool IsRecv() const { return class_ == NC_RECV || class_ == NC_HOST_RECV; }
+  bool IsConstant() const { return class_ == NC_CONSTANT; }
+  bool IsVariable() const { return class_ == NC_VARIABLE; }
+  bool IsIdentity() const { return class_ == NC_IDENTITY; }
+  bool IsGetSessionHandle() const { return class_ == NC_GET_SESSION_HANDLE; }
+  bool IsGetSessionTensor() const { return class_ == NC_GET_SESSION_TENSOR; }
   bool IsDeleteSessionTensor() const {
-    return (class_ == NC_DELETE_SESSION_TENSOR);
+    return class_ == NC_DELETE_SESSION_TENSOR;
   }
   bool IsControlFlow() const {
     return (class_ != NC_OTHER) &&  // Fast path
            (IsSwitch() || IsMerge() || IsEnter() || IsExit() ||
             IsNextIteration());
   }
-  bool IsHostSend() const { return is_host_send_; }
-  bool IsHostRecv() const { return is_host_recv_; }
+  bool IsHostSend() const { return class_ == NC_HOST_SEND; }
+  bool IsHostRecv() const { return class_ == NC_HOST_RECV; }
 
   template <typename T>
   void AddAttr(const string& name, const T& val) {
@@ -187,7 +187,9 @@ class Node {
     NC_LOOP_COND,
     NC_CONTROL_TRIGGER,
     NC_SEND,
+    NC_HOST_SEND,
     NC_RECV,
+    NC_HOST_RECV,
     NC_CONSTANT,
     NC_VARIABLE,
     NC_IDENTITY,
@@ -200,8 +202,6 @@ class Node {
   int id_;       // -1 until Initialize() is called
   int cost_id_;  // -1 if there is no corresponding cost accounting node
   NodeClass class_;
-  bool is_host_send_;
-  bool is_host_recv_;
 
   EdgeSet in_edges_;
   EdgeSet out_edges_;
-- 
GitLab


From e207eff09537b3ff428da31b45b00dfd34d2a06c Mon Sep 17 00:00:00 2001
From: Ian Langmore <langmore@google.com>
Date: Wed, 24 Aug 2016 14:49:13 -0800
Subject: [PATCH 110/223] Test time increase for entropy_test.  Was timing out
 in gcudacc tests. Change: 131225577

---
 tensorflow/contrib/bayesflow/BUILD | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/bayesflow/BUILD b/tensorflow/contrib/bayesflow/BUILD
index 7ba2ac4515..cf88489e8a 100644
--- a/tensorflow/contrib/bayesflow/BUILD
+++ b/tensorflow/contrib/bayesflow/BUILD
@@ -19,7 +19,7 @@ py_library(
 
 cuda_py_test(
     name = "entropy_test",
-    size = "small",
+    size = "medium",
     srcs = ["python/kernel_tests/entropy_test.py"],
     additional_deps = [
         ":bayesflow_py",
-- 
GitLab


From 399384b96981200e394721935c6231645207d10b Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@google.com>
Date: Wed, 24 Aug 2016 14:52:55 -0800
Subject: [PATCH 111/223] Add individual arg_scopes for each inception, and
 test them. Change: 131226037

---
 .../contrib/slim/python/slim/learning.py      |  2 +-
 .../slim/python/slim/nets/inception.py        |  2 +
 .../slim/python/slim/nets/inception_v1.py     | 49 +++++++++++++++++++
 .../python/slim/nets/inception_v1_test.py     |  3 +-
 .../slim/python/slim/nets/inception_v2.py     | 40 +++++++++++++++
 .../python/slim/nets/inception_v2_test.py     |  5 +-
 .../slim/python/slim/nets/inception_v3.py     |  7 ++-
 .../python/slim/nets/inception_v3_test.py     |  3 +-
 .../slim/python/slim/nets/resnet_utils.py     |  5 +-
 9 files changed, 102 insertions(+), 14 deletions(-)

diff --git a/tensorflow/contrib/slim/python/slim/learning.py b/tensorflow/contrib/slim/python/slim/learning.py
index 4e48f9c6fd..59fecea0c9 100644
--- a/tensorflow/contrib/slim/python/slim/learning.py
+++ b/tensorflow/contrib/slim/python/slim/learning.py
@@ -534,7 +534,7 @@ def train_step(sess, train_op, global_step, train_step_kwargs):
 
   if 'should_log' in train_step_kwargs:
     if sess.run(train_step_kwargs['should_log']):
-      logging.info('global step %d: loss = %.4f (%.2f sec)',
+      logging.info('global step %d: loss = %.4f (%.2f sec/step)',
                    np_global_step, total_loss, time_elapsed)
 
   # TODO(nsilberman): figure out why we can't put this into sess.run. The
diff --git a/tensorflow/contrib/slim/python/slim/nets/inception.py b/tensorflow/contrib/slim/python/slim/nets/inception.py
index b6ec1b9da6..6f50025644 100644
--- a/tensorflow/contrib/slim/python/slim/nets/inception.py
+++ b/tensorflow/contrib/slim/python/slim/nets/inception.py
@@ -20,8 +20,10 @@ from __future__ import print_function
 
 # pylint: disable=unused-import
 from tensorflow.contrib.slim.python.slim.nets.inception_v1 import inception_v1
+from tensorflow.contrib.slim.python.slim.nets.inception_v1 import inception_v1_arg_scope
 from tensorflow.contrib.slim.python.slim.nets.inception_v1 import inception_v1_base
 from tensorflow.contrib.slim.python.slim.nets.inception_v2 import inception_v2
+from tensorflow.contrib.slim.python.slim.nets.inception_v2 import inception_v2_arg_scope
 from tensorflow.contrib.slim.python.slim.nets.inception_v2 import inception_v2_base
 from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3
 from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3_arg_scope
diff --git a/tensorflow/contrib/slim/python/slim/nets/inception_v1.py b/tensorflow/contrib/slim/python/slim/nets/inception_v1.py
index 1e4ed212a8..8b9e3254a3 100644
--- a/tensorflow/contrib/slim/python/slim/nets/inception_v1.py
+++ b/tensorflow/contrib/slim/python/slim/nets/inception_v1.py
@@ -299,3 +299,52 @@ def inception_v1(inputs,
         end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
   return logits, end_points
 inception_v1.default_image_size = 224
+
+
+def inception_v1_arg_scope(weight_decay=0.00004,
+                           use_batch_norm=True,
+                           batch_norm_var_collection='moving_vars'):
+  """Defines the default InceptionV1 arg scope.
+
+  Note: Althougth the original paper didn't use batch_norm we found it useful.
+
+  Args:
+    weight_decay: The weight decay to use for regularizing the model.
+    use_batch_norm: "If `True`, batch_norm is applied after each convolution.
+    batch_norm_var_collection: The name of the collection for the batch norm
+      variables.
+
+  Returns:
+    An `arg_scope` to use for the inception v3 model.
+  """
+  batch_norm_params = {
+      # Decay for the moving averages.
+      'decay': 0.9997,
+      # epsilon to prevent 0s in variance.
+      'epsilon': 0.001,
+      # collection containing update_ops.
+      'updates_collections': tf.GraphKeys.UPDATE_OPS,
+      # collection containing the moving mean and moving variance.
+      'variables_collections': {
+          'beta': None,
+          'gamma': None,
+          'moving_mean': [batch_norm_var_collection],
+          'moving_variance': [batch_norm_var_collection],
+      }
+  }
+  if use_batch_norm:
+    normalizer_fn = slim.batch_norm
+    normalizer_params = batch_norm_params
+  else:
+    normalizer_fn = None
+    normalizer_params = {}
+  # Set weight_decay for weights in Conv and FC layers.
+  with slim.arg_scope([slim.conv2d, slim.fully_connected],
+                      weights_regularizer=slim.l2_regularizer(weight_decay)):
+    with slim.arg_scope(
+        [slim.conv2d],
+        weights_initializer=slim.variance_scaling_initializer(),
+        activation_fn=tf.nn.relu,
+        normalizer_fn=normalizer_fn,
+        normalizer_params=normalizer_params) as sc:
+      return sc
diff --git a/tensorflow/contrib/slim/python/slim/nets/inception_v1_test.py b/tensorflow/contrib/slim/python/slim/nets/inception_v1_test.py
index 3be79f792e..aeae7cffe6 100644
--- a/tensorflow/contrib/slim/python/slim/nets/inception_v1_test.py
+++ b/tensorflow/contrib/slim/python/slim/nets/inception_v1_test.py
@@ -110,8 +110,7 @@ class InceptionV1Test(tf.test.TestCase):
     batch_size = 5
     height, width = 224, 224
     inputs = tf.random_uniform((batch_size, height, width, 3))
-    with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
-                        normalizer_fn=slim.batch_norm):
+    with slim.arg_scope(inception.inception_v1_arg_scope()):
       inception.inception_v1_base(inputs)
     total_params, _ = slim.model_analyzer.analyze_vars(
         slim.get_model_variables())
diff --git a/tensorflow/contrib/slim/python/slim/nets/inception_v2.py b/tensorflow/contrib/slim/python/slim/nets/inception_v2.py
index cf9372df3a..2e8ddbd133 100644
--- a/tensorflow/contrib/slim/python/slim/nets/inception_v2.py
+++ b/tensorflow/contrib/slim/python/slim/nets/inception_v2.py
@@ -513,3 +513,43 @@ def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
     kernel_size_out = [min(shape[1], kernel_size[0]),
                        min(shape[2], kernel_size[1])]
   return kernel_size_out
+
+
+def inception_v2_arg_scope(weight_decay=0.00004,
+                           batch_norm_var_collection='moving_vars'):
+  """Defines the default InceptionV2 arg scope.
+
+  Args:
+    weight_decay: The weight decay to use for regularizing the model.
+    batch_norm_var_collection: The name of the collection for the batch norm
+      variables.
+
+  Returns:
+    An `arg_scope` to use for the inception v3 model.
+  """
+  batch_norm_params = {
+      # Decay for the moving averages.
+      'decay': 0.9997,
+      # epsilon to prevent 0s in variance.
+      'epsilon': 0.001,
+      # collection containing update_ops.
+      'updates_collections': tf.GraphKeys.UPDATE_OPS,
+      # collection containing the moving mean and moving variance.
+      'variables_collections': {
+          'beta': None,
+          'gamma': None,
+          'moving_mean': [batch_norm_var_collection],
+          'moving_variance': [batch_norm_var_collection],
+      }
+  }
+
+  # Set weight_decay for weights in Conv and FC layers.
+  with slim.arg_scope([slim.conv2d, slim.fully_connected],
+                      weights_regularizer=slim.l2_regularizer(weight_decay)):
+    with slim.arg_scope(
+        [slim.conv2d],
+        weights_initializer=slim.variance_scaling_initializer(),
+        activation_fn=tf.nn.relu,
+        normalizer_fn=slim.batch_norm,
+        normalizer_params=batch_norm_params) as sc:
+      return sc
diff --git a/tensorflow/contrib/slim/python/slim/nets/inception_v2_test.py b/tensorflow/contrib/slim/python/slim/nets/inception_v2_test.py
index 15ab355d64..f9d26b9b4d 100644
--- a/tensorflow/contrib/slim/python/slim/nets/inception_v2_test.py
+++ b/tensorflow/contrib/slim/python/slim/nets/inception_v2_test.py
@@ -107,12 +107,11 @@ class InceptionV2Test(tf.test.TestCase):
     batch_size = 5
     height, width = 224, 224
     inputs = tf.random_uniform((batch_size, height, width, 3))
-    with slim.arg_scope([slim.conv2d, slim.separable_conv2d],
-                        normalizer_fn=slim.batch_norm):
+    with slim.arg_scope(inception.inception_v2_arg_scope()):
       inception.inception_v2_base(inputs)
     total_params, _ = slim.model_analyzer.analyze_vars(
         slim.get_model_variables())
-    self.assertAlmostEqual(10173240, total_params)
+    self.assertAlmostEqual(10173112, total_params)
 
   def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
     batch_size = 5
diff --git a/tensorflow/contrib/slim/python/slim/nets/inception_v3.py b/tensorflow/contrib/slim/python/slim/nets/inception_v3.py
index b938288ce4..0efd901352 100644
--- a/tensorflow/contrib/slim/python/slim/nets/inception_v3.py
+++ b/tensorflow/contrib/slim/python/slim/nets/inception_v3.py
@@ -555,14 +555,12 @@ def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
   return kernel_size_out
 
 
-def inception_v3_arg_scope(is_training=True,
-                           weight_decay=0.00004,
+def inception_v3_arg_scope(weight_decay=0.00004,
                            stddev=0.1,
                            batch_norm_var_collection='moving_vars'):
   """Defines the default InceptionV3 arg scope.
 
   Args:
-    is_training: Whether or not we're training the model.
     weight_decay: The weight decay to use for regularizing the model.
     stddev: The standard deviation of the trunctated normal weight initializer.
     batch_norm_var_collection: The name of the collection for the batch norm
@@ -572,11 +570,12 @@ def inception_v3_arg_scope(is_training=True,
     An `arg_scope` to use for the inception v3 model.
   """
   batch_norm_params = {
-      'is_training': is_training,
       # Decay for the moving averages.
       'decay': 0.9997,
       # epsilon to prevent 0s in variance.
       'epsilon': 0.001,
+      # collection containing update_ops.
+      'updates_collections': tf.GraphKeys.UPDATE_OPS,
       # collection containing the moving mean and moving variance.
       'variables_collections': {
           'beta': None,
diff --git a/tensorflow/contrib/slim/python/slim/nets/inception_v3_test.py b/tensorflow/contrib/slim/python/slim/nets/inception_v3_test.py
index 786715f327..ca978e30fe 100644
--- a/tensorflow/contrib/slim/python/slim/nets/inception_v3_test.py
+++ b/tensorflow/contrib/slim/python/slim/nets/inception_v3_test.py
@@ -113,8 +113,7 @@ class InceptionV3Test(tf.test.TestCase):
     batch_size = 5
     height, width = 299, 299
     inputs = tf.random_uniform((batch_size, height, width, 3))
-    with slim.arg_scope([slim.conv2d],
-                        normalizer_fn=slim.batch_norm):
+    with slim.arg_scope(inception.inception_v3_arg_scope()):
       inception.inception_v3_base(inputs)
     total_params, _ = slim.model_analyzer.analyze_vars(
         slim.get_model_variables())
diff --git a/tensorflow/contrib/slim/python/slim/nets/resnet_utils.py b/tensorflow/contrib/slim/python/slim/nets/resnet_utils.py
index b375488cb0..de8c2effc2 100644
--- a/tensorflow/contrib/slim/python/slim/nets/resnet_utils.py
+++ b/tensorflow/contrib/slim/python/slim/nets/resnet_utils.py
@@ -206,7 +206,7 @@ def stack_blocks_dense(net, blocks, output_stride=None,
   return net
 
 
-def resnet_arg_scope(is_training=False,
+def resnet_arg_scope(is_training=True,
                      weight_decay=0.0001,
                      batch_norm_decay=0.997,
                      batch_norm_epsilon=1e-5,
@@ -236,7 +236,8 @@ def resnet_arg_scope(is_training=False,
       'is_training': is_training,
       'decay': batch_norm_decay,
       'epsilon': batch_norm_epsilon,
-      'scale': batch_norm_scale
+      'scale': batch_norm_scale,
+      'updates_collections': tf.GraphKeys.UPDATE_OPS,
   }
 
   with slim.arg_scope(
-- 
GitLab


From 18e2e13ff28620c111c6f510e23873f39a5d17a6 Mon Sep 17 00:00:00 2001
From: Justine Tunney <jart@google.com>
Date: Wed, 24 Aug 2016 15:24:28 -0800
Subject: [PATCH 112/223] Add copyright headers to TensorBoard

All HTML comments have been updated to have the same year so vulcanizer
compresses them down to just one comment in the production HTML.
Change: 131229494
---
 tensorflow/tensorboard/components/index.html  | 19 +++++++++++++-
 .../tf-audio-dashboard/demo/index.html        | 19 +++++++++++++-
 .../tf-audio-dashboard/test/index.html        | 19 +++++++++++++-
 .../tf-audio-dashboard.html                   | 20 +++++++++++++++
 .../tf-audio-dashboard/tf-audio-grid.html     | 18 +++++++++++++
 .../tf-audio-dashboard/tf-audio-loader.html   | 19 ++++++++++++++
 .../components/tf-backend/behavior.ts         | 15 +++++++++++
 .../components/tf-backend/test/index.html     | 11 +++++---
 .../components/tf-backend/tf-backend.html     | 17 +++++++++++++
 .../components/tf-color-scale/palettes.ts     | 15 +++++++++++
 .../components/tf-color-scale/test/index.html | 11 +++++---
 .../tf-color-scale/tf-color-scale.html        | 19 ++++++++++++++
 .../tf-dashboard-common/dashboard-style.html  | 17 +++++++++++++
 .../demo/tf-categorizer-demo.html             | 19 +++++++++++++-
 .../demo/tf-collapsable-pane-demo.html        | 19 +++++++++++++-
 .../demo/tf-multi-checkbox-demo.html          | 19 +++++++++++++-
 .../demo/tf-regex-group-demo.html             | 19 +++++++++++++-
 .../tf-dashboard-common/reload-behavior.ts    | 15 +++++++++++
 .../tf-dashboard-common/run-color-style.html  | 17 +++++++++++++
 .../tf-dashboard-common/scrollbar-style.html  | 17 +++++++++++++
 .../tensorboard-color.html                    | 18 +++++++++++++
 .../tf-dashboard-common/test/index.html       | 17 +++++++++++++
 .../tf-dashboard-common/tf-categorizer.html   | 17 +++++++++++++
 .../tf-chart-scaffold.html                    | 17 +++++++++++++
 .../tf-collapsable-pane.html                  | 17 +++++++++++++
 .../tf-dashboard-layout.html                  | 18 +++++++++++++
 .../tf-dashboard-common/tf-dashboard.html     | 18 +++++++++++++
 .../tf-dashboard-common/tf-downloader.html    | 17 +++++++++++++
 .../tf-multi-checkbox.html                    | 18 ++++++++++++-
 .../tf-no-data-warning.html                   | 17 +++++++++++++
 .../tf-option-selector.html                   | 17 +++++++++++++
 .../tf-dashboard-common/tf-regex-group.html   | 17 +++++++++++++
 .../tf-dashboard-common/tf-run-selector.html  | 17 +++++++++++++
 .../tf-distribution-chart.html                | 17 +++++++++++++
 .../tf-distribution-dashboard.html            | 17 +++++++++++++
 .../tf-event-dashboard/demo/index.html        | 19 +++++++++++++-
 .../tf-event-dashboard.html                   | 17 +++++++++++++
 .../tf-smoothing-input.html                   | 17 +++++++++++++
 .../components/tf-globals/globals.ts          |  2 +-
 .../components/tf-globals/tf-globals.html     |  8 +++---
 .../components/tf-graph-app/demo/index.html   | 19 +++++++++++++-
 .../components/tf-graph-app/index.html        | 16 ++++++++++++
 .../components/tf-graph-app/tf-graph-app.html | 21 ++++++++++++++--
 .../tf-graph-board/tf-graph-board.html        | 24 +++++++++++++++---
 .../tf-graph-common/test/index.html           | 17 +++++++++++++
 .../tf-graph-common/tf-graph-common.html      | 17 +++++++++++++
 .../tf-graph-dashboard.html                   | 18 ++++++++++++-
 .../tf-graph-info/tf-graph-info.html          | 18 +++++++++++++
 .../tf-graph-info/tf-node-info.html           | 17 +++++++++++++
 .../tf-graph-info/tf-node-list-item.html      | 17 +++++++++++++
 .../tf-graph-loader/test/index.html           | 17 +++++++++++++
 .../tf-graph-loader/tf-graph-loader.html      | 17 +++++++++++++
 .../components/tf-graph/demo/index.html       | 17 +++++++++++++
 .../tf-graph/demo/tf-graph-demo.html          | 25 ++++++++++++++++---
 .../tf-graph/tf-graph-controls.html           | 17 +++++++++++++
 .../components/tf-graph/tf-graph-icon.html    | 17 +++++++++++++
 .../components/tf-graph/tf-graph-minimap.html | 18 +++++++++++++
 .../components/tf-graph/tf-graph-scene.html   | 17 +++++++++++++
 .../components/tf-graph/tf-graph.html         | 18 +++++++++++++
 .../tf-histogram-dashboard.html               | 17 +++++++++++++
 .../tf-image-dashboard/demo/index.html        | 19 +++++++++++++-
 .../test/imageDashboardTests.ts               | 15 +++++++++++
 .../tf-image-dashboard/test/index.html        | 19 +++++++++++++-
 .../tf-image-dashboard.html                   | 17 +++++++++++++
 .../tf-image-dashboard/tf-image-grid.html     | 17 +++++++++++++
 .../tf-image-dashboard/tf-image-loader.html   | 17 +++++++++++++
 .../tensorboard/components/tf-imports/d3.html | 17 +++++++++++++
 .../components/tf-imports/dagre.html          | 17 +++++++++++++
 .../components/tf-imports/graphlib.html       | 17 +++++++++++++
 .../components/tf-imports/lodash.html         | 17 +++++++++++++
 .../components/tf-imports/plottable.html      | 17 +++++++++++++
 .../components/tf-storage/storage.ts          |  2 +-
 .../components/tf-storage/test/index.html     | 11 +++++---
 .../tf-storage/test/storageTests.ts           |  3 ++-
 .../components/tf-storage/tf-storage.html     |  8 +++---
 .../tf-tensorboard/test/e2eTests.html         | 19 +++++++++++++-
 .../tf-tensorboard/test/e2eTests.ts           | 15 +++++++++++
 .../tf-tensorboard/test/fastTabSwitch.html    | 19 +++++++++++++-
 .../tf-tensorboard/test/fastTabSwitch.ts      | 15 +++++++++++
 .../components/tf-tensorboard/test/index.html | 17 +++++++++++++
 .../tf-tensorboard/test/tensorboardTests.html | 19 +++++++++++++-
 .../tf-tensorboard/tf-tensorboard.html        | 17 +++++++++++++
 .../components/vz-heatmap/demo/index.html     | 19 +++++++++++++-
 .../components/vz-heatmap/index.html          | 19 +++++++++++++-
 .../components/vz-heatmap/vz-heatmap.html     | 19 +++++++++++++-
 .../vz-histogram-timeseries/demo/index.html   | 19 +++++++++++++-
 .../vz-histogram-timeseries/index.html        | 16 ++++++++++++
 .../vz-histogram-timeseries.html              | 20 ++++++++++++++-
 .../components/vz-line-chart/demo/index.html  | 19 +++++++++++++-
 .../components/vz-line-chart/index.html       | 16 ++++++++++++
 .../vz-line-chart/vz-line-chart.html          | 20 ++++++++++++++-
 .../components/vz-sorting/vz-sorting.html     | 21 ++++++++--------
 tensorflow/tensorboard/demo/index.html        | 19 +++++++++++++-
 tensorflow/tensorboard/dist/index.html        | 19 +++++++++++++-
 tensorflow/tensorboard/lib/css/global.css     | 17 ++++++++++++-
 95 files changed, 1541 insertions(+), 66 deletions(-)

diff --git a/tensorflow/tensorboard/components/index.html b/tensorflow/tensorboard/components/index.html
index 7bf454dba0..afd151869b 100644
--- a/tensorflow/tensorboard/components/index.html
+++ b/tensorflow/tensorboard/components/index.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
   <head>
     <script src="webcomponentsjs/webcomponents-lite.min.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-audio-dashboard/demo/index.html b/tensorflow/tensorboard/components/tf-audio-dashboard/demo/index.html
index 2f53b1fdde..e6c92b095e 100644
--- a/tensorflow/tensorboard/components/tf-audio-dashboard/demo/index.html
+++ b/tensorflow/tensorboard/components/tf-audio-dashboard/demo/index.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
   <head>
     <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-audio-dashboard/test/index.html b/tensorflow/tensorboard/components/tf-audio-dashboard/test/index.html
index 75349fbabe..421a6658b0 100644
--- a/tensorflow/tensorboard/components/tf-audio-dashboard/test/index.html
+++ b/tensorflow/tensorboard/components/tf-audio-dashboard/test/index.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
 <head>
   <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-audio-dashboard/tf-audio-dashboard.html b/tensorflow/tensorboard/components/tf-audio-dashboard/tf-audio-dashboard.html
index 6b7ccb0f27..ad879210d6 100644
--- a/tensorflow/tensorboard/components/tf-audio-dashboard/tf-audio-dashboard.html
+++ b/tensorflow/tensorboard/components/tf-audio-dashboard/tf-audio-dashboard.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../paper-icon-button/paper-icon-button.html">
 <link rel="import" href="tf-audio-grid.html">
@@ -6,6 +23,9 @@
 
 <!--
 tf-audio-dashboard displays a dashboard that loads audio from a TensorFlow run.
+
+@element tf-audio-dashboard
+@demo demo/index.html
 -->
 <dom-module id="tf-audio-dashboard">
   <template>
diff --git a/tensorflow/tensorboard/components/tf-audio-dashboard/tf-audio-grid.html b/tensorflow/tensorboard/components/tf-audio-dashboard/tf-audio-grid.html
index d48dddcd1d..25b97c0d6f 100644
--- a/tensorflow/tensorboard/components/tf-audio-dashboard/tf-audio-grid.html
+++ b/tensorflow/tensorboard/components/tf-audio-dashboard/tf-audio-grid.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../paper-styles/paper-styles.html">
 <link rel="import" href="tf-audio-loader.html">
@@ -29,6 +46,7 @@ is high)
 - Fix column/row names
 - Include hook for loading past audio (by step/timestamp? or index?)
 
+@element tf-audio-grid
 -->
 <dom-module id="tf-audio-grid">
   <template>
diff --git a/tensorflow/tensorboard/components/tf-audio-dashboard/tf-audio-loader.html b/tensorflow/tensorboard/components/tf-audio-dashboard/tf-audio-loader.html
index 5a27593656..0a62b13243 100644
--- a/tensorflow/tensorboard/components/tf-audio-dashboard/tf-audio-loader.html
+++ b/tensorflow/tensorboard/components/tf-audio-dashboard/tf-audio-loader.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-imports/lodash.html">
 
@@ -6,6 +23,8 @@ tf-audio-loader loads an individual audio clip from the TensorBoard backend.
 
 Right now it always loads the most recent audio clip. We should add support in the
 future for loading older clips.
+
+@element tf-audio-loader
 -->
 <dom-module id="tf-audio-loader">
   <style>
diff --git a/tensorflow/tensorboard/components/tf-backend/behavior.ts b/tensorflow/tensorboard/components/tf-backend/behavior.ts
index 3731345982..de6590456f 100644
--- a/tensorflow/tensorboard/components/tf-backend/behavior.ts
+++ b/tensorflow/tensorboard/components/tf-backend/behavior.ts
@@ -1,3 +1,18 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
 module TF.Backend {
   export var Behavior = {
     properties: {
diff --git a/tensorflow/tensorboard/components/tf-backend/test/index.html b/tensorflow/tensorboard/components/tf-backend/test/index.html
index a1ee080b0e..7f51861d25 100644
--- a/tensorflow/tensorboard/components/tf-backend/test/index.html
+++ b/tensorflow/tensorboard/components/tf-backend/test/index.html
@@ -1,18 +1,21 @@
-<!-- Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
 
-http://www.apache.org/licenses/LICENSE-2.0
+    http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
-=============================================================================-->
-<!doctype html>
+-->
+
 <html>
 <head>
   <meta charset="utf-8">
diff --git a/tensorflow/tensorboard/components/tf-backend/tf-backend.html b/tensorflow/tensorboard/components/tf-backend/tf-backend.html
index d416787e3f..0e07c7fdb1 100644
--- a/tensorflow/tensorboard/components/tf-backend/tf-backend.html
+++ b/tensorflow/tensorboard/components/tf-backend/tf-backend.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-imports/lodash.html">
 <link rel="import" href="../tf-imports/d3.html">
diff --git a/tensorflow/tensorboard/components/tf-color-scale/palettes.ts b/tensorflow/tensorboard/components/tf-color-scale/palettes.ts
index 09a7339da5..c53ed599ae 100644
--- a/tensorflow/tensorboard/components/tf-color-scale/palettes.ts
+++ b/tensorflow/tensorboard/components/tf-color-scale/palettes.ts
@@ -1,3 +1,18 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
 module TF {
   export const palettes = {
     googleStandard: [
diff --git a/tensorflow/tensorboard/components/tf-color-scale/test/index.html b/tensorflow/tensorboard/components/tf-color-scale/test/index.html
index 143444ba57..9a2a174349 100644
--- a/tensorflow/tensorboard/components/tf-color-scale/test/index.html
+++ b/tensorflow/tensorboard/components/tf-color-scale/test/index.html
@@ -1,18 +1,21 @@
-<!-- Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
 
-http://www.apache.org/licenses/LICENSE-2.0
+    http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
-=============================================================================-->
-<!doctype html>
+-->
+
 <html>
 <head>
   <meta charset="utf-8">
diff --git a/tensorflow/tensorboard/components/tf-color-scale/tf-color-scale.html b/tensorflow/tensorboard/components/tf-color-scale/tf-color-scale.html
index 5a4c51467a..743996f624 100644
--- a/tensorflow/tensorboard/components/tf-color-scale/tf-color-scale.html
+++ b/tensorflow/tensorboard/components/tf-color-scale/tf-color-scale.html
@@ -1,9 +1,28 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 
 <!--
 tf-color-scale is a plumbing component that takes in an array of runs, and produces
 an upward-bindable outColorScale, which is a color scale mapping from those runs to
 a set of colors.
+
+@element tf-color-scale
 -->
 <dom-module id="tf-color-scale">
   <script src="palettes.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/dashboard-style.html b/tensorflow/tensorboard/components/tf-dashboard-common/dashboard-style.html
index 2870845f8d..1591994292 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/dashboard-style.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/dashboard-style.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../paper-styles/paper-styles.html">
 <link rel="import" href="../tf-dashboard-common/tensorboard-color.html">
 
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/demo/tf-categorizer-demo.html b/tensorflow/tensorboard/components/tf-dashboard-common/demo/tf-categorizer-demo.html
index 2c632e81c0..3918f045dd 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/demo/tf-categorizer-demo.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/demo/tf-categorizer-demo.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
  <head>
    <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/demo/tf-collapsable-pane-demo.html b/tensorflow/tensorboard/components/tf-dashboard-common/demo/tf-collapsable-pane-demo.html
index 237870408b..15253a8331 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/demo/tf-collapsable-pane-demo.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/demo/tf-collapsable-pane-demo.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
  <head>
    <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/demo/tf-multi-checkbox-demo.html b/tensorflow/tensorboard/components/tf-dashboard-common/demo/tf-multi-checkbox-demo.html
index 723b90f7df..bad4a1a66e 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/demo/tf-multi-checkbox-demo.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/demo/tf-multi-checkbox-demo.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
 <head>
 <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/demo/tf-regex-group-demo.html b/tensorflow/tensorboard/components/tf-dashboard-common/demo/tf-regex-group-demo.html
index 2eb6516c26..eaf10999bf 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/demo/tf-regex-group-demo.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/demo/tf-regex-group-demo.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
  <head>
    <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/reload-behavior.ts b/tensorflow/tensorboard/components/tf-dashboard-common/reload-behavior.ts
index 9a6719ae88..f629576ae6 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/reload-behavior.ts
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/reload-behavior.ts
@@ -1,3 +1,18 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
 module TF.Dashboard {
   /**
    * ReloadBehavior: A simple behavior for dashboards where the
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/run-color-style.html b/tensorflow/tensorboard/components/tf-dashboard-common/run-color-style.html
index 9bb64ec618..b15861694f 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/run-color-style.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/run-color-style.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../paper-styles/paper-styles.html">
 
 <dom-module id="run-color-style">
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/scrollbar-style.html b/tensorflow/tensorboard/components/tf-dashboard-common/scrollbar-style.html
index ddae16adb9..82a22e78cb 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/scrollbar-style.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/scrollbar-style.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../paper-styles/paper-styles.html">
 
 <dom-module id="scrollbar-style">
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/tensorboard-color.html b/tensorflow/tensorboard/components/tf-dashboard-common/tensorboard-color.html
index 2badf3a264..7f9ca64614 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/tensorboard-color.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/tensorboard-color.html
@@ -1,4 +1,22 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
+
 <style is="custom-style">
 
   :root {
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/test/index.html b/tensorflow/tensorboard/components/tf-dashboard-common/test/index.html
index 6336c876b8..270a2522b9 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/test/index.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/test/index.html
@@ -1,4 +1,21 @@
 <!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
 <head>
   <meta charset="utf-8">
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/tf-categorizer.html b/tensorflow/tensorboard/components/tf-dashboard-common/tf-categorizer.html
index 9eeef71a23..4b588f6323 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/tf-categorizer.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/tf-categorizer.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../paper-toggle-button/paper-toggle-button.html">
 <link rel="import" href="../tf-storage/tf-storage.html">
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/tf-chart-scaffold.html b/tensorflow/tensorboard/components/tf-dashboard-common/tf-chart-scaffold.html
index 840d16116c..c908aa56ff 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/tf-chart-scaffold.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/tf-chart-scaffold.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-imports/plottable.html">
 <link rel="import" href="../tf-imports/lodash.html">
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/tf-collapsable-pane.html b/tensorflow/tensorboard/components/tf-dashboard-common/tf-collapsable-pane.html
index e43cbfaf25..7115afae42 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/tf-collapsable-pane.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/tf-collapsable-pane.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../iron-collapse/iron-collapse.html">
 
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/tf-dashboard-layout.html b/tensorflow/tensorboard/components/tf-dashboard-common/tf-dashboard-layout.html
index faf1139922..e0e8a2b52c 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/tf-dashboard-layout.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/tf-dashboard-layout.html
@@ -1,6 +1,24 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="scrollbar-style.html">
 <link rel="import" href="tensorboard-color.html">
+
 <!--
 Generic layout for a dashboard.
 -->
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/tf-dashboard.html b/tensorflow/tensorboard/components/tf-dashboard-common/tf-dashboard.html
index 12beb70bb5..4c7c91d171 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/tf-dashboard.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/tf-dashboard.html
@@ -1,7 +1,25 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="tf-dashboard-layout.html">
 <link rel="import" href="tensorboard-color.html">
 <link rel="import" href="dashboard-style.html">
 <link rel="import" href="tf-downloader.html">
 <link rel="import" href="tf-no-data-warning.html">
+
 <script src="reload-behavior.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/tf-downloader.html b/tensorflow/tensorboard/components/tf-dashboard-common/tf-downloader.html
index e3a491389b..24879ef813 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/tf-downloader.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/tf-downloader.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../paper-dropdown-menu/paper-dropdown-menu.html">
 <link rel="import" href="../paper-menu/paper-menu.html">
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/tf-multi-checkbox.html b/tensorflow/tensorboard/components/tf-dashboard-common/tf-multi-checkbox.html
index b9ae4bef0f..1a881c9917 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/tf-multi-checkbox.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/tf-multi-checkbox.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../paper-checkbox/paper-checkbox.html">
 <link rel="import" href="../paper-input/paper-input.html">
@@ -15,7 +32,6 @@ tf-multi-checkbox assumes that the names may be very long compared to the width
 of the checkbox, and the number of names may also be very large, and works to
 handle these situations gracefully.
 -->
-
 <dom-module id="tf-multi-checkbox">
   <style include="scrollbar-style"></style>
   <style include="run-color-style"></style>
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/tf-no-data-warning.html b/tensorflow/tensorboard/components/tf-dashboard-common/tf-no-data-warning.html
index 7798d7fe82..ea0047f32e 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/tf-no-data-warning.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/tf-no-data-warning.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 
 <!--
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/tf-option-selector.html b/tensorflow/tensorboard/components/tf-dashboard-common/tf-option-selector.html
index 50b542ce69..d6fc9d6861 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/tf-option-selector.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/tf-option-selector.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-dashboard-common/tensorboard-color.html">
 
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/tf-regex-group.html b/tensorflow/tensorboard/components/tf-dashboard-common/tf-regex-group.html
index ad890984a6..67d6fa5e33 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/tf-regex-group.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/tf-regex-group.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../paper-icon-button/paper-icon-button.html">
 <link rel="import" href="../iron-icons/iron-icons.html">
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/tf-run-selector.html b/tensorflow/tensorboard/components/tf-dashboard-common/tf-run-selector.html
index e602233963..5981a51a37 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/tf-run-selector.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/tf-run-selector.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../paper-button/paper-button.html">
 <link rel="import" href="../tf-imports/lodash.html">
diff --git a/tensorflow/tensorboard/components/tf-distribution-dashboard/tf-distribution-chart.html b/tensorflow/tensorboard/components/tf-distribution-dashboard/tf-distribution-chart.html
index e5459a8276..9cedf64174 100644
--- a/tensorflow/tensorboard/components/tf-distribution-dashboard/tf-distribution-chart.html
+++ b/tensorflow/tensorboard/components/tf-distribution-dashboard/tf-distribution-chart.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-imports/plottable.html">
 <link rel="import" href="../tf-imports/lodash.html">
diff --git a/tensorflow/tensorboard/components/tf-distribution-dashboard/tf-distribution-dashboard.html b/tensorflow/tensorboard/components/tf-distribution-dashboard/tf-distribution-dashboard.html
index 47728d32d1..74b18c44bb 100644
--- a/tensorflow/tensorboard/components/tf-distribution-dashboard/tf-distribution-dashboard.html
+++ b/tensorflow/tensorboard/components/tf-distribution-dashboard/tf-distribution-dashboard.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-backend/tf-backend.html">
 <link rel="import" href="../tf-color-scale/tf-color-scale.html">
diff --git a/tensorflow/tensorboard/components/tf-event-dashboard/demo/index.html b/tensorflow/tensorboard/components/tf-event-dashboard/demo/index.html
index 22087786a5..3e43831e82 100644
--- a/tensorflow/tensorboard/components/tf-event-dashboard/demo/index.html
+++ b/tensorflow/tensorboard/components/tf-event-dashboard/demo/index.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
   <head>
     <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html b/tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html
index db9089280e..417f64b42b 100644
--- a/tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html
+++ b/tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="tf-smoothing-input.html">
 <link rel="import" href="../tf-backend/tf-backend.html">
diff --git a/tensorflow/tensorboard/components/tf-event-dashboard/tf-smoothing-input.html b/tensorflow/tensorboard/components/tf-event-dashboard/tf-smoothing-input.html
index d39d8b85b5..a076033000 100644
--- a/tensorflow/tensorboard/components/tf-event-dashboard/tf-smoothing-input.html
+++ b/tensorflow/tensorboard/components/tf-event-dashboard/tf-smoothing-input.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../paper-slider/paper-slider.html">
 <link rel="import" href="../paper-input/paper-input.html">
diff --git a/tensorflow/tensorboard/components/tf-globals/globals.ts b/tensorflow/tensorboard/components/tf-globals/globals.ts
index f2902bf0c0..aaa2ffebf9 100644
--- a/tensorflow/tensorboard/components/tf-globals/globals.ts
+++ b/tensorflow/tensorboard/components/tf-globals/globals.ts
@@ -1,4 +1,4 @@
-/* Copyright 2015 Google Inc. All Rights Reserved.
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
diff --git a/tensorflow/tensorboard/components/tf-globals/tf-globals.html b/tensorflow/tensorboard/components/tf-globals/tf-globals.html
index a76f21d96c..952979d0be 100644
--- a/tensorflow/tensorboard/components/tf-globals/tf-globals.html
+++ b/tensorflow/tensorboard/components/tf-globals/tf-globals.html
@@ -1,17 +1,19 @@
-<!-- Copyright 2015 Google Inc. All Rights Reserved.
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
 
-http://www.apache.org/licenses/LICENSE-2.0
+    http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
-=============================================================================-->
+-->
 
 <dom-module id="tf-globals">
   <script src="globals.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-graph-app/demo/index.html b/tensorflow/tensorboard/components/tf-graph-app/demo/index.html
index 965bd09afc..0897cdd08b 100644
--- a/tensorflow/tensorboard/components/tf-graph-app/demo/index.html
+++ b/tensorflow/tensorboard/components/tf-graph-app/demo/index.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
 <head>
   <meta charset="utf-8">
diff --git a/tensorflow/tensorboard/components/tf-graph-app/index.html b/tensorflow/tensorboard/components/tf-graph-app/index.html
index 5e41499009..c80fbf4f63 100644
--- a/tensorflow/tensorboard/components/tf-graph-app/index.html
+++ b/tensorflow/tensorboard/components/tf-graph-app/index.html
@@ -1,4 +1,20 @@
 <!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
 
 <html>
   <head>
diff --git a/tensorflow/tensorboard/components/tf-graph-app/tf-graph-app.html b/tensorflow/tensorboard/components/tf-graph-app/tf-graph-app.html
index 5f74903b04..07308d38e4 100644
--- a/tensorflow/tensorboard/components/tf-graph-app/tf-graph-app.html
+++ b/tensorflow/tensorboard/components/tf-graph-app/tf-graph-app.html
@@ -1,7 +1,25 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-graph-board/tf-graph-board.html">
 <link rel="import" href="../tf-graph-loader/tf-graph-loader.html">
 <link rel="import" href="../tf-graph/tf-graph-controls.html">
+
 <!--
 Stand alone element of tf-graph for embedding.
 
@@ -17,9 +35,8 @@ The pbtxt format is the stringified version of the graphdef.
     answer = tf.add(lifeuniverse, everything, name='answer')
     open("graph.pbtxt", "w").write(str(tf.get_default_graph().as_graph_def()))
 
-@demo
+@demo demo/index.html
 -->
-
 <dom-module id="tf-graph-app">
 <template>
 <style>
diff --git a/tensorflow/tensorboard/components/tf-graph-board/tf-graph-board.html b/tensorflow/tensorboard/components/tf-graph-board/tf-graph-board.html
index 78caa0c7d4..2bc16b15c0 100644
--- a/tensorflow/tensorboard/components/tf-graph-board/tf-graph-board.html
+++ b/tensorflow/tensorboard/components/tf-graph-board/tf-graph-board.html
@@ -1,15 +1,33 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-graph/tf-graph.html">
 <link rel="import" href="../tf-graph-info/tf-graph-info.html">
 <link rel="import" href="../paper-progress/paper-progress.html">
-<!-- Element for putting tf-graph and tf-graph-info side by side.
+
+<!--
+Element for putting tf-graph and tf-graph-info side by side.
 
 Example
 
-<tf-graph-board graph=[[graph]]></tf-graph-board>
+  <tf-graph-board graph=[[graph]]></tf-graph-board>
 
 -->
-
 <dom-module id="tf-graph-board">
 <template>
 <style>
diff --git a/tensorflow/tensorboard/components/tf-graph-common/test/index.html b/tensorflow/tensorboard/components/tf-graph-common/test/index.html
index 1897893833..00b76e462a 100644
--- a/tensorflow/tensorboard/components/tf-graph-common/test/index.html
+++ b/tensorflow/tensorboard/components/tf-graph-common/test/index.html
@@ -1,4 +1,21 @@
 <!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
 <head>
   <meta charset="utf-8">
diff --git a/tensorflow/tensorboard/components/tf-graph-common/tf-graph-common.html b/tensorflow/tensorboard/components/tf-graph-common/tf-graph-common.html
index f9c876c649..f4f98c8b9a 100644
--- a/tensorflow/tensorboard/components/tf-graph-common/tf-graph-common.html
+++ b/tensorflow/tensorboard/components/tf-graph-common/tf-graph-common.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../tf-imports/d3.html">
 <link rel="import" href="../tf-imports/dagre.html">
 <link rel="import" href="../tf-imports/graphlib.html">
diff --git a/tensorflow/tensorboard/components/tf-graph-dashboard/tf-graph-dashboard.html b/tensorflow/tensorboard/components/tf-graph-dashboard/tf-graph-dashboard.html
index 911ad08c7a..bd63f5361b 100644
--- a/tensorflow/tensorboard/components/tf-graph-dashboard/tf-graph-dashboard.html
+++ b/tensorflow/tensorboard/components/tf-graph-dashboard/tf-graph-dashboard.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-graph-loader/tf-graph-loader.html">
 <link rel="import" href="../tf-graph-board/tf-graph-board.html">
@@ -15,7 +32,6 @@ associated graphs) along with the url generator into tf-graph-board for display.
 If there are multiple runs with graphs, the first run's graph is shown
 by default. The user can select a different run from a dropdown menu.
 -->
-
 <dom-module id="tf-graph-dashboard">
 <template>
 <tf-no-data-warning
diff --git a/tensorflow/tensorboard/components/tf-graph-info/tf-graph-info.html b/tensorflow/tensorboard/components/tf-graph-info/tf-graph-info.html
index 9422d02b00..527cbb7888 100644
--- a/tensorflow/tensorboard/components/tf-graph-info/tf-graph-info.html
+++ b/tensorflow/tensorboard/components/tf-graph-info/tf-graph-info.html
@@ -1,5 +1,23 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="tf-node-info.html">
+
 <dom-module id="tf-graph-info">
 <template>
 <style>
diff --git a/tensorflow/tensorboard/components/tf-graph-info/tf-node-info.html b/tensorflow/tensorboard/components/tf-graph-info/tf-node-info.html
index d283ac725e..1e60cda66a 100644
--- a/tensorflow/tensorboard/components/tf-graph-info/tf-node-info.html
+++ b/tensorflow/tensorboard/components/tf-graph-info/tf-node-info.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../iron-collapse/iron-collapse.html">
 <link rel="import" href="../iron-list/iron-list.html">
 <link rel="import" href="../polymer/polymer.html">
diff --git a/tensorflow/tensorboard/components/tf-graph-info/tf-node-list-item.html b/tensorflow/tensorboard/components/tf-graph-info/tf-node-list-item.html
index 6a3acc8e74..9e9bface5d 100644
--- a/tensorflow/tensorboard/components/tf-graph-info/tf-node-list-item.html
+++ b/tensorflow/tensorboard/components/tf-graph-info/tf-node-list-item.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-graph/tf-graph-icon.html">
 <link rel="import" href="../tf-dashboard-common/tensorboard-color.html">
diff --git a/tensorflow/tensorboard/components/tf-graph-loader/test/index.html b/tensorflow/tensorboard/components/tf-graph-loader/test/index.html
index 082eee314b..c8e2027f42 100644
--- a/tensorflow/tensorboard/components/tf-graph-loader/test/index.html
+++ b/tensorflow/tensorboard/components/tf-graph-loader/test/index.html
@@ -1,4 +1,21 @@
 <!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
 <head>
   <meta charset="utf-8">
diff --git a/tensorflow/tensorboard/components/tf-graph-loader/tf-graph-loader.html b/tensorflow/tensorboard/components/tf-graph-loader/tf-graph-loader.html
index 0501258e9f..c675877be7 100644
--- a/tensorflow/tensorboard/components/tf-graph-loader/tf-graph-loader.html
+++ b/tensorflow/tensorboard/components/tf-graph-loader/tf-graph-loader.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 
 <!--
diff --git a/tensorflow/tensorboard/components/tf-graph/demo/index.html b/tensorflow/tensorboard/components/tf-graph/demo/index.html
index 5eb2e76031..c89490f44d 100644
--- a/tensorflow/tensorboard/components/tf-graph/demo/index.html
+++ b/tensorflow/tensorboard/components/tf-graph/demo/index.html
@@ -1,4 +1,21 @@
 <!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
   <head>
     <meta charset="utf-8">
diff --git a/tensorflow/tensorboard/components/tf-graph/demo/tf-graph-demo.html b/tensorflow/tensorboard/components/tf-graph/demo/tf-graph-demo.html
index 934115a906..d5fd41dfeb 100644
--- a/tensorflow/tensorboard/components/tf-graph/demo/tf-graph-demo.html
+++ b/tensorflow/tensorboard/components/tf-graph/demo/tf-graph-demo.html
@@ -1,15 +1,32 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../../polymer/polymer.html">
 <link rel="import" href="../../tf-graph-board/tf-graph-board.html">
 <link rel="import" href="../../tf-graph-loader/tf-graph-loader.html">
 <link rel="import" href="../../tf-graph/tf-graph-controls.html">
-<!-- Element for tf-graph demo page
 
-Example
+<!--
+Element for tf-graph demo page
 
-<tf-graph-demo></tf-graph-demo>
+Example:
 
+<tf-graph-demo></tf-graph-demo>
 -->
-
 <dom-module id="tf-graph-demo">
 <template>
 <style>
diff --git a/tensorflow/tensorboard/components/tf-graph/tf-graph-controls.html b/tensorflow/tensorboard/components/tf-graph/tf-graph-controls.html
index 2f2999231d..f7913262bb 100644
--- a/tensorflow/tensorboard/components/tf-graph/tf-graph-controls.html
+++ b/tensorflow/tensorboard/components/tf-graph/tf-graph-controls.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../paper-menu/paper-menu.html">
 <link rel="import" href="../paper-dropdown-menu/paper-dropdown-menu.html">
diff --git a/tensorflow/tensorboard/components/tf-graph/tf-graph-icon.html b/tensorflow/tensorboard/components/tf-graph/tf-graph-icon.html
index 985637d057..a3e9dc59c5 100644
--- a/tensorflow/tensorboard/components/tf-graph/tf-graph-icon.html
+++ b/tensorflow/tensorboard/components/tf-graph/tf-graph-icon.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-dashboard-common/tensorboard-color.html">
 
diff --git a/tensorflow/tensorboard/components/tf-graph/tf-graph-minimap.html b/tensorflow/tensorboard/components/tf-graph/tf-graph-minimap.html
index b9386bc19d..cdbee3af5e 100644
--- a/tensorflow/tensorboard/components/tf-graph/tf-graph-minimap.html
+++ b/tensorflow/tensorboard/components/tf-graph/tf-graph-minimap.html
@@ -1,5 +1,23 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <script src="../tf-graph-common/lib/scene/minimap.js"></script>
+
 <dom-module id="tf-graph-minimap">
 <template>
 <style>
diff --git a/tensorflow/tensorboard/components/tf-graph/tf-graph-scene.html b/tensorflow/tensorboard/components/tf-graph/tf-graph-scene.html
index 04c34bf935..44ef3abdfa 100644
--- a/tensorflow/tensorboard/components/tf-graph/tf-graph-scene.html
+++ b/tensorflow/tensorboard/components/tf-graph/tf-graph-scene.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-dashboard-common/tensorboard-color.html">
 <link rel="import" href="tf-graph-minimap.html">
diff --git a/tensorflow/tensorboard/components/tf-graph/tf-graph.html b/tensorflow/tensorboard/components/tf-graph/tf-graph.html
index 883ce5d14c..368479f1a3 100644
--- a/tensorflow/tensorboard/components/tf-graph/tf-graph.html
+++ b/tensorflow/tensorboard/components/tf-graph/tf-graph.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../iron-flex-layout/iron-flex-layout.html">
 <link rel="import" href="../iron-icons/iron-icons.html">
@@ -6,6 +23,7 @@
 <link rel="import" href="../paper-toggle-button/paper-toggle-button.html">
 <link rel="import" href="../tf-graph-common/tf-graph-common.html">
 <link rel="import" href="tf-graph-scene.html">
+
 <dom-module id="tf-graph">
 <template>
 <style>
diff --git a/tensorflow/tensorboard/components/tf-histogram-dashboard/tf-histogram-dashboard.html b/tensorflow/tensorboard/components/tf-histogram-dashboard/tf-histogram-dashboard.html
index 1261f5985d..1295afb073 100644
--- a/tensorflow/tensorboard/components/tf-histogram-dashboard/tf-histogram-dashboard.html
+++ b/tensorflow/tensorboard/components/tf-histogram-dashboard/tf-histogram-dashboard.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-backend/tf-backend.html">
 <link rel="import" href="../tf-color-scale/tf-color-scale.html">
diff --git a/tensorflow/tensorboard/components/tf-image-dashboard/demo/index.html b/tensorflow/tensorboard/components/tf-image-dashboard/demo/index.html
index e163b1487f..9689c13f3b 100644
--- a/tensorflow/tensorboard/components/tf-image-dashboard/demo/index.html
+++ b/tensorflow/tensorboard/components/tf-image-dashboard/demo/index.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
   <head>
     <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-image-dashboard/test/imageDashboardTests.ts b/tensorflow/tensorboard/components/tf-image-dashboard/test/imageDashboardTests.ts
index bcbbc6c8ed..f903326428 100644
--- a/tensorflow/tensorboard/components/tf-image-dashboard/test/imageDashboardTests.ts
+++ b/tensorflow/tensorboard/components/tf-image-dashboard/test/imageDashboardTests.ts
@@ -1,3 +1,18 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
 declare function stub(el: string, obj: any): void;
 
     describe('image dashboard tests', function() {
diff --git a/tensorflow/tensorboard/components/tf-image-dashboard/test/index.html b/tensorflow/tensorboard/components/tf-image-dashboard/test/index.html
index 39139b9ef5..c0c84d1e71 100644
--- a/tensorflow/tensorboard/components/tf-image-dashboard/test/index.html
+++ b/tensorflow/tensorboard/components/tf-image-dashboard/test/index.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
 <head>
   <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-image-dashboard/tf-image-dashboard.html b/tensorflow/tensorboard/components/tf-image-dashboard/tf-image-dashboard.html
index a79745b87e..74eb737148 100644
--- a/tensorflow/tensorboard/components/tf-image-dashboard/tf-image-dashboard.html
+++ b/tensorflow/tensorboard/components/tf-image-dashboard/tf-image-dashboard.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../paper-icon-button/paper-icon-button.html">
 <link rel="import" href="tf-image-grid.html">
diff --git a/tensorflow/tensorboard/components/tf-image-dashboard/tf-image-grid.html b/tensorflow/tensorboard/components/tf-image-dashboard/tf-image-grid.html
index ec21649666..d34968ca70 100644
--- a/tensorflow/tensorboard/components/tf-image-dashboard/tf-image-grid.html
+++ b/tensorflow/tensorboard/components/tf-image-dashboard/tf-image-grid.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../paper-styles/paper-styles.html">
 <link rel="import" href="tf-image-loader.html">
diff --git a/tensorflow/tensorboard/components/tf-image-dashboard/tf-image-loader.html b/tensorflow/tensorboard/components/tf-image-dashboard/tf-image-loader.html
index f0635b6de2..5156af5110 100644
--- a/tensorflow/tensorboard/components/tf-image-dashboard/tf-image-loader.html
+++ b/tensorflow/tensorboard/components/tf-image-dashboard/tf-image-loader.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-imports/lodash.html">
 
diff --git a/tensorflow/tensorboard/components/tf-imports/d3.html b/tensorflow/tensorboard/components/tf-imports/d3.html
index b703802fe7..8e7295f3a0 100644
--- a/tensorflow/tensorboard/components/tf-imports/d3.html
+++ b/tensorflow/tensorboard/components/tf-imports/d3.html
@@ -1 +1,18 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <script src="../d3/d3.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-imports/dagre.html b/tensorflow/tensorboard/components/tf-imports/dagre.html
index 0301ec5698..71d69ac93a 100644
--- a/tensorflow/tensorboard/components/tf-imports/dagre.html
+++ b/tensorflow/tensorboard/components/tf-imports/dagre.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <!--
 HTML imports are non-blocking thus getting the dependency 'graphlib'
 and 'lodash' via script imports instead.
diff --git a/tensorflow/tensorboard/components/tf-imports/graphlib.html b/tensorflow/tensorboard/components/tf-imports/graphlib.html
index 7039c9de95..d093b528c2 100644
--- a/tensorflow/tensorboard/components/tf-imports/graphlib.html
+++ b/tensorflow/tensorboard/components/tf-imports/graphlib.html
@@ -1,2 +1,19 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <script src="../lodash/lodash.min.js"></script>
 <script src="../graphlib/dist/graphlib.core.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-imports/lodash.html b/tensorflow/tensorboard/components/tf-imports/lodash.html
index 2e2a22f431..9d8c43728c 100644
--- a/tensorflow/tensorboard/components/tf-imports/lodash.html
+++ b/tensorflow/tensorboard/components/tf-imports/lodash.html
@@ -1 +1,18 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <script src="../lodash/lodash.min.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-imports/plottable.html b/tensorflow/tensorboard/components/tf-imports/plottable.html
index dc62eb32d2..6d619e0d72 100644
--- a/tensorflow/tensorboard/components/tf-imports/plottable.html
+++ b/tensorflow/tensorboard/components/tf-imports/plottable.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="d3.html">
 <script src="../plottable/plottable.js"></script>
 <link rel="stylesheet" type="text/css" href="../plottable/plottable.css">
diff --git a/tensorflow/tensorboard/components/tf-storage/storage.ts b/tensorflow/tensorboard/components/tf-storage/storage.ts
index 63674c2ed9..39827aabf9 100644
--- a/tensorflow/tensorboard/components/tf-storage/storage.ts
+++ b/tensorflow/tensorboard/components/tf-storage/storage.ts
@@ -1,4 +1,4 @@
-/* Copyright 2015 Google Inc. All Rights Reserved.
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
diff --git a/tensorflow/tensorboard/components/tf-storage/test/index.html b/tensorflow/tensorboard/components/tf-storage/test/index.html
index 17546216a2..6d395b0702 100644
--- a/tensorflow/tensorboard/components/tf-storage/test/index.html
+++ b/tensorflow/tensorboard/components/tf-storage/test/index.html
@@ -1,18 +1,21 @@
-<!-- Copyright 2015 Google Inc. All Rights Reserved.
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
 
-http://www.apache.org/licenses/LICENSE-2.0
+    http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
-=============================================================================-->
-<!doctype html>
+-->
+
 <html>
 <head>
   <meta charset="utf-8">
diff --git a/tensorflow/tensorboard/components/tf-storage/test/storageTests.ts b/tensorflow/tensorboard/components/tf-storage/test/storageTests.ts
index b79bd8b7b7..f3486095b3 100644
--- a/tensorflow/tensorboard/components/tf-storage/test/storageTests.ts
+++ b/tensorflow/tensorboard/components/tf-storage/test/storageTests.ts
@@ -1,4 +1,4 @@
-/* Copyright 2015 Google Inc. All Rights Reserved.
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the 'License');
 you may not use this file except in compliance with the License.
@@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
+
 // tslint:disable-next-line:no-var-keyword
 var assert = chai.assert;
 
diff --git a/tensorflow/tensorboard/components/tf-storage/tf-storage.html b/tensorflow/tensorboard/components/tf-storage/tf-storage.html
index db1bed389a..b20a23bd44 100644
--- a/tensorflow/tensorboard/components/tf-storage/tf-storage.html
+++ b/tensorflow/tensorboard/components/tf-storage/tf-storage.html
@@ -1,17 +1,19 @@
-<!-- Copyright 2015 Google Inc. All Rights Reserved.
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at
 
-http://www.apache.org/licenses/LICENSE-2.0
+    http://www.apache.org/licenses/LICENSE-2.0
 
 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
-=============================================================================-->
+-->
 
 <link rel="import" href="../tf-globals/tf-globals.html">
 
diff --git a/tensorflow/tensorboard/components/tf-tensorboard/test/e2eTests.html b/tensorflow/tensorboard/components/tf-tensorboard/test/e2eTests.html
index b0667b56a3..5efc02ef98 100644
--- a/tensorflow/tensorboard/components/tf-tensorboard/test/e2eTests.html
+++ b/tensorflow/tensorboard/components/tf-tensorboard/test/e2eTests.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
 <head>
   <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-tensorboard/test/e2eTests.ts b/tensorflow/tensorboard/components/tf-tensorboard/test/e2eTests.ts
index 3c5bcdc2ee..2025ac6a59 100644
--- a/tensorflow/tensorboard/components/tf-tensorboard/test/e2eTests.ts
+++ b/tensorflow/tensorboard/components/tf-tensorboard/test/e2eTests.ts
@@ -1,3 +1,18 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
 describe('end-to-end test', () => {
   let assert = chai.assert;
   window.HTMLImports.whenReady(() => {
diff --git a/tensorflow/tensorboard/components/tf-tensorboard/test/fastTabSwitch.html b/tensorflow/tensorboard/components/tf-tensorboard/test/fastTabSwitch.html
index 2a81a06cde..88bb6edc48 100644
--- a/tensorflow/tensorboard/components/tf-tensorboard/test/fastTabSwitch.html
+++ b/tensorflow/tensorboard/components/tf-tensorboard/test/fastTabSwitch.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
 <head>
   <script src="../../webcomponentsjs/webcomponents-lite.min.js"></script>
diff --git a/tensorflow/tensorboard/components/tf-tensorboard/test/fastTabSwitch.ts b/tensorflow/tensorboard/components/tf-tensorboard/test/fastTabSwitch.ts
index 3a65eb87bc..1366695419 100644
--- a/tensorflow/tensorboard/components/tf-tensorboard/test/fastTabSwitch.ts
+++ b/tensorflow/tensorboard/components/tf-tensorboard/test/fastTabSwitch.ts
@@ -1,3 +1,18 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
 describe('fast tab switch', () => {
   let assert = chai.assert;
   window.HTMLImports.whenReady(() => {
diff --git a/tensorflow/tensorboard/components/tf-tensorboard/test/index.html b/tensorflow/tensorboard/components/tf-tensorboard/test/index.html
index 317bff4135..8806f36fad 100644
--- a/tensorflow/tensorboard/components/tf-tensorboard/test/index.html
+++ b/tensorflow/tensorboard/components/tf-tensorboard/test/index.html
@@ -1,4 +1,21 @@
 <!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
 <head>
   <meta charset="utf-8">
diff --git a/tensorflow/tensorboard/components/tf-tensorboard/test/tensorboardTests.html b/tensorflow/tensorboard/components/tf-tensorboard/test/tensorboardTests.html
index b8ca209f96..2122cb79b1 100644
--- a/tensorflow/tensorboard/components/tf-tensorboard/test/tensorboardTests.html
+++ b/tensorflow/tensorboard/components/tf-tensorboard/test/tensorboardTests.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
 <head>
   <link rel="import" href="../../polymer/polymer.html">
diff --git a/tensorflow/tensorboard/components/tf-tensorboard/tf-tensorboard.html b/tensorflow/tensorboard/components/tf-tensorboard/tf-tensorboard.html
index c45bdf00d5..ec62c60633 100644
--- a/tensorflow/tensorboard/components/tf-tensorboard/tf-tensorboard.html
+++ b/tensorflow/tensorboard/components/tf-tensorboard/tf-tensorboard.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../paper-tabs/paper-tabs.html">
 <link rel="import" href="../paper-dialog/paper-dialog.html">
diff --git a/tensorflow/tensorboard/components/vz-heatmap/demo/index.html b/tensorflow/tensorboard/components/vz-heatmap/demo/index.html
index 0abc131795..63dd04485f 100644
--- a/tensorflow/tensorboard/components/vz-heatmap/demo/index.html
+++ b/tensorflow/tensorboard/components/vz-heatmap/demo/index.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html lang="en">
 <head>
   <meta charset="UTF-8">
diff --git a/tensorflow/tensorboard/components/vz-heatmap/index.html b/tensorflow/tensorboard/components/vz-heatmap/index.html
index 3384d4cb3f..656306499e 100644
--- a/tensorflow/tensorboard/components/vz-heatmap/index.html
+++ b/tensorflow/tensorboard/components/vz-heatmap/index.html
@@ -1,4 +1,21 @@
 <!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
   <head>
     <meta charset="utf-8">
@@ -11,4 +28,4 @@
     <iron-component-page src="vz-heatmap.html"></iron-component-page>
 
   </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/tensorflow/tensorboard/components/vz-heatmap/vz-heatmap.html b/tensorflow/tensorboard/components/vz-heatmap/vz-heatmap.html
index 78c01036e3..b1108051f4 100644
--- a/tensorflow/tensorboard/components/vz-heatmap/vz-heatmap.html
+++ b/tensorflow/tensorboard/components/vz-heatmap/vz-heatmap.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-imports/d3.html">
 
@@ -317,4 +334,4 @@ fits itself to the width of the container it is placed in.
       }
     });
   </script>
-</dom-module>
\ No newline at end of file
+</dom-module>
diff --git a/tensorflow/tensorboard/components/vz-histogram-timeseries/demo/index.html b/tensorflow/tensorboard/components/vz-histogram-timeseries/demo/index.html
index 56fb436b98..56543fb66b 100644
--- a/tensorflow/tensorboard/components/vz-histogram-timeseries/demo/index.html
+++ b/tensorflow/tensorboard/components/vz-histogram-timeseries/demo/index.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
   <head>
     <meta charset="utf-8">
diff --git a/tensorflow/tensorboard/components/vz-histogram-timeseries/index.html b/tensorflow/tensorboard/components/vz-histogram-timeseries/index.html
index 483a746c48..20a157ea0e 100644
--- a/tensorflow/tensorboard/components/vz-histogram-timeseries/index.html
+++ b/tensorflow/tensorboard/components/vz-histogram-timeseries/index.html
@@ -1,4 +1,20 @@
 <!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
 
 <html>
   <head>
diff --git a/tensorflow/tensorboard/components/vz-histogram-timeseries/vz-histogram-timeseries.html b/tensorflow/tensorboard/components/vz-histogram-timeseries/vz-histogram-timeseries.html
index 10db2e2d3b..015cf7b31e 100644
--- a/tensorflow/tensorboard/components/vz-histogram-timeseries/vz-histogram-timeseries.html
+++ b/tensorflow/tensorboard/components/vz-histogram-timeseries/vz-histogram-timeseries.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-imports/d3.html">
 
@@ -8,7 +25,8 @@ displaying how data is distributed over time.
 This histogram supports changing the time axis type and different modes of
 visualization.
 
-@demo
+@element vz-histogram-timeseries
+@demo demo/index.html
 -->
 <dom-module id="vz-histogram-timeseries">
     <template>
diff --git a/tensorflow/tensorboard/components/vz-line-chart/demo/index.html b/tensorflow/tensorboard/components/vz-line-chart/demo/index.html
index b518c13c90..fec8e8bed8 100644
--- a/tensorflow/tensorboard/components/vz-line-chart/demo/index.html
+++ b/tensorflow/tensorboard/components/vz-line-chart/demo/index.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
   <head>
     <meta charset="utf-8">
diff --git a/tensorflow/tensorboard/components/vz-line-chart/index.html b/tensorflow/tensorboard/components/vz-line-chart/index.html
index 58f90bb36e..b7b399d3fc 100644
--- a/tensorflow/tensorboard/components/vz-line-chart/index.html
+++ b/tensorflow/tensorboard/components/vz-line-chart/index.html
@@ -1,4 +1,20 @@
 <!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
 
 <html>
   <head>
diff --git a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html
index 1d68cc90f7..c08e512ffe 100644
--- a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html
+++ b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html
@@ -1,3 +1,20 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <link rel="import" href="../polymer/polymer.html">
 <link rel="import" href="../tf-imports/plottable.html">
 <link rel="import" href="../tf-imports/lodash.html">
@@ -9,7 +26,8 @@ displaying event values.
 This line chart supports drawing multiple lines at the same time, with features
 such as different X scales (linear and temporal), tooltips and smoothing.
 
-@demo
+@element vz-line-chart
+@demo demo/index.html
 -->
 <dom-module id="vz-line-chart">
   <template>
diff --git a/tensorflow/tensorboard/components/vz-sorting/vz-sorting.html b/tensorflow/tensorboard/components/vz-sorting/vz-sorting.html
index 9376663dae..5ff6f31158 100644
--- a/tensorflow/tensorboard/components/vz-sorting/vz-sorting.html
+++ b/tensorflow/tensorboard/components/vz-sorting/vz-sorting.html
@@ -1,17 +1,18 @@
 <!--
-  Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 
-  Licensed under the Apache License, Version 2.0 (the "License");
-  you may not use this file except in compliance with the License.
-  You may obtain a copy of the License at
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
 
-      http://www.apache.org/licenses/LICENSE-2.0
+    http://www.apache.org/licenses/LICENSE-2.0
 
-  Unless required by applicable law or agreed to in writing, software
-  distributed under the License is distributed on an "AS IS" BASIS,
-  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-  See the License for the specific language governing permissions and
-  limitations under the License.
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
 -->
 
 <script src="sorting.js"></script>
diff --git a/tensorflow/tensorboard/demo/index.html b/tensorflow/tensorboard/demo/index.html
index c9f84b13a4..66af5bfaa0 100644
--- a/tensorflow/tensorboard/demo/index.html
+++ b/tensorflow/tensorboard/demo/index.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <!-- This demo index file serves statically serialized TensorBoard json.
 It is essentially a mocked version of the TensorBoard backend. -->
 <html>
diff --git a/tensorflow/tensorboard/dist/index.html b/tensorflow/tensorboard/dist/index.html
index 66194def06..fa3020e1cb 100644
--- a/tensorflow/tensorboard/dist/index.html
+++ b/tensorflow/tensorboard/dist/index.html
@@ -1,4 +1,21 @@
-<!DOCTYPE html>
+<!doctype html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
 <html>
   <head>
     <title>TensorBoard</title>
diff --git a/tensorflow/tensorboard/lib/css/global.css b/tensorflow/tensorboard/lib/css/global.css
index b3681d766f..cb6e966fdd 100644
--- a/tensorflow/tensorboard/lib/css/global.css
+++ b/tensorflow/tensorboard/lib/css/global.css
@@ -1,4 +1,19 @@
-html,body {
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+html, body {
   margin: 0;
   padding: 0;
   height: 100%;
-- 
GitLab


From 829a2365229580074cff806e725bca9f2a34788c Mon Sep 17 00:00:00 2001
From: Wei Ho <weiho@google.com>
Date: Wed, 24 Aug 2016 15:26:40 -0800
Subject: [PATCH 113/223] Adds _PartitionInfo to variable initializer signature
 Change: 131229727

---
 RELEASE.md                                    |   3 +
 .../framework/python/ops/variables_test.py    |   3 +-
 .../layers/python/layers/initializers.py      |   3 +-
 .../partitioned_variables_test.py             |   2 +-
 .../kernel_tests/variable_scope_test.py       |  58 +++++++
 tensorflow/python/ops/array_ops.py            |   2 +-
 tensorflow/python/ops/init_ops.py             |  54 ++++---
 tensorflow/python/ops/variable_scope.py       | 153 +++++++++++++++++-
 .../saver_large_partitioned_variable_test.py  |   3 +-
 9 files changed, 249 insertions(+), 32 deletions(-)

diff --git a/RELEASE.md b/RELEASE.md
index f1a8859c35..abb99a2ebb 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -10,6 +10,9 @@
 * Int32 elements of list(type) arguments are no longer placed in host memory by
   default. If necessary, a list(type) argument to a kernel can be placed in host
   memory using a HostMemory annotation.
+* uniform_unit_scaling_initializer() no longer takes a full_shape arg, instead
+  relying on the partition info passed to the initializer function when it's
+  called.
 
 # Release 0.10.0
 
diff --git a/tensorflow/contrib/framework/python/ops/variables_test.py b/tensorflow/contrib/framework/python/ops/variables_test.py
index 11bd723179..d6e1d03a56 100644
--- a/tensorflow/contrib/framework/python/ops/variables_test.py
+++ b/tensorflow/contrib/framework/python/ops/variables_test.py
@@ -472,7 +472,8 @@ class ModelVariablesTest(tf.test.TestCase):
 
   def testInitializedVariableValue(self):
     with self.test_session() as sess:
-      a = tf.contrib.framework.model_variable('a', [5], initializer=tf.ones)
+      a = tf.contrib.framework.model_variable(
+          'a', [5], initializer=tf.ones_initializer)
       sess.run(tf.initialize_all_variables())
       self.assertAllEqual(a.eval(), [1]*5)
 
diff --git a/tensorflow/contrib/layers/python/layers/initializers.py b/tensorflow/contrib/layers/python/layers/initializers.py
index 1786b71dcf..fef925ca7e 100644
--- a/tensorflow/contrib/layers/python/layers/initializers.py
+++ b/tensorflow/contrib/layers/python/layers/initializers.py
@@ -105,7 +105,8 @@ def variance_scaling_initializer(factor=2.0, mode='FAN_IN', uniform=False,
     raise TypeError('Cannot create initializer for non-floating point type.')
   if mode not in ['FAN_IN', 'FAN_OUT', 'FAN_AVG']:
     raise TypeError('Unknow mode %s [FAN_IN, FAN_OUT, FAN_AVG]', mode)
-  def _initializer(shape, dtype=dtype):
+
+  def _initializer(shape, dtype=dtype, partition_info=None):
     """Initializer function."""
     if not dtype.is_floating:
       raise TypeError('Cannot create initializer for non-floating point type.')
diff --git a/tensorflow/python/kernel_tests/partitioned_variables_test.py b/tensorflow/python/kernel_tests/partitioned_variables_test.py
index 77feacad19..efa7322f65 100644
--- a/tensorflow/python/kernel_tests/partitioned_variables_test.py
+++ b/tensorflow/python/kernel_tests/partitioned_variables_test.py
@@ -219,7 +219,7 @@ class PartitionerCreatorsTest(tf.test.TestCase):
                                           expected_partitions=[4, 1, 1])
 
 
-def _IotaInitializer(shape, dtype=tf.float32):
+def _IotaInitializer(shape, dtype=tf.float32, partition_info=None):
   assert dtype == tf.float32
   if len(shape) == 1:
     return range(shape[0])
diff --git a/tensorflow/python/kernel_tests/variable_scope_test.py b/tensorflow/python/kernel_tests/variable_scope_test.py
index ff9716438a..bce249d8c5 100644
--- a/tensorflow/python/kernel_tests/variable_scope_test.py
+++ b/tensorflow/python/kernel_tests/variable_scope_test.py
@@ -751,5 +751,63 @@ class VariableScopeWithCustomGetterTest(tf.test.TestCase):
       np_vars, np_v = sess.run([true_vars, v])
       self.assertAllClose(np_v, sum(np_vars))
 
+
+class PartitionInfoTest(tf.test.TestCase):
+
+  def testConstructorChecks(self):
+    # Invalid arg types.
+    with self.assertRaises(TypeError):
+      variable_scope._PartitionInfo(full_shape=None, var_offset=[0, 1])
+    with self.assertRaises(TypeError):
+      variable_scope._PartitionInfo(full_shape=[0, 1], var_offset=None)
+    with self.assertRaises(TypeError):
+      variable_scope._PartitionInfo(full_shape="foo", var_offset=[0, 1])
+    with self.assertRaises(TypeError):
+      variable_scope._PartitionInfo(full_shape=[0, 1], var_offset="foo")
+
+    # full_shape and var_offset must have same length.
+    with self.assertRaises(ValueError):
+      variable_scope._PartitionInfo(full_shape=[0, 1], var_offset=[0])
+    # Offset must always be less than shape.
+    with self.assertRaises(ValueError):
+      variable_scope._PartitionInfo(full_shape=[1, 1], var_offset=[0, 1])
+
+  def testSingleOffset(self):
+    partition_info = variable_scope._PartitionInfo(
+        full_shape=[9, 3], var_offset=[4, 0])
+    self.assertEqual(4, partition_info.single_offset([1, 3]))
+
+    # Tests when the variable isn't partitioned at all.
+    partition_info = variable_scope._PartitionInfo(
+        full_shape=[9, 3], var_offset=[0, 0])
+    self.assertEqual(0, partition_info.single_offset([9, 3]))
+
+  def testSingleSliceDim(self):
+    partition_info = variable_scope._PartitionInfo(
+        full_shape=[9, 3], var_offset=[4, 0])
+    # Invalid shape.
+    with self.assertRaises(TypeError):
+      partition_info.single_slice_dim(None)
+
+    # Rank of shape differs from full_shape.
+    with self.assertRaises(ValueError):
+      partition_info.single_slice_dim([1, 2, 3])
+
+    # Shape is too large given var_offset (4+6 > 9).
+    with self.assertRaises(ValueError):
+      partition_info.single_slice_dim([6, 3])
+
+    # Multiple possible slice dim from shape.
+    with self.assertRaises(ValueError):
+      partition_info.single_slice_dim([1, 1])
+
+    partition_info = variable_scope._PartitionInfo(
+        full_shape=[9, 3], var_offset=[0, 0])
+    self.assertEqual(1, partition_info.single_slice_dim([9, 2]))
+    partition_info = variable_scope._PartitionInfo(
+        full_shape=[9, 3], var_offset=[4, 0])
+    self.assertEqual(0, partition_info.single_slice_dim([2, 3]))
+
+
 if __name__ == "__main__":
   tf.test.main()
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index c556e8e213..16267fd481 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -254,7 +254,7 @@ def rank_internal(input, name=None, optimize=True):
 
 # DEPRECATED use init_ops.zeros_initializer
 # TODO(irving) Move it to init_ops.py
-def zeros_initializer(shape, dtype=dtypes.float32):
+def zeros_initializer(shape, dtype=dtypes.float32, partition_info=None):
   """An adaptor for zeros() to match the Initializer spec."""
   return zeros(shape, dtype)
 
diff --git a/tensorflow/python/ops/init_ops.py b/tensorflow/python/ops/init_ops.py
index 77a8ced837..24699b868b 100644
--- a/tensorflow/python/ops/init_ops.py
+++ b/tensorflow/python/ops/init_ops.py
@@ -13,7 +13,22 @@
 # limitations under the License.
 # ==============================================================================
 
-"""Operations often used for initializing tensors."""
+"""Operations often used for initializing tensors.
+
+All variable initializers returned by functions in this file should have the
+following signature:
+
+def _initializer(shape, dtype=dtypes.float32, partition_info=None):
+  Args:
+    shape: List of `int` representing the shape of the output `Tensor`. Some
+      initializers may also be able to accept a `Tensor`.
+    dtype: (Optional) Type of the output `Tensor`.
+    partition_info: (Optional) variable_scope._PartitionInfo object holding
+      additional information about how the variable is partitioned. May be
+      `None` if the variable is not partitioned.
+  Returns:
+    A `Tensor` of type `dtype` and `shape`.
+"""
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -50,7 +65,7 @@ def _assert_float_dtype(dtype):
 zeros_initializer = array_ops.zeros_initializer
 
 
-def ones_initializer(shape, dtype=dtypes.float32):
+def ones_initializer(shape, dtype=dtypes.float32, partition_info=None):
   """An adaptor for ones() to match the Initializer spec."""
   return array_ops.ones(shape, dtype)
 
@@ -125,7 +140,7 @@ def constant_initializer(value=0, dtype=dtypes.float32):
     ValueError: Too many elements provided. Needed at most 6, but received 8
     ```
   """
-  def _initializer(shape, dtype=dtype):
+  def _initializer(shape, dtype=dtype, partition_info=None):
     return constant_op.constant(value, dtype=dtype, shape=shape)
   return _initializer
 
@@ -147,7 +162,7 @@ def random_uniform_initializer(minval=0, maxval=None, seed=None,
   Returns:
     An initializer that generates tensors with a uniform distribution.
   """
-  def _initializer(shape, dtype=dtype):
+  def _initializer(shape, dtype=dtype, partition_info=None):
     return random_ops.random_uniform(shape, minval, maxval, dtype, seed=seed)
   return _initializer
 
@@ -172,7 +187,8 @@ def random_normal_initializer(mean=0.0, stddev=1.0, seed=None,
   Raises:
     ValueError: if `dtype` is not a floating point type.
   """
-  def _initializer(shape, dtype=_assert_float_dtype(dtype)):
+  def _initializer(shape, dtype=_assert_float_dtype(dtype),
+                   partition_info=None):
     return random_ops.random_normal(shape, mean, stddev, dtype, seed=seed)
   return _initializer
 
@@ -203,13 +219,16 @@ def truncated_normal_initializer(mean=0.0, stddev=1.0, seed=None,
   Raises:
     ValueError: if `dtype` is not a floating point type.
   """
-  def _initializer(shape, dtype=_assert_float_dtype(dtype)):
+  def _initializer(shape, dtype=_assert_float_dtype(dtype),
+                   partition_info=None):
     return random_ops.truncated_normal(shape, mean, stddev, dtype, seed=seed)
+
   return _initializer
 
 
-def uniform_unit_scaling_initializer(factor=1.0, seed=None,
-                                     dtype=dtypes.float32, full_shape=None):
+def uniform_unit_scaling_initializer(factor=1.0,
+                                     seed=None,
+                                     dtype=dtypes.float32):
   """Returns an initializer that generates tensors without scaling variance.
 
   When initializing a deep network, it is in principle advantageous to keep
@@ -228,21 +247,12 @@ def uniform_unit_scaling_initializer(factor=1.0, seed=None,
   and the calculation of constants. In section 2.3 there, the constants were
   numerically computed: for a linear layer it's 1.0, relu: ~1.43, tanh: ~1.15.
 
-  If the shape tuple `full_shape` is provided, the scale will be calculated from
-  this predefined shape.  This is useful when a `Variable` is being partitioned
-  across several shards, and each shard has a smaller shape than the whole.
-  Since the shards are usually concatenated when used, the scale should be
-  based on the shape of the whole.
-
   Args:
     factor: Float.  A multiplicative factor by which the values will be scaled.
     seed: A Python integer. Used to create random seeds. See
       [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
       for behavior.
     dtype: The data type. Only floating point types are supported.
-    full_shape: Tuple or list of integers.  The shape used for calculating
-      scale normalization (instead of the shape passed at creation time).
-      Useful when creating sharded variables via partitioning.
 
   Returns:
     An initializer that generates tensors with unit variance.
@@ -250,8 +260,12 @@ def uniform_unit_scaling_initializer(factor=1.0, seed=None,
   Raises:
     ValueError: if `dtype` is not a floating point type.
   """
-  def _initializer(shape, dtype=_assert_float_dtype(dtype)):
-    scale_shape = full_shape if full_shape is not None else shape
+  def _initializer(shape, dtype=_assert_float_dtype(dtype),
+                   partition_info=None):
+    scale_shape = shape
+    if partition_info is not None:
+      scale_shape = partition_info.full_shape
+
     input_size = 1.0
     # Estimating input size is not possible to do perfectly, but we try.
     # The estimate, obtained by multiplying all dimensions but the last one,
@@ -319,7 +333,7 @@ class _RandomWalkInitializer(object):
     self._nonlinearity = nonlinearity
     self._seed = seed
 
-  def __call__(self, shape, dtype=dtypes.float32):
+  def __call__(self, shape, dtype=dtypes.float32, partition_info=None):
     """Generate a tensor used to initialize a variable."""
     return random_ops._random_walk(shape, self._nonlinearity, dtype,
                                    seed=self._seed)
diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py
index c4c51aa443..217556df3c 100644
--- a/tensorflow/python/ops/variable_scope.py
+++ b/tensorflow/python/ops/variable_scope.py
@@ -39,6 +39,133 @@ __all__ = ["VariableScope", "get_variable_scope",
            "no_regularizer"]
 
 
+class _PartitionInfo(object):
+  """Holds partition info used by initializer functions.
+  """
+
+  def __init__(self, full_shape, var_offset):
+    """Constructor.
+
+    Args:
+      full_shape: Tuple or list of `int` indicating the full combined shape
+        of the partitioned variables.
+      var_offset: Tuple or list of `int` specifying offset of this partition
+        with respect to the full variable for each dimension.
+
+    Raises:
+      TypeError: If `full_shape` or `var_offset` is not a sequence.
+      ValueError: If `full_shape` or `var_offset` differ in length. If
+        `var_offset` exceeds `full_shape` in any dimension.
+    """
+    if not isinstance(full_shape, collections_lib.Sequence) or isinstance(
+        full_shape, six.string_types):
+      raise TypeError(
+          "`full_shape` must be a sequence (like tuple or list) instead of " +
+          type(full_shape).__name__)
+
+    if not isinstance(var_offset, collections_lib.Sequence) or isinstance(
+        var_offset, six.string_types):
+      raise TypeError(
+          "`var_offset` must be a sequence (like tuple or list) instead of " +
+          type(var_offset).__name__)
+
+    if len(var_offset) != len(full_shape):
+      raise ValueError(
+          "Expected equal length, but `var_offset` is of length {} while "
+          "full_shape is of length {}.".format(
+              len(var_offset), len(full_shape)))
+
+    for i in xrange(len(full_shape)):
+      offset = var_offset[i]
+      shape = full_shape[i]
+      if offset < 0 or offset >= shape:
+        raise ValueError(
+            "Expected 0 <= offset < shape but found offset={}, shape={} for "
+            "var_offset={}, full_shape={}".format(offset, shape, var_offset,
+                                                  full_shape))
+
+    self._full_shape = full_shape
+    self._var_offset = var_offset
+
+  @property
+  def full_shape(self):
+    return self._full_shape
+
+  @property
+  def var_offset(self):
+    return self._var_offset
+
+  def single_offset(self, shape):
+    """Returns the offset when the variable is partitioned in at most one dim.
+
+    Args:
+      shape: Tuple or list of `int` indicating the shape of one specific
+        variable partition.
+
+    Returns:
+      `int` representing the offset in the dimension along which the variable is
+       partitioned. Returns 0 if the variable is not being partitioned.
+
+    Raises:
+      ValueError: Depending on self.single_slice_dim().
+    """
+
+    single_slice_dim = self.single_slice_dim(shape)
+    # If this variable is not being partitioned at all, single_slice_dim() could
+    # return None.
+    if single_slice_dim is None:
+      return 0
+    return self.var_offset[single_slice_dim]
+
+  def single_slice_dim(self, shape):
+    """Returns the slice dim when the variable is partitioned only in one dim.
+
+    Args:
+      shape: Tuple or list of `int` indicating the shape of one specific
+        variable partition.
+
+    Returns:
+      `int` representing the dimension that the variable is partitioned in, or
+      `None` if the variable doesn't seem to be partitioned at all.
+
+    Raises:
+      TypeError: If `shape` is not a sequence.
+      ValueError: If `shape` is not the same length as `self.full_shape`. If
+        the variable is partitioned in more than one dimension.
+    """
+    if not isinstance(shape, collections_lib.Sequence) or isinstance(
+        shape, six.string_types):
+      raise TypeError(
+          "`shape` must be a sequence (like tuple or list) instead of " +
+          type(shape).__name__)
+
+    if len(shape) != len(self.full_shape):
+      raise ValueError(
+          "Expected equal length, but received shape={} of length {} while "
+          "self.full_shape={} is of length {}.".format(shape, len(
+              shape), self.full_shape, len(self.full_shape)))
+
+    for i in xrange(len(shape)):
+      if self.var_offset[i] + shape[i] > self.full_shape[i]:
+        raise ValueError(
+            "With self.var_offset={}, a partition of shape={} would exceed "
+            "self.full_shape={} in dimension {}.".format(
+                self.var_offset, shape, self.full_shape, i))
+
+    slice_dim = None
+    for i in xrange(len(shape)):
+      if shape[i] == self.full_shape[i]:
+        continue
+      if slice_dim is not None:
+        raise ValueError(
+            "Cannot use single_slice_dim() with shape={} and "
+            "self.full_shape={} since slice dim could be either dimension {} "
+            "or {}.".format(shape, self.full_shape, i, slice_dim))
+      slice_dim = i
+
+    return slice_dim
+
+
 class _VariableStore(object):
   """Variable store that carries a number of named Variables.
 
@@ -390,6 +517,8 @@ class _VariableStore(object):
     for i in xrange(num_slices):
       var_shape = slice_shape[:]
       var_offset = slice_offset[:]
+      partition_info = _PartitionInfo(
+          full_shape=shape.as_list(), var_offset=var_offset)
       if i < num_slices_with_excess:
         var_shape[slice_dim] += 1
       slice_offset[slice_dim] += var_shape[slice_dim]
@@ -397,8 +526,7 @@ class _VariableStore(object):
       var_full_name = "%s/part_%d" % (name, i)
       with ops.name_scope(var_full_name + "/PartitionedInitializer"):
         if initializer is None:
-          init = init_ops.uniform_unit_scaling_initializer(
-              full_shape=shape.as_list())
+          init = init_ops.uniform_unit_scaling_initializer()
           init_shape = var_shape
         elif callable(initializer):
           init = initializer
@@ -419,6 +547,7 @@ class _VariableStore(object):
             shape=init_shape,
             dtype=dtype,
             initializer=init,
+            partition_info=partition_info,
             regularizer=regularizer,
             reuse=reuse,
             trainable=trainable,
@@ -443,10 +572,18 @@ class _VariableStore(object):
     self._partitioned_vars[name] = partitioned_var
     return partitioned_var
 
-  def _get_single_variable(self, name, shape=None, dtype=dtypes.float32,
-                           initializer=None, regularizer=None, reuse=None,
-                           trainable=True, collections=None,
-                           caching_device=None, validate_shape=True):
+  def _get_single_variable(self,
+                           name,
+                           shape=None,
+                           dtype=dtypes.float32,
+                           initializer=None,
+                           regularizer=None,
+                           partition_info=None,
+                           reuse=None,
+                           trainable=True,
+                           collections=None,
+                           caching_device=None,
+                           validate_shape=True):
     """Get or create a single Variable (e.g. a shard or entire variable).
 
     See the documentation of get_variable above (ignore partitioning components)
@@ -458,6 +595,7 @@ class _VariableStore(object):
       dtype: see get_variable.
       initializer: see get_variable.
       regularizer: see get_variable.
+      partition_info: _PartitionInfo object.
       reuse: see get_variable.
       trainable: see get_variable.
       collections: see get_variable.
@@ -523,7 +661,8 @@ class _VariableStore(object):
         init_val = initializer
         variable_dtype = None
       else:
-        init_val = lambda: initializer(shape.as_list(), dtype=dtype)
+        init_val = lambda: initializer(
+            shape.as_list(), dtype=dtype, partition_info=partition_info)
         variable_dtype = dtype.base_dtype
 
     # Create the variable.
diff --git a/tensorflow/python/training/saver_large_partitioned_variable_test.py b/tensorflow/python/training/saver_large_partitioned_variable_test.py
index bc071eb270..4c0526cc42 100644
--- a/tensorflow/python/training/saver_large_partitioned_variable_test.py
+++ b/tensorflow/python/training/saver_large_partitioned_variable_test.py
@@ -34,7 +34,8 @@ class SaverLargePartitionedVariableTest(tf.test.TestCase):
       with tf.device("/cpu:0"):
         # Create a partitioned variable which is larger than int32 size but
         # split into smaller sized variables.
-        init = lambda shape, dtype: tf.constant(True, dtype, shape)
+        init = lambda shape, dtype, partition_info: tf.constant(
+            True, dtype, shape)
         partitioned_var = tf.create_partitioned_variables(
             [1 << 31], [4], init, dtype=tf.bool, name=var_name)
         tf.initialize_all_variables().run()
-- 
GitLab


From 2174f5e1dfd24a55aaced21990e3c53148035dc6 Mon Sep 17 00:00:00 2001
From: Yuan Yu <yuanbyu@google.com>
Date: Wed, 24 Aug 2016 15:41:57 -0800
Subject: [PATCH 114/223] Fix a bug introduced in cl/130951050. The correct CL
 description should be: make sure that no control edges by an outer control
 dependency context are added to nodes inside a while loop. Change: 131231314

---
 tensorflow/python/ops/control_flow_ops.py | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index aab4042f34..2af82cf9de 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -1158,8 +1158,17 @@ class ControlFlowContext(object):
 
   def _MaybeRemoveExternalControlEdges(self, op):
     """Remove any external control dependency on this op."""
-    internal_control_inputs = [x for x in op.control_inputs
-                               if _GetOutputContext(x) == self]
+    while_ctxt = self.GetWhileContext()
+    # A control input of `op` is internal if it is in the same while
+    # loop context as the enclosing while loop context of self.
+    if while_ctxt is None:
+      internal_control_inputs = op.control_inputs
+    else:
+      internal_control_inputs = []
+      for x in op.control_inputs:
+        ctxt = _GetOutputContext(x)
+        if ctxt is not None and ctxt.GetWhileContext() == while_ctxt:
+          internal_control_inputs.append(x)
     if len(internal_control_inputs) != len(op.control_inputs):
       del op.control_inputs[:]
       op._add_control_inputs(internal_control_inputs)
-- 
GitLab


From 351545f840bf0c5e34e5963f62066693817fb724 Mon Sep 17 00:00:00 2001
From: Ben Dilday <bdilday@users.noreply.github.com>
Date: Wed, 24 Aug 2016 16:57:27 -0700
Subject: [PATCH 115/223] Tensorboard log plot option (#3876)

* yScaleType property

* restored changes inadvertently deleted by rebase

* log-option-button style
---
 .../tf-dashboard-common/dashboard-style.html  | 10 +++++++++-
 .../tf-event-dashboard.html                   | 20 +++++++++++++++++++
 .../vz-line-chart/vz-line-chart.html          | 16 ++++++++++++---
 .../components/vz-line-chart/vz-line-chart.ts | 18 +++++++++++++----
 4 files changed, 56 insertions(+), 8 deletions(-)

diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/dashboard-style.html b/tensorflow/tensorboard/components/tf-dashboard-common/dashboard-style.html
index 2870845f8d..d3e3a6d81f 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/dashboard-style.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/dashboard-style.html
@@ -36,7 +36,7 @@
       }
       .card .card-bottom-row {
         position: absolute;
-        left: 50px;
+        left: 75px;
         bottom: 0;
         padding-right: 10px;
       }
@@ -54,6 +54,14 @@
         display: block;
       }
 
+      .log-option-button {
+        position: absolute;
+        left: 25px;
+        bottom: 0px;
+        color: #2196F3;
+        display: block;
+      }
+
       #content-container{
         display: block;
       }
diff --git a/tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html b/tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html
index db9089280e..0ec8f5bbed 100644
--- a/tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html
+++ b/tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html
@@ -128,6 +128,13 @@ The #center div contains tf-line-charts embedded inside tf-collapsable-panes.
                       icon="fullscreen"
                       on-tap="toggleSelected"
                     ></paper-icon-button>
+                      <paper-icon-button
+                              class="log-option-button"
+                              icon="line-weight"
+                              on-tap="toggleYScale"
+                              title="Toggle y-axis scale (log, linear)"
+                              ></paper-icon-button>
+
                   </div>
                   <template is="dom-if" if="[[_showDownloadLinks]]">
                     <div class="card-bottom-row">
@@ -225,6 +232,19 @@ The #center div contains tf-line-charts embedded inside tf-collapsable-panes.
           chartScaffold.chart().redraw();
         }
       },
+
+      toggleYScale: function(e) {
+        var currentTarget = Polymer.dom(e.currentTarget);
+
+        var b = currentTarget.parentNode.querySelector('.log-option-button');
+        var c = currentTarget.parentNode.querySelector('vz-line-chart');
+        if (c !== null) {
+          c.yScaleType = c.yScaleType === 'log' ? 'linear' : 'log';
+          b.icon = c.yScaleType === 'log' ? 'line-weight' : 'reorder';
+          c.redraw();
+        }
+      },
+
       validRuns: function(tag, runsChange, run2tagChange) {
         var _this = this;
         var result = this.selectedRuns.filter(function(r) {
diff --git a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html
index 1d68cc90f7..0faf99482f 100644
--- a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html
+++ b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html
@@ -172,6 +172,16 @@ such as different X scales (linear and temporal), tooltips and smoothing.
           value: 'step'
         },
 
+        /**
+         * The scale for the y-axis. Allows:
+         * - "linear" - linear scale (Plottable.Scales.Linear)
+         * - "log" - modified-log scale (Plottable.Scales.ModifiedLog)
+         */
+        yScaleType: {
+          type: String,
+          value: 'linear'
+        },
+
         /**
          * Change how the tooltip is sorted. Allows:
          * - "default" - Sort the tooltip by input order.
@@ -209,7 +219,7 @@ such as different X scales (linear and temporal), tooltips and smoothing.
         }
       },
       observers: [
-        "_makeChart(xType, colorScale, _attached)",
+        "_makeChart(xType, yScaleType, colorScale, _attached)",
         "_reloadFromCache(_chart)",
         "_smoothingChanged(smoothingEnabled, smoothingWeight, _chart)",
         "_tooltipSortingMethodChanged(tooltipSortingMethod, _chart)",
@@ -265,7 +275,7 @@ such as different X scales (linear and temporal), tooltips and smoothing.
         this.scopeSubtree(this.$.tooltip, true);
         this.scopeSubtree(this.$.chartsvg, true);
       },
-      _makeChart: function(xType, colorScale, _attached) {
+      _makeChart: function(xType, yScaleType, colorScale, _attached) {
         if (this._makeChartAsyncCallbackId === null) {
           this.cancelAsync(this._makeChartAsyncCallbackId);
         }
@@ -275,7 +285,7 @@ such as different X scales (linear and temporal), tooltips and smoothing.
           if (!this._attached) return;
           if (this._chart) this._chart.destroy();
           var tooltip = d3.select(this.$.tooltip);
-          var chart = new VZ.LineChart(xType, colorScale, tooltip);
+          var chart = new VZ.LineChart(xType, yScaleType, colorScale, tooltip);
           var svg = d3.select(this.$.chartsvg);
           chart.renderTo(svg);
           this._chart = chart;
diff --git a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts
index 6fde2706f1..17c02b9afb 100644
--- a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts
+++ b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts
@@ -47,7 +47,7 @@ module VZ {
     private tooltipPosition: string;
 
     constructor(
-        xType: string, colorScale: Plottable.Scales.Color,
+        xType: string, yScaleType: string, colorScale: Plottable.Scales.Color,
         tooltip: d3.Selection<any>) {
       this.seriesNames = [];
       this.name2datasets = {};
@@ -61,10 +61,10 @@ module VZ {
       // need to do a single bind, so we can deregister the callback from
       // old Plottable.Datasets. (Deregistration is done by identity checks.)
       this.onDatasetChanged = this._onDatasetChanged.bind(this);
-      this.buildChart(xType);
+      this.buildChart(xType, yScaleType);
     }
 
-    private buildChart(xType: string) {
+    private buildChart(xType: string, yScaleType: string) {
       if (this.outer) {
         this.outer.destroy();
       }
@@ -73,7 +73,7 @@ module VZ {
       this.xScale = xComponents.scale;
       this.xAxis = xComponents.axis;
       this.xAxis.margin(0).tickLabelPadding(3);
-      this.yScale = new Plottable.Scales.Linear();
+      this.yScale = LineChart.getYScaleFromType(yScaleType);
       this.yAxis = new Plottable.Axes.Numeric(this.yScale, 'left');
       let yFormatter = VZ.ChartHelpers.multiscaleFormatter(
           VZ.ChartHelpers.Y_AXIS_FORMATTER_PRECISION);
@@ -448,6 +448,16 @@ module VZ {
       return this.name2datasets[name];
     }
 
+    static getYScaleFromType(yScaleType: string): Plottable.QuantitativeScale<number> {
+      if (yScaleType === 'log') {
+        return new Plottable.Scales.ModifiedLog();
+      } else if (yScaleType === 'linear') {
+        return new Plottable.Scales.Linear();
+      } else {
+        throw new Error('Unrecognized yScale type ' + yScaleType);
+      }
+    }
+
     /**
      * Update the selected series on the chart.
      */
-- 
GitLab


From 04da3f89c9c5bee16ce98b6c6e745b8ab2f8e7d2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 24 Aug 2016 15:49:00 -0800
Subject: [PATCH 116/223] Update generated Python Op docs. Change: 131232000

---
 .../shard0/tf.zeros_initializer.md                |  2 +-
 .../shard2/tf.uniform_unit_scaling_initializer.md | 11 +----------
 .../shard8/tf.ones_initializer.md                 |  2 +-
 tensorflow/g3doc/api_docs/python/state_ops.md     | 15 +++------------
 4 files changed, 6 insertions(+), 24 deletions(-)

diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.zeros_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.zeros_initializer.md
index 707393f8be..0936c08b1a 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.zeros_initializer.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.zeros_initializer.md
@@ -1,4 +1,4 @@
-### `tf.zeros_initializer(shape, dtype=tf.float32)` {#zeros_initializer}
+### `tf.zeros_initializer(shape, dtype=tf.float32, partition_info=None)` {#zeros_initializer}
 
 An adaptor for zeros() to match the Initializer spec.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.uniform_unit_scaling_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.uniform_unit_scaling_initializer.md
index 6033fbf53a..e2613a77a1 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.uniform_unit_scaling_initializer.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.uniform_unit_scaling_initializer.md
@@ -1,4 +1,4 @@
-### `tf.uniform_unit_scaling_initializer(factor=1.0, seed=None, dtype=tf.float32, full_shape=None)` {#uniform_unit_scaling_initializer}
+### `tf.uniform_unit_scaling_initializer(factor=1.0, seed=None, dtype=tf.float32)` {#uniform_unit_scaling_initializer}
 
 Returns an initializer that generates tensors without scaling variance.
 
@@ -18,12 +18,6 @@ See [Sussillo et al., 2014](https://arxiv.org/abs/1412.6558)
 and the calculation of constants. In section 2.3 there, the constants were
 numerically computed: for a linear layer it's 1.0, relu: ~1.43, tanh: ~1.15.
 
-If the shape tuple `full_shape` is provided, the scale will be calculated from
-this predefined shape.  This is useful when a `Variable` is being partitioned
-across several shards, and each shard has a smaller shape than the whole.
-Since the shards are usually concatenated when used, the scale should be
-based on the shape of the whole.
-
 ##### Args:
 
 
@@ -32,9 +26,6 @@ based on the shape of the whole.
     [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
     for behavior.
 *  <b>`dtype`</b>: The data type. Only floating point types are supported.
-*  <b>`full_shape`</b>: Tuple or list of integers.  The shape used for calculating
-    scale normalization (instead of the shape passed at creation time).
-    Useful when creating sharded variables via partitioning.
 
 ##### Returns:
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.ones_initializer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.ones_initializer.md
index 0ddbc8b801..ebb90069ad 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.ones_initializer.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.ones_initializer.md
@@ -1,4 +1,4 @@
-### `tf.ones_initializer(shape, dtype=tf.float32)` {#ones_initializer}
+### `tf.ones_initializer(shape, dtype=tf.float32, partition_info=None)` {#ones_initializer}
 
 An adaptor for ones() to match the Initializer spec.
 
diff --git a/tensorflow/g3doc/api_docs/python/state_ops.md b/tensorflow/g3doc/api_docs/python/state_ops.md
index 95f7405391..2ad42d9a47 100644
--- a/tensorflow/g3doc/api_docs/python/state_ops.md
+++ b/tensorflow/g3doc/api_docs/python/state_ops.md
@@ -1721,7 +1721,7 @@ Returns an initializer that generates tensors with a uniform distribution.
 
 - - -
 
-### `tf.uniform_unit_scaling_initializer(factor=1.0, seed=None, dtype=tf.float32, full_shape=None)` {#uniform_unit_scaling_initializer}
+### `tf.uniform_unit_scaling_initializer(factor=1.0, seed=None, dtype=tf.float32)` {#uniform_unit_scaling_initializer}
 
 Returns an initializer that generates tensors without scaling variance.
 
@@ -1741,12 +1741,6 @@ See [Sussillo et al., 2014](https://arxiv.org/abs/1412.6558)
 and the calculation of constants. In section 2.3 there, the constants were
 numerically computed: for a linear layer it's 1.0, relu: ~1.43, tanh: ~1.15.
 
-If the shape tuple `full_shape` is provided, the scale will be calculated from
-this predefined shape.  This is useful when a `Variable` is being partitioned
-across several shards, and each shard has a smaller shape than the whole.
-Since the shards are usually concatenated when used, the scale should be
-based on the shape of the whole.
-
 ##### Args:
 
 
@@ -1755,9 +1749,6 @@ based on the shape of the whole.
     [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
     for behavior.
 *  <b>`dtype`</b>: The data type. Only floating point types are supported.
-*  <b>`full_shape`</b>: Tuple or list of integers.  The shape used for calculating
-    scale normalization (instead of the shape passed at creation time).
-    Useful when creating sharded variables via partitioning.
 
 ##### Returns:
 
@@ -1771,14 +1762,14 @@ based on the shape of the whole.
 
 - - -
 
-### `tf.zeros_initializer(shape, dtype=tf.float32)` {#zeros_initializer}
+### `tf.zeros_initializer(shape, dtype=tf.float32, partition_info=None)` {#zeros_initializer}
 
 An adaptor for zeros() to match the Initializer spec.
 
 
 - - -
 
-### `tf.ones_initializer(shape, dtype=tf.float32)` {#ones_initializer}
+### `tf.ones_initializer(shape, dtype=tf.float32, partition_info=None)` {#ones_initializer}
 
 An adaptor for ones() to match the Initializer spec.
 
-- 
GitLab


From e78fadb28627e82c978f6ef4f57915fda05062fc Mon Sep 17 00:00:00 2001
From: Sergio Guadarrama <sguada@google.com>
Date: Wed, 24 Aug 2016 16:20:08 -0800
Subject: [PATCH 117/223] Improve stability of batch_norm, by using a copy of
 moving_mean in tf.moments Removes race condition in contrib.layers.batch_norm
 about updating moving_mean. Change: 131235105

---
 .../contrib/layers/python/layers/layers.py    | 19 ++++++-
 .../layers/python/layers/layers_test.py       | 51 +++++++++++++++++++
 2 files changed, 68 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index cf1e13b97e..3290d92551 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -128,6 +128,18 @@ def batch_norm(inputs,
 
   Can be used as a normalizer function for conv2d and fully_connected.
 
+  Note: When is_training is True the moving_mean and moving_variance need to be
+  updated, by default the update_ops are placed in tf.GraphKeys.UPDATE_OPS so
+  they need to be added as a dependency to the train_op, example:
+
+    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+    if update_ops:
+      updates = tf.group(update_ops)
+      total_loss = control_flow_ops.with_dependencies([updates], total_loss)
+
+  One can set update_collections=None to force the updates in place, but that
+  can have speed penalty, specially in distributed settings.
+
   Args:
     inputs: a tensor with 2 or more dimensions, where the first dimension has
       `batch_size`. The normalization is over all but the last dimension.
@@ -139,8 +151,9 @@ def batch_norm(inputs,
     epsilon: small float added to variance to avoid dividing by zero.
     activation_fn: Optional activation function.
     updates_collections: collections to collect the update ops for computation.
+      The updates_ops need to be excuted with the train_op.
       If None, a control dependency would be added to make sure the updates are
-      computed.
+      computed in place.
     is_training: whether or not the layer is in training mode. In training mode
       it would accumulate the statistics of the moments into `moving_mean` and
       `moving_variance` using an exponential moving average with the given
@@ -221,7 +234,9 @@ def batch_norm(inputs,
     need_moments = is_training_value is None or is_training_value
     if need_moments:
       # Calculate the moments based on the individual batch.
-      mean, variance = nn.moments(inputs, axis, shift=moving_mean)
+      # Use a copy of moving_mean as a shift to compute more reliable moments.
+      shift = math_ops.add(moving_mean, 0)
+      mean, variance = nn.moments(inputs, axis, shift=shift)
       moving_vars_fn = lambda: (moving_mean, moving_variance)
       if updates_collections is None:
         def _force_updates():
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index 4d84989405..711d17cb29 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -1490,6 +1490,57 @@ class BatchNormTest(tf.test.TestCase):
       output_false = sess.run([output], {is_training: False})
       self.assertTrue(np.allclose(output_true, output_false))
 
+  def testTrainMovingVars(self):
+    """Test that the gradients are stable while the moving_mean is updated.
+
+    Since the moving_mean is used as shift to compute the tf.momments, the
+    gradients could diverge, this test checks that gradients remains stable
+    while the moving_mean is updated.
+    """
+    height, width = 7, 7
+    num_channels = 32
+    with self.test_session() as sess:
+      image_shape = (10, height, width, num_channels)
+      image_values = np.random.rand(*image_shape) + 2
+      expected_mean = np.mean(image_values, axis=(0, 1, 2))
+      expected_var = np.var(image_values, axis=(0, 1, 2))
+      images = tf.constant(image_values, shape=image_shape, dtype=tf.float32)
+      output = tf.contrib.layers.batch_norm(images,
+                                            decay=0.2,
+                                            updates_collections=None,
+                                            is_training=True)
+      self.assertEquals(tf.get_collection(tf.GraphKeys.UPDATE_OPS), [])
+
+      objective = tf.reduce_sum(output)
+
+      [images_gradients] = tf.gradients(objective, images)
+      # Initialize all variables
+      sess.run(tf.initialize_all_variables())
+      moving_mean = tf.contrib.framework.get_variables(
+          'BatchNorm/moving_mean')[0]
+      moving_variance = tf.contrib.framework.get_variables(
+          'BatchNorm/moving_variance')[0]
+      mean, variance = sess.run([moving_mean, moving_variance])
+      # After initialization moving_mean == 0 and moving_variance == 1.
+      self.assertAllClose(mean, [0] * num_channels)
+      self.assertAllClose(variance, [1] * num_channels)
+
+      # Initial input gradients.
+      images_gradients_value = sess.run(images_gradients)
+      for _ in range(10):
+        np_output, new_images_gradients = sess.run([output, images_gradients])
+        # The outputs should be close to 0.0 mean and 1.0 variance
+        self.assertAllClose(np.mean(np_output, axis=(0, 1, 2)),
+                            [0] * num_channels, rtol=0.1, atol=0.1)
+        self.assertAllClose(np.var(np_output, axis=(0, 1, 2)),
+                            [1] * num_channels, rtol=0.1, atol=0.1)
+        # The gradients should change slowly while updating moving_mean.
+        max_diff = np.max(np.abs(images_gradients_value - new_images_gradients))
+        self.assertGreater(max_diff, 0.0)
+        self.assertLess(max_diff, 5e-5)
+      self.assertAllClose(moving_mean.eval(), expected_mean)
+      self.assertAllClose(moving_variance.eval(), expected_var)
+
 
 class MaxPool2DTest(tf.test.TestCase):
 
-- 
GitLab


From 336726435c7a02f113a7c054a20f2a6b0259c06e Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 24 Aug 2016 16:36:01 -0800
Subject: [PATCH 118/223] Update generated Python Op docs. Change: 131236504

---
 .../g3doc/api_docs/python/contrib.layers.md       | 15 ++++++++++++++-
 .../shard4/tf.contrib.layers.batch_norm.md        | 15 ++++++++++++++-
 2 files changed, 28 insertions(+), 2 deletions(-)

diff --git a/tensorflow/g3doc/api_docs/python/contrib.layers.md b/tensorflow/g3doc/api_docs/python/contrib.layers.md
index b43a0c9c32..7d756b217d 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.layers.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.layers.md
@@ -51,6 +51,18 @@ Adds a Batch Normalization layer from http://arxiv.org/abs/1502.03167.
 
 Can be used as a normalizer function for conv2d and fully_connected.
 
+Note: When is_training is True the moving_mean and moving_variance need to be
+updated, by default the update_ops are placed in tf.GraphKeys.UPDATE_OPS so
+they need to be added as a dependency to the train_op, example:
+
+  update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+  if update_ops:
+    updates = tf.group(update_ops)
+    total_loss = control_flow_ops.with_dependencies([updates], total_loss)
+
+One can set update_collections=None to force the updates in place, but that
+can have speed penalty, specially in distributed settings.
+
 ##### Args:
 
 
@@ -64,8 +76,9 @@ Can be used as a normalizer function for conv2d and fully_connected.
 *  <b>`epsilon`</b>: small float added to variance to avoid dividing by zero.
 *  <b>`activation_fn`</b>: Optional activation function.
 *  <b>`updates_collections`</b>: collections to collect the update ops for computation.
+    The updates_ops need to be excuted with the train_op.
     If None, a control dependency would be added to make sure the updates are
-    computed.
+    computed in place.
 *  <b>`is_training`</b>: whether or not the layer is in training mode. In training mode
     it would accumulate the statistics of the moments into `moving_mean` and
     `moving_variance` using an exponential moving average with the given
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.batch_norm.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.batch_norm.md
index a8c2a66341..ad852cc608 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.batch_norm.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.layers.batch_norm.md
@@ -9,6 +9,18 @@ Adds a Batch Normalization layer from http://arxiv.org/abs/1502.03167.
 
 Can be used as a normalizer function for conv2d and fully_connected.
 
+Note: When is_training is True the moving_mean and moving_variance need to be
+updated, by default the update_ops are placed in tf.GraphKeys.UPDATE_OPS so
+they need to be added as a dependency to the train_op, example:
+
+  update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
+  if update_ops:
+    updates = tf.group(update_ops)
+    total_loss = control_flow_ops.with_dependencies([updates], total_loss)
+
+One can set update_collections=None to force the updates in place, but that
+can have speed penalty, specially in distributed settings.
+
 ##### Args:
 
 
@@ -22,8 +34,9 @@ Can be used as a normalizer function for conv2d and fully_connected.
 *  <b>`epsilon`</b>: small float added to variance to avoid dividing by zero.
 *  <b>`activation_fn`</b>: Optional activation function.
 *  <b>`updates_collections`</b>: collections to collect the update ops for computation.
+    The updates_ops need to be excuted with the train_op.
     If None, a control dependency would be added to make sure the updates are
-    computed.
+    computed in place.
 *  <b>`is_training`</b>: whether or not the layer is in training mode. In training mode
     it would accumulate the statistics of the moments into `moving_mean` and
     `moving_variance` using an exponential moving average with the given
-- 
GitLab


From d6af4de6f0ea8758cc8ee4f5a36d321e08dca672 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 24 Aug 2016 18:23:20 -0800
Subject: [PATCH 119/223] Experiment.train now starts a server_lib.Server when
 necessary. Change: 131243727

---
 tensorflow/contrib/learn/BUILD                | 13 +++
 .../python/learn/estimators/run_config.py     | 10 ++-
 .../contrib/learn/python/learn/experiment.py  | 24 +++++-
 .../python/learn/tests/experiment_test.py     | 83 +++++++++++++++++++
 .../python/learn/tests/run_config_test.py     | 53 ++++++++++++
 5 files changed, 181 insertions(+), 2 deletions(-)
 create mode 100644 tensorflow/contrib/learn/python/learn/tests/run_config_test.py

diff --git a/tensorflow/contrib/learn/BUILD b/tensorflow/contrib/learn/BUILD
index 057a80f1a6..8278ab89a4 100644
--- a/tensorflow/contrib/learn/BUILD
+++ b/tensorflow/contrib/learn/BUILD
@@ -359,6 +359,19 @@ py_test(
     ],
 )
 
+py_test(
+    name = "run_config_test",
+    size = "small",
+    srcs = [
+        "python/learn/tests/run_config_test.py",
+    ],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow:tensorflow_py",
+        "//tensorflow/python:framework_test_lib",
+    ],
+)
+
 py_test(
     name = "basic_session_run_hooks_test",
     size = "small",
diff --git a/tensorflow/contrib/learn/python/learn/estimators/run_config.py b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
index d04b854ce6..e5317b6a08 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/run_config.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
@@ -44,7 +44,8 @@ class RunConfig(object):
                save_summary_steps=100,
                save_checkpoints_secs=60,
                keep_checkpoint_max=5,
-               keep_checkpoint_every_n_hours=10000):
+               keep_checkpoint_every_n_hours=10000,
+               job_name=None):
     """Constructor.
 
     Args:
@@ -69,9 +70,12 @@ class RunConfig(object):
       keep_checkpoint_every_n_hours: Number of hours between each checkpoint
         to be saved. The default value of 10,000 hours effectively disables
         the feature.
+      job_name: the type of task, e.g., 'ps', 'worker', etc. Must exist in
+        cluster_spec.jobs.
     """
     self.master = master
     self.task = task
+    self._job_name = job_name
     self.num_ps_replicas = num_ps_replicas
     gpu_options = GPUOptions(
         per_process_gpu_memory_fraction=gpu_memory_fraction)
@@ -85,3 +89,7 @@ class RunConfig(object):
     self.save_checkpoints_secs = save_checkpoints_secs
     self.keep_checkpoint_max = keep_checkpoint_max
     self.keep_checkpoint_every_n_hours = keep_checkpoint_every_n_hours
+
+  @property
+  def job_name(self):
+    return self._job_name
diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index f26a194bb4..30dafefbe1 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -27,6 +27,7 @@ from tensorflow.contrib.learn.python.learn import trainable
 from tensorflow.contrib.learn.python.learn.estimators._sklearn import NotFittedError
 from tensorflow.python.platform import flags
 from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import server_lib
 
 FLAGS = flags.FLAGS
 
@@ -105,12 +106,18 @@ class Experiment(object):
     Returns:
       The trained estimator.
     """
+    start = time.time()
     if delay_secs is None:
       task_id = self._estimator.config.task or 0
       delay_secs = min(60, task_id * 5)
 
+    # Start the server, if needed.
+    self._maybe_start_server()
+
     if delay_secs:
-      logging.info("Waiting %d secs before starting training.", delay_secs)
+      elapsed_secs = time.time() - start
+      remaining = delay_secs - elapsed_secs
+      logging.info("Waiting %d secs before starting training.", remaining)
       time.sleep(delay_secs)
 
     return self._estimator.fit(input_fn=self._train_input_fn,
@@ -234,3 +241,18 @@ class Experiment(object):
                                     steps=1,
                                     metrics=self._eval_metrics,
                                     name="one_pass")
+
+  def _maybe_start_server(self):
+    config = self._estimator.config
+    if config.cluster_spec:
+      if not config.job_name or not config.master or config.task is None:
+        raise ValueError("Could not start server; be sure to specify "
+                         "cluster_spec, job_name, master, and task in "
+                         "RunConfig or set the TF_CONFIG environment variable.")
+      server = server_lib.Server(
+          config.cluster_spec,
+          job_name=config.job_name,
+          task_index=config.task,
+          config=config.tf_config,
+          start=False)
+      server.start()
diff --git a/tensorflow/contrib/learn/python/learn/tests/experiment_test.py b/tensorflow/contrib/learn/python/learn/tests/experiment_test.py
index 7980881ea6..5acb4e4ab9 100644
--- a/tensorflow/contrib/learn/python/learn/tests/experiment_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/experiment_test.py
@@ -88,6 +88,60 @@ class ExperimentTest(tf.test.TestCase):
       duration = time.time() - start
       self.assertAlmostEqual(duration, task*5, delta=0.5)
 
+  @tf.test.mock.patch('tensorflow.python.training.server_lib.Server')  # pylint: disable=line-too-long
+  def test_train_starts_server(self, mock_server):
+    # Arrange.
+    config = tf.contrib.learn.RunConfig(
+        master='host4:2222',
+        cluster_spec=tf.train.ClusterSpec(
+            {'ps': ['host1:2222', 'host2:2222'],
+             'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
+        ),
+        job_name='worker',
+        task=1,
+        num_cores=15,
+        gpu_memory_fraction=0.314,
+    )
+
+    est = TestEstimator(config)
+    ex = tf.contrib.learn.Experiment(est,
+                                     train_input_fn='train_input',
+                                     eval_input_fn='eval_input')
+
+    # Act.
+    # We want to make sure we discount the time it takes to start the server
+    # in our accounting of the delay, so we set a small delay here.
+    start = time.time()
+    ex.train(delay_secs=1)
+    duration = time.time() - start
+
+    # Assert.
+    expected_config_proto = tf.ConfigProto()
+    expected_config_proto.inter_op_parallelism_threads = 15
+    expected_config_proto.intra_op_parallelism_threads = 15
+    expected_config_proto.gpu_options.per_process_gpu_memory_fraction = 0.314
+    mock_server.assert_called_with(
+        config.cluster_spec,
+        job_name='worker',
+        task_index=1,
+        config=expected_config_proto,
+        start=False)
+    mock_server.assert_has_calls([tf.test.mock.call().start()])
+
+    # Ensure that the delay takes into account the time to start the server.
+    self.assertAlmostEqual(duration, 1.0, delta=0.5)
+
+  @tf.test.mock.patch('tensorflow.python.training.server_lib.Server')  # pylint: disable=line-too-long
+  def test_server_does_not_start_without_cluster_spec(self, mock_server):
+    config = tf.contrib.learn.RunConfig(master='host4:2222')
+    ex = tf.contrib.learn.Experiment(TestEstimator(config),
+                                     train_input_fn='train_input',
+                                     eval_input_fn='eval_input')
+    ex.train()
+
+    # The server should not have started because there was no ClusterSpec.
+    self.assertFalse(mock_server.called)
+
   def test_evaluate(self):
     est = TestEstimator()
     ex = tf.contrib.learn.Experiment(est,
@@ -167,6 +221,35 @@ class ExperimentTest(tf.test.TestCase):
     self.assertEquals(1, est.fit_count)
     self.assertEquals(1, est.eval_count)
 
+  def test_start_server_raises_without_all_info(self):
+    # Job name is missing.
+    no_job_name = tf.contrib.learn.RunConfig(
+        cluster_spec=tf.train.ClusterSpec(
+            {'ps': ['host1:2222', 'host2:2222'],
+             'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
+        ),
+        task=1,
+    )
+    with self.assertRaises(ValueError):
+      ex = tf.contrib.learn.Experiment(TestEstimator(no_job_name),
+                                       train_input_fn='train_input',
+                                       eval_input_fn='eval_input')
+      ex.train()
+
+    # Task index is missing.
+    no_task = tf.contrib.learn.RunConfig(
+        cluster_spec=tf.train.ClusterSpec(
+            {'ps': ['host1:2222', 'host2:2222'],
+             'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
+        ),
+        job_name='worker',
+    )
+    with self.assertRaises(ValueError):
+      ex = tf.contrib.learn.Experiment(TestEstimator(no_task),
+                                       train_input_fn='train_input',
+                                       eval_input_fn='eval_input')
+      ex.train()
+
 
 if __name__ == '__main__':
   tf.test.main()
diff --git a/tensorflow/contrib/learn/python/learn/tests/run_config_test.py b/tensorflow/contrib/learn/python/learn/tests/run_config_test.py
new file mode 100644
index 0000000000..5086f00caf
--- /dev/null
+++ b/tensorflow/contrib/learn/python/learn/tests/run_config_test.py
@@ -0,0 +1,53 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""run_config.py tests."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+from tensorflow.contrib.learn.python.learn import run_config
+
+
+class RunConfigTest(tf.test.TestCase):
+
+  def test_defaults(self):
+    config = run_config.RunConfig()
+    self.assertEquals(config.master, "")
+    self.assertEquals(config.task, 0)
+    self.assertEquals(config.num_ps_replicas, 0)
+    self.assertEquals(config.cluster_spec, None)
+    self.assertEquals(config.job_name, None)
+
+  def test_explicitly_specified_values(self):
+    cluster_spec = tf.train.ClusterSpec({
+        "ps": ["localhost:9990"],
+        "my_job_name": ["localhost:9991", "localhost:9992", "localhost:0"]
+    })
+    config = run_config.RunConfig(
+        master="localhost:0",
+        task=2,
+        job_name="my_job_name",
+        cluster_spec=cluster_spec,)
+
+    self.assertEquals(config.master, "localhost:0")
+    self.assertEquals(config.task, 2)
+    self.assertEquals(config.cluster_spec, cluster_spec)
+    self.assertEquals(config.job_name, "my_job_name")
+
+
+if __name__ == "__main__":
+  tf.test.main()
-- 
GitLab


From 37528511de7059df173e8e728866490ec06feded Mon Sep 17 00:00:00 2001
From: Corey Wharton <corey@cwharton.com>
Date: Wed, 24 Aug 2016 19:50:19 -0700
Subject: [PATCH 120/223] Add layer_norm op to contrib.layers. (#3671)

* Add layer_norm op to contrib.layers.

* Fix unit tests for layer_norm op.

* Remove the epsilon parameter and change defaults for the layer_norm op.

* Add unit test on 4-D input tensor for the layer_norm op.

* Address code review comments for #3671.
---
 tensorflow/contrib/layers/__init__.py         |  1 +
 .../contrib/layers/python/layers/layers.py    | 88 +++++++++++++++++++
 .../layers/python/layers/layers_test.py       | 83 +++++++++++++++++
 3 files changed, 172 insertions(+)

diff --git a/tensorflow/contrib/layers/__init__.py b/tensorflow/contrib/layers/__init__.py
index 20a82a235e..4bec7c2661 100644
--- a/tensorflow/contrib/layers/__init__.py
+++ b/tensorflow/contrib/layers/__init__.py
@@ -27,6 +27,7 @@ common machine learning algorithms.
 @@convolution2d_transpose
 @@flatten
 @@fully_connected
+@@layer_norm
 @@max_pool2d
 @@one_hot_encoding
 @@repeat
diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index 2a91711868..7f87d42452 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -52,6 +52,7 @@ __all__ = ['avg_pool2d',
            'dropout',
            'flatten',
            'fully_connected',
+           'layer_norm',
            'linear',
            'max_pool2d',
            'one_hot_encoding',
@@ -839,6 +840,93 @@ def fully_connected(inputs,
                                        sc.original_name_scope, outputs)
 
 
+@add_arg_scope
+def layer_norm(inputs,
+               center=True,
+               scale=True,
+               activation_fn=None,
+               reuse=None,
+               variables_collections=None,
+               outputs_collections=None,
+               trainable=True,
+               scope=None):
+  """Adds a Layer Normalization layer from https://arxiv.org/abs/1607.06450.
+
+    "Layer Normalization"
+
+    Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton
+
+  Can be used as a normalizer function for conv2d and fully_connected.
+
+  Args:
+    inputs: a tensor with 2 or more dimensions. The normalization
+            occurs over all but the first dimension.
+    center: If True, subtract `beta`. If False, `beta` is ignored.
+    scale: If True, multiply by `gamma`. If False, `gamma` is
+      not used. When the next layer is linear (also e.g. `nn.relu`), this can be
+      disabled since the scaling can be done by the next layer.
+    activation_fn: Optional activation function.
+    reuse: whether or not the layer and its variables should be reused. To be
+      able to reuse the layer scope must be given.
+    variables_collections: optional collections for the variables.
+    outputs_collections: collections to add the outputs.
+    trainable: If `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
+    scope: Optional scope for `variable_op_scope`.
+
+  Returns:
+    A `Tensor` representing the output of the operation.
+
+  Raises:
+    ValueError: if rank or last dimension of `inputs` is undefined.
+  """
+  with variable_scope.variable_scope(scope, 'LayerNorm', [inputs],
+                                     reuse=reuse) as sc:
+    inputs = ops.convert_to_tensor(inputs)
+    inputs_shape = inputs.get_shape()
+    inputs_rank = inputs_shape.ndims
+    if inputs_rank is None:
+      raise ValueError('Inputs %s has undefined rank.' % inputs.name)
+    dtype = inputs.dtype.base_dtype
+    axis = list(range(1, inputs_rank))
+    params_shape = inputs_shape[-1:]
+    if not params_shape.is_fully_defined():
+      raise ValueError('Inputs %s has undefined last dimension %s.' % (
+          inputs.name, params_shape))
+    # Allocate parameters for the beta and gamma of the normalization.
+    beta, gamma = None, None
+    if center:
+      beta_collections = utils.get_variable_collections(variables_collections,
+                                                        'beta')
+      beta = variables.model_variable('beta',
+                                      shape=params_shape,
+                                      dtype=dtype,
+                                      initializer=init_ops.zeros_initializer,
+                                      collections=beta_collections,
+                                      trainable=trainable)
+    if scale:
+      gamma_collections = utils.get_variable_collections(variables_collections,
+                                                         'gamma')
+      gamma = variables.model_variable('gamma',
+                                       shape=params_shape,
+                                       dtype=dtype,
+                                       initializer=init_ops.ones_initializer,
+                                       collections=gamma_collections,
+                                       trainable=trainable)
+    # Calculate the moments on the last axis (layer activations).
+    mean, variance = nn.moments(inputs, axis, keep_dims=True)
+    # Compute layer normalization using the batch_normalization function.
+    variance_epsilon = 1E-12
+    outputs = nn.batch_normalization(
+        inputs, mean, variance, beta, gamma, variance_epsilon)
+    outputs.set_shape(inputs_shape)
+    if activation_fn:
+      outputs = activation_fn(outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope,
+                                       outputs)
+
+
 @add_arg_scope
 def max_pool2d(inputs,
                kernel_size,
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index 02eb517476..2cf47467b5 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -1511,6 +1511,89 @@ class BatchNormTest(tf.test.TestCase):
       self.assertTrue(np.allclose(output_true, output_false))
 
 
+class LayerNormTest(tf.test.TestCase):
+
+  def testUnknownShape(self):
+    with tf.Graph().as_default() as g, self.test_session(g):
+      inputs = tf.placeholder(dtype=tf.float32)
+      with self.assertRaisesRegexp(ValueError, 'undefined rank'):
+        tf.contrib.layers.layer_norm(inputs)
+
+  def testUnknownLastDim(self):
+    with tf.Graph().as_default() as g, self.test_session(g):
+      inputs = tf.placeholder(dtype=tf.float32)
+      inputs.set_shape(tf.TensorShape((5, 3, 3, None)))
+      with self.assertRaisesRegexp(ValueError, 'undefined last dimension'):
+        tf.contrib.layers.layer_norm(inputs)
+
+  def testCreateOp(self):
+    height, width = 3, 3
+    with self.test_session():
+      images = np.random.uniform(size=(5, height, width, 3))
+      output = tf.contrib.layers.layer_norm(images)
+      self.assertTrue(output.op.name.startswith('LayerNorm/batchnorm'))
+      self.assertListEqual(output.get_shape().as_list(), [5, height, width, 3])
+
+  def testCreateVariables(self):
+    height, width = 3, 3
+    with self.test_session():
+      images = tf.random_uniform((5, height, width, 3), seed=1)
+      tf.contrib.layers.layer_norm(images)
+      beta = tf.contrib.framework.get_variables_by_name('beta')[0]
+      gamma = tf.contrib.framework.get_variables_by_name('gamma')[0]
+      self.assertEquals(beta.op.name, 'LayerNorm/beta')
+      self.assertEquals(gamma.op.name, 'LayerNorm/gamma')
+
+  def testReuseVariables(self):
+    height, width = 3, 3
+    with self.test_session():
+      images = tf.random_uniform((5, height, width, 3), seed=1)
+      tf.contrib.layers.layer_norm(images, scope='ln')
+      tf.contrib.layers.layer_norm(images, scope='ln', reuse=True)
+      beta = tf.contrib.framework.get_variables_by_name('beta')
+      gamma = tf.contrib.framework.get_variables_by_name('gamma')
+      self.assertEquals(len(beta), 1)
+      self.assertEquals(len(gamma), 1)
+
+  def testReuseVars(self):
+    height, width = 3, 3
+    with self.test_session() as sess:
+      image_shape = (10, height, width, 3)
+      image_values = np.random.rand(*image_shape)
+      images = tf.constant(image_values, shape=image_shape, dtype=tf.float32)
+      output_train = tf.contrib.layers.layer_norm(images, scope='LN')
+      output_eval = tf.contrib.layers.layer_norm(images,
+                                                 scope='LN',
+                                                 reuse=True)
+      # Initialize all variables
+      sess.run(tf.initialize_all_variables())
+      # output_train and output_eval should be the same.
+      self.assertAllClose(sess.run([output_train]), sess.run([output_eval]))
+
+  def doOutputTest(self, input_shape):
+    with self.test_session() as sess:
+      input_values = np.random.rand(*input_shape)
+      inputs = tf.constant(input_values, shape=input_shape, dtype=tf.float32)
+      output_op = tf.contrib.layers.layer_norm(inputs, scope='LN')
+      # Initialize all variables
+      sess.run(tf.initialize_all_variables())
+      # The mean and variance of the output should be close to 0 and 1
+      # respectively.
+      moments_axis = tuple([i for i in range(1, len(input_shape))])
+      outputs = sess.run(output_op)
+      expected_mean = np.zeros(input_shape[0])
+      expected_var = np.ones(input_shape[0])
+      mean = np.mean(outputs, axis=moments_axis)
+      var = np.var(outputs, axis=moments_axis)
+      self.assertAllClose(mean, expected_mean, rtol=1e-5)
+      self.assertAllClose(var, expected_var, rtol=1e-5)
+
+  def testOutput2DInput(self):
+    self.doOutputTest((10, 300))
+
+  def testOutput4DInput(self):
+    self.doOutputTest((100, 10, 10, 3))
+
 class MaxPool2DTest(tf.test.TestCase):
 
   def testCreateMaxPool(self):
-- 
GitLab


From 7a78d2e12407c3a478ca62a0992a7d6f3163e27a Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 24 Aug 2016 18:49:10 -0800
Subject: [PATCH 121/223] Update generated Python Op docs. Change: 131244802

---
 tensorflow/g3doc/api_docs/python/contrib.learn.md     | 11 ++++++++++-
 .../shard4/tf.contrib.learn.RunConfig.md              | 11 ++++++++++-
 2 files changed, 20 insertions(+), 2 deletions(-)

diff --git a/tensorflow/g3doc/api_docs/python/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md
index c6ffd5fa13..10df20c4e9 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.learn.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md
@@ -3573,7 +3573,7 @@ If you're a Google-internal user using command line flags with learn_runner.py
 probably want to use learn_runner.EstimatorConfig instead.
 - - -
 
-#### `tf.contrib.learn.RunConfig.__init__(master='', task=0, num_ps_replicas=0, num_cores=4, log_device_placement=False, gpu_memory_fraction=1, cluster_spec=None, tf_random_seed=None, save_summary_steps=100, save_checkpoints_secs=60, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000)` {#RunConfig.__init__}
+#### `tf.contrib.learn.RunConfig.__init__(master='', task=0, num_ps_replicas=0, num_cores=4, log_device_placement=False, gpu_memory_fraction=1, cluster_spec=None, tf_random_seed=None, save_summary_steps=100, save_checkpoints_secs=60, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000, job_name=None)` {#RunConfig.__init__}
 
 Constructor.
 
@@ -3601,6 +3601,15 @@ Constructor.
 *  <b>`keep_checkpoint_every_n_hours`</b>: Number of hours between each checkpoint
     to be saved. The default value of 10,000 hours effectively disables
     the feature.
+*  <b>`job_name`</b>: the type of task, e.g., 'ps', 'worker', etc. Must exist in
+    cluster_spec.jobs.
+
+
+- - -
+
+#### `tf.contrib.learn.RunConfig.job_name` {#RunConfig.job_name}
+
+
 
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.RunConfig.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.RunConfig.md
index 8dec07572f..61169a8511 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.RunConfig.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.RunConfig.md
@@ -5,7 +5,7 @@ If you're a Google-internal user using command line flags with learn_runner.py
 probably want to use learn_runner.EstimatorConfig instead.
 - - -
 
-#### `tf.contrib.learn.RunConfig.__init__(master='', task=0, num_ps_replicas=0, num_cores=4, log_device_placement=False, gpu_memory_fraction=1, cluster_spec=None, tf_random_seed=None, save_summary_steps=100, save_checkpoints_secs=60, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000)` {#RunConfig.__init__}
+#### `tf.contrib.learn.RunConfig.__init__(master='', task=0, num_ps_replicas=0, num_cores=4, log_device_placement=False, gpu_memory_fraction=1, cluster_spec=None, tf_random_seed=None, save_summary_steps=100, save_checkpoints_secs=60, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000, job_name=None)` {#RunConfig.__init__}
 
 Constructor.
 
@@ -33,5 +33,14 @@ Constructor.
 *  <b>`keep_checkpoint_every_n_hours`</b>: Number of hours between each checkpoint
     to be saved. The default value of 10,000 hours effectively disables
     the feature.
+*  <b>`job_name`</b>: the type of task, e.g., 'ps', 'worker', etc. Must exist in
+    cluster_spec.jobs.
+
+
+- - -
+
+#### `tf.contrib.learn.RunConfig.job_name` {#RunConfig.job_name}
+
+
 
 
-- 
GitLab


From b04c9f48ce69377a1172fd58e8ab210b435f359e Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Wed, 24 Aug 2016 19:35:47 -0800
Subject: [PATCH 122/223] Fix a lost wakeup bug in
 `Notification::WaitForNotificationWithTimeout()`.

If the notification was set before waiting, the waiter would never wake up.

Added a test to cover this case. Fixes #3978.
Change: 131247138
---
 tensorflow/core/BUILD                         |  3 +
 .../core/common_runtime/direct_session.cc     |  6 +-
 tensorflow/core/lib/core/notification.h       | 50 +------------
 tensorflow/core/lib/core/notification_test.cc |  6 ++
 .../core/platform/default/notification.h      | 73 +++++++++++++++++++
 tensorflow/core/platform/notification.h       | 31 ++++++++
 tensorflow/python/client/session_test.py      | 15 ++++
 tensorflow/stream_executor/lib/notification.h |  2 +-
 8 files changed, 135 insertions(+), 51 deletions(-)
 create mode 100644 tensorflow/core/platform/default/notification.h
 create mode 100644 tensorflow/core/platform/notification.h

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 7f0f876afa..d30a94b928 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -178,6 +178,7 @@ cc_library(
         "platform/mem.h",
         "platform/net.h",
         "platform/mutex.h",
+        "platform/notification.h",
         "platform/protobuf.h",  # TODO(josh11b): make internal
         "platform/regexp.h",
         "platform/strong_hash.h",
@@ -1048,6 +1049,7 @@ filegroup(
         "platform/default/integral_types.h",
         "platform/default/logging.h",
         "platform/default/mutex.h",
+        "platform/default/notification.h",
         "platform/default/protobuf.h",
         "platform/default/thread_annotations.h",
         "platform/env.h",
@@ -1059,6 +1061,7 @@ filegroup(
         "platform/mem.h",
         "platform/mutex.h",
         "platform/net.h",
+        "platform/notification.h",
         "platform/platform.h",
         "platform/protobuf.h",
         "platform/strong_hash.h",
diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc
index f64ab7c4ff..6aedcf4e7e 100644
--- a/tensorflow/core/common_runtime/direct_session.cc
+++ b/tensorflow/core/common_runtime/direct_session.cc
@@ -1031,9 +1031,9 @@ DirectSession::RunState::~RunState() {
 void DirectSession::WaitForNotification(RunState* run_state,
                                         int64 timeout_in_ms) {
   if (timeout_in_ms > 0) {
-    bool timed_out =
-        run_state->executors_done.WaitForNotificationWithTimeout(timeout_in_ms);
-    if (timed_out) {
+    bool notified = WaitForNotificationWithTimeout(&run_state->executors_done,
+                                                   timeout_in_ms);
+    if (!notified) {
       {
         mutex_lock l(run_state->mu_);
         run_state->status.Update(Status(error::DEADLINE_EXCEEDED,
diff --git a/tensorflow/core/lib/core/notification.h b/tensorflow/core/lib/core/notification.h
index 56e8ac6b2a..b3e515e28f 100644
--- a/tensorflow/core/lib/core/notification.h
+++ b/tensorflow/core/lib/core/notification.h
@@ -16,52 +16,8 @@ limitations under the License.
 #ifndef TENSORFLOW_UTIL_NOTIFICATION_H_
 #define TENSORFLOW_UTIL_NOTIFICATION_H_
 
-#include <assert.h>
-#include <chrono>              // NOLINT
-#include <condition_variable>  // NOLINT
-
-#include "tensorflow/core/platform/mutex.h"
-#include "tensorflow/core/platform/types.h"
-
-namespace tensorflow {
-
-class Notification {
- public:
-  Notification() : notified_(false) {}
-  ~Notification() {}
-
-  void Notify() {
-    mutex_lock l(mu_);
-    assert(!notified_);
-    notified_ = true;
-    cv_.notify_all();
-  }
-
-  bool HasBeenNotified() {
-    mutex_lock l(mu_);
-    return notified_;
-  }
-
-  void WaitForNotification() {
-    mutex_lock l(mu_);
-    while (!notified_) {
-      cv_.wait(l);
-    }
-  }
-
-  bool WaitForNotificationWithTimeout(int64 timeout_in_ms) {
-    mutex_lock l(mu_);
-    std::cv_status s =
-        cv_.wait_for(l, std::chrono::milliseconds(timeout_in_ms));
-    return (s == std::cv_status::timeout) ? true : false;
-  }
-
- private:
-  mutex mu_;
-  condition_variable cv_;
-  bool notified_;
-};
-
-}  // namespace tensorflow
+// Notification implementation is platform-dependent, to support
+// alternative synchronization primitives.
+#include "tensorflow/core/platform/notification.h"
 
 #endif  // TENSORFLOW_UTIL_NOTIFICATION_H_
diff --git a/tensorflow/core/lib/core/notification_test.cc b/tensorflow/core/lib/core/notification_test.cc
index a281437d47..8cb1c895ad 100644
--- a/tensorflow/core/lib/core/notification_test.cc
+++ b/tensorflow/core/lib/core/notification_test.cc
@@ -76,5 +76,11 @@ TEST(NotificationTest, TestMultipleThreadsWaitingOnNotification) {
   EXPECT_EQ(4, counter);
 }
 
+TEST(NotificationTest, TestWaitWithTimeoutOnNotifiedNotification) {
+  Notification n;
+  n.Notify();
+  EXPECT_TRUE(WaitForNotificationWithTimeout(&n, 1000));
+}
+
 }  // namespace
 }  // namespace tensorflow
diff --git a/tensorflow/core/platform/default/notification.h b/tensorflow/core/platform/default/notification.h
new file mode 100644
index 0000000000..13d2317e38
--- /dev/null
+++ b/tensorflow/core/platform/default/notification.h
@@ -0,0 +1,73 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CORE_PLATFORM_DEFAULT_NOTIFICATION_H_
+#define TENSORFLOW_CORE_PLATFORM_DEFAULT_NOTIFICATION_H_
+
+#include <assert.h>
+#include <chrono>              // NOLINT
+#include <condition_variable>  // NOLINT
+
+#include "tensorflow/core/platform/mutex.h"
+#include "tensorflow/core/platform/types.h"
+
+namespace tensorflow {
+
+class Notification {
+ public:
+  Notification() : notified_(false) {}
+  ~Notification() {}
+
+  void Notify() {
+    mutex_lock l(mu_);
+    assert(!notified_);
+    notified_ = true;
+    cv_.notify_all();
+  }
+
+  bool HasBeenNotified() {
+    mutex_lock l(mu_);
+    return notified_;
+  }
+
+  void WaitForNotification() {
+    mutex_lock l(mu_);
+    while (!notified_) {
+      cv_.wait(l);
+    }
+  }
+
+ private:
+  friend bool WaitForNotificationWithTimeout(Notification* n,
+                                             int64 timeout_in_ms);
+  bool WaitForNotificationWithTimeout(int64 timeout_in_ms) {
+    mutex_lock l(mu_);
+    return cv_.wait_for(l, std::chrono::milliseconds(timeout_in_ms),
+                        [this]() { return notified_; });
+  }
+
+  mutex mu_;
+  condition_variable cv_;
+  bool notified_;
+};
+
+inline bool WaitForNotificationWithTimeout(Notification* n,
+                                           int64 timeout_in_ms) {
+  return n->WaitForNotificationWithTimeout(timeout_in_ms);
+}
+
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_CORE_PLATFORM_DEFAULT_NOTIFICATION_H_
diff --git a/tensorflow/core/platform/notification.h b/tensorflow/core/platform/notification.h
new file mode 100644
index 0000000000..dfea9e8168
--- /dev/null
+++ b/tensorflow/core/platform/notification.h
@@ -0,0 +1,31 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_CORE_PLATFORM_NOTIFICATION_H_
+#define TENSORFLOW_CORE_PLATFORM_NOTIFICATION_H_
+
+#include "tensorflow/core/platform/platform.h"
+
+// Include appropriate platform-dependent implementations of Notification.
+#if defined(PLATFORM_GOOGLE)
+#include "tensorflow/core/platform/google/notification.h"
+#elif defined(PLATFORM_POSIX) || defined(PLATFORM_POSIX_ANDROID) || \
+    defined(PLATFORM_GOOGLE_ANDROID)
+#include "tensorflow/core/platform/default/notification.h"
+#else
+#error Define the appropriate PLATFORM_<foo> macro for this platform
+#endif
+
+#endif  // TENSORFLOW_CORE_PLATFORM_NOTIFICATION_H_
diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py
index ad3c681b83..f0214d2bbe 100644
--- a/tensorflow/python/client/session_test.py
+++ b/tensorflow/python/client/session_test.py
@@ -38,6 +38,7 @@ from tensorflow.python.framework import test_util
 from tensorflow.python.framework import versions
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import data_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import state_ops
 from tensorflow.python.ops import variables
@@ -1441,6 +1442,20 @@ class SessionTest(test_util.TensorFlowTestCase):
     with self.assertRaisesRegexp(TypeError, 'graph must be a tf.Graph'):
       session.Session(graph=37)
 
+  def testTimeoutWithShortOperations(self):
+    num_epochs = 5
+    q = data_flow_ops.FIFOQueue(
+        capacity=50, dtypes=[dtypes.int32], shapes=[()])
+    enqueue_op = q.enqueue_many(constant_op.constant([1, 2]))
+
+    # Use a 10-second timeout, which should be longer than any
+    # non-blocking enqueue_many op.
+    config = config_pb2.ConfigProto(operation_timeout_in_ms=10000)
+    with session.Session(config=config) as sess:
+      for _ in range(num_epochs):
+        sess.run(enqueue_op)
+      self.assertEqual(sess.run(q.size()), num_epochs * 2)
+
 
 if __name__ == '__main__':
   googletest.main()
diff --git a/tensorflow/stream_executor/lib/notification.h b/tensorflow/stream_executor/lib/notification.h
index 26873c7a95..9bb3e170dc 100644
--- a/tensorflow/stream_executor/lib/notification.h
+++ b/tensorflow/stream_executor/lib/notification.h
@@ -16,7 +16,7 @@ limitations under the License.
 #ifndef TENSORFLOW_STREAM_EXECUTOR_LIB_NOTIFICATION_H_
 #define TENSORFLOW_STREAM_EXECUTOR_LIB_NOTIFICATION_H_
 
-#include "tensorflow/core/lib/core/notification.h"
+#include "tensorflow/core/platform/notification.h"
 
 namespace perftools {
 namespace gputools {
-- 
GitLab


From 55d8c205133f8ee020eca413ccc9057bb35b9ee6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Wed, 24 Aug 2016 21:16:40 -0800
Subject: [PATCH 123/223] Adds a run_server method to Experiment that starts a
 TF server; intended for use by parameter servers. Change: 131252553

---
 .../contrib/learn/python/learn/experiment.py  | 42 ++++++---
 .../python/learn/tests/experiment_test.py     | 91 +++++++++++++------
 2 files changed, 91 insertions(+), 42 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/experiment.py b/tensorflow/contrib/learn/python/learn/experiment.py
index 30dafefbe1..b8c791ae79 100644
--- a/tensorflow/contrib/learn/python/learn/experiment.py
+++ b/tensorflow/contrib/learn/python/learn/experiment.py
@@ -112,7 +112,8 @@ class Experiment(object):
       delay_secs = min(60, task_id * 5)
 
     # Start the server, if needed.
-    self._maybe_start_server()
+    if self._estimator.config.cluster_spec:
+      self._start_server()
 
     if delay_secs:
       elapsed_secs = time.time() - start
@@ -227,6 +228,17 @@ class Experiment(object):
                           delay_secs=delay_secs,
                           throttle_delay_secs=throttle_delay_secs)
 
+  def run_std_server(self):
+    """Starts a TensorFlow server and joins the serving thread.
+
+    Typically used for parameter servers.
+
+    Raises:
+      ValueError: if not enough information is available in the estimator's
+        config to create a server.
+    """
+    self._start_server().join()
+
   def test(self):
     """Tests training and evaluating the estimator both for a single step.
 
@@ -242,17 +254,19 @@ class Experiment(object):
                                     metrics=self._eval_metrics,
                                     name="one_pass")
 
-  def _maybe_start_server(self):
+  def _start_server(self):
+    """Creates, starts, and returns a server_lib.Server."""
     config = self._estimator.config
-    if config.cluster_spec:
-      if not config.job_name or not config.master or config.task is None:
-        raise ValueError("Could not start server; be sure to specify "
-                         "cluster_spec, job_name, master, and task in "
-                         "RunConfig or set the TF_CONFIG environment variable.")
-      server = server_lib.Server(
-          config.cluster_spec,
-          job_name=config.job_name,
-          task_index=config.task,
-          config=config.tf_config,
-          start=False)
-      server.start()
+    if (not config.cluster_spec or not config.job_name or not config.master or
+        config.task is None):
+      raise ValueError("Could not start server; be sure to specify "
+                       "cluster_spec, job_name, master, and task in "
+                       "RunConfig or set the TF_CONFIG environment variable.")
+    server = server_lib.Server(
+        config.cluster_spec,
+        job_name=config.job_name,
+        task_index=config.task,
+        config=config.tf_config,
+        start=False)
+    server.start()
+    return server
diff --git a/tensorflow/contrib/learn/python/learn/tests/experiment_test.py b/tensorflow/contrib/learn/python/learn/tests/experiment_test.py
index 5acb4e4ab9..19cca2ac53 100644
--- a/tensorflow/contrib/learn/python/learn/tests/experiment_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/experiment_test.py
@@ -132,7 +132,7 @@ class ExperimentTest(tf.test.TestCase):
     self.assertAlmostEqual(duration, 1.0, delta=0.5)
 
   @tf.test.mock.patch('tensorflow.python.training.server_lib.Server')  # pylint: disable=line-too-long
-  def test_server_does_not_start_without_cluster_spec(self, mock_server):
+  def test_train_server_does_not_start_without_cluster_spec(self, mock_server):
     config = tf.contrib.learn.RunConfig(master='host4:2222')
     ex = tf.contrib.learn.Experiment(TestEstimator(config),
                                      train_input_fn='train_input',
@@ -142,6 +142,35 @@ class ExperimentTest(tf.test.TestCase):
     # The server should not have started because there was no ClusterSpec.
     self.assertFalse(mock_server.called)
 
+  def test_train_raises_if_not_enough_info_to_start_server(self):
+    # Job name is missing.
+    no_job_name = tf.contrib.learn.RunConfig(
+        cluster_spec=tf.train.ClusterSpec(
+            {'ps': ['host1:2222', 'host2:2222'],
+             'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
+        ),
+        task=1,
+    )
+    with self.assertRaises(ValueError):
+      ex = tf.contrib.learn.Experiment(TestEstimator(no_job_name),
+                                       train_input_fn='train_input',
+                                       eval_input_fn='eval_input')
+      ex.train()
+
+    # Task index is missing.
+    no_task = tf.contrib.learn.RunConfig(
+        cluster_spec=tf.train.ClusterSpec(
+            {'ps': ['host1:2222', 'host2:2222'],
+             'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
+        ),
+        job_name='worker',
+    )
+    with self.assertRaises(ValueError):
+      ex = tf.contrib.learn.Experiment(TestEstimator(no_task),
+                                       train_input_fn='train_input',
+                                       eval_input_fn='eval_input')
+      ex.train()
+
   def test_evaluate(self):
     est = TestEstimator()
     ex = tf.contrib.learn.Experiment(est,
@@ -212,43 +241,49 @@ class ExperimentTest(tf.test.TestCase):
     self.assertTrue(isinstance(est.monitors[0],
                                tf.contrib.learn.monitors.ValidationMonitor))
 
-  def test_test(self):
-    est = TestEstimator()
-    ex = tf.contrib.learn.Experiment(est,
-                                     train_input_fn='train_input',
-                                     eval_input_fn='eval_input')
-    ex.test()
-    self.assertEquals(1, est.fit_count)
-    self.assertEquals(1, est.eval_count)
-
-  def test_start_server_raises_without_all_info(self):
-    # Job name is missing.
-    no_job_name = tf.contrib.learn.RunConfig(
+  @tf.test.mock.patch('tensorflow.python.training.server_lib.Server')  # pylint: disable=line-too-long
+  def test_run_std_server(self, mock_server):
+    # Arrange.
+    config = tf.contrib.learn.RunConfig(
+        master='host2:2222',
         cluster_spec=tf.train.ClusterSpec(
             {'ps': ['host1:2222', 'host2:2222'],
              'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
         ),
+        job_name='ps',
         task=1,
+        num_cores=15,
+        gpu_memory_fraction=0.314,
     )
-    with self.assertRaises(ValueError):
-      ex = tf.contrib.learn.Experiment(TestEstimator(no_job_name),
-                                       train_input_fn='train_input',
-                                       eval_input_fn='eval_input')
-      ex.train()
+    est = TestEstimator(config)
+    ex = tf.contrib.learn.Experiment(est,
+                                     train_input_fn='train_input',
+                                     eval_input_fn='eval_input')
 
-    # Task index is missing.
-    no_task = tf.contrib.learn.RunConfig(
-        cluster_spec=tf.train.ClusterSpec(
-            {'ps': ['host1:2222', 'host2:2222'],
-             'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
-        ),
-        job_name='worker',
-    )
+    # Act.
+    ex.run_std_server()
+
+    # Assert.
+    mock_server.assert_has_calls([tf.test.mock.call().start(),
+                                  tf.test.mock.call().join()])
+
+  @tf.test.mock.patch('tensorflow.python.training.server_lib.Server')  # pylint: disable=line-too-long
+  def test_run_std_server_raises_without_cluster_spec(self, mock_server):
+    config = tf.contrib.learn.RunConfig(master='host4:2222')
     with self.assertRaises(ValueError):
-      ex = tf.contrib.learn.Experiment(TestEstimator(no_task),
+      ex = tf.contrib.learn.Experiment(TestEstimator(config),
                                        train_input_fn='train_input',
                                        eval_input_fn='eval_input')
-      ex.train()
+      ex.run_std_server()
+
+  def test_test(self):
+    est = TestEstimator()
+    ex = tf.contrib.learn.Experiment(est,
+                                     train_input_fn='train_input',
+                                     eval_input_fn='eval_input')
+    ex.test()
+    self.assertEquals(1, est.fit_count)
+    self.assertEquals(1, est.eval_count)
 
 
 if __name__ == '__main__':
-- 
GitLab


From e1a89ef3f181cd1f877e7739ae0549148132c1c6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 03:56:41 -0800
Subject: [PATCH 124/223] Adding reduce_logsumexp to core math_ops.py.

This function computes log(sum(exp(elements across dimensions of a tensor))), avoiding underflow/overflows in most cases.
Change: 131276538
---
 tensorflow/python/ops/math_ops.py      | 51 ++++++++++++++++++++
 tensorflow/python/ops/math_ops_test.py | 64 ++++++++++++++++++++++++++
 2 files changed, 115 insertions(+)

diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index b976e953f8..4e1bf7898a 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -150,6 +150,7 @@ common math computations that reduce various dimensions of a tensor.
 @@reduce_mean
 @@reduce_all
 @@reduce_any
+@@reduce_logsumexp
 
 @@accumulate_n
 
@@ -1250,6 +1251,56 @@ def reduce_any(input_tensor, reduction_indices=None, keep_dims=False,
                            keep_dims, name=name)
 
 
+def reduce_logsumexp(input_tensor, reduction_indices=None, keep_dims=False,
+                     name=None):
+  """Computes log(sum(exp(elements across dimensions of a tensor))).
+
+  Reduces `input_tensor` along the dimensions given in `reduction_indices`.
+  Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
+  entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
+  are retained with length 1.
+
+  If `reduction_indices` has no entries, all dimensions are reduced, and a
+  tensor with a single element is returned.
+
+  This funciton is more numerically stable than log(sum(exp(input))). It avoids
+  overflows caused by taking the exp of large inputs and underflows caused by
+  taking the log of small inputs.
+
+  For example:
+
+  ```python
+  # 'x' is [[0, 0, 0]]
+  #         [0, 0, 0]]
+  tf.reduce_logsumexp(x) ==> log(6)
+  tf.reduce_logsumexp(x, 0) ==> [log(2), log(2), log(2)]
+  tf.reduce_logsumexp(x, 1) ==> [log(3), log(3)]
+  tf.reduce_logsumexp(x, 1, keep_dims=True) ==> [[log(3)], [log(3)]]
+  tf.reduce_logsumexp(x, [0, 1]) ==> log(6)
+  ```
+
+  Args:
+    input_tensor: The tensor to reduce. Should have numeric type.
+    reduction_indices: The dimensions to reduce. If `None` (the defaut),
+      reduces all dimensions.
+    keep_dims: If true, retains reduced dimensions with length 1.
+    name: A name for the operation (optional).
+
+  Returns:
+    The reduced tensor.
+  """
+  with ops.name_scope(name, "ReduceLogSumExp", [input_tensor]) as name:
+    my_max = array_ops.stop_gradient(
+        reduce_max(input_tensor, reduction_indices, keep_dims=True))
+    result = gen_math_ops.log(reduce_sum(
+        gen_math_ops.exp(input_tensor - my_max),
+        reduction_indices,
+        keep_dims=True)) + my_max
+    if not keep_dims:
+      result = array_ops.squeeze(result, reduction_indices)
+    return result
+
+
 def trace(x, name=None):
   """ Compute the trace of a tensor `x`.
 
diff --git a/tensorflow/python/ops/math_ops_test.py b/tensorflow/python/ops/math_ops_test.py
index 1a71479ba8..0968bd0992 100644
--- a/tensorflow/python/ops/math_ops_test.py
+++ b/tensorflow/python/ops/math_ops_test.py
@@ -47,6 +47,70 @@ class ReduceTest(test_util.TensorFlowTestCase):
       math_ops.reduce_sum(x, axis)
 
 
+class LogSumExpTest(test_util.TensorFlowTestCase):
+
+  def testReduceLogSumExp(self):
+    for dtype in [np.float16, np.float32, np.double]:
+      x_np = np.random.rand(5, 5).astype(dtype)
+      with self.test_session():
+        y_tf_np = math_ops.reduce_logsumexp(x_np).eval()
+        y_np = log(np.sum(exp(x_np)))
+        self.assertAllClose(y_tf_np, y_np)
+
+  def testReductionIndices(self):
+    for dtype in [np.float16, np.float32, np.double]:
+      x_np = np.random.rand(5, 5).astype(dtype)
+      with self.test_session():
+        y_tf = math_ops.reduce_logsumexp(x_np, reduction_indices=[0])
+        y_np = log(np.sum(exp(x_np), axis=0))
+        self.assertShapeEqual(y_np, y_tf)
+        y_tf_np = y_tf.eval()
+        self.assertAllClose(y_tf_np, y_np)
+
+  def testKeepDims(self):
+    for dtype in [np.float16, np.float32, np.double]:
+      x_np = np.random.rand(5, 5).astype(dtype)
+      with self.test_session():
+        y_tf_np = math_ops.reduce_logsumexp(x_np, keep_dims=True).eval()
+        self.assertEqual(y_tf_np.ndim, x_np.ndim)
+        y_np = log(np.sum(exp(x_np), keepdims=True))
+        self.assertAllClose(y_tf_np, y_np)
+
+  def testOverflow(self):
+    x = [1000, 1001, 1002, 1003]
+    for dtype in [np.float16, np.float32, np.double]:
+      x_np = np.array(x, dtype=dtype)
+      max_np = np.max(x_np)
+      with self.assertRaisesRegexp(RuntimeWarning,
+                                   "overflow encountered in exp"):
+        out = log(np.sum(exp(x_np)))
+        if out == np.inf:
+          raise RuntimeWarning("overflow encountered in exp")
+
+      with self.test_session():
+        x_tf = constant_op.constant(x_np, shape=x_np.shape)
+        y_tf_np = math_ops.reduce_logsumexp(x_tf).eval()
+        y_np = log(np.sum(exp(x_np - max_np))) + max_np
+        self.assertAllClose(y_tf_np, y_np)
+
+  def testUnderflow(self):
+    x = [-1000, -1001, -1002, -1003]
+    for dtype in [np.float16, np.float32, np.double]:
+      x_np = np.array(x, dtype=dtype)
+      max_np = np.max(x_np)
+      with self.assertRaisesRegexp(RuntimeWarning,
+                                   "divide by zero encountered in log"):
+        out = log(np.sum(exp(x_np)))
+        if out == -np.inf:
+          raise RuntimeWarning("divide by zero encountered in log")
+
+      with self.test_session():
+        x_tf = constant_op.constant(x_np, shape=x_np.shape)
+        y_tf_np = math_ops.reduce_logsumexp(x_tf).eval()
+        y_np = log(np.sum(exp(x_np - max_np))) + max_np
+        self.assertAllClose(y_tf_np, y_np)
+
+
 class RoundTest(test_util.TensorFlowTestCase):
 
   def testRounding(self):
-- 
GitLab


From 740c713cd559b408cde64721842cc568116c409f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 04:34:49 -0800
Subject: [PATCH 125/223] Update generated Python Op docs. Change: 131279298

---
 .../shard1/tf.reduce_logsumexp.md             | 41 +++++++++++++++++
 tensorflow/g3doc/api_docs/python/index.md     |  1 +
 tensorflow/g3doc/api_docs/python/math_ops.md  | 44 +++++++++++++++++++
 3 files changed, 86 insertions(+)
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.reduce_logsumexp.md

diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.reduce_logsumexp.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.reduce_logsumexp.md
new file mode 100644
index 0000000000..29c3c1c0fe
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.reduce_logsumexp.md
@@ -0,0 +1,41 @@
+### `tf.reduce_logsumexp(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_logsumexp}
+
+Computes log(sum(exp(elements across dimensions of a tensor))).
+
+Reduces `input_tensor` along the dimensions given in `reduction_indices`.
+Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
+entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
+are retained with length 1.
+
+If `reduction_indices` has no entries, all dimensions are reduced, and a
+tensor with a single element is returned.
+
+This funciton is more numerically stable than log(sum(exp(input))). It avoids
+overflows caused by taking the exp of large inputs and underflows caused by
+taking the log of small inputs.
+
+For example:
+
+```python
+# 'x' is [[0, 0, 0]]
+#         [0, 0, 0]]
+tf.reduce_logsumexp(x) ==> log(6)
+tf.reduce_logsumexp(x, 0) ==> [log(2), log(2), log(2)]
+tf.reduce_logsumexp(x, 1) ==> [log(3), log(3)]
+tf.reduce_logsumexp(x, 1, keep_dims=True) ==> [[log(3)], [log(3)]]
+tf.reduce_logsumexp(x, [0, 1]) ==> log(6)
+```
+
+##### Args:
+
+
+*  <b>`input_tensor`</b>: The tensor to reduce. Should have numeric type.
+*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the defaut),
+    reduces all dimensions.
+*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The reduced tensor.
+
diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md
index 74ae9b0557..b760c5ab1d 100644
--- a/tensorflow/g3doc/api_docs/python/index.md
+++ b/tensorflow/g3doc/api_docs/python/index.md
@@ -239,6 +239,7 @@
   * [`real`](../../api_docs/python/math_ops.md#real)
   * [`reduce_all`](../../api_docs/python/math_ops.md#reduce_all)
   * [`reduce_any`](../../api_docs/python/math_ops.md#reduce_any)
+  * [`reduce_logsumexp`](../../api_docs/python/math_ops.md#reduce_logsumexp)
   * [`reduce_max`](../../api_docs/python/math_ops.md#reduce_max)
   * [`reduce_mean`](../../api_docs/python/math_ops.md#reduce_mean)
   * [`reduce_min`](../../api_docs/python/math_ops.md#reduce_min)
diff --git a/tensorflow/g3doc/api_docs/python/math_ops.md b/tensorflow/g3doc/api_docs/python/math_ops.md
index 906e5073ca..5131493f75 100644
--- a/tensorflow/g3doc/api_docs/python/math_ops.md
+++ b/tensorflow/g3doc/api_docs/python/math_ops.md
@@ -2667,6 +2667,50 @@ tf.reduce_any(x, 1) ==> [True, False]
   The reduced tensor.
 
 
+- - -
+
+### `tf.reduce_logsumexp(input_tensor, reduction_indices=None, keep_dims=False, name=None)` {#reduce_logsumexp}
+
+Computes log(sum(exp(elements across dimensions of a tensor))).
+
+Reduces `input_tensor` along the dimensions given in `reduction_indices`.
+Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
+entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
+are retained with length 1.
+
+If `reduction_indices` has no entries, all dimensions are reduced, and a
+tensor with a single element is returned.
+
+This funciton is more numerically stable than log(sum(exp(input))). It avoids
+overflows caused by taking the exp of large inputs and underflows caused by
+taking the log of small inputs.
+
+For example:
+
+```python
+# 'x' is [[0, 0, 0]]
+#         [0, 0, 0]]
+tf.reduce_logsumexp(x) ==> log(6)
+tf.reduce_logsumexp(x, 0) ==> [log(2), log(2), log(2)]
+tf.reduce_logsumexp(x, 1) ==> [log(3), log(3)]
+tf.reduce_logsumexp(x, 1, keep_dims=True) ==> [[log(3)], [log(3)]]
+tf.reduce_logsumexp(x, [0, 1]) ==> log(6)
+```
+
+##### Args:
+
+
+*  <b>`input_tensor`</b>: The tensor to reduce. Should have numeric type.
+*  <b>`reduction_indices`</b>: The dimensions to reduce. If `None` (the defaut),
+    reduces all dimensions.
+*  <b>`keep_dims`</b>: If true, retains reduced dimensions with length 1.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  The reduced tensor.
+
+
 
 - - -
 
-- 
GitLab


From a42b40bcb5c47ca5ccdd233808476fbd507d2205 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 25 Aug 2016 09:22:33 -0400
Subject: [PATCH 126/223] Disable export_test in OSS builds temporarily

http://b/31032996
---
 tensorflow/contrib/learn/BUILD | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/contrib/learn/BUILD b/tensorflow/contrib/learn/BUILD
index 057a80f1a6..a422fea589 100644
--- a/tensorflow/contrib/learn/BUILD
+++ b/tensorflow/contrib/learn/BUILD
@@ -671,6 +671,9 @@ py_test(
     size = "small",
     srcs = ["python/learn/utils/export_test.py"],
     srcs_version = "PY2AND3",
+    tags = [
+        "manual",  # http://b/31032996
+    ],
     deps = [
         ":learn",
         "//tensorflow:tensorflow_py",
-- 
GitLab


From cff3c607b2204e8db2b63188a545620343f68959 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 25 Aug 2016 09:39:49 -0400
Subject: [PATCH 127/223] Fix docker tag step in parameterized_docker_build.sh

---
 .../tools/docker/parameterized_docker_build.sh   | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/tensorflow/tools/docker/parameterized_docker_build.sh b/tensorflow/tools/docker/parameterized_docker_build.sh
index bfae655076..aae9a868b9 100755
--- a/tensorflow/tools/docker/parameterized_docker_build.sh
+++ b/tensorflow/tools/docker/parameterized_docker_build.sh
@@ -323,7 +323,21 @@ fi
 
 # Apply the final image name and tag
 FINAL_IMG="${FINAL_IMAGE_NAME}:${FINAL_TAG}"
-docker tag -f "${IMG}" "${FINAL_IMG}" || \
+
+DOCKER_VER=$(docker version | grep Version | head -1 | awk '{print $NF}')
+if [[ -z "${DOCKER_VER}" ]]; then
+  die "ERROR: Failed to determine docker version"
+fi
+DOCKER_MAJOR_VER=$(echo "${DOCKER_VER}" | cut -d. -f 1)
+DOCKER_MINOR_VER=$(echo "${DOCKER_VER}" | cut -d. -f 2)
+
+FORCE_TAG=""
+if [[ "${DOCKER_MAJOR_VER}" -le 1 ]] && \
+   [[ "${DOCKER_MINOR_VER}" -le 9 ]]; then
+  FORCE_TAG="--force"
+fi
+
+docker tag ${FORCE_TAG} "${IMG}" "${FINAL_IMG}" || \
     die "Failed to tag intermediate docker image ${IMG} as ${FINAL_IMG}"
 
 echo ""
-- 
GitLab


From 77d37ddde43b03bda29764885a8da0ed80ed5cfd Mon Sep 17 00:00:00 2001
From: "David Z. Chen" <davidzchen@users.noreply.github.com>
Date: Thu, 25 Aug 2016 07:45:54 -0700
Subject: [PATCH 128/223] cuda_configure: Use env variables that match those
 used in configure script. (#4034)

Issue #4002
---
 configure                                | 10 +++---
 tensorflow/tools/ci_build/Dockerfile.gpu |  2 +-
 third_party/gpus/cuda_configure.bzl      | 40 ++++++++++++++----------
 3 files changed, 29 insertions(+), 23 deletions(-)

diff --git a/configure b/configure
index bcef37bd26..f4b772c55e 100755
--- a/configure
+++ b/configure
@@ -98,7 +98,7 @@ while true; do
     fi
   fi
   if [ -e "$GCC_HOST_COMPILER_PATH" ]; then
-    export CC=$GCC_HOST_COMPILER_PATH
+    export GCC_HOST_COMPILER_PATH
     break
   fi
   echo "Invalid gcc path. ${GCC_HOST_COMPILER_PATH} cannot be found" 1>&2
@@ -142,7 +142,7 @@ while true; do
 
   if [ -e "${CUDA_TOOLKIT_PATH}/${CUDA_RT_LIB_PATH}" ]; then
     export CUDA_TOOLKIT_PATH
-    export CUDA_VERSION=$TF_CUDA_VERSION
+    export TF_CUDA_VERSION
     break
   fi
   echo "Invalid path to CUDA $TF_CUDA_VERSION toolkit. ${CUDA_TOOLKIT_PATH}/${CUDA_RT_LIB_PATH} cannot be found"
@@ -203,7 +203,7 @@ while true; do
   fi
 
   if [ -e "$CUDNN_INSTALL_PATH/${CUDA_DNN_LIB_ALT_PATH}" -o -e "$CUDNN_INSTALL_PATH/${CUDA_DNN_LIB_PATH}" ]; then
-    export CUDNN_VERSION=$TF_CUDNN_VERSION
+    export TF_CUDNN_VERSION
     export CUDNN_INSTALL_PATH
     break
   fi
@@ -211,7 +211,7 @@ while true; do
   if [ "$OSNAME" == "Linux" ]; then
     CUDNN_PATH_FROM_LDCONFIG="$(ldconfig -p | sed -n 's/.*libcudnn.so .* => \(.*\)/\1/p')"
     if [ -e "${CUDNN_PATH_FROM_LDCONFIG}${TF_CUDNN_EXT}" ]; then
-      export CUDNN_VERSION=$TF_CUDNN_VERSION
+      export TF_CUDNN_VERSION
       export CUDNN_INSTALL_PATH="$(dirname ${CUDNN_PATH_FROM_LDCONFIG})"
       break
     fi
@@ -263,7 +263,7 @@ EOF
       exit 1
     fi
   else
-    export CUDA_COMPUTE_CAPABILITIES=$TF_CUDA_COMPUTE_CAPABILITIES
+    export TF_CUDA_COMPUTE_CAPABILITIES
     break
   fi
   TF_CUDA_COMPUTE_CAPABILITIES=""
diff --git a/tensorflow/tools/ci_build/Dockerfile.gpu b/tensorflow/tools/ci_build/Dockerfile.gpu
index b36edb9bde..4ada062986 100644
--- a/tensorflow/tools/ci_build/Dockerfile.gpu
+++ b/tensorflow/tools/ci_build/Dockerfile.gpu
@@ -24,4 +24,4 @@ ENV LD_LIBRARY_PATH /usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
 ENV CUDA_TOOLKIT_PATH /usr/local/cuda
 ENV CUDNN_INSTALL_PATH /usr/lib/x86_64-linux-gnu
 ENV TF_NEED_CUDA 1
-ENV CUDA_COMPUTE_CAPABILITIES 3.0,5.2
+ENV TF_CUDA_COMPUTE_CAPABILITIES 3.0,5.2
diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl
index 3682cb305d..1e47bfac78 100644
--- a/third_party/gpus/cuda_configure.bzl
+++ b/third_party/gpus/cuda_configure.bzl
@@ -3,19 +3,25 @@
 
 `cuda_configure` depends on the following environment variables:
 
-  * `ENABLE_CUDA`: Whether to enable building with CUDA.
-  * `CC`: The GCC host compiler path
+  * `TF_NEED_CUDA`: Whether to enable building with CUDA.
+  * `GCC_HOST_COMPILER_PATH`: The GCC host compiler path
   * `CUDA_TOOLKIT_PATH`: The path to the CUDA toolkit. Default is
     `/usr/local/cuda`.
-  * `CUDA_VERSION`: The version of the CUDA toolkit. If this is blank, then
+  * `TF_CUDA_VERSION`: The version of the CUDA toolkit. If this is blank, then
     use the system default.
-  * `CUDNN_VERSION`: The version of the cuDNN library.
+  * `TF_CUDNN_VERSION`: The version of the cuDNN library.
   * `CUDNN_INSTALL_PATH`: The path to the cuDNN library. Default is
     `/usr/local/cuda`.
-  * `CUDA_COMPUTE_CAPABILITIES`: The CUDA compute capabilities. Default is
+  * `TF_CUDA_COMPUTE_CAPABILITIES`: The CUDA compute capabilities. Default is
     `3.5,5.2`.
 """
 
+_GCC_HOST_COMPILER_PATH = "GCC_HOST_COMPILER_PATH"
+_CUDA_TOOLKIT_PATH = "CUDA_TOOLKIT_PATH"
+_TF_CUDA_VERSION = "TF_CUDA_VERSION"
+_TF_CUDNN_VERSION = "TF_CUDNN_VERSION"
+_CUDNN_INSTALL_PATH = "CUDNN_INSTALL_PATH"
+_TF_CUDA_COMPUTE_CAPABILITIES = "TF_CUDA_COMPUTE_CAPABILITIES"
 
 _DEFAULT_CUDA_VERSION = ""
 _DEFAULT_CUDNN_VERSION = ""
@@ -30,8 +36,8 @@ _DEFAULT_CUDA_COMPUTE_CAPABILITIES = ["3.5", "5.2"]
 def find_cc(repository_ctx):
   """Find the C++ compiler."""
   cc_name = "gcc"
-  if "CC" in repository_ctx.os.environ:
-    cc_name = repository_ctx.os.environ["CC"].strip()
+  if _GCC_HOST_COMPILER_PATH in repository_ctx.os.environ:
+    cc_name = repository_ctx.os.environ[_GCC_HOST_COMPILER_PATH].strip()
     if not cc_name:
       cc_name = "gcc"
   if cc_name.startswith("/"):
@@ -93,8 +99,8 @@ def _enable_cuda(repository_ctx):
 def _cuda_toolkit_path(repository_ctx):
   """Finds the cuda toolkit directory."""
   cuda_toolkit_path = _DEFAULT_CUDA_TOOLKIT_PATH
-  if "CUDA_TOOLKIT_PATH" in repository_ctx.os.environ:
-    cuda_toolkit_path = repository_ctx.os.environ["CUDA_TOOLKIT_PATH"].strip()
+  if _CUDA_TOOLKIT_PATH in repository_ctx.os.environ:
+    cuda_toolkit_path = repository_ctx.os.environ[_CUDA_TOOLKIT_PATH].strip()
   if not repository_ctx.path(cuda_toolkit_path).exists:
     fail("Cannot find cuda toolkit path.")
   return cuda_toolkit_path
@@ -103,8 +109,8 @@ def _cuda_toolkit_path(repository_ctx):
 def _cudnn_install_basedir(repository_ctx):
   """Finds the cudnn install directory."""
   cudnn_install_path = _DEFAULT_CUDNN_INSTALL_PATH
-  if "CUDNN_INSTALL_PATH" in repository_ctx.os.environ:
-    cudnn_install_path = repository_ctx.os.environ["CUDNN_INSTALL_PATH"].strip()
+  if _CUDNN_INSTALL_PATH in repository_ctx.os.environ:
+    cudnn_install_path = repository_ctx.os.environ[_CUDNN_INSTALL_PATH].strip()
   if not repository_ctx.path(cudnn_install_path).exists:
     fail("Cannot find cudnn install path.")
   return cudnn_install_path
@@ -112,25 +118,25 @@ def _cudnn_install_basedir(repository_ctx):
 
 def _cuda_version(repository_ctx):
   """Detects the cuda version."""
-  if "CUDA_VERSION" in repository_ctx.os.environ:
-    return repository_ctx.os.environ["CUDA_VERSION"].strip()
+  if _TF_CUDA_VERSION in repository_ctx.os.environ:
+    return repository_ctx.os.environ[_TF_CUDA_VERSION].strip()
   else:
     return ""
 
 
 def _cudnn_version(repository_ctx):
   """Detects the cudnn version."""
-  if "CUDNN_VERSION" in repository_ctx.os.environ:
-    return repository_ctx.os.environ["CUDNN_VERSION"].strip()
+  if _TF_CUDNN_VERSION in repository_ctx.os.environ:
+    return repository_ctx.os.environ[_TF_CUDNN_VERSION].strip()
   else:
     return ""
 
 
 def _compute_capabilities(repository_ctx):
   """Returns a list of strings representing cuda compute capabilities."""
-  if "CUDA_COMPUTE_CAPABILITIES" not in repository_ctx.os.environ:
+  if _TF_CUDA_COMPUTE_CAPABILITIES not in repository_ctx.os.environ:
     return _DEFAULT_CUDA_COMPUTE_CAPABILITIES
-  capabilities_str = repository_ctx.os.environ["CUDA_COMPUTE_CAPABILITIES"]
+  capabilities_str = repository_ctx.os.environ[_TF_CUDA_COMPUTE_CAPABILITIES]
   capabilities = capabilities_str.split(",")
   for capability in capabilities:
     # Workaround for Skylark's lack of support for regex. This check should
-- 
GitLab


From 672eebd8f455531f4826fc265864ff51d4cf1408 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 07:19:38 -0800
Subject: [PATCH 129/223] graph.proto no longer needs to export NodeDef,
 clients should use node_def.proto instead. Change: 131290995

---
 RELEASE.md                            | 2 ++
 tensorflow/core/framework/graph.proto | 4 +---
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/RELEASE.md b/RELEASE.md
index abb99a2ebb..02cc73bf13 100644
--- a/RELEASE.md
+++ b/RELEASE.md
@@ -13,6 +13,8 @@
 * uniform_unit_scaling_initializer() no longer takes a full_shape arg, instead
   relying on the partition info passed to the initializer function when it's
   called.
+* The NodeDef protocol message is now defined in its own file node_def.proto
+  instead of graph.proto.
 
 # Release 0.10.0
 
diff --git a/tensorflow/core/framework/graph.proto b/tensorflow/core/framework/graph.proto
index 7c669db319..7d6e16d5c1 100644
--- a/tensorflow/core/framework/graph.proto
+++ b/tensorflow/core/framework/graph.proto
@@ -6,9 +6,7 @@ option java_outer_classname = "GraphProtos";
 option java_multiple_files = true;
 option java_package = "org.tensorflow.framework";
 
-// Temporarily include node_def.proto here as a public import until
-// users can be migrated to reference node_def.proto to get NodeDef.
-import public "tensorflow/core/framework/node_def.proto";
+import "tensorflow/core/framework/node_def.proto";
 import "tensorflow/core/framework/function.proto";
 import "tensorflow/core/framework/versions.proto";
 
-- 
GitLab


From a856685175f0919dd2ab03ac447d2708dc0fffe3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 07:57:24 -0800
Subject: [PATCH 130/223] Deprecates _FeatureColumn.to_weighted_sum in favor of
 moving weight-constructing logic to layers.weighted_sum_from_feature_columns.
 Change: 131294219

---
 .../layers/python/layers/feature_column.py    | 102 ++++++++++++++++++
 .../python/layers/feature_column_ops.py       |  97 ++++++++++++++---
 .../python/layers/feature_column_test.py      |  30 ++++--
 .../estimators/dnn_linear_combined_test.py    |   1 +
 .../learn/python/learn/tests/base_test.py     |   8 +-
 5 files changed, 212 insertions(+), 26 deletions(-)

diff --git a/tensorflow/contrib/layers/python/layers/feature_column.py b/tensorflow/contrib/layers/python/layers/feature_column.py
index dbb6b30152..9f68498bc6 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column.py
@@ -76,6 +76,7 @@ import collections
 import math
 
 from tensorflow.contrib.framework.python.framework import checkpoint_utils
+from tensorflow.contrib.framework.python.framework import deprecation
 from tensorflow.contrib.framework.python.ops import variables as contrib_variables
 from tensorflow.contrib.layers.python.layers import embedding_ops
 from tensorflow.contrib.layers.python.ops import bucketization_op
@@ -92,6 +93,20 @@ from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 
 
+class _EmbeddingLookupArguments(
+    collections.namedtuple("_EmbeddingLookupArguments",
+                           ["input_tensor",
+                            "weight_tensor",
+                            "vocab_size",
+                            "initializer",
+                            "combiner"])):
+  """Represents the information needed from a column for embedding lookup.
+
+  Used to to compute DNN inputs and weighted sum.
+  """
+  pass
+
+
 class _FeatureColumn(object):
   """Represents a feature column abstraction.
 
@@ -106,21 +121,33 @@ class _FeatureColumn(object):
   __metaclass__ = abc.ABCMeta
 
   @abc.abstractproperty
+  @deprecation.deprecated(
+      "2016-09-25",
+      "Should be private.")
   def name(self):
     """Returns the name of column or transformed column."""
     pass
 
   @abc.abstractproperty
+  @deprecation.deprecated(
+      "2016-09-25",
+      "Should be private.")
   def config(self):
     """Returns configuration of the base feature for `tf.parse_example`."""
     pass
 
   @abc.abstractproperty
+  @deprecation.deprecated(
+      "2016-09-25",
+      "Should be private.")
   def key(self):
     """Returns a string which will be used as a key when we do sorting."""
     pass
 
   @abc.abstractmethod
+  @deprecation.deprecated(
+      "2016-09-25",
+      "Should be private.")
   def insert_transformed_feature(self, columns_to_tensors):
     """Apply transformation and inserts it into columns_to_tensors.
 
@@ -133,6 +160,9 @@ class _FeatureColumn(object):
         self))
 
   @abc.abstractmethod
+  @deprecation.deprecated(
+      "2016-09-25",
+      "Use layers.input_from_feature_columns instead.")
   def to_dnn_input_layer(self,
                          input_tensor,
                          weight_collection=None,
@@ -141,6 +171,9 @@ class _FeatureColumn(object):
     raise ValueError("Calling an abstract method.")
 
   @abc.abstractmethod
+  @deprecation.deprecated(
+      "2016-09-25",
+      "Use layers.weighted_sum_from_feature_columns instead.")
   def to_weighted_sum(self,
                       input_tensor,
                       num_outputs=1,
@@ -149,6 +182,22 @@ class _FeatureColumn(object):
     """Returns a Tensor as linear predictions and a list of created Variable."""
     raise ValueError("Calling an abstract method.")
 
+  # It is expected that classes implement either to_embedding_lookup_arguments
+  # or to_dense_tensor to be used in linear models.
+  # pylint: disable=unused-argument
+  def _to_embedding_lookup_arguments(self, input_tensor):
+    """Returns arguments to look up embeddings for this column."""
+    raise NotImplementedError("Calling an abstract method.")
+
+  # pylint: disable=unused-argument
+  def _to_dense_tensor(self, input_tensor):
+    """Returns a dense tensor representing this column's values."""
+    raise NotImplementedError("Calling an abstract method.")
+
+  def _checkpoint_path(self):
+    """Returns None, or a (path,tensor_name) to load a checkpoint from."""
+    return None
+
   def _key_without_properties(self, properties):
     """Helper method for self.key() that omits particular properties."""
     fields_values = []
@@ -303,6 +352,14 @@ class _SparseColumn(_FeatureColumn,
         combiner=self.combiner,
         trainable=trainable)
 
+  def _to_embedding_lookup_arguments(self, input_tensor):
+    return _EmbeddingLookupArguments(
+        input_tensor=self.id_tensor(input_tensor),
+        weight_tensor=self.weight_tensor(input_tensor),
+        vocab_size=self.length,
+        initializer=init_ops.zeros_initializer,
+        combiner=self.combiner)
+
 
 class _SparseColumnIntegerized(_SparseColumn):
   """See `sparse_column_with_integerized_feature`."""
@@ -547,6 +604,14 @@ class _WeightedSparseColumn(_FeatureColumn, collections.namedtuple(
         combiner=self.sparse_id_column.combiner,
         trainable=trainable)
 
+  def _to_embedding_lookup_arguments(self, input_tensor):
+    return _EmbeddingLookupArguments(
+        input_tensor=self.id_tensor(input_tensor),
+        weight_tensor=self.weight_tensor(input_tensor),
+        vocab_size=self.length,
+        initializer=init_ops.zeros_initializer,
+        combiner=self.sparse_id_column.combiner)
+
 
 def weighted_sparse_column(sparse_id_column,
                            weight_column_name,
@@ -691,6 +756,11 @@ class _EmbeddingColumn(_FeatureColumn, collections.namedtuple(
           {self.tensor_name_in_ckpt: weights_to_restore})
     return output
 
+  def _checkpoint_path(self):
+    if self.ckpt_to_load_from is not None:
+      return self.ckpt_to_load_from, self.tensor_name_in_ckpt
+    return None
+
   # pylint: disable=unused-argument
   def to_weighted_sum(self,
                       input_tensor,
@@ -700,6 +770,11 @@ class _EmbeddingColumn(_FeatureColumn, collections.namedtuple(
     raise ValueError("EmbeddingColumn is not supported in linear models. "
                      "Please use sparse_column. column: {}".format(self))
 
+  # pylint: disable=unused-argument
+  def _to_embedding_lookup_arguments(self, input_tensor):
+    raise ValueError("Column {} is not supported in linear models. "
+                     "Please use sparse_column.".format(self))
+
 
 def embedding_column(sparse_id_column,
                      dimension,
@@ -929,6 +1004,9 @@ class _RealValuedColumn(_FeatureColumn, collections.namedtuple(
         transformed_input_tensor, weight, name="matmul")
     return log_odds_by_dim, [weight]
 
+  def _to_dense_tensor(self, input_tensor):
+    return input_tensor
+
 
 def real_valued_column(column_name,
                        dimension=1,
@@ -1172,6 +1250,14 @@ class _BucketizedColumn(_FeatureColumn, collections.namedtuple(
         combiner="sum",
         trainable=trainable)
 
+  def _to_embedding_lookup_arguments(self, input_tensor):
+    return _EmbeddingLookupArguments(
+        input_tensor=self.to_sparse_tensor(input_tensor),
+        weight_tensor=None,
+        vocab_size=self.length * self.source_column.dimension,
+        initializer=init_ops.zeros_initializer,
+        combiner="sum")
+
 
 def bucketized_column(source_column, boundaries):
   """Creates a _BucketizedColumn.
@@ -1371,6 +1457,19 @@ class _CrossedColumn(_FeatureColumn, collections.namedtuple(
           {self.tensor_name_in_ckpt: weights_to_restore})
     return output, embedding_weights
 
+  def _checkpoint_path(self):
+    if self.ckpt_to_load_from is not None:
+      return self.ckpt_to_load_from, self.tensor_name_in_ckpt
+    return None
+
+  def _to_embedding_lookup_arguments(self, input_tensor):
+    return _EmbeddingLookupArguments(
+        input_tensor=input_tensor,
+        weight_tensor=None,
+        vocab_size=self.length,
+        initializer=init_ops.zeros_initializer,
+        combiner=self.combiner)
+
 
 def crossed_column(columns, hash_bucket_size, combiner="sum",
                    ckpt_to_load_from=None,
@@ -1488,6 +1587,9 @@ class DataFrameColumn(_FeatureColumn,
     log_odds_by_dim = math_ops.matmul(input_tensor, weight, name="matmul")
     return log_odds_by_dim, [weight]
 
+  def _to_dense_tensor(self, input_tensor):
+    return self.to_dnn_input_layer(input_tensor)
+
   def __eq__(self, other):
     if isinstance(other, self.__class__):
       return self.__dict__ == other.__dict__
diff --git a/tensorflow/contrib/layers/python/layers/feature_column_ops.py b/tensorflow/contrib/layers/python/layers/feature_column_ops.py
index a676647d04..fcad9bde19 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column_ops.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column_ops.py
@@ -18,8 +18,11 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from tensorflow.contrib.framework.python.framework import checkpoint_utils
 from tensorflow.contrib.framework.python.ops import variables as contrib_variables
+from tensorflow.contrib.layers.python.layers import embedding_ops
 from tensorflow.contrib.layers.python.layers import feature_column as fc
+from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
@@ -111,6 +114,60 @@ def input_from_feature_columns(columns_to_tensors,
     return array_ops.concat(1, output_tensors)
 
 
+def _create_embedding_lookup(column,
+                             columns_to_tensors,
+                             embedding_lookup_arguments,
+                             num_outputs,
+                             trainable,
+                             weight_collections):
+  """Creates variables and returns predictions for linear weights in a model.
+
+  Args:
+   column: the column we're working on.
+   columns_to_tensors: a map from column name to tensors.
+   embedding_lookup_arguments: arguments for embedding lookup.
+   num_outputs: how many outputs.
+   trainable: whether the variable we create is trainable.
+   weight_collections: weights will be placed here.
+
+  Returns:
+  variables: the created embeddings.
+  predictions: the computed predictions.
+  """
+  with variable_scope.variable_scope(
+      None, default_name=column.name, values=columns_to_tensors.values()):
+    variable = contrib_variables.model_variable(
+        name='weights',
+        shape=[embedding_lookup_arguments.vocab_size, num_outputs],
+        dtype=dtypes.float32,
+        initializer=embedding_lookup_arguments.initializer,
+        trainable=trainable,
+        collections=weight_collections)
+    if isinstance(variable, variables.Variable):
+      variable = [variable]
+    else:
+      variable = variable._get_variable_list()  # pylint: disable=protected-access
+    predictions = embedding_ops.safe_embedding_lookup_sparse(
+        variable,
+        embedding_lookup_arguments.input_tensor,
+        sparse_weights=embedding_lookup_arguments.weight_tensor,
+        default_id=0,
+        combiner=embedding_lookup_arguments.combiner,
+        name=column.name + '_weights')
+    return variable, predictions
+
+
+def _maybe_restore_from_checkpoint(checkpoint_path, variable):
+  if checkpoint_path is not None:
+    path, tensor_name = checkpoint_path
+    weights_to_restore = variable
+    if len(variable) == 1:
+      weights_to_restore = variable[0]
+    checkpoint_utils.init_from_checkpoint(path,
+                                          {tensor_name: weights_to_restore})
+
+
+
 def weighted_sum_from_feature_columns(columns_to_tensors,
                                       feature_columns,
                                       num_outputs,
@@ -180,22 +237,36 @@ def weighted_sum_from_feature_columns(columns_to_tensors,
     column_to_variable = dict()
     transformer = _Transformer(columns_to_tensors)
     for column in sorted(set(feature_columns), key=lambda x: x.key):
-      with variable_scope.variable_scope(
-          None,
-          default_name=column.name,
-          values=columns_to_tensors.values()):
-        try:
-          transformed_tensor = transformer.transform(column)
-          predictions, variable = column.to_weighted_sum(transformed_tensor,
-                                                         num_outputs,
-                                                         weight_collections,
-                                                         trainable)
-        except ValueError as e:
-          raise ValueError('Error creating weighted sum for column: {}.\n'
-                           '{}'.format(column.name, e))
+      transformed_tensor = transformer.transform(column)
+      try:
+        embedding_lookup_arguments = column._to_embedding_lookup_arguments(  # pylint: disable=protected-access
+            transformed_tensor)
+        variable, predictions = _create_embedding_lookup(
+            column,
+            columns_to_tensors,
+            embedding_lookup_arguments,
+            num_outputs,
+            trainable,
+            weight_collections)
+      except NotImplementedError:
+        with variable_scope.variable_scope(
+            None,
+            default_name=column.name,
+            values=columns_to_tensors.values()):
+          tensor = column._to_dense_tensor(transformed_tensor)  # pylint: disable=protected-access
+          variable = [contrib_variables.model_variable(
+              name='weight',
+              shape=[tensor.get_shape()[1], num_outputs],
+              initializer=array_ops.zeros_initializer,
+              collections=weight_collections)]
+          predictions = math_ops.matmul(tensor, variable[0], name='matmul')
+      except ValueError as ee:
+        raise ValueError('Error creating weighted sum for column: {}.\n'
+                         '{}'.format(column.name, ee))
       output_tensors.append(predictions)
       column_to_variable[column] = variable
       _log_variable(variable)
+      _maybe_restore_from_checkpoint(column._checkpoint_path(), variable)  # pylint: disable=protected-access
 
     predictions_no_bias = math_ops.add_n(output_tensors)
     bias = contrib_variables.model_variable(
diff --git a/tensorflow/contrib/layers/python/layers/feature_column_test.py b/tensorflow/contrib/layers/python/layers/feature_column_test.py
index f8f098d41b..91ff677b1b 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column_test.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column_test.py
@@ -418,17 +418,22 @@ class FeatureColumnTest(tf.test.TestCase):
                                    values=[0, 1, 2, 3],
                                    shape=[4, 4])
 
-    # Invoking 'crossed_col.to_weighted_sum' will create the crossed column
-    # weights variable.
+    # Invoking 'weighted_sum_from_feature_columns' will create the crossed
+    # column weights variable.
     with tf.variable_scope("run_1"):
       with tf.variable_scope(crossed_col.name):
         # Returns looked up column weights which is same as crossed column
         # weights as well as actual references to weights variables.
-        col_weights, weights = crossed_col.to_weighted_sum(input_tensor)
+        _, col_weights, _ = (
+            tf.contrib.layers.weighted_sum_from_feature_columns(
+                {sparse_col_1.name: input_tensor,
+                 sparse_col_2.name: input_tensor},
+                [crossed_col],
+                1))
         # Update the weights since default initializer initializes all weights
         # to 0.0.
-        for weight in weights:
-          assign_op = tf.assign(weight, weight + 0.5)
+        for weight in col_weights.values():
+          assign_op = tf.assign(weight[0], weight[0] + 0.5)
 
     save = tf.train.Saver()
     checkpoint_path = os.path.join(self.get_temp_dir(), "model.ckpt")
@@ -436,21 +441,28 @@ class FeatureColumnTest(tf.test.TestCase):
     with self.test_session() as sess:
       sess.run(tf.initialize_all_variables())
       sess.run(assign_op)
-      saved_col_weights = col_weights.eval()
+      saved_col_weights = col_weights[crossed_col][0].eval()
       save.save(sess, checkpoint_path)
 
     crossed_col_initialized = tf.contrib.layers.crossed_column(
         columns=[sparse_col_1, sparse_col_2],
         hash_bucket_size=4,
         ckpt_to_load_from=checkpoint_path,
-        tensor_name_in_ckpt="run_1/col_1_X_col_2/weights")
+        tensor_name_in_ckpt=("run_1/col_1_X_col_2/"
+                             "weighted_sum_from_feature_columns/"
+                             "col_1_X_col_2/weights"))
 
     with tf.variable_scope("run_2"):
       # This will initialize the crossed column weights from provided checkpoint
       # and return a [4, 1] tensor which is same as weights variable. Since we
       # won't modify weights, this should be same as 'saved_col_weights'.
-      col_weights_from_ckpt, _ = crossed_col_initialized.to_weighted_sum(
-          input_tensor)
+      _, col_weights, _ = (
+          tf.contrib.layers.weighted_sum_from_feature_columns(
+              {sparse_col_1.name: input_tensor,
+               sparse_col_2.name: input_tensor},
+              [crossed_col_initialized],
+              1))
+      col_weights_from_ckpt = col_weights[crossed_col_initialized][0]
 
     with self.test_session() as sess:
       sess.run(tf.initialize_all_variables())
diff --git a/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined_test.py b/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined_test.py
index f3706a312c..9e3a6dfd7f 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined_test.py
@@ -498,6 +498,7 @@ class DNNLinearCombinedClassifierTest(tf.test.TestCase):
     self.assertNotIn('dnn/logits/weights', classifier.get_variable_names())
     self.assertEquals(1, len(classifier.linear_bias_))
     self.assertEquals(2, len(classifier.linear_weights_))
+    print(classifier.linear_weights_)
     self.assertEquals(1, len(classifier.linear_weights_['linear/age/weight']))
     self.assertEquals(
         100, len(classifier.linear_weights_['linear/language/weights']))
diff --git a/tensorflow/contrib/learn/python/learn/tests/base_test.py b/tensorflow/contrib/learn/python/learn/tests/base_test.py
index 421fb4b9f4..115f86aa7e 100644
--- a/tensorflow/contrib/learn/python/learn/tests/base_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/base_test.py
@@ -67,10 +67,10 @@ class BaseTest(tf.test.TestCase):
          "centered_bias_weight/Adagrad",
          "global_step",
          # Double slashes appear because the column name is empty. If it was not
-         # empty, the variable names would be "linear/column_name/_weight" etc.
-         "linear//_weight",
-         "linear//_weight/Ftrl",
-         "linear//_weight/Ftrl_1",
+         # empty, the variable names would be "linear/column_name/weight" etc.
+         "linear//weight",
+         "linear//weight/Ftrl",
+         "linear//weight/Ftrl_1",
          "linear/bias_weight",
          "linear/bias_weight/Ftrl",
          "linear/bias_weight/Ftrl_1"])
-- 
GitLab


From e78cbe072bb8fd50dd8be6033de9bcb5f62d59fd Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 07:59:14 -0800
Subject: [PATCH 131/223] Fix gradient of pow for complex types Change:
 131294380

---
 tensorflow/core/framework/tensor_testutil.cc  |  6 +++
 tensorflow/core/ops/math_grad.cc              | 54 ++++++++++++-------
 tensorflow/core/ops/math_grad_test.cc         | 19 +++++++
 .../python/kernel_tests/cwise_ops_test.py     | 23 +++++++-
 tensorflow/python/ops/math_grad.py            |  8 ++-
 5 files changed, 89 insertions(+), 21 deletions(-)

diff --git a/tensorflow/core/framework/tensor_testutil.cc b/tensorflow/core/framework/tensor_testutil.cc
index e307d25268..1bae7b08d5 100644
--- a/tensorflow/core/framework/tensor_testutil.cc
+++ b/tensorflow/core/framework/tensor_testutil.cc
@@ -50,6 +50,12 @@ void ExpectClose(const Tensor& x, const Tensor& y, double atol, double rtol) {
     case DT_DOUBLE:
       ExpectClose<double>(x, y, atol, rtol);
       break;
+    case DT_COMPLEX64:
+      ExpectClose<complex64>(x, y, atol, rtol);
+      break;
+    case DT_COMPLEX128:
+      ExpectClose<complex128>(x, y, atol, rtol);
+      break;
     default:
       LOG(FATAL) << "Unexpected type : " << DataTypeString(x.dtype());
   }
diff --git a/tensorflow/core/ops/math_grad.cc b/tensorflow/core/ops/math_grad.cc
index 1d8f45ea7a..f74ce32cef 100644
--- a/tensorflow/core/ops/math_grad.cc
+++ b/tensorflow/core/ops/math_grad.cc
@@ -375,26 +375,42 @@ REGISTER_OP_GRADIENT("Div", DivGrad);
 
 Status PowGrad(const AttrSlice& attrs, FunctionDef* g) {
   // clang-format off
-  return GradForBinaryCwise(g, {
-      {{"z"}, "Pow", {"x", "y"}},
-      // dz * y * Pow(x, y - 1)
-      FDH::Const("const_zero", 0.0f),
-      FDH::Const("const_one", 1.0f),
-      {{"zero"}, "Cast", {"const_zero"}, {{"SrcT", DT_FLOAT}, {"DstT", "$T"}}},
-      {{"one"}, "Cast", {"const_one"}, {{"SrcT", DT_FLOAT}, {"DstT", "$T"}}},
-      {{"t0"}, "Sub", {"y", "one"}, {}, {"dz"}},
-      {{"t1"}, "Pow", {"x", "t0"}},
-      {{"t2"}, "Mul", {"dz", "y"}},
-      {{"gx"}, "Mul", {"t1", "t2"}},
-      // dz * z * (x > 0 ? Log(x) : 0)
+  std::vector<FDH::Node> nodes = {
+    {{"z"}, "Pow", {"x", "y"}},
+    // dz * y * Pow(x, y - 1)
+    FDH::Const("const_zero", 0.0f),
+    FDH::Const("const_one", 1.0f),
+    {{"zero"}, "Cast", {"const_zero"}, {{"SrcT", DT_FLOAT}, {"DstT", "$T"}}},
+    {{"one"}, "Cast", {"const_one"}, {{"SrcT", DT_FLOAT}, {"DstT", "$T"}}},
+    {{"t0"}, "Sub", {"y", "one"}, {}, {"dz"}},
+    {{"t1"}, "Pow", {"x", "t0"}},
+    {{"t2"}, "Mul", {"dz", "y"}},
+    {{"gx"}, "Mul", {"t1", "t2"}},
+    {{"unsafe_log"}, "Log", {"x"}, {}, {"dz"}},
+    {{"zeros"}, "ZerosLike", {"x"}}};
+  // clang-format on
+  std::vector<FDH::Node> log_x_handling;
+  DataType T;
+  TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "T", &T));
+  if (T == DT_COMPLEX64 || T == DT_COMPLEX128) {
+    // dz * z * (x != 0 ? Log(x) : 0)
+    // clang-format off
+    log_x_handling = {
+      {{"nz_x"}, "NotEqual", {"x", "zero"}},
+      {{"safe_log"}, "Select", {"nz_x", "unsafe_log", "zeros"}}};
+    // clang-format on
+  } else {
+    // dz * z * (x > 0 ? Log(x) : 0)
+    // clang-format off
+    log_x_handling = {
       {{"pos_x"}, "Greater", {"x", "zero"}},
-      {{"unsafe_log"}, "Log", {"x"}, {}, {"dz"}},
-      {{"zeros"}, "ZerosLike", {"x"}},
-      {{"safe_log"}, "Select", {"pos_x", "unsafe_log", "zeros"}},
-      {{"t4"}, "Mul", {"dz", "z"}},
-      {{"gy"}, "Mul", {"safe_log", "t4"}},
-  });
-  // clang-format on
+      {{"safe_log"}, "Select", {"pos_x", "unsafe_log", "zeros"}}};
+    // clang-format on
+  }
+  nodes.insert(nodes.end(), log_x_handling.begin(), log_x_handling.end());
+  nodes.push_back({{"t4"}, "Mul", {"dz", "z"}});
+  nodes.push_back({{"gy"}, "Mul", {"safe_log", "t4"}});
+  return GradForBinaryCwise(g, nodes);
 }
 REGISTER_OP_GRADIENT("Pow", PowGrad);
 
diff --git a/tensorflow/core/ops/math_grad_test.cc b/tensorflow/core/ops/math_grad_test.cc
index 9af73b2da0..e937fc5ab1 100644
--- a/tensorflow/core/ops/math_grad_test.cc
+++ b/tensorflow/core/ops/math_grad_test.cc
@@ -684,6 +684,25 @@ TEST_F(MathGradTest, Pow) {
   }
 }
 
+TEST_F(MathGradTest, ComplexPow) {
+  auto x = test::AsTensor<complex64>({0.f, 2.f, -2.f}, TensorShape({3}));
+  auto y = test::AsTensor<complex64>({2.f, 2.f, 2.f}, TensorShape({3}));
+  Tensor dx;
+  Tensor dy;
+  auto g = [](complex64 x, complex64 y) { return y * std::pow(x, y - 1.f); };
+  auto h = [](complex64 x, complex64 y) {
+    return std::pow(x, y) * (x != complex64(0) ? std::log(x) : 0);
+  };
+  SymGrad("Pow", x, y, &dx, &dy);
+
+  test::ExpectClose(
+      dx, test::AsTensor<complex64>({g(0.f, 2.f), g(2.f, 2.f), g(-2.f, 2.f)},
+                                    TensorShape({3})));
+  test::ExpectClose(
+      dy, test::AsTensor<complex64>({h(0.f, 2.f), h(2.f, 2.f), h(-2.f, 2.f)},
+                                    TensorShape({3})));
+}
+
 TEST_F(MathGradTest, Maximum) {
   auto x = test::AsTensor<float>({-3.f, -2.f, -1.f, 1.f, 2.f, 3.f},
                                  TensorShape({2, 3}));
diff --git a/tensorflow/python/kernel_tests/cwise_ops_test.py b/tensorflow/python/kernel_tests/cwise_ops_test.py
index e5406ff87a..0890ea4a2a 100644
--- a/tensorflow/python/kernel_tests/cwise_ops_test.py
+++ b/tensorflow/python/kernel_tests/cwise_ops_test.py
@@ -992,13 +992,34 @@ class BinaryOpTest(tf.test.TestCase):
 
   def testZeroPowGrad(self):
     with self.test_session():
-      for dtype in np.float16, np.float32, np.float64:
+      for dtype in (np.float16, np.float32, np.float64, np.complex64,
+                    np.complex128):
         x = tf.constant(0.0, dtype=dtype)
         y = tf.constant(2.0, dtype=dtype)
         z = tf.pow(x, y)
         error = tf.test.compute_gradient_error(y, [], z, [])
         self.assertEqual(error, 0)
 
+  def testComplexPowGradPositiveBase(self):
+    with self.test_session():
+      for dtype in np.complex64, np.complex128:
+        x = tf.constant(2.0, dtype=dtype)
+        y = tf.constant(2.0, dtype=dtype)
+        z = tf.pow(x, y)
+        error = tf.test.compute_gradient_error(y, [], z, [])
+        self.assertLess(error, 1e-4)
+
+  def testComplexPowGradNegativeBase(self):
+    with self.test_session() as session:
+      for dtype in np.complex64, np.complex128:
+        x = tf.constant(-2.0, dtype=dtype)
+        y = tf.constant(2.0, dtype=dtype)
+        z = tf.pow(x, y)
+        expected_x_grad = -4
+        expected_y_grad = (-2)**2 * (np.log(2) + np.pi * 1j)
+        self.assertAllClose([expected_x_grad, expected_y_grad],
+                            session.run(tf.gradients(z, [x, y])))
+
 
 class ComparisonOpTest(tf.test.TestCase):
 
diff --git a/tensorflow/python/ops/math_grad.py b/tensorflow/python/ops/math_grad.py
index 61d998ec4f..315fd4ffca 100644
--- a/tensorflow/python/ops/math_grad.py
+++ b/tensorflow/python/ops/math_grad.py
@@ -552,7 +552,13 @@ def _PowGrad(op, grad):
   gx = array_ops.reshape(
       math_ops.reduce_sum(grad * y * math_ops.pow(x, y - 1), rx), sx)
   # Avoid false singularity at x = 0
-  log_x = math_ops.select(x > 0, math_ops.log(x), array_ops.zeros_like(x))
+  if x.dtype.is_complex:
+    # real(x) < 0 is fine for the complex case
+    log_x = math_ops.select(
+        math_ops.not_equal(x, 0), math_ops.log(x), array_ops.zeros_like(x))
+  else:
+    # There's no sensible real value to return if x < 0, so return 0
+    log_x = math_ops.select(x > 0, math_ops.log(x), array_ops.zeros_like(x))
   gy = array_ops.reshape(
       math_ops.reduce_sum(grad * z * log_x, ry), sy)
   return gx, gy
-- 
GitLab


From a85ed2e4601a8111d25f30b14421cac7a94ad372 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 08:15:49 -0800
Subject: [PATCH 132/223] Make _tp_tn_fp_fn have weights as an optional
 parameter. I was using this in a few places in prototype code, and noticed
 that the docstring did not match the declaration. Change: 131296046

---
 tensorflow/contrib/metrics/python/ops/metric_ops.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py
index 249c25b072..c4d20e5db9 100644
--- a/tensorflow/contrib/metrics/python/ops/metric_ops.py
+++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py
@@ -628,7 +628,7 @@ def streaming_recall(predictions, labels, ignore_mask=None,
     return recall, update_op
 
 
-def _tp_fn_tn_fp(predictions, labels, thresholds, weights):
+def _tp_fn_tn_fp(predictions, labels, thresholds, weights=None):
   """Computes true_positives, false_negatives, true_negatives, false_positives.
 
   The `_tp_fn_tn_fp` function creates four local variables, `true_positives`,
-- 
GitLab


From 6bca9eab85082cf3bc988f9588ddddfb3ed60cb1 Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Thu, 25 Aug 2016 08:55:38 -0800
Subject: [PATCH 133/223] Add to the C++ Node class the ability to fetch input
 nodes and edges by index.  There are various locations in code where users
 currently use iteration to find the edge by its already known index, and
 these functions would be useful to accomplish. In addition, this implements
 the equivalent functionality of 'op.inputs[i]' in our python Operation class.

Given the new functionality, it exposed a weird use of NoOp for nodes
that actually had multiple inputs.  Modified the test to use custom op
definitions to be more correct.

Currently this iterates over the edge list, which in the common case
will be fast and introduces no additional state to Node.  In the future
we may want to revisit this.
Change: 131299794
---
 tensorflow/core/graph/graph.cc      |  37 ++++++++++
 tensorflow/core/graph/graph.h       |   7 ++
 tensorflow/core/graph/graph_test.cc | 107 ++++++++++++++++++++++++++--
 3 files changed, 145 insertions(+), 6 deletions(-)

diff --git a/tensorflow/core/graph/graph.cc b/tensorflow/core/graph/graph.cc
index 95f1c06a2c..648f439607 100644
--- a/tensorflow/core/graph/graph.cc
+++ b/tensorflow/core/graph/graph.cc
@@ -144,6 +144,43 @@ void Node::ClearAttr(const string& name) {
   (*props_->node_def_.mutable_attr()).erase(name);
 }
 
+Status Node::input_edge(int idx, const Edge** e) const {
+  if (idx < 0 || idx >= num_inputs()) {
+    return errors::InvalidArgument("Invalid input_edge index: ", idx, ", Node ",
+                                   name(), " only has ", num_inputs(),
+                                   " inputs.");
+  }
+
+  // This does a linear search over the edges.  In the common case,
+  // the number of elements is small enough that this search isn't
+  // expensive.  Should it become a bottleneck, one can make an
+  // optimization where, if the number of edges is small, we use
+  // linear iteration, and if the number of edges is large, we perform
+  // an indexing step during construction that keeps an array of Edges
+  // indexed by pointer.  This would keep the size of each Node small
+  // in the common case but make this function faster when the number
+  // of edges is large.
+  for (const Edge* edge : in_edges()) {
+    if (edge->dst_input() == idx) {
+      *e = edge;
+      return Status::OK();
+    }
+  }
+
+  return errors::NotFound("Could not find input edge ", idx, " for ", name());
+}
+
+Status Node::input_node(int idx, const Node** n) const {
+  const Edge* e;
+  TF_RETURN_IF_ERROR(input_edge(idx, &e));
+  if (e == nullptr) {
+    *n = nullptr;
+  } else {
+    *n = e->src();
+  }
+  return Status::OK();
+}
+
 // Node::Properties
 
 Node::Properties::Properties(const OpDef* op_def, const NodeDef& node_def,
diff --git a/tensorflow/core/graph/graph.h b/tensorflow/core/graph/graph.h
index 5a3008b317..6bf88984c9 100644
--- a/tensorflow/core/graph/graph.h
+++ b/tensorflow/core/graph/graph.h
@@ -139,6 +139,13 @@ class Node {
 
   void ClearAttr(const string& name);
 
+  // Returns into '*e' the edge connecting to the 'idx' input of this Node.
+  Status input_edge(int idx, const Edge** e) const;
+
+  // Returns into '*n' the node that has an output connected to the
+  // 'idx' input of this Node.
+  Status input_node(int idx, const Node** n) const;
+
  private:
   friend class Graph;
   Node();
diff --git a/tensorflow/core/graph/graph_test.cc b/tensorflow/core/graph/graph_test.cc
index 1ae3ba5454..e42e3d2bcd 100644
--- a/tensorflow/core/graph/graph_test.cc
+++ b/tensorflow/core/graph/graph_test.cc
@@ -20,6 +20,7 @@ limitations under the License.
 #include "tensorflow/core/graph/graph_constructor.h"
 #include "tensorflow/core/graph/node_builder.h"
 #include "tensorflow/core/kernels/ops_util.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
 #include "tensorflow/core/lib/random/simple_philox.h"
 #include "tensorflow/core/lib/strings/stringprintf.h"
 #include "tensorflow/core/platform/logging.h"
@@ -30,6 +31,20 @@ limitations under the License.
 namespace tensorflow {
 namespace {
 
+REGISTER_OP("OneInput").Input("x: float");
+
+REGISTER_OP("OneOutput").Output("y: float");
+
+REGISTER_OP("OneInputTwoOutputs")
+    .Input("x: float")
+    .Output("y: float")
+    .Output("z: float");
+
+REGISTER_OP("TwoInputsOneOutput")
+    .Input("x: float")
+    .Input("y: float")
+    .Output("z: float");
+
 class GraphTest : public ::testing::Test {
  protected:
   GraphTest() : graph_(OpRegistry::Global()) {}
@@ -71,6 +86,22 @@ class GraphTest : public ::testing::Test {
     return node;
   }
 
+  Node* FromNodeDef(const string& name, const string& node_type,
+                    int num_inputs) {
+    auto builder = NodeDefBuilder(name, node_type);
+    for (int i = 0; i < num_inputs; ++i) {
+      builder = builder.Input(strings::StrCat("node_", i), i, DT_FLOAT);
+    }
+
+    NodeDef node_def;
+    TF_CHECK_OK(builder.Finalize(&node_def));
+
+    Status s;
+    Node* node = graph_.AddNode(node_def, &s);
+    TF_CHECK_OK(s);
+    return node;
+  }
+
   Graph graph_;
 
  private:
@@ -112,9 +143,9 @@ TEST_F(GraphTest, RemoveThenAdd) {
 }
 
 TEST_F(GraphTest, InNodesAndOutNodes) {
-  Node* a = AddNodeWithName("A");
+  Node* a = FromNodeDef("A", "OneOutput", 0);
   Node* b = AddNodeWithName("B");
-  Node* c = AddNodeWithName("C");
+  Node* c = FromNodeDef("C", "OneInput", 1);
   graph_.RemoveNode(b);
   Node* d = AddNodeWithName("D");
 
@@ -147,11 +178,75 @@ TEST_F(GraphTest, InNodesAndOutNodes) {
   VerifyGraphStats();
 }
 
+TEST_F(GraphTest, NodeByIndex) {
+  Node* a = FromNodeDef("A", "OneOutput", 0);
+  Node* c = FromNodeDef("C", "OneInput", 1);
+  graph_.AddEdge(a, 0, c, 0);
+
+  // Ask for 'a' from 'c' by index.
+  const Node* a_copy;
+  TF_ASSERT_OK(c->input_node(0, &a_copy));
+  EXPECT_EQ(a, a_copy);
+
+  const Edge* e;
+  TF_ASSERT_OK(c->input_edge(0, &e));
+  EXPECT_EQ(0, e->dst_input());
+  EXPECT_EQ(a, e->src());
+  EXPECT_EQ(c, e->dst());
+  EXPECT_EQ(0, e->src_output());
+
+  Node* t = FromNodeDef("T", "TwoInputsOneOutput", 2);
+  graph_.AddEdge(a, 0, t, 0);
+  // Weird self edge
+  graph_.AddEdge(t, 0, t, 1);
+
+  const Node* t_0;
+  const Node* t_1;
+  TF_ASSERT_OK(t->input_node(0, &t_0));
+  EXPECT_EQ(a, t_0);
+  TF_ASSERT_OK(t->input_node(1, &t_1));
+  EXPECT_EQ(t, t_1);
+
+  TF_ASSERT_OK(t->input_edge(1, &e));
+  EXPECT_EQ(1, e->dst_input());
+  EXPECT_EQ(t, e->src());
+
+  // Check out of bounds access
+  EXPECT_FALSE(c->input_node(1, &a_copy).ok());
+  EXPECT_FALSE(c->input_node(-1, &a_copy).ok());
+
+  graph_.RemoveNode(a);
+
+  // 'c's input_node entry should be invalidated.
+  Status s = c->input_node(0, &a_copy);
+  EXPECT_FALSE(s.ok());
+
+  // Add two new nodes.
+  Node* a_new = FromNodeDef("A_new", "OneOutput", 0);
+  Node* b_new = FromNodeDef("B_new", "OneOutput", 0);
+
+  // Connect one up to c.
+  graph_.AddEdge(a_new, 0, c, 0);
+  const Edge* a_new_c_edge;
+  TF_ASSERT_OK(c->input_edge(0, &a_new_c_edge));
+
+  // Connect up the second edge
+  graph_.AddEdge(b_new, 0, c, 0);
+  const Edge* b_new_c_edge;
+  TF_ASSERT_OK(c->input_edge(0, &b_new_c_edge));
+
+  // Now remove the old one
+  graph_.RemoveEdge(a_new_c_edge);
+
+  // Check that the second edge can still be retrieved
+  TF_ASSERT_OK(c->input_edge(0, &b_new_c_edge));
+}
+
 TEST_F(GraphTest, NodeIteration) {
   // Set up the graph with some holes due to removals.
-  Node* a = AddNodeWithName("A");
+  Node* a = FromNodeDef("A", "OneOutput", 0);
   Node* b = AddNodeWithName("B");
-  Node* c = AddNodeWithName("C");
+  Node* c = FromNodeDef("C", "OneInput", 1);
   graph_.RemoveNode(b);
   Node* d = AddNodeWithName("D");
   const Edge* source_to_a = graph_.AddControlEdge(graph_.source_node(), a);
@@ -245,8 +340,8 @@ static string EdgeIter(const Graph& g) {
 TEST_F(GraphTest, EdgeIteration) {
   EXPECT_EQ("0->1;", EdgeIter(graph_));
 
-  Node* a = AddNodeWithName("A");
-  Node* b = AddNodeWithName("B");
+  Node* a = FromNodeDef("A", "OneInputTwoOutputs", 1);
+  Node* b = FromNodeDef("B", "OneInput", 1);
   EXPECT_EQ("0->1;", EdgeIter(graph_));  // Since a,b are currently disconnected
 
   graph_.AddEdge(a, 0, b, 0);
-- 
GitLab


From 51b23d50e3a3d021d54094bd25f22b9dde434202 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 08:56:54 -0800
Subject: [PATCH 134/223] Set default '' for master arg, do not create a saver
 when the graph has no saveable variables.

Also delete extraneous build(), as the saver does that in its constructor.
Change: 131299934
---
 .../learn/python/learn/monitored_session.py   |  4 +-
 .../learn/tests/monitored_session_test.py     | 12 +++++
 tensorflow/python/training/saver.py           | 44 ++++++++++++++-----
 tensorflow/python/training/saver_test.py      | 11 +++++
 4 files changed, 57 insertions(+), 14 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/monitored_session.py b/tensorflow/contrib/learn/python/learn/monitored_session.py
index c87633e976..6f9957f25e 100644
--- a/tensorflow/contrib/learn/python/learn/monitored_session.py
+++ b/tensorflow/contrib/learn/python/learn/monitored_session.py
@@ -143,7 +143,7 @@ class Scaffold(object):
       self._saver = Scaffold._get_or_default(
           'saver',
           ops.GraphKeys.SAVERS,
-          lambda: training_saver.Saver(sharded=True))
+          lambda: training_saver.Saver(sharded=True, allow_empty=True))
     # pylint: enable=g-long-lambda
     self._saver.build()
 
@@ -240,7 +240,7 @@ class MonitoredSession(object):
   """
 
   def __init__(self,
-               master,
+               master='',
                is_chief=True,
                checkpoint_dir=None,
                hooks=None,
diff --git a/tensorflow/contrib/learn/python/learn/tests/monitored_session_test.py b/tensorflow/contrib/learn/python/learn/tests/monitored_session_test.py
index 4011d15827..af790f2a91 100644
--- a/tensorflow/contrib/learn/python/learn/tests/monitored_session_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/monitored_session_test.py
@@ -61,6 +61,18 @@ class ScaffoldTest(tf.test.TestCase):
         sess.run([scaffold.init_op, scaffold.local_init_op])
         self.assertEquals(0, len(sess.run(scaffold.ready_op)))
 
+  def test_defaults_no_variables(self):
+    with tf.Graph().as_default():
+      scaffold = monitored_session.Scaffold()
+      tf.constant(1, name='my_const')
+      scaffold.finalize()
+      self.assertTrue(isinstance(scaffold.init_op, tf.Operation))
+      self.assertEqual(None, scaffold.init_feed_dict)
+      self.assertEqual(None, scaffold.init_fn)
+      self.assertTrue(isinstance(scaffold.ready_op, tf.Tensor))
+      self.assertTrue(isinstance(scaffold.local_init_op, tf.Operation))
+      self.assertTrue(isinstance(scaffold.saver, tf.train.Saver))
+
   def test_caches_values(self):
     with tf.Graph().as_default():
       tf.Variable([1])
diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py
index 4d3db66ba4..c4f86b7a9e 100644
--- a/tensorflow/python/training/saver.py
+++ b/tensorflow/python/training/saver.py
@@ -881,7 +881,8 @@ class Saver(object):
                restore_sequentially=False,
                saver_def=None,
                builder=None,
-               defer_build=False):
+               defer_build=False,
+               allow_empty=False):
     """Creates a `Saver`.
 
     The constructor adds ops to save and restore variables.
@@ -943,6 +944,9 @@ class Saver(object):
       defer_build: If `True`, defer adding the save and restore ops to the
         `build()` call. In that case `build()` should be called before
         finalizing the graph or using the saver.
+      allow_empty: If `False` (default) raise an error if there are no
+        variables in the graph. Otherwise, construct the saver anyway and make
+        it a no-op.
 
     Raises:
       TypeError: If `var_list` is invalid.
@@ -962,6 +966,8 @@ class Saver(object):
     self.saver_def = saver_def
     self._builder = builder
     self._is_built = False
+    self._allow_empty = allow_empty
+    self._is_empty = None
     if not defer_build:
       self.build()
     if self.saver_def:
@@ -979,7 +985,12 @@ class Saver(object):
         # pylint: disable=protected-access
         self._var_list = variables._all_saveable_objects()
       if not self._var_list:
-        raise ValueError("No variables to save")
+        if self._allow_empty:
+          self._is_empty = True
+          return
+        else:
+          raise ValueError("No variables to save")
+      self._is_empty = False
       self.saver_def = self._builder.build(
           self._var_list,
           reshape=self._reshape,
@@ -1207,6 +1218,7 @@ class Saver(object):
       A string: path at which the variables were saved.  If the saver is
         sharded, this string ends with: '-?????-of-nnnnn' where 'nnnnn'
         is the number of shards created.
+      If the saver is empty, returns None.
 
     Raises:
       TypeError: If `sess` is not a `Session`.
@@ -1216,7 +1228,7 @@ class Saver(object):
     """
     if not self._is_built:
       raise RuntimeError(
-          "`build()` should be called before save if deffer_build==True")
+          "`build()` should be called before save if defer_build==True")
     if latest_filename is None:
       latest_filename = "checkpoint"
 
@@ -1244,21 +1256,26 @@ class Saver(object):
     if not isinstance(sess, session.SessionInterface):
       raise TypeError("'sess' must be a Session; %s" % sess)
 
-    model_checkpoint_path = sess.run(
-        self.saver_def.save_tensor_name,
-        {self.saver_def.filename_tensor_name: checkpoint_file})
-    model_checkpoint_path = compat.as_str(model_checkpoint_path)
-    self._MaybeDeleteOldCheckpoints(
-        model_checkpoint_path, meta_graph_suffix=meta_graph_suffix)
-    update_checkpoint_state(save_path, model_checkpoint_path,
-                            self.last_checkpoints, latest_filename)
+    if not self._is_empty:
+      model_checkpoint_path = sess.run(
+          self.saver_def.save_tensor_name,
+          {self.saver_def.filename_tensor_name: checkpoint_file})
+      model_checkpoint_path = compat.as_str(model_checkpoint_path)
+      self._MaybeDeleteOldCheckpoints(
+          model_checkpoint_path, meta_graph_suffix=meta_graph_suffix)
+      update_checkpoint_state(save_path, model_checkpoint_path,
+                              self.last_checkpoints, latest_filename)
+
     if write_meta_graph:
       meta_graph_filename = self._MetaGraphFilename(
           checkpoint_file, meta_graph_suffix=meta_graph_suffix)
       with sess.graph.as_default():
         self.export_meta_graph(meta_graph_filename)
 
-    return model_checkpoint_path
+    if self._is_empty:
+      return None
+    else:
+      return model_checkpoint_path
 
   def export_meta_graph(self,
                         filename=None,
@@ -1299,6 +1316,9 @@ class Saver(object):
     Raises:
       ValueError: If the given `save_path` does not point to a file.
     """
+    if self._is_empty:
+      return
+
     if not file_io.get_matching_files(
         _prefix_to_checkpoint_path(save_path, self.saver_def.version)):
       raise ValueError("Restore called with invalid save path %s" % save_path)
diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py
index 85d0392b0d..92700c9414 100644
--- a/tensorflow/python/training/saver_test.py
+++ b/tensorflow/python/training/saver_test.py
@@ -338,6 +338,17 @@ class SaverTest(tf.test.TestCase):
     # The cached readers should know to re-read the file.
     self._SaveAndLoad("var1", 1.1, 2.2, save_path)
 
+  def testAllowEmpty(self):
+    save_path = os.path.join(self.get_temp_dir(), "allow_empty")
+    with self.test_session() as sess:
+      _ = tf.constant(1)
+      save = tf.train.Saver(allow_empty=True)
+      val = save.save(sess, save_path)
+      self.assertIsNone(val)
+    with self.test_session() as sess:
+      save = tf.train.Saver(allow_empty=True)
+      save.restore(sess, save_path)
+
   def testGPU(self):
     if not tf.test.is_gpu_available():
       return
-- 
GitLab


From 10be3ebc790fa3db974a5df90c78ec0acb420cb8 Mon Sep 17 00:00:00 2001
From: ichuang <ichuang@mit.edu>
Date: Thu, 25 Aug 2016 13:10:32 -0400
Subject: [PATCH 135/223] =?UTF-8?q?fix=20small=20typo=20in=20contrib.metri?=
 =?UTF-8?q?cs.confusion=5Fmatrix=20docs,=20and=20use=20less=E2=80=A6=20(#4?=
 =?UTF-8?q?040)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* fix small typo in contrib.metrics.confusion_matrix docs, and use less confusing variable

* fix small typo in contrib.metrics.confusion_matrix docs, and use less confusing variable
---
 tensorflow/contrib/metrics/python/ops/confusion_matrix_ops.py   | 2 +-
 .../shard8/tf.contrib.metrics.confusion_matrix.md               | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/contrib/metrics/python/ops/confusion_matrix_ops.py b/tensorflow/contrib/metrics/python/ops/confusion_matrix_ops.py
index cf000f06d3..5620001f1a 100644
--- a/tensorflow/contrib/metrics/python/ops/confusion_matrix_ops.py
+++ b/tensorflow/contrib/metrics/python/ops/confusion_matrix_ops.py
@@ -59,7 +59,7 @@ def confusion_matrix(predictions, labels, num_classes=None,
     name: Scope name.
 
   Returns:
-    A l X l matrix represeting the confusion matrix, where l in the number of
+    A k X k matrix represeting the confusion matrix, where k is the number of
     possible labels in the classification task.
 
   Raises:
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.confusion_matrix.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.confusion_matrix.md
index f06295f081..50d7917926 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.confusion_matrix.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.confusion_matrix.md
@@ -35,7 +35,7 @@ the same shape in order for this function to work.
 
 ##### Returns:
 
-  A l X l matrix represeting the confusion matrix, where l in the number of
+  A k X k matrix represeting the confusion matrix, where k is the number of
   possible labels in the classification task.
 
 ##### Raises:
-- 
GitLab


From dce14921a25dc784f96d724d54edbcba12dbb4ad Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 09:08:24 -0800
Subject: [PATCH 136/223] Update generated Python Op docs. Change: 131301254

---
 .../python/functions_and_classes/shard5/tf.train.Saver.md   | 6 +++++-
 tensorflow/g3doc/api_docs/python/state_ops.md               | 6 +++++-
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Saver.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Saver.md
index 0dacbd809a..58b112a979 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Saver.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Saver.md
@@ -72,7 +72,7 @@ protocol buffer file in the call to `save()`.
 
 - - -
 
-#### `tf.train.Saver.__init__(var_list=None, reshape=False, sharded=False, max_to_keep=5, keep_checkpoint_every_n_hours=10000.0, name=None, restore_sequentially=False, saver_def=None, builder=None, defer_build=False)` {#Saver.__init__}
+#### `tf.train.Saver.__init__(var_list=None, reshape=False, sharded=False, max_to_keep=5, keep_checkpoint_every_n_hours=10000.0, name=None, restore_sequentially=False, saver_def=None, builder=None, defer_build=False, allow_empty=False)` {#Saver.__init__}
 
 Creates a `Saver`.
 
@@ -137,6 +137,9 @@ checkpoints per device.
 *  <b>`defer_build`</b>: If `True`, defer adding the save and restore ops to the
     `build()` call. In that case `build()` should be called before
     finalizing the graph or using the saver.
+*  <b>`allow_empty`</b>: If `False` (default) raise an error if there are no
+    variables in the graph. Otherwise, construct the saver anyway and make
+    it a no-op.
 
 ##### Raises:
 
@@ -181,6 +184,7 @@ path can be passed directly to a call to `restore()`.
   A string: path at which the variables were saved.  If the saver is
     sharded, this string ends with: '-?????-of-nnnnn' where 'nnnnn'
     is the number of shards created.
+  If the saver is empty, returns None.
 
 ##### Raises:
 
diff --git a/tensorflow/g3doc/api_docs/python/state_ops.md b/tensorflow/g3doc/api_docs/python/state_ops.md
index 2ad42d9a47..d9ad4e0e12 100644
--- a/tensorflow/g3doc/api_docs/python/state_ops.md
+++ b/tensorflow/g3doc/api_docs/python/state_ops.md
@@ -736,7 +736,7 @@ protocol buffer file in the call to `save()`.
 
 - - -
 
-#### `tf.train.Saver.__init__(var_list=None, reshape=False, sharded=False, max_to_keep=5, keep_checkpoint_every_n_hours=10000.0, name=None, restore_sequentially=False, saver_def=None, builder=None, defer_build=False)` {#Saver.__init__}
+#### `tf.train.Saver.__init__(var_list=None, reshape=False, sharded=False, max_to_keep=5, keep_checkpoint_every_n_hours=10000.0, name=None, restore_sequentially=False, saver_def=None, builder=None, defer_build=False, allow_empty=False)` {#Saver.__init__}
 
 Creates a `Saver`.
 
@@ -801,6 +801,9 @@ checkpoints per device.
 *  <b>`defer_build`</b>: If `True`, defer adding the save and restore ops to the
     `build()` call. In that case `build()` should be called before
     finalizing the graph or using the saver.
+*  <b>`allow_empty`</b>: If `False` (default) raise an error if there are no
+    variables in the graph. Otherwise, construct the saver anyway and make
+    it a no-op.
 
 ##### Raises:
 
@@ -845,6 +848,7 @@ path can be passed directly to a call to `restore()`.
   A string: path at which the variables were saved.  If the saver is
     sharded, this string ends with: '-?????-of-nnnnn' where 'nnnnn'
     is the number of shards created.
+  If the saver is empty, returns None.
 
 ##### Raises:
 
-- 
GitLab


From a54aa42d4e1d5b60782bcee9ac346d7509195567 Mon Sep 17 00:00:00 2001
From: Illia Polosukhin <ipolosukhin@google.com>
Date: Thu, 25 Aug 2016 09:09:57 -0800
Subject: [PATCH 137/223] Fixed size partitioner: partitions variable into
 fixed number of shards. Change: 131301480

---
 .../partitioned_variables_test.py             | 10 ++++++++++
 .../python/ops/partitioned_variables.py       | 19 +++++++++++++++++++
 tensorflow/python/ops/state_ops.py            |  1 +
 3 files changed, 30 insertions(+)

diff --git a/tensorflow/python/kernel_tests/partitioned_variables_test.py b/tensorflow/python/kernel_tests/partitioned_variables_test.py
index efa7322f65..fda8264bbc 100644
--- a/tensorflow/python/kernel_tests/partitioned_variables_test.py
+++ b/tensorflow/python/kernel_tests/partitioned_variables_test.py
@@ -25,6 +25,16 @@ import tensorflow as tf
 
 class PartitionerCreatorsTest(tf.test.TestCase):
 
+  def testFixedSizePartitioner(self):
+    with self.test_session():
+      partitioner = tf.fixed_size_partitioner(5, axis=0)
+      with tf.variable_scope("root", partitioner=partitioner):
+        v0 = tf.get_variable("v0", dtype=tf.float32, shape=(10, 10))
+        v0_list = v0._get_variable_list()
+        v0_part = v0._get_partitions()
+        self.assertEqual(len(v0_list), 5)
+        self.assertAllEqual(v0_part, (5, 1))
+
   def _testVariableAxisSizePartitioner(self, name, axis, max_shard_bytes,
                                        expected_axis_shards,
                                        expected_partitions,
diff --git a/tensorflow/python/ops/partitioned_variables.py b/tensorflow/python/ops/partitioned_variables.py
index 23e0c858ff..fb7524b56b 100644
--- a/tensorflow/python/ops/partitioned_variables.py
+++ b/tensorflow/python/ops/partitioned_variables.py
@@ -65,6 +65,7 @@ __all__ = [
     "create_partitioned_variables",
     "variable_axis_size_partitioner",
     "min_max_variable_partitioner",
+    "fixed_size_partitioner",
 ]
 
 
@@ -215,6 +216,24 @@ def min_max_variable_partitioner(max_partitions=1, axis=0,
   return _partitioner
 
 
+def fixed_size_partitioner(num_shards, axis=0):
+  """Partitioner to specify a fixed number of shards along given axis.
+
+  Args:
+    num_shards: `int`, number of shards to partition variable.
+    axis: `int`, axis to partition on.
+
+  Returns:
+    A partition function usable as the `partitioner` argument to
+    `variable_scope`, `get_variable`, and `get_partitioned_variable_list`.
+  """
+  def _partitioner(shape, **unused_args):
+    partitions_list = [1] * len(shape)
+    partitions_list[axis] = min(num_shards, shape[axis].value)
+    return partitions_list
+  return _partitioner
+
+
 def create_partitioned_variables(
     shape, slicing, initializer, dtype=dtypes.float32,
     trainable=True, collections=None, name=None, reuse=None):
diff --git a/tensorflow/python/ops/state_ops.py b/tensorflow/python/ops/state_ops.py
index 7c188ca651..63fae74812 100644
--- a/tensorflow/python/ops/state_ops.py
+++ b/tensorflow/python/ops/state_ops.py
@@ -67,6 +67,7 @@ create variables contingent on certain conditions.
 
 ## Variable Partitioners for Sharding
 
+@@fixed_size_partitioner
 @@variable_axis_size_partitioner
 @@min_max_variable_partitioner
 
-- 
GitLab


From b057e0d6fd6441ad03d4834c09f831948a7905cd Mon Sep 17 00:00:00 2001
From: Geoffrey Irving <geoffreyi@google.com>
Date: Thu, 25 Aug 2016 09:20:48 -0800
Subject: [PATCH 138/223] Fix scan ops for empty tensors Change: 131302762

---
 tensorflow/core/kernels/scan_ops.cc             | 3 +++
 tensorflow/python/kernel_tests/scan_ops_test.py | 9 +++++++++
 2 files changed, 12 insertions(+)

diff --git a/tensorflow/core/kernels/scan_ops.cc b/tensorflow/core/kernels/scan_ops.cc
index e6505dff6e..604e712b0f 100644
--- a/tensorflow/core/kernels/scan_ops.cc
+++ b/tensorflow/core/kernels/scan_ops.cc
@@ -62,6 +62,9 @@ public:
     Tensor* output = nullptr;
     OP_REQUIRES_OK(ctx, ctx->allocate_output(0, output_shape, &output));
 
+    // Exit early if there's nothing to compute
+    if (output_shape.num_elements() == 0) return;
+
     const Device& d = ctx->eigen_device<Device>();
     Reducer reducer;
 
diff --git a/tensorflow/python/kernel_tests/scan_ops_test.py b/tensorflow/python/kernel_tests/scan_ops_test.py
index 4db5cf51c4..677dac8cda 100644
--- a/tensorflow/python/kernel_tests/scan_ops_test.py
+++ b/tensorflow/python/kernel_tests/scan_ops_test.py
@@ -71,6 +71,11 @@ class CumsumTest(tf.test.TestCase):
       for reverse in [True, False]:
         self._compare(x, axis, exclusive, reverse)
 
+  def testEmpty(self):
+    for dtype in self.valid_dtypes:
+      x = np.zeros([0]).astype(dtype)
+      self._compareAll(x, 0)
+
   def test1D(self):
     for dtype in self.valid_dtypes:
       x = np.arange(1, 6).reshape([5]).astype(dtype)
@@ -155,6 +160,10 @@ class CumprodTest(tf.test.TestCase):
       for reverse in [True, False]:
         self._compare(x, axis, exclusive, reverse)
 
+  def testEmpty(self):
+    for dtype in self.valid_dtypes:
+      x = np.zeros([0]).astype(dtype)
+      self._compareAll(x, 0)
 
   def test1D(self):
     for dtype in self.valid_dtypes:
-- 
GitLab


From 4747f6587777d915d9df199592e5d080d7820778 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 09:48:52 -0800
Subject: [PATCH 139/223] Update generated Python Op docs. Change: 131306335

---
 .../shard4/tf.fixed_size_partitioner.md        | 15 +++++++++++++++
 tensorflow/g3doc/api_docs/python/index.md      |  1 +
 tensorflow/g3doc/api_docs/python/state_ops.md  | 18 ++++++++++++++++++
 3 files changed, 34 insertions(+)
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.fixed_size_partitioner.md

diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.fixed_size_partitioner.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.fixed_size_partitioner.md
new file mode 100644
index 0000000000..fdeea7f207
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.fixed_size_partitioner.md
@@ -0,0 +1,15 @@
+### `tf.fixed_size_partitioner(num_shards, axis=0)` {#fixed_size_partitioner}
+
+Partitioner to specify a fixed number of shards along given axis.
+
+##### Args:
+
+
+*  <b>`num_shards`</b>: `int`, number of shards to partition variable.
+*  <b>`axis`</b>: `int`, axis to partition on.
+
+##### Returns:
+
+  A partition function usable as the `partitioner` argument to
+  `variable_scope`, `get_variable`, and `get_partitioned_variable_list`.
+
diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md
index b760c5ab1d..12aa7c6a03 100644
--- a/tensorflow/g3doc/api_docs/python/index.md
+++ b/tensorflow/g3doc/api_docs/python/index.md
@@ -79,6 +79,7 @@
   * [`count_up_to`](../../api_docs/python/state_ops.md#count_up_to)
   * [`device`](../../api_docs/python/state_ops.md#device)
   * [`export_meta_graph`](../../api_docs/python/state_ops.md#export_meta_graph)
+  * [`fixed_size_partitioner`](../../api_docs/python/state_ops.md#fixed_size_partitioner)
   * [`get_checkpoint_state`](../../api_docs/python/state_ops.md#get_checkpoint_state)
   * [`get_variable`](../../api_docs/python/state_ops.md#get_variable)
   * [`get_variable_scope`](../../api_docs/python/state_ops.md#get_variable_scope)
diff --git a/tensorflow/g3doc/api_docs/python/state_ops.md b/tensorflow/g3doc/api_docs/python/state_ops.md
index d9ad4e0e12..39574c4cb0 100644
--- a/tensorflow/g3doc/api_docs/python/state_ops.md
+++ b/tensorflow/g3doc/api_docs/python/state_ops.md
@@ -1781,6 +1781,24 @@ An adaptor for ones() to match the Initializer spec.
 
 ## Variable Partitioners for Sharding
 
+- - -
+
+### `tf.fixed_size_partitioner(num_shards, axis=0)` {#fixed_size_partitioner}
+
+Partitioner to specify a fixed number of shards along given axis.
+
+##### Args:
+
+
+*  <b>`num_shards`</b>: `int`, number of shards to partition variable.
+*  <b>`axis`</b>: `int`, axis to partition on.
+
+##### Returns:
+
+  A partition function usable as the `partitioner` argument to
+  `variable_scope`, `get_variable`, and `get_partitioned_variable_list`.
+
+
 - - -
 
 ### `tf.variable_axis_size_partitioner(max_shard_bytes, axis=0, bytes_per_string_element=16, max_shards=None)` {#variable_axis_size_partitioner}
-- 
GitLab


From 1fa09b5560144e8dd16756d7877e01647fa68325 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 25 Aug 2016 10:03:53 -0800
Subject: [PATCH 140/223] Fix abs()-related build failure on mac os Change:
 131308407

---
 tensorflow/core/framework/tensor_testutil.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/framework/tensor_testutil.cc b/tensorflow/core/framework/tensor_testutil.cc
index 1bae7b08d5..cc117ad19e 100644
--- a/tensorflow/core/framework/tensor_testutil.cc
+++ b/tensorflow/core/framework/tensor_testutil.cc
@@ -22,7 +22,7 @@ namespace test {
 template <typename T>
 bool IsClose(const T& x, const T& y, double atol, double rtol) {
   // Need x == y so that infinities are close to themselves
-  return x == y || fabs(x - y) < atol + rtol * fabs(x);
+  return x == y || std::abs(x - y) < atol + rtol * std::abs(x);
 }
 
 template <typename T>
-- 
GitLab


From 2c598e874e6a7b6b3185846ce9bac97a7d5d0169 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 10:22:44 -0800
Subject: [PATCH 141/223] Merge changes from github. Change: 131310818

---
 configure                                     |  73 +--
 .../core/common_runtime/gpu/gpu_device.cc     |   4 +-
 tensorflow/core/kernels/lrn_op.cc             |   2 +-
 tensorflow/core/kernels/matmul_op.cc          |   2 +-
 tensorflow/core/ops/ops.pbtxt                 |  36 ++
 .../core/platform/default/build_config/BUILD  |  18 +-
 tensorflow/core/platform/default/gpu/BUILD    |   6 +-
 .../core/platform/default/gpu/cupti_wrapper.h |   2 +-
 tensorflow/core/util/port.cc                  |   2 +-
 .../shard5/tf.train.Optimizer.md              |   5 +-
 tensorflow/g3doc/api_docs/python/train.md     |   5 +-
 tensorflow/g3doc/get_started/os_setup.md      |   9 +-
 tensorflow/python/ops/ctc_ops.py              |  18 +-
 tensorflow/python/ops/variables.py            |   3 +-
 tensorflow/python/training/optimizer.py       |   6 +-
 tensorflow/stream_executor/BUILD              |   9 +-
 tensorflow/stream_executor/cuda/cuda_blas.cc  |   4 +-
 tensorflow/stream_executor/cuda/cuda_dnn.cc   |   2 +-
 tensorflow/stream_executor/cuda/cuda_driver.h |   2 +-
 tensorflow/stream_executor/cuda/cuda_fft.h    |   2 +-
 .../stream_executor/cuda/cuda_helpers.h       |   4 +-
 tensorflow/stream_executor/cuda/cuda_kernel.h |   2 +-
 tensorflow/stream_executor/cuda/cuda_rng.cc   |   2 +-
 tensorflow/stream_executor/dso_loader.cc      |  60 +--
 tensorflow/stream_executor/dso_loader.h       |   1 +
 tensorflow/tensorboard/backend/handler.py     |   5 +-
 tensorflow/tensorflow.bzl                     |  32 +-
 tensorflow/tools/ci_build/Dockerfile.gpu      |   3 +-
 tensorflow/tools/dist_test/README.md          |   2 +-
 tensorflow/workspace.bzl                      |   3 +
 third_party/gpus/BUILD                        |   0
 third_party/gpus/crosstool/BUILD.tpl          |  42 ++
 third_party/gpus/crosstool/CROSSTOOL.tpl      | 254 +++++++++++
 .../crosstool_wrapper_driver_is_not_gcc.tpl   | 315 +++++++++++++
 third_party/gpus/cuda/BUILD                   | 224 ----------
 third_party/gpus/cuda/BUILD.tpl               | 172 +++++++
 third_party/gpus/cuda/build_defs.bzl.tpl      |  14 +
 third_party/gpus/cuda/cuda_config.h.tpl       |  24 +
 third_party/gpus/cuda/platform.bzl.tpl        |  57 +++
 third_party/gpus/cuda_configure.bzl           | 423 ++++++++++++++++++
 tools/bazel.rc.template                       |   2 +-
 41 files changed, 1469 insertions(+), 382 deletions(-)
 create mode 100644 third_party/gpus/BUILD
 create mode 100644 third_party/gpus/crosstool/BUILD.tpl
 create mode 100644 third_party/gpus/crosstool/CROSSTOOL.tpl
 create mode 100755 third_party/gpus/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc.tpl
 create mode 100644 third_party/gpus/cuda/BUILD.tpl
 create mode 100644 third_party/gpus/cuda/build_defs.bzl.tpl
 create mode 100644 third_party/gpus/cuda/cuda_config.h.tpl
 create mode 100644 third_party/gpus/cuda/platform.bzl.tpl
 create mode 100644 third_party/gpus/cuda_configure.bzl

diff --git a/configure b/configure
index 9ab6ea6b1c..bcef37bd26 100755
--- a/configure
+++ b/configure
@@ -80,6 +80,7 @@ while [ "$TF_NEED_CUDA" == "" ]; do
   esac
 done
 
+export TF_NEED_CUDA
 if [ "$TF_NEED_CUDA" == "0" ]; then
   echo "Configuration finished"
   exit
@@ -97,6 +98,7 @@ while true; do
     fi
   fi
   if [ -e "$GCC_HOST_COMPILER_PATH" ]; then
+    export CC=$GCC_HOST_COMPILER_PATH
     break
   fi
   echo "Invalid gcc path. ${GCC_HOST_COMPILER_PATH} cannot be found" 1>&2
@@ -107,7 +109,6 @@ while true; do
   # Retry
 done
 
-
 # Find out where the CUDA toolkit is installed
 OSNAME=`uname -s`
 
@@ -140,6 +141,8 @@ while true; do
   fi
 
   if [ -e "${CUDA_TOOLKIT_PATH}/${CUDA_RT_LIB_PATH}" ]; then
+    export CUDA_TOOLKIT_PATH
+    export CUDA_VERSION=$TF_CUDA_VERSION
     break
   fi
   echo "Invalid path to CUDA $TF_CUDA_VERSION toolkit. ${CUDA_TOOLKIT_PATH}/${CUDA_RT_LIB_PATH} cannot be found"
@@ -200,13 +203,16 @@ while true; do
   fi
 
   if [ -e "$CUDNN_INSTALL_PATH/${CUDA_DNN_LIB_ALT_PATH}" -o -e "$CUDNN_INSTALL_PATH/${CUDA_DNN_LIB_PATH}" ]; then
+    export CUDNN_VERSION=$TF_CUDNN_VERSION
+    export CUDNN_INSTALL_PATH
     break
   fi
 
   if [ "$OSNAME" == "Linux" ]; then
     CUDNN_PATH_FROM_LDCONFIG="$(ldconfig -p | sed -n 's/.*libcudnn.so .* => \(.*\)/\1/p')"
     if [ -e "${CUDNN_PATH_FROM_LDCONFIG}${TF_CUDNN_EXT}" ]; then
-      CUDNN_INSTALL_PATH="$(dirname ${CUDNN_PATH_FROM_LDCONFIG})"
+      export CUDNN_VERSION=$TF_CUDNN_VERSION
+      export CUDNN_INSTALL_PATH="$(dirname ${CUDNN_PATH_FROM_LDCONFIG})"
       break
     fi
   fi
@@ -225,42 +231,11 @@ while true; do
   CUDNN_INSTALL_PATH=""
 done
 
-cat > third_party/gpus/cuda/cuda.config <<EOF
-# CUDA_TOOLKIT_PATH refers to the CUDA toolkit.
-CUDA_TOOLKIT_PATH="$CUDA_TOOLKIT_PATH"
-# CUDNN_INSTALL_PATH refers to the cuDNN toolkit. The cuDNN header and library
-# files can be either in this directory, or under include/ and lib64/
-# directories separately.
-CUDNN_INSTALL_PATH="$CUDNN_INSTALL_PATH"
-
-# The Cuda SDK version that should be used in this build (empty to use libcudart.so symlink)
-TF_CUDA_VERSION=$TF_CUDA_VERSION
-
-# The Cudnn version that should be used in this build
-TF_CUDNN_VERSION=$TF_CUDNN_VERSION
-EOF
-
-# Configure the gcc host compiler to use
-export WARNING=$DO_NOT_SUBMIT_WARNING
-perl -pi -e "s,CPU_COMPILER = \('.*'\),# \$ENV{WARNING}\nCPU_COMPILER = ('$GCC_HOST_COMPILER_PATH'),s" third_party/gpus/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc
-perl -pi -e "s,GCC_HOST_COMPILER_PATH = \('.*'\),# \$ENV{WARNING}\nGCC_HOST_COMPILER_PATH = ('$GCC_HOST_COMPILER_PATH'),s" third_party/gpus/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc
-
-# Configure the platform name.
-perl -pi -e "s,PLATFORM = \".*\",PLATFORM = \"$OSNAME\",s" third_party/gpus/cuda/platform.bzl
-
-# Configure the Cuda toolkit version to work with.
-perl -pi -e "s,(GetCudaVersion.*return )\"[0-9\.]*\",\1\"$TF_CUDA_VERSION\",s" tensorflow/stream_executor/dso_loader.cc
-perl -pi -e "s,CUDA_VERSION = \"[0-9\.]*\",CUDA_VERSION = \"$TF_CUDA_VERSION\",s" third_party/gpus/cuda/platform.bzl
-
-# Configure the Cudnn version to work with.
-perl -pi -e "s,(GetCudnnVersion.*return )\"[0-9\.]*\",\1\"$TF_CUDNN_VERSION\",s" tensorflow/stream_executor/dso_loader.cc
-perl -pi -e "s,CUDNN_VERSION = \"[0-9\.]*\",CUDNN_VERSION = \"$TF_CUDNN_VERSION\",s" third_party/gpus/cuda/platform.bzl
-
-
 # Configure the compute capabilities that TensorFlow builds for.
 # Since Cuda toolkit is not backward-compatible, this is not guaranteed to work.
 while true; do
   fromuser=""
+  default_cuda_compute_capabilities="3.5,5.2"
   if [ -z "$TF_CUDA_COMPUTE_CAPABILITIES" ]; then
 cat << EOF
 Please specify a list of comma-separated Cuda compute capabilities you want to build with.
@@ -270,6 +245,9 @@ EOF
     read -p "[Default is: \"3.5,5.2\"]: " TF_CUDA_COMPUTE_CAPABILITIES
     fromuser=1
   fi
+  if [ -z "$TF_CUDA_COMPUTE_CAPABILITIES" ]; then
+    TF_CUDA_COMPUTE_CAPABILITIES=$default_cuda_compute_capabilities
+  fi
   # Check whether all capabilities from the input is valid
   COMPUTE_CAPABILITIES=${TF_CUDA_COMPUTE_CAPABILITIES//,/ }
   ALL_VALID=1
@@ -285,34 +263,13 @@ EOF
       exit 1
     fi
   else
+    export CUDA_COMPUTE_CAPABILITIES=$TF_CUDA_COMPUTE_CAPABILITIES
     break
   fi
   TF_CUDA_COMPUTE_CAPABILITIES=""
 done
 
-if [ ! -z "$TF_CUDA_COMPUTE_CAPABILITIES" ]; then
-  export WARNING=$DO_NOT_SUBMIT_WARNING
-  function CudaGenCodeOpts() {
-    OUTPUT=""
-    for CAPABILITY in $@; do
-      OUTPUT=${OUTPUT}"   \"${CAPABILITY}\",     "
-    done
-    echo $OUTPUT
-  }
-  export CUDA_GEN_CODES_OPTS=$(CudaGenCodeOpts ${TF_CUDA_COMPUTE_CAPABILITIES//,/ })
-  perl -pi -0 -e 's,\n( *)([^\n]*supported_cuda_compute_capabilities\s*=\s*\[).*?(\]),\n\1# $ENV{WARNING}\n\1\2$ENV{CUDA_GEN_CODES_OPTS}\3,s' third_party/gpus/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc
-  function CudaVersionOpts() {
-    OUTPUT=""
-    for CAPABILITY in $@; do
-      OUTPUT=$OUTPUT"CudaVersion(\"${CAPABILITY}\"), "
-    done
-    echo $OUTPUT
-  }
-  export CUDA_VERSION_OPTS=$(CudaVersionOpts ${TF_CUDA_COMPUTE_CAPABILITIES//,/ })
-  perl -pi -0 -e 's,\n( *)([^\n]*supported_cuda_compute_capabilities\s*=\s*\{).*?(\}),\n\1// $ENV{WARNING}\n\1\2$ENV{CUDA_VERSION_OPTS}\3,s' tensorflow/core/common_runtime/gpu/gpu_device.cc
-fi
-
-# Invoke the cuda_config.sh and set up the TensorFlow's canonical view of the Cuda libraries
-(cd third_party/gpus/cuda; ./cuda_config.sh;) || exit -1
+bazel clean --expunge
+bazel fetch //...
 
 echo "Configuration finished"
diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc
index 0ff64dd939..c08abc5689 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc
@@ -785,10 +785,8 @@ struct CudaVersion {
   int minor_part = -1;
 };
 
-// "configure" uses the specific name to substitute the following string.
-// If you change it, make sure you modify "configure" as well.
 std::vector<CudaVersion> supported_cuda_compute_capabilities = {
-    CudaVersion("3.5"), CudaVersion("5.2")};
+    TF_CUDA_CAPABILITIES,};
 
 std::vector<CudaVersion> GetSupportedCudaComputeCapabilities() {
   auto cuda_caps = supported_cuda_compute_capabilities;
diff --git a/tensorflow/core/kernels/lrn_op.cc b/tensorflow/core/kernels/lrn_op.cc
index 7b99aee6c6..3435486c95 100644
--- a/tensorflow/core/kernels/lrn_op.cc
+++ b/tensorflow/core/kernels/lrn_op.cc
@@ -31,7 +31,7 @@ limitations under the License.
 #endif
 
 #if GOOGLE_CUDA
-#include "third_party/gpus/cuda/include/cuda.h"
+#include "cuda/include/cuda.h"
 #include "tensorflow/core/platform/stream_executor.h"
 #include "tensorflow/core/util/stream_executor_util.h"
 #endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/kernels/matmul_op.cc b/tensorflow/core/kernels/matmul_op.cc
index 7852b90c73..03a6d29839 100644
--- a/tensorflow/core/kernels/matmul_op.cc
+++ b/tensorflow/core/kernels/matmul_op.cc
@@ -25,7 +25,7 @@ limitations under the License.
 #include "tensorflow/core/kernels/fill_functor.h"
 
 #if GOOGLE_CUDA
-#include "third_party/gpus/cuda/include/cuda.h"
+#include "cuda/include/cuda.h"
 #include "tensorflow/core/platform/stream_executor.h"
 #endif  // GOOGLE_CUDA
 
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 2583128d8d..bd5593a6a2 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -4680,6 +4680,42 @@ op {
   summary: "Decode a PNG-encoded image to a uint8 or uint16 tensor."
   description: "The attr `channels` indicates the desired number of color channels for the\ndecoded image.\n\nAccepted values are:\n\n*   0: Use the number of channels in the PNG-encoded image.\n*   1: output a grayscale image.\n*   3: output an RGB image.\n*   4: output an RGBA image.\n\nIf needed, the PNG-encoded image is transformed to match the requested number\nof color channels."
 }
+op {
+  name: "DecodeGif"
+  input_arg {
+    name: "contents"
+    description: "0-D.  The GIF-encoded image."
+    type: DT_STRING
+  }
+  output_arg {
+    name: "image"
+    description: "3-D with shape `[height, width, channels]`."
+    type_attr: "dtype"
+  }
+  attr {
+    name: "channels"
+    type: "int"
+    default_value {
+      i: 0
+    }
+    description: "Number of color channels for the decoded image."
+  }
+  attr {
+    name: "dtype"
+    type: "type"
+    default_value {
+      type: DT_UINT8
+    }
+    allowed_values {
+      list {
+        type: DT_UINT8
+        type: DT_UINT16
+      }
+    }
+  }
+  summary: "Decode a GIF-encoded image to a uint8 or uint16 tensor."
+  description: "The attr `channels` indicates the desired number of color channels for the\ndecoded image.\n\nAccepted values are:\n\n*   0: Use the number of channels in the GIF-encoded image.\n*   1: output a grayscale image.\n*   3: output an RGB image.\n*   4: output an RGBA image.\n\nIf needed, the GIF-encoded image is transformed to match the requested number\nof color channels."
+}
 op {
   name: "DecodeRaw"
   input_arg {
diff --git a/tensorflow/core/platform/default/build_config/BUILD b/tensorflow/core/platform/default/build_config/BUILD
index f372d2ef0d..158c42b5ad 100644
--- a/tensorflow/core/platform/default/build_config/BUILD
+++ b/tensorflow/core/platform/default/build_config/BUILD
@@ -9,7 +9,7 @@ exports_files(["LICENSE"])
 
 load("//tensorflow:tensorflow.bzl", "tf_copts")
 load("//tensorflow:tensorflow.bzl", "tf_cuda_library")
-load("//third_party/gpus/cuda:platform.bzl", "cuda_library_path")
+load("@local_config_cuda//cuda:platform.bzl", "cuda_library_path")
 
 cc_library(
     name = "gtest",
@@ -32,7 +32,7 @@ tf_cuda_library(
     deps = [
         "//tensorflow/stream_executor",
     ] + select({
-        "//third_party/gpus/cuda:darwin": ["IOKit"],
+        "@local_config_cuda//cuda:darwin": ["IOKit"],
         "//conditions:default": [],
     }),
 )
@@ -91,20 +91,20 @@ filegroup(
 cc_library(
     name = "cuda",
     data = [
-        "//third_party/gpus/cuda:{}".format(cuda_library_path("cudart")),
+        "@local_config_cuda//cuda:{}".format(cuda_library_path("cudart")),
     ],
     linkopts = select({
-        "//third_party/gpus/cuda:darwin": [
-            "-Wl,-rpath,third_party/gpus/cuda/lib",
-            "-Wl,-rpath,third_party/gpus/cuda/extras/CUPTI/lib",
+        "@local_config_cuda//cuda:darwin": [
+            "-Wl,-rpath,../local_config_cuda/cuda/lib",
+            "-Wl,-rpath,../local_config_cuda/cuda/extras/CUPTI/lib",
         ],
         "//conditions:default": [
-            "-Wl,-rpath,third_party/gpus/cuda/lib64",
-            "-Wl,-rpath,third_party/gpus/cuda/extras/CUPTI/lib64",
+            "-Wl,-rpath,../local_config_cuda/cuda/lib64",
+            "-Wl,-rpath,../local_config_cuda/cuda/extras/CUPTI/lib64",
         ],
     }),
     deps = [
-        "//third_party/gpus/cuda:cudart",
+        "@local_config_cuda//cuda:cudart",
     ],
 )
 
diff --git a/tensorflow/core/platform/default/gpu/BUILD b/tensorflow/core/platform/default/gpu/BUILD
index 93b6227848..6b0c919a89 100644
--- a/tensorflow/core/platform/default/gpu/BUILD
+++ b/tensorflow/core/platform/default/gpu/BUILD
@@ -15,9 +15,9 @@ tf_cuda_library(
     copts = tf_copts(),
     cuda_deps = [
         "//tensorflow/core:stream_executor",
-        "//third_party/gpus/cuda:cuda_headers",
-        "//third_party/gpus/cuda:cupti_headers",
+        "@local_config_cuda//cuda:cuda_headers",
+        "@local_config_cuda//cuda:cupti_headers",
     ],
-    data = ["//third_party/gpus/cuda:cupti_dsos"],
+    data = ["@local_config_cuda//cuda:cupti_dsos"],
     visibility = ["//visibility:public"],
 )
diff --git a/tensorflow/core/platform/default/gpu/cupti_wrapper.h b/tensorflow/core/platform/default/gpu/cupti_wrapper.h
index 5829172c47..e482f8607f 100644
--- a/tensorflow/core/platform/default/gpu/cupti_wrapper.h
+++ b/tensorflow/core/platform/default/gpu/cupti_wrapper.h
@@ -21,7 +21,7 @@ limitations under the License.
 #include <stddef.h>
 #include <stdint.h>
 
-#include "third_party/gpus/cuda/extras/CUPTI/include/cupti.h"
+#include "cuda/extras/CUPTI/include/cupti.h"
 
 namespace perftools {
 namespace gputools {
diff --git a/tensorflow/core/util/port.cc b/tensorflow/core/util/port.cc
index 42375770f4..d93b971f85 100644
--- a/tensorflow/core/util/port.cc
+++ b/tensorflow/core/util/port.cc
@@ -16,7 +16,7 @@ limitations under the License.
 #include "tensorflow/core/util/port.h"
 
 #if GOOGLE_CUDA
-#include "third_party/gpus/cuda/include/cuda.h"
+#include "cuda/include/cuda.h"
 #endif
 
 namespace tensorflow {
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Optimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Optimizer.md
index 02bb5b5ac4..ff14086bb4 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Optimizer.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Optimizer.md
@@ -184,9 +184,8 @@ applies gradients.
 
 ### Gating Gradients
 
-Both `minimize()` and `compute_gradients()` accept a `gate_gradient` argument
-that controls the degree of parallelism during the application of the
-gradients.
+Both `minimize()` and `compute_gradients()` accept a `gate_gradients` argument
+that controls the degree of parallelism during the application of the gradients.
 
 The possible values are: `GATE_NONE`, `GATE_OP`, and `GATE_GRAPH`.
 
diff --git a/tensorflow/g3doc/api_docs/python/train.md b/tensorflow/g3doc/api_docs/python/train.md
index 94fa7a3ed5..3d0329ad3f 100644
--- a/tensorflow/g3doc/api_docs/python/train.md
+++ b/tensorflow/g3doc/api_docs/python/train.md
@@ -204,9 +204,8 @@ applies gradients.
 
 ### Gating Gradients
 
-Both `minimize()` and `compute_gradients()` accept a `gate_gradient` argument
-that controls the degree of parallelism during the application of the
-gradients.
+Both `minimize()` and `compute_gradients()` accept a `gate_gradients` argument
+that controls the degree of parallelism during the application of the gradients.
 
 The possible values are: `GATE_NONE`, `GATE_OP`, and `GATE_GRAPH`.
 
diff --git a/tensorflow/g3doc/get_started/os_setup.md b/tensorflow/g3doc/get_started/os_setup.md
index d85ae429fa..53afca4ccb 100644
--- a/tensorflow/g3doc/get_started/os_setup.md
+++ b/tensorflow/g3doc/get_started/os_setup.md
@@ -7,11 +7,10 @@ github source.
 
 The TensorFlow Python API supports Python 2.7 and Python 3.3+.
 
-The GPU version (Linux only) works best with Cuda Toolkit 7.5 and
-cuDNN v4.  other versions are supported (Cuda toolkit >= 7.0 and
-cuDNN 6.5(v2), 7.0(v3), v5) only when installing from sources.
-Please see [Cuda installation](#optional-install-cuda-gpus-on-linux)
-for details.
+The GPU version (Linux & Mac OS X only) works best with Cuda Toolkit 7.5 and
+cuDNN v4. other versions are supported (Cuda toolkit >= 7.0 and cuDNN 6.5(v2),
+7.0(v3), v5) only when installing from sources. Please see [Cuda installation]
+(#optional-install-cuda-gpus-on-linux) for details.
 
 ## Overview
 
diff --git a/tensorflow/python/ops/ctc_ops.py b/tensorflow/python/ops/ctc_ops.py
index bab9dc0ef5..2f85112172 100644
--- a/tensorflow/python/ops/ctc_ops.py
+++ b/tensorflow/python/ops/ctc_ops.py
@@ -48,6 +48,18 @@ def ctc_loss(inputs, labels, sequence_length,
     <= sequence_length(b) for all b.
   ```
 
+  Notes:
+
+  This class performs the softmax operation for you, so inputs should
+  be e.g. linear projections of outputs by an LSTM.
+
+  The `inputs` Tensor's innermost dimension size, `num_classes`, represents
+  `num_labels + 1` classes, where num_labels is the number of true labels, and
+  the largest value `(num_classes - 1)` is reserved for the blank label.
+
+  For example, for a vocabulary containing 3 labels `[a, b, c]`,
+  `num_classes = 4` and the labels indexing is `{a: 0, b: 1, c: 2, blank: 3}`.
+
   Regarding the arguments `preprocess_collapse_repeated` and
   `ctc_merge_repeated`:
 
@@ -84,10 +96,12 @@ def ctc_loss(inputs, labels, sequence_length,
 
   Args:
     inputs: 3-D `float` `Tensor` sized
-      `[max_time x batch_size x num_classes]`.  The logits.
+      `[max_time x batch_size x num_classes]`. The logits.
     labels: An `int32` `SparseTensor`.
       `labels.indices[i, :] == [b, t]` means `labels.values[i]` stores
-      the id for (batch b, time t).  See `core/ops/ctc_ops.cc` for more details.
+      the id for (batch b, time t).
+      `labels.values[i]` must take on values in `[0, num_labels)`.
+      See `core/ops/ctc_ops.cc` for more details.
     sequence_length: 1-D `int32` vector, size `[batch_size]`.
       The sequence lengths.
     preprocess_collapse_repeated: Boolean.  Default: False.
diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py
index a720d7d26a..d3d78dad5f 100644
--- a/tensorflow/python/ops/variables.py
+++ b/tensorflow/python/ops/variables.py
@@ -1036,8 +1036,7 @@ def report_uninitialized_variables(var_list=None,
 
   Returns:
     A 1-D tensor containing names of the uninitialized variables, or an empty
-    1-D
-    tensor if there are no variables or no uninitialized variables.
+    1-D tensor if there are no variables or no uninitialized variables.
   """
   if var_list is None:
     var_list = all_variables() + local_variables()
diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py
index 2627f9fce0..deeec2f6e3 100644
--- a/tensorflow/python/training/optimizer.py
+++ b/tensorflow/python/training/optimizer.py
@@ -89,9 +89,9 @@ class Optimizer(object):
 
   ### Gating Gradients
 
-  Both `minimize()` and `compute_gradients()` accept a `gate_gradient` argument
-  that controls the degree of parallelism during the application of the
-  gradients.
+  Both `minimize()` and `compute_gradients()` accept a `gate_gradients`
+  argument that controls the degree of parallelism during the application of
+  the gradients.
 
   The possible values are: `GATE_NONE`, `GATE_OP`, and `GATE_GRAPH`.
 
diff --git a/tensorflow/stream_executor/BUILD b/tensorflow/stream_executor/BUILD
index 24e8305d31..256b128750 100644
--- a/tensorflow/stream_executor/BUILD
+++ b/tensorflow/stream_executor/BUILD
@@ -27,9 +27,10 @@ cc_library(
     ]),
     data = [
         "//tensorflow/core:cuda",
-        "//third_party/gpus/cuda:cublas",
-        "//third_party/gpus/cuda:cudnn",
-        "//third_party/gpus/cuda:cufft",
+        "@local_config_cuda//cuda:cublas",
+        "@local_config_cuda//cuda:cudnn",
+        "@local_config_cuda//cuda:cufft",
+        "@local_config_cuda//cuda:curand",
     ],
     linkopts = [
         "-ldl",
@@ -37,7 +38,7 @@ cc_library(
     visibility = ["//visibility:public"],
     deps = [
         "//tensorflow/core:lib",
-        "//third_party/gpus/cuda:cuda_headers",
+        "@local_config_cuda//cuda:cuda_headers",
     ],
     alwayslink = 1,
 )
diff --git a/tensorflow/stream_executor/cuda/cuda_blas.cc b/tensorflow/stream_executor/cuda/cuda_blas.cc
index a9dd2953e5..e2611cd3d0 100644
--- a/tensorflow/stream_executor/cuda/cuda_blas.cc
+++ b/tensorflow/stream_executor/cuda/cuda_blas.cc
@@ -18,8 +18,8 @@ limitations under the License.
 // cuda.h). This ensures that Eigen's Half.h does not attempt to make its own
 // __half typedef if CUDA has already defined one (and conversely, that we do
 // not include <cuda_fp16.h> after Half.h has made its typedef).
-#include "third_party/gpus/cuda/include/cuda.h"
-#include "third_party/gpus/cuda/include/cublas_v2.h"
+#include "cuda/include/cuda.h"
+#include "cuda/include/cublas_v2.h"
 
 #if CUDA_VERSION >= 7050
 #define EIGEN_HAS_CUDA_FP16
diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc
index 7fbafa3d7e..55535f9ce5 100644
--- a/tensorflow/stream_executor/cuda/cuda_dnn.cc
+++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc
@@ -39,7 +39,7 @@ limitations under the License.
 #include "tensorflow/stream_executor/stream.h"
 #include "tensorflow/stream_executor/stream_executor_pimpl.h"
 // clang-format off
-#include "third_party/gpus/cuda/include/cudnn.h"
+#include "cuda/include/cudnn.h"
 // clang-format on
 
 namespace {
diff --git a/tensorflow/stream_executor/cuda/cuda_driver.h b/tensorflow/stream_executor/cuda/cuda_driver.h
index a5de5d0f59..ab118e5d40 100644
--- a/tensorflow/stream_executor/cuda/cuda_driver.h
+++ b/tensorflow/stream_executor/cuda/cuda_driver.h
@@ -25,7 +25,7 @@ limitations under the License.
 #include "tensorflow/stream_executor/lib/status.h"
 #include "tensorflow/stream_executor/lib/statusor.h"
 #include "tensorflow/stream_executor/platform/port.h"
-#include "third_party/gpus/cuda/include/cuda.h"
+#include "cuda/include/cuda.h"
 
 namespace perftools {
 namespace gputools {
diff --git a/tensorflow/stream_executor/cuda/cuda_fft.h b/tensorflow/stream_executor/cuda/cuda_fft.h
index 0c7aa34df3..95b3e8de63 100644
--- a/tensorflow/stream_executor/cuda/cuda_fft.h
+++ b/tensorflow/stream_executor/cuda/cuda_fft.h
@@ -23,7 +23,7 @@ limitations under the License.
 #include "tensorflow/stream_executor/fft.h"
 #include "tensorflow/stream_executor/platform/port.h"
 #include "tensorflow/stream_executor/plugin_registry.h"
-#include "third_party/gpus/cuda/include/cufft.h"
+#include "cuda/include/cufft.h"
 
 namespace perftools {
 namespace gputools {
diff --git a/tensorflow/stream_executor/cuda/cuda_helpers.h b/tensorflow/stream_executor/cuda/cuda_helpers.h
index 7753866560..6a6134bf88 100644
--- a/tensorflow/stream_executor/cuda/cuda_helpers.h
+++ b/tensorflow/stream_executor/cuda/cuda_helpers.h
@@ -24,8 +24,8 @@ limitations under the License.
 #include <stddef.h>
 #include <complex>
 
-#include "third_party/gpus/cuda/include/cuComplex.h"
-#include "third_party/gpus/cuda/include/cuda.h"
+#include "cuda/include/cuComplex.h"
+#include "cuda/include/cuda.h"
 
 namespace perftools {
 namespace gputools {
diff --git a/tensorflow/stream_executor/cuda/cuda_kernel.h b/tensorflow/stream_executor/cuda/cuda_kernel.h
index 412e7d9a40..88d29fddd0 100644
--- a/tensorflow/stream_executor/cuda/cuda_kernel.h
+++ b/tensorflow/stream_executor/cuda/cuda_kernel.h
@@ -28,7 +28,7 @@ limitations under the License.
 #include "tensorflow/stream_executor/lib/casts.h"
 #include "tensorflow/stream_executor/platform/port.h"
 #include "tensorflow/stream_executor/platform/logging.h"
-#include "third_party/gpus/cuda/include/cuda.h"
+#include "cuda/include/cuda.h"
 
 #ifdef PLATFORMS_GPUS_CUDA_DYNAMIC_LIBCUDA_DYNAMIC_LIBCUDA_H_
 #error \
diff --git a/tensorflow/stream_executor/cuda/cuda_rng.cc b/tensorflow/stream_executor/cuda/cuda_rng.cc
index 334c6af970..367eba4d51 100644
--- a/tensorflow/stream_executor/cuda/cuda_rng.cc
+++ b/tensorflow/stream_executor/cuda/cuda_rng.cc
@@ -28,7 +28,7 @@ limitations under the License.
 #include "tensorflow/stream_executor/lib/status.h"
 #include "tensorflow/stream_executor/platform/logging.h"
 #include "tensorflow/stream_executor/rng.h"
-#include "third_party/gpus/cuda/include/curand.h"
+#include "cuda/include/curand.h"
 
 // Formats curandStatus_t to output prettified values into a log stream.
 std::ostream &operator<<(std::ostream &in, const curandStatus_t &status) {
diff --git a/tensorflow/stream_executor/dso_loader.cc b/tensorflow/stream_executor/dso_loader.cc
index cce31ef4dc..83f2cadfad 100644
--- a/tensorflow/stream_executor/dso_loader.cc
+++ b/tensorflow/stream_executor/dso_loader.cc
@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+// TODO(jhen): Replace hardcoded, platform specific path strings in GetXXXPath()
+// with a function in e.g. cuda.h.
+
 #include "tensorflow/stream_executor/dso_loader.h"
 
 #include <dlfcn.h>
@@ -32,19 +35,17 @@ limitations under the License.
 #include "tensorflow/stream_executor/lib/stringprintf.h"
 #include "tensorflow/stream_executor/platform/logging.h"
 #include "tensorflow/stream_executor/platform/port.h"
-#include "tensorflow/stream_executor/lib/str_util.h"
 
 namespace perftools {
 namespace gputools {
 namespace internal {
 
-// TensorFlow OSS configure uses the following lines to configure versions. For
-// any modifications of the format, please make sure the script still works.
-string GetCudaVersion() { return ""; }
-string GetCudnnVersion() { return ""; }
+string GetCudaVersion() { return TF_CUDA_VERSION; }
+string GetCudnnVersion() { return TF_CUDNN_VERSION; }
 
 /* static */ port::Status DsoLoader::GetCublasDsoHandle(void** dso_handle) {
-  return GetDsoHandle(FindDsoPath(tensorflow::internal::FormatLibraryFileName("cublas", GetCudaVersion()),
+  return GetDsoHandle(FindDsoPath(tensorflow::internal::FormatLibraryFileName(
+                                      "cublas", GetCudaVersion()),
                                   GetCudaLibraryDirPath()),
                       dso_handle);
 }
@@ -53,35 +54,38 @@ string GetCudnnVersion() { return ""; }
   // libcudnn is versioned differently than the other libraries and may have a
   // different version number than other CUDA libraries.  See b/22397368 for
   // some details about the complications surrounding this.
-  return GetDsoHandle(
-      FindDsoPath(tensorflow::internal::FormatLibraryFileName("cudnn", GetCudnnVersion()),
-                              GetCudaLibraryDirPath()),
+  return GetDsoHandle(FindDsoPath(tensorflow::internal::FormatLibraryFileName(
+                                      "cudnn", GetCudnnVersion()),
+                                  GetCudaLibraryDirPath()),
                       dso_handle);
 }
 
 /* static */ port::Status DsoLoader::GetCufftDsoHandle(void** dso_handle) {
-  return GetDsoHandle(FindDsoPath(tensorflow::internal::FormatLibraryFileName("cufft", GetCudaVersion()),
+  return GetDsoHandle(FindDsoPath(tensorflow::internal::FormatLibraryFileName(
+                                      "cufft", GetCudaVersion()),
                                   GetCudaLibraryDirPath()),
                       dso_handle);
 }
 
 /* static */ port::Status DsoLoader::GetCurandDsoHandle(void** dso_handle) {
-  return GetDsoHandle(FindDsoPath(tensorflow::internal::FormatLibraryFileName("curand", GetCudaVersion()),
+  return GetDsoHandle(FindDsoPath(tensorflow::internal::FormatLibraryFileName(
+                                      "curand", GetCudaVersion()),
                                   GetCudaLibraryDirPath()),
                       dso_handle);
 }
 
 /* static */ port::Status DsoLoader::GetLibcudaDsoHandle(void** dso_handle) {
-  return GetDsoHandle(FindDsoPath(tensorflow::internal::FormatLibraryFileName("cuda", "1"),
-                                  GetCudaDriverLibraryPath()),
-                      dso_handle);
+  return GetDsoHandle(
+      FindDsoPath(tensorflow::internal::FormatLibraryFileName("cuda", "1"),
+                  GetCudaDriverLibraryPath()),
+      dso_handle);
 }
 
 /* static */ port::Status DsoLoader::GetLibcuptiDsoHandle(void** dso_handle) {
-  return GetDsoHandle(
-      FindDsoPath(tensorflow::internal::FormatLibraryFileName("cupti", GetCudaVersion()),
-                  GetCudaCuptiLibraryPath()),
-      dso_handle);
+  return GetDsoHandle(FindDsoPath(tensorflow::internal::FormatLibraryFileName(
+                                      "cupti", GetCudaVersion()),
+                                  GetCudaCuptiLibraryPath()),
+                      dso_handle);
 }
 
 /* static */ void DsoLoader::RegisterRpath(port::StringPiece path) {
@@ -89,11 +93,9 @@ string GetCudnnVersion() { return ""; }
   GetRpaths()->push_back(path.ToString());
 }
 
-
 /* static */ port::Status DsoLoader::GetDsoHandle(port::StringPiece path,
                                                   void** dso_handle,
                                                   LoadKind load_kind) {
-
   int dynload_flags =
       RTLD_LAZY | (load_kind == LoadKind::kLocal ? RTLD_LOCAL : RTLD_GLOBAL);
   string path_string = path.ToString();
@@ -138,9 +140,9 @@ string GetCudnnVersion() { return ""; }
 static std::vector<string>* CreatePrimordialRpaths() {
   auto rpaths = new std::vector<string>;
 #if defined(__APPLE__)
-  rpaths->push_back("driver/driver_sh.runfiles/org_tensorflow/third_party/gpus/cuda/lib");
+  rpaths->push_back("driver/driver_sh.runfiles/local_config_cuda/cuda/lib");
 #else
-  rpaths->push_back("driver/driver_sh.runfiles/org_tensorflow/third_party/gpus/cuda/lib64");
+  rpaths->push_back("driver/driver_sh.runfiles/local_config_cuda/cuda/lib64");
 #endif
   return rpaths;
 }
@@ -165,7 +167,6 @@ static std::vector<string>* CreatePrimordialRpaths() {
 
 /* static */ string DsoLoader::FindDsoPath(port::StringPiece library_name,
                                            port::StringPiece runfiles_relpath) {
-
   // Keep a record of the paths we attempted so we can dump out meaningful
   // diagnostics if no path is found.
   std::vector<string> attempted;
@@ -191,29 +192,28 @@ static std::vector<string>* CreatePrimordialRpaths() {
 
 /* static */ string DsoLoader::GetCudaLibraryDirPath() {
 #if defined(__APPLE__)
-  return "third_party/gpus/cuda/lib";
+  return "external/local_config_cuda/cuda/lib";
 #else
-  return "third_party/gpus/cuda/lib64";
+  return "external/local_config_cuda/cuda/lib64";
 #endif
 }
 
 /* static */ string DsoLoader::GetCudaDriverLibraryPath() {
 #if defined(__APPLE__)
-  return "third_party/gpus/cuda/driver/lib";
+  return "external/local_config_cuda/cuda/driver/lib";
 #else
-  return "third_party/gpus/cuda/driver/lib64";
+  return "external/local_config_cuda/cuda/driver/lib64";
 #endif
 }
 
 /* static */ string DsoLoader::GetCudaCuptiLibraryPath() {
 #if defined(__APPLE__)
-  return "third_party/gpus/cuda/extras/CUPTI/lib";
+  return "external/local_config_cuda/cuda/extras/CUPTI/lib";
 #else
-  return "third_party/gpus/cuda/extras/CUPTI/lib64";
+  return "external/local_config_cuda/cuda/extras/CUPTI/lib64";
 #endif
 }
 
-
 // -- CachedDsoLoader
 
 /* static */ port::StatusOr<void*> CachedDsoLoader::GetCublasDsoHandle() {
diff --git a/tensorflow/stream_executor/dso_loader.h b/tensorflow/stream_executor/dso_loader.h
index a3d4678255..64419e46f9 100644
--- a/tensorflow/stream_executor/dso_loader.h
+++ b/tensorflow/stream_executor/dso_loader.h
@@ -22,6 +22,7 @@ limitations under the License.
 #include "tensorflow/stream_executor/platform/port.h"
 #include <vector>
 
+#include "cuda/cuda_config.h"
 #include "tensorflow/stream_executor/lib/status.h"
 #include "tensorflow/stream_executor/lib/statusor.h"
 #include "tensorflow/stream_executor/lib/stringpiece.h"
diff --git a/tensorflow/tensorboard/backend/handler.py b/tensorflow/tensorboard/backend/handler.py
index a8ecf73a5b..1c1e9411a4 100644
--- a/tensorflow/tensorboard/backend/handler.py
+++ b/tensorflow/tensorboard/backend/handler.py
@@ -32,6 +32,7 @@ import os
 import re
 
 from six import BytesIO
+from six import StringIO
 from six.moves import BaseHTTPServer
 from six.moves import urllib
 from six.moves import xrange  # pylint: disable=redefined-builtin
@@ -276,7 +277,7 @@ class TensorboardHandler(BaseHTTPServer.BaseHTTPRequestHandler):
       values = self._multiplexer.Scalars(run, tag)
 
     if query_params.get('format') == _OutputFormat.CSV:
-      string_io = BytesIO()
+      string_io = StringIO()
       writer = csv.writer(string_io)
       writer.writerow(['Wall time', 'Step', 'Value'])
       writer.writerows(values)
@@ -353,7 +354,7 @@ class TensorboardHandler(BaseHTTPServer.BaseHTTPRequestHandler):
     run = query_params.get('run')
     compressed_histograms = self._multiplexer.CompressedHistograms(run, tag)
     if query_params.get('format') == _OutputFormat.CSV:
-      string_io = BytesIO()
+      string_io = StringIO()
       writer = csv.writer(string_io)
 
       # Build the headers; we have two columns for timing and two columns for
diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl
index 898a8b7309..ffa1965c3b 100644
--- a/tensorflow/tensorflow.bzl
+++ b/tensorflow/tensorflow.bzl
@@ -32,7 +32,7 @@ load(
     "tf_cuda_tests_tags",
 )
 load(
-    "//third_party/gpus/cuda:build_defs.bzl",
+    "@local_config_cuda//cuda:build_defs.bzl",
     "if_cuda",
 )
 
@@ -324,11 +324,11 @@ def tf_cc_tests(tests, deps, linkstatic=0, tags=[], size="medium", args=None,
     tf_cc_test(t, deps, linkstatic, tags=tags, size=size, args=args,
                linkopts=linkopts)
 
-def tf_cc_tests_gpu(tests, deps, linkstatic=0, tags=[], size="medium", args=None):
+def tf_cc_tests_gpu(tests, deps, linkstatic=0, tags=[], size="medium",
+                    args=None):
   tf_cc_tests(tests, deps, linkstatic, tags=tags, size=size, args=args)
 
 
-
 def tf_cuda_cc_tests(tests, deps, tags=[], size="medium", linkstatic=0,
                      args=None, linkopts=[]):
   for t in tests:
@@ -345,29 +345,29 @@ def _cuda_copts():
     common_cuda_opts = ["-x", "cuda", "-DGOOGLE_CUDA=1"]
     return select({
         "//conditions:default": [],
-        "//third_party/gpus/cuda:using_nvcc": (
+        "@local_config_cuda//cuda:using_nvcc": (
             common_cuda_opts +
             [
                 "-nvcc_options=relaxed-constexpr",
                 "-nvcc_options=ftz=true",
             ]
         ),
-        "//third_party/gpus/cuda:using_gcudacc": (
+        "@local_config_cuda//cuda:using_gcudacc": (
             common_cuda_opts +
             ["--gcudacc_flag=-ftz=true"]
         ),
-        "//third_party/gpus/cuda:using_clang": (
+        "@local_config_cuda//cuda:using_clang": (
             common_cuda_opts +
             [
                 "-fcuda-flush-denormals-to-zero",
-                "--cuda-path=third_party/gpus/cuda",
+                "--cuda-path=external/local_config_cuda/cuda",
                 "--cuda-gpu-arch=sm_35",
             ]
         ),
     }) + select({
         # Pass -O3 when building CUDA code with clang; some important
         # optimizations are not enabled at O2.
-        "//third_party/gpus/cuda:using_clang_opt": ["-O3"],
+        "@local_config_cuda//cuda:using_clang_opt": ["-O3"],
         "//conditions:default": [],
     })
 
@@ -438,7 +438,8 @@ def tf_kernel_library(name, prefix=None, srcs=None, gpu_srcs=None, hdrs=None,
     * srcs = ["cwise_op_abs.cc", ..., "cwise_op_tanh.cc"],
     * hdrs = ["cwise_ops.h", "cwise_ops_common.h"],
     * gpu_srcs = ["cwise_op_gpu_abs.cu.cc", ..., "cwise_op_gpu_tanh.cu.cc",
-                  "cwise_ops.h", "cwise_ops_common.h", "cwise_ops_gpu_common.cu.h"]
+                  "cwise_ops.h", "cwise_ops_common.h",
+                  "cwise_ops_gpu_common.cu.h"]
     * "cwise_ops_test.cc" is excluded
   """
   if not srcs:
@@ -642,7 +643,7 @@ check_deps = rule(
 def tf_custom_op_library(name, srcs=[], gpu_srcs=[], deps=[]):
   cuda_deps = [
       "//tensorflow/core:stream_executor_headers_lib",
-      "//third_party/gpus/cuda:cudart_static",
+      "@local_config_cuda//cuda:cudart_static",
   ]
   deps = deps + tf_custom_op_library_additional_deps()
   if gpu_srcs:
@@ -692,7 +693,7 @@ def tf_py_wrap_cc(name, srcs, swig_includes=[], deps=[], copts=[], **kwargs):
               module_name=module_name,
               py_module_name=name)
   extra_linkopts = select({
-      "//third_party/gpus/cuda:darwin": [
+      "@local_config_cuda//cuda:darwin": [
           "-Wl,-exported_symbols_list",
           "//tensorflow:tf_exported_symbols.lds"
       ],
@@ -701,7 +702,7 @@ def tf_py_wrap_cc(name, srcs, swig_includes=[], deps=[], copts=[], **kwargs):
           "//tensorflow:tf_version_script.lds"
       ]})
   extra_deps += select({
-      "//third_party/gpus/cuda:darwin": [
+      "@local_config_cuda//cuda:darwin": [
         "//tensorflow:tf_exported_symbols.lds"
       ],
       "//conditions:default": [
@@ -775,13 +776,14 @@ def py_tests(name,
                data=data,
                additional_deps=additional_deps)
 
-def cuda_py_tests(name, srcs, size="medium", additional_deps=[], data=[], shard_count=1, tags=[], prefix=""):
+def cuda_py_tests(name, srcs, size="medium", additional_deps=[], data=[],
+                  shard_count=1, tags=[], prefix=""):
   test_tags = tags + tf_cuda_tests_tags()
   py_tests(name=name, size=size, srcs=srcs, additional_deps=additional_deps,
            data=data, tags=test_tags, shard_count=shard_count,prefix=prefix)
 
-# Creates a genrule named <name> for running tools/proto_text's generator to make
-# the proto_text functions, for the protos passed in <srcs>.
+# Creates a genrule named <name> for running tools/proto_text's generator to
+# make the proto_text functions, for the protos passed in <srcs>.
 #
 # Return a struct with fields (hdrs, srcs) containing the names of the
 # generated files.
diff --git a/tensorflow/tools/ci_build/Dockerfile.gpu b/tensorflow/tools/ci_build/Dockerfile.gpu
index 7b09691178..b36edb9bde 100644
--- a/tensorflow/tools/ci_build/Dockerfile.gpu
+++ b/tensorflow/tools/ci_build/Dockerfile.gpu
@@ -22,5 +22,6 @@ ENV LD_LIBRARY_PATH /usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
 
 # Configure the build for our CUDA configuration.
 ENV CUDA_TOOLKIT_PATH /usr/local/cuda
-ENV CUDNN_INSTALL_PATH /usr/local/cuda
+ENV CUDNN_INSTALL_PATH /usr/lib/x86_64-linux-gnu
 ENV TF_NEED_CUDA 1
+ENV CUDA_COMPUTE_CAPABILITIES 3.0,5.2
diff --git a/tensorflow/tools/dist_test/README.md b/tensorflow/tools/dist_test/README.md
index b7f411f847..b042bcf3a2 100644
--- a/tensorflow/tools/dist_test/README.md
+++ b/tensorflow/tools/dist_test/README.md
@@ -35,7 +35,7 @@ For example:
 
     export TF_DIST_GCLOUD_PROJECT="tensorflow-testing"
     export TF_DIST_GCLOUD_COMPUTE_ZONE="us-central1-f"
-    export CONTAINER_CLUSTER="test-cluster-1"
+    export TF_DIST_CONTAINER_CLUSTER="test-cluster-1"
     export TF_DIST_GCLOUD_KEY_FILE_DIR="/tmp/gcloud-secrets"
     ./remote_test.sh
 
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index 3b2112a026..d47c446bbe 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -1,9 +1,12 @@
 # TensorFlow external dependencies that can be loaded in WORKSPACE files.
 
+load("//third_party/gpus:cuda_configure.bzl", "cuda_configure")
+
 # If TensorFlow is linked as a submodule, path_prefix is TensorFlow's directory
 # within the workspace (e.g. "tensorflow/"), and tf_repo_name is the name of the
 # local_repository rule (e.g. "@tf").
 def tf_workspace(path_prefix = "", tf_repo_name = ""):
+  cuda_configure(name = "local_config_cuda")
 
   # These lines need to be changed when updating Eigen. They are parsed from
   # this file by the cmake and make builds to determine the eigen version and hash.
diff --git a/third_party/gpus/BUILD b/third_party/gpus/BUILD
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/third_party/gpus/crosstool/BUILD.tpl b/third_party/gpus/crosstool/BUILD.tpl
new file mode 100644
index 0000000000..7c9c8ab884
--- /dev/null
+++ b/third_party/gpus/crosstool/BUILD.tpl
@@ -0,0 +1,42 @@
+licenses(["restricted"])
+
+package(default_visibility = ["//visibility:public"])
+
+filegroup(
+    name = "crosstool",
+    srcs = ["CROSSTOOL"],
+    output_licenses = ["unencumbered"],
+)
+
+cc_toolchain(
+    name = "cc-compiler-local",
+    all_files = ":empty",
+    compiler_files = ":empty",
+    cpu = "local",
+    dwp_files = ":empty",
+    dynamic_runtime_libs = [":empty"],
+    linker_files = ":empty",
+    objcopy_files = ":empty",
+    static_runtime_libs = [":empty"],
+    strip_files = ":empty",
+    supports_param_files = 0,
+)
+
+cc_toolchain(
+    name = "cc-compiler-darwin",
+    all_files = ":empty",
+    compiler_files = ":empty",
+    cpu = "darwin",
+    dwp_files = ":empty",
+    dynamic_runtime_libs = [":empty"],
+    linker_files = ":empty",
+    objcopy_files = ":empty",
+    static_runtime_libs = [":empty"],
+    strip_files = ":empty",
+    supports_param_files = 0,
+)
+
+filegroup(
+    name = "empty",
+    srcs = [],
+)
diff --git a/third_party/gpus/crosstool/CROSSTOOL.tpl b/third_party/gpus/crosstool/CROSSTOOL.tpl
new file mode 100644
index 0000000000..a367aa8f66
--- /dev/null
+++ b/third_party/gpus/crosstool/CROSSTOOL.tpl
@@ -0,0 +1,254 @@
+major_version: "local"
+minor_version: ""
+default_target_cpu: "same_as_host"
+
+default_toolchain {
+  cpu: "k8"
+  toolchain_identifier: "local_linux"
+}
+default_toolchain {
+  cpu: "piii"
+  toolchain_identifier: "local_linux"
+}
+default_toolchain {
+  cpu: "arm"
+  toolchain_identifier: "local_linux"
+}
+default_toolchain {
+  cpu: "darwin"
+  toolchain_identifier: "local_darwin"
+}
+default_toolchain {
+  cpu: "ppc"
+  toolchain_identifier: "local_linux"
+}
+
+toolchain {
+  abi_version: "local"
+  abi_libc_version: "local"
+  builtin_sysroot: ""
+  compiler: "compiler"
+  host_system_name: "local"
+  needsPic: true
+  supports_gold_linker: false
+  supports_incremental_linker: false
+  supports_fission: false
+  supports_interface_shared_objects: false
+  supports_normalizing_ar: false
+  supports_start_end_lib: false
+  supports_thin_archives: false
+  target_libc: "local"
+  target_cpu: "local"
+  target_system_name: "local"
+  toolchain_identifier: "local_linux"
+
+  tool_path { name: "ar" path: "/usr/bin/ar" }
+  tool_path { name: "compat-ld" path: "/usr/bin/ld" }
+  tool_path { name: "cpp" path: "/usr/bin/cpp" }
+  tool_path { name: "dwp" path: "/usr/bin/dwp" }
+  # As part of the TensorFlow release, we place some cuda-related compilation
+  # files in @local_config_cuda//crosstool/clang/bin, and this relative
+  # path, combined with the rest of our Bazel configuration causes our
+  # compilation to use those files.
+  tool_path { name: "gcc" path: "clang/bin/crosstool_wrapper_driver_is_not_gcc" }
+  # Use "-std=c++11" for nvcc. For consistency, force both the host compiler
+  # and the device compiler to use "-std=c++11".
+  cxx_flag: "-std=c++11"
+  linker_flag: "-lstdc++"
+  linker_flag: "-B/usr/bin/"
+
+  # TODO(bazel-team): In theory, the path here ought to exactly match the path
+  # used by gcc. That works because bazel currently doesn't track files at
+  # absolute locations and has no remote execution, yet. However, this will need
+  # to be fixed, maybe with auto-detection?
+  cxx_builtin_include_directory: "/usr/lib/gcc/"
+  cxx_builtin_include_directory: "/usr/local/include"
+  cxx_builtin_include_directory: "/usr/include"
+  tool_path { name: "gcov" path: "/usr/bin/gcov" }
+
+  # C(++) compiles invoke the compiler (as that is the one knowing where
+  # to find libraries), but we provide LD so other rules can invoke the linker.
+  tool_path { name: "ld" path: "/usr/bin/ld" }
+
+  tool_path { name: "nm" path: "/usr/bin/nm" }
+  tool_path { name: "objcopy" path: "/usr/bin/objcopy" }
+  objcopy_embed_flag: "-I"
+  objcopy_embed_flag: "binary"
+  tool_path { name: "objdump" path: "/usr/bin/objdump" }
+  tool_path { name: "strip" path: "/usr/bin/strip" }
+
+  # Anticipated future default.
+  unfiltered_cxx_flag: "-no-canonical-prefixes"
+
+  # Make C++ compilation deterministic. Use linkstamping instead of these
+  # compiler symbols.
+  unfiltered_cxx_flag: "-Wno-builtin-macro-redefined"
+  unfiltered_cxx_flag: "-D__DATE__=\"redacted\""
+  unfiltered_cxx_flag: "-D__TIMESTAMP__=\"redacted\""
+  unfiltered_cxx_flag: "-D__TIME__=\"redacted\""
+
+  # Security hardening on by default.
+  # Conservative choice; -D_FORTIFY_SOURCE=2 may be unsafe in some cases.
+  # We need to undef it before redefining it as some distributions now have
+  # it enabled by default.
+  compiler_flag: "-U_FORTIFY_SOURCE"
+  compiler_flag: "-D_FORTIFY_SOURCE=1"
+  compiler_flag: "-fstack-protector"
+  compiler_flag: "-fPIE"
+  linker_flag: "-pie"
+  linker_flag: "-Wl,-z,relro,-z,now"
+
+  # Enable coloring even if there's no attached terminal. Bazel removes the
+  # escape sequences if --nocolor is specified. This isn't supported by gcc
+  # on Ubuntu 14.04.
+  # compiler_flag: "-fcolor-diagnostics"
+
+  # All warnings are enabled. Maybe enable -Werror as well?
+  compiler_flag: "-Wall"
+  # Enable a few more warnings that aren't part of -Wall.
+  compiler_flag: "-Wunused-but-set-parameter"
+  # But disable some that are problematic.
+  compiler_flag: "-Wno-free-nonheap-object" # has false positives
+
+  # Keep stack frames for debugging, even in opt mode.
+  compiler_flag: "-fno-omit-frame-pointer"
+
+  # Anticipated future default.
+  linker_flag: "-no-canonical-prefixes"
+  unfiltered_cxx_flag: "-fno-canonical-system-headers"
+  # Have gcc return the exit code from ld.
+  linker_flag: "-pass-exit-codes"
+  # Stamp the binary with a unique identifier.
+  linker_flag: "-Wl,--build-id=md5"
+  linker_flag: "-Wl,--hash-style=gnu"
+  # Gold linker only? Can we enable this by default?
+  # linker_flag: "-Wl,--warn-execstack"
+  # linker_flag: "-Wl,--detect-odr-violations"
+
+  # Include directory for cuda headers.
+  cxx_builtin_include_directory: "/usr/local/cuda%{cuda_version}/include"
+
+  compilation_mode_flags {
+    mode: DBG
+    # Enable debug symbols.
+    compiler_flag: "-g"
+  }
+  compilation_mode_flags {
+    mode: OPT
+
+    # No debug symbols.
+    # Maybe we should enable https://gcc.gnu.org/wiki/DebugFission for opt or
+    # even generally? However, that can't happen here, as it requires special
+    # handling in Bazel.
+    compiler_flag: "-g0"
+
+    # Conservative choice for -O
+    # -O3 can increase binary size and even slow down the resulting binaries.
+    # Profile first and / or use FDO if you need better performance than this.
+    compiler_flag: "-O2"
+
+    # Disable assertions
+    compiler_flag: "-DNDEBUG"
+
+    # Removal of unused code and data at link time (can this increase binary size in some cases?).
+    compiler_flag: "-ffunction-sections"
+    compiler_flag: "-fdata-sections"
+    linker_flag: "-Wl,--gc-sections"
+  }
+  linking_mode_flags { mode: DYNAMIC }
+}
+
+toolchain {
+  abi_version: "local"
+  abi_libc_version: "local"
+  builtin_sysroot: ""
+  compiler: "compiler"
+  host_system_name: "local"
+  needsPic: true
+  target_libc: "macosx"
+  target_cpu: "darwin"
+  target_system_name: "local"
+  toolchain_identifier: "local_darwin"
+
+  tool_path { name: "ar" path: "/usr/bin/libtool" }
+  tool_path { name: "compat-ld" path: "/usr/bin/ld" }
+  tool_path { name: "cpp" path: "/usr/bin/cpp" }
+  tool_path { name: "dwp" path: "/usr/bin/dwp" }
+  tool_path { name: "gcc" path: "clang/bin/crosstool_wrapper_driver_is_not_gcc" }
+  cxx_flag: "-std=c++11"
+  ar_flag: "-static"
+  ar_flag: "-s"
+  ar_flag: "-o"
+  linker_flag: "-lc++"
+  linker_flag: "-undefined"
+  linker_flag: "dynamic_lookup"
+  # TODO(ulfjack): This is wrong on so many levels. Figure out a way to auto-detect the proper
+  # setting from the local compiler, and also how to make incremental builds correct.
+  cxx_builtin_include_directory: "/"
+  tool_path { name: "gcov" path: "/usr/bin/gcov" }
+  tool_path { name: "ld" path: "/usr/bin/ld" }
+  tool_path { name: "nm" path: "/usr/bin/nm" }
+  tool_path { name: "objcopy" path: "/usr/bin/objcopy" }
+  objcopy_embed_flag: "-I"
+  objcopy_embed_flag: "binary"
+  tool_path { name: "objdump" path: "/usr/bin/objdump" }
+  tool_path { name: "strip" path: "/usr/bin/strip" }
+
+  # Anticipated future default.
+  unfiltered_cxx_flag: "-no-canonical-prefixes"
+  # Make C++ compilation deterministic. Use linkstamping instead of these
+  # compiler symbols.
+  unfiltered_cxx_flag: "-Wno-builtin-macro-redefined"
+  unfiltered_cxx_flag: "-D__DATE__=\"redacted\""
+  unfiltered_cxx_flag: "-D__TIMESTAMP__=\"redacted\""
+  unfiltered_cxx_flag: "-D__TIME__=\"redacted\""
+
+  # Security hardening on by default.
+  # Conservative choice; -D_FORTIFY_SOURCE=2 may be unsafe in some cases.
+  compiler_flag: "-D_FORTIFY_SOURCE=1"
+  compiler_flag: "-fstack-protector"
+
+  # Enable coloring even if there's no attached terminal. Bazel removes the
+  # escape sequences if --nocolor is specified.
+  compiler_flag: "-fcolor-diagnostics"
+
+  # All warnings are enabled. Maybe enable -Werror as well?
+  compiler_flag: "-Wall"
+  # Enable a few more warnings that aren't part of -Wall.
+  compiler_flag: "-Wthread-safety"
+  compiler_flag: "-Wself-assign"
+
+  # Keep stack frames for debugging, even in opt mode.
+  compiler_flag: "-fno-omit-frame-pointer"
+
+  # Anticipated future default.
+  linker_flag: "-no-canonical-prefixes"
+
+  # Include directory for cuda headers.
+  cxx_builtin_include_directory: "/usr/local/cuda%{cuda_version}/include"
+
+  compilation_mode_flags {
+    mode: DBG
+    # Enable debug symbols.
+    compiler_flag: "-g"
+  }
+  compilation_mode_flags {
+    mode: OPT
+    # No debug symbols.
+    # Maybe we should enable https://gcc.gnu.org/wiki/DebugFission for opt or even generally?
+    # However, that can't happen here, as it requires special handling in Bazel.
+    compiler_flag: "-g0"
+
+    # Conservative choice for -O
+    # -O3 can increase binary size and even slow down the resulting binaries.
+    # Profile first and / or use FDO if you need better performance than this.
+    compiler_flag: "-O2"
+
+    # Disable assertions
+    compiler_flag: "-DNDEBUG"
+
+    # Removal of unused code and data at link time (can this increase binary size in some cases?).
+    compiler_flag: "-ffunction-sections"
+    compiler_flag: "-fdata-sections"
+  }
+}
diff --git a/third_party/gpus/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc.tpl b/third_party/gpus/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc.tpl
new file mode 100755
index 0000000000..20449a1137
--- /dev/null
+++ b/third_party/gpus/crosstool/clang/bin/crosstool_wrapper_driver_is_not_gcc.tpl
@@ -0,0 +1,315 @@
+#!/usr/bin/env python
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Crosstool wrapper for compiling CUDA programs.
+
+SYNOPSIS:
+  crosstool_wrapper_is_not_gcc [options passed in by cc_library()
+                                or cc_binary() rule]
+
+DESCRIPTION:
+  This script is expected to be called by the cc_library() or cc_binary() bazel
+  rules. When the option "-x cuda" is present in the list of arguments passed
+  to this script, it invokes the nvcc CUDA compiler. Most arguments are passed
+  as is as a string to --compiler-options of nvcc. When "-x cuda" is not
+  present, this wrapper invokes hybrid_driver_is_not_gcc with the input
+  arguments as is.
+
+NOTES:
+  Changes to the contents of this file must be propagated from
+  //third_party/gpus/crosstool/crosstool_wrapper_is_not_gcc to
+  //third_party/gpus/crosstool/v*/*/clang/bin/crosstool_wrapper_is_not_gcc
+"""
+
+from __future__ import print_function
+
+__author__ = 'keveman@google.com (Manjunath Kudlur)'
+
+from argparse import ArgumentParser
+import os
+import subprocess
+import re
+import sys
+import pipes
+
+# Template values set by cuda_autoconf.
+CPU_COMPILER = ('%{cpu_compiler}')
+GCC_HOST_COMPILER_PATH = ('%{gcc_host_compiler_path}')
+
+CURRENT_DIR = os.path.dirname(sys.argv[0])
+NVCC_PATH = CURRENT_DIR + '/../../../cuda/bin/nvcc'
+LLVM_HOST_COMPILER_PATH = ('/usr/bin/gcc')
+PREFIX_DIR = os.path.dirname(GCC_HOST_COMPILER_PATH)
+
+def Log(s):
+  print('gpus/crosstool: {0}'.format(s))
+
+
+def GetOptionValue(argv, option):
+  """Extract the list of values for option from the argv list.
+
+  Args:
+    argv: A list of strings, possibly the argv passed to main().
+    option: The option whose value to extract, without the leading '-'.
+
+  Returns:
+    A list of values, either directly following the option,
+    (eg., -opt val1 val2) or values collected from multiple occurrences of
+    the option (eg., -opt val1 -opt val2).
+  """
+
+  parser = ArgumentParser()
+  parser.add_argument('-' + option, nargs='*', action='append')
+  args, _ = parser.parse_known_args(argv)
+  if not args or not vars(args)[option]:
+    return []
+  else:
+    return sum(vars(args)[option], [])
+
+
+def GetHostCompilerOptions(argv):
+  """Collect the -isystem, -iquote, and --sysroot option values from argv.
+
+  Args:
+    argv: A list of strings, possibly the argv passed to main().
+
+  Returns:
+    The string that can be used as the --compiler-options to nvcc.
+  """
+
+  parser = ArgumentParser()
+  parser.add_argument('-isystem', nargs='*', action='append')
+  parser.add_argument('-iquote', nargs='*', action='append')
+  parser.add_argument('--sysroot', nargs=1)
+  parser.add_argument('-g', nargs='*', action='append')
+
+  args, _ = parser.parse_known_args(argv)
+
+  opts = ''
+
+  if args.isystem:
+    opts += ' -isystem ' + ' -isystem '.join(sum(args.isystem, []))
+  if args.iquote:
+    opts += ' -iquote ' + ' -iquote '.join(sum(args.iquote, []))
+  if args.g:
+    opts += ' -g' + ' -g'.join(sum(args.g, []))
+  if args.sysroot:
+    opts += ' --sysroot ' + args.sysroot[0]
+
+  return opts
+
+def GetNvccOptions(argv):
+  """Collect the -nvcc_options values from argv.
+
+  Args:
+    argv: A list of strings, possibly the argv passed to main().
+
+  Returns:
+    The string that can be passed directly to nvcc.
+  """
+
+  parser = ArgumentParser()
+  parser.add_argument('-nvcc_options', nargs='*', action='append')
+
+  args, _ = parser.parse_known_args(argv)
+
+  if args.nvcc_options:
+    return ' '.join(['--'+a for a in sum(args.nvcc_options, [])])
+  return ''
+
+
+def StripAndTransformNvccOptions(argv):
+  """Strips the -nvcc_options values from argv and transforms define-macros.
+
+  Args:
+    argv: A list of strings, possibly the argv passed to main().
+
+  Returns:
+    A list of strings that can be passed directly to gcudacc.
+  """
+  parser = ArgumentParser()
+  parser.add_argument('-nvcc_options', nargs='*', action='store')
+  args, leftover = parser.parse_known_args(argv)
+  if args.nvcc_options:
+    for option in args.nvcc_options:
+      (flag, _, value) = option.partition('=')
+      if 'define-macro' in flag:
+        leftover.append('-D' + value)
+  return leftover
+
+
+def InvokeGcudacc(argv, gcudacc_version, gcudacc_flags, log=False):
+  """Call gcudacc with arguments assembled from argv.
+
+  Args:
+    argv: A list of strings, possibly the argv passed to main().
+    gcudacc_version: The version of gcudacc; this is a subdirectory name under
+      the gcudacc bin/ directory.
+    gcudacc_flags: A list of extra arguments passed just for gcudacc.
+    log: True if logging is requested.
+
+  Returns:
+    The return value of calling os.system('gcudacc ' + args)
+  """
+
+  gcudacc_cmd = os.path.join(GCUDACC_PATH_BASE, gcudacc_version, 'gcudacc.par')
+  gcudacc_cmd = (
+      gcudacc_cmd +
+      ' --google_host_compiler={0} '.format(LLVM_HOST_COMPILER_PATH) +
+      ' '.join(sum(gcudacc_flags, [])) +
+      ' -- ' +
+      ' '.join(StripAndTransformNvccOptions(argv)))
+  if log: Log(gcudacc_cmd)
+  return os.system(gcudacc_cmd)
+
+
+def InvokeNvcc(argv, log=False):
+  """Call nvcc with arguments assembled from argv.
+
+  Args:
+    argv: A list of strings, possibly the argv passed to main().
+    log: True if logging is requested.
+
+  Returns:
+    The return value of calling os.system('nvcc ' + args)
+  """
+
+  host_compiler_options = GetHostCompilerOptions(argv)
+  nvcc_compiler_options = GetNvccOptions(argv)
+  opt_option = GetOptionValue(argv, 'O')
+  m_options = GetOptionValue(argv, 'm')
+  m_options = ''.join([' -m' + m for m in m_options if m in ['32', '64']])
+  include_options = GetOptionValue(argv, 'I')
+  out_file = GetOptionValue(argv, 'o')
+  depfiles = GetOptionValue(argv, 'MF')
+  defines = GetOptionValue(argv, 'D')
+  defines = ''.join([' -D' + define for define in defines])
+  undefines = GetOptionValue(argv, 'U')
+  undefines = ''.join([' -U' + define for define in undefines])
+  std_options = GetOptionValue(argv, 'std')
+  # currently only c++11 is supported by Cuda 7.0 std argument
+  nvcc_allowed_std_options = ["c++11"]
+  std_options = ''.join([' -std=' + define
+      for define in std_options if define in nvcc_allowed_std_options])
+
+  # The list of source files get passed after the -c option. I don't know of
+  # any other reliable way to just get the list of source files to be compiled.
+  src_files = GetOptionValue(argv, 'c')
+
+  if len(src_files) == 0:
+    return 1
+  if len(out_file) != 1:
+    return 1
+
+  opt = (' -O2' if (len(opt_option) > 0 and int(opt_option[0]) > 0)
+         else ' -g -G')
+
+  includes = (' -I ' + ' -I '.join(include_options)
+              if len(include_options) > 0
+              else '')
+
+  # Unfortunately, there are other options that have -c prefix too.
+  # So allowing only those look like C/C++ files.
+  src_files = [f for f in src_files if
+               re.search('\.cpp$|\.cc$|\.c$|\.cxx$|\.C$', f)]
+  srcs = ' '.join(src_files)
+  out = ' -o ' + out_file[0]
+
+  supported_cuda_compute_capabilities = [ %{cuda_compute_capabilities} ]
+  nvccopts = ''
+  for capability in supported_cuda_compute_capabilities:
+    capability = capability.replace('.', '')
+    nvccopts += r'-gencode=arch=compute_%s,\"code=sm_%s,compute_%s\" ' % (
+        capability, capability, capability)
+  nvccopts += ' ' + nvcc_compiler_options
+  nvccopts += undefines
+  nvccopts += defines
+  nvccopts += std_options
+  nvccopts += m_options
+
+  if depfiles:
+    # Generate the dependency file
+    depfile = depfiles[0]
+    cmd = (NVCC_PATH + ' ' + nvccopts +
+           ' --compiler-options "' + host_compiler_options + '"' +
+           ' --compiler-bindir=' + GCC_HOST_COMPILER_PATH +
+           ' -I .' +
+           ' -x cu ' + includes + ' ' + srcs + ' -M -o ' + depfile)
+    if log: Log(cmd)
+    exit_status = os.system(cmd)
+    if exit_status != 0:
+      return exit_status
+
+  cmd = (NVCC_PATH + ' ' + nvccopts +
+         ' --compiler-options "' + host_compiler_options + ' -fPIC"' +
+         ' --compiler-bindir=' + GCC_HOST_COMPILER_PATH +
+         ' -I .' +
+         ' -x cu ' + opt + includes + ' -c ' + srcs + out)
+
+  # TODO(zhengxq): for some reason, 'gcc' needs this help to find 'as'.
+  # Need to investigate and fix.
+  cmd = 'PATH=' + PREFIX_DIR + ' ' + cmd
+  if log: Log(cmd)
+  return os.system(cmd)
+
+
+def main():
+  parser = ArgumentParser()
+  parser.add_argument('-x', nargs=1)
+  parser.add_argument('--cuda_log', action='store_true')
+  parser.add_argument('--use_gcudacc', action='store_true')
+  parser.add_argument('--gcudacc_version', action='store', default='v8')
+  parser.add_argument('--gcudacc_flag', nargs='*', action='append', default=[])
+  args, leftover = parser.parse_known_args(sys.argv[1:])
+
+  if args.x and args.x[0] == 'cuda':
+    if args.cuda_log: Log('-x cuda')
+    leftover = [pipes.quote(s) for s in leftover]
+    if args.use_gcudacc:
+      if args.cuda_log: Log('using gcudacc')
+      return InvokeGcudacc(argv=leftover,
+                           gcudacc_version=args.gcudacc_version,
+                           gcudacc_flags=args.gcudacc_flag,
+                           log=args.cuda_log)
+    if args.cuda_log: Log('using nvcc')
+    return InvokeNvcc(leftover, log=args.cuda_log)
+
+  # Strip our flags before passing through to the CPU compiler for files which
+  # are not -x cuda. We can't just pass 'leftover' because it also strips -x.
+  # We not only want to pass -x to the CPU compiler, but also keep it in its
+  # relative location in the argv list (the compiler is actually sensitive to
+  # this).
+  cpu_compiler_flags = [flag for flag in sys.argv[1:]
+                             if not flag.startswith(('--cuda_log',
+                                                     '--use_gcudacc',
+                                                     '--gcudacc_version',
+                                                     '--gcudacc_flag'))]
+  if args.use_gcudacc:
+    # This macro is defined for TUs that are not marked with "-x cuda" but are
+    # built as part of a -config=cuda --use_gcudacc compilation. They are
+    # compiled with the default CPU compiler. Since the objects built from
+    # these TUs are later linked with objects that come from gcudacc, some
+    # parts of the code need to be marked for these special cases. For example,
+    # some types have to be defined similarly for gcudacc-compiled TUs and
+    # default CPU compiler-compiled TUs linked with them, but differently when
+    # nvcc is used.
+    # TODO(eliben): rename to a more descriptive name.
+    cpu_compiler_flags.append('-D__GCUDACC_HOST__')
+
+  return subprocess.call([CPU_COMPILER] + cpu_compiler_flags)
+
+if __name__ == '__main__':
+  sys.exit(main())
diff --git a/third_party/gpus/cuda/BUILD b/third_party/gpus/cuda/BUILD
index 79c6227687..e69de29bb2 100644
--- a/third_party/gpus/cuda/BUILD
+++ b/third_party/gpus/cuda/BUILD
@@ -1,224 +0,0 @@
-licenses(["restricted"])  # MPL2, portions GPL v3, LGPL v3, BSD-like
-
-load("//third_party/gpus/cuda:build_defs.bzl", "if_cuda")
-load("platform", "cuda_library_path")
-load("platform", "cuda_static_library_path")
-load("platform", "cudnn_library_path")
-load("platform", "cupti_library_path")
-load("platform", "readlink_command")
-
-package(default_visibility = ["//visibility:public"])
-
-config_setting(
-    name = "using_gcudacc",
-    values = {
-        "define": "using_cuda_gcudacc=true",
-    },
-    visibility = ["//visibility:public"],
-)
-
-config_setting(
-    name = "using_nvcc",
-    values = {
-        "define": "using_cuda_nvcc=true",
-    },
-)
-
-config_setting(
-    name = "using_clang",
-    values = {
-        "define": "using_cuda_clang=true",
-    },
-)
-
-# Equivalent to using_clang && -c opt.
-config_setting(
-    name = "using_clang_opt",
-    values = {
-        "define": "using_cuda_clang=true",
-        "compilation_mode": "opt",
-    },
-)
-
-config_setting(
-    name = "darwin",
-    values = {"cpu": "darwin"},
-    visibility = ["//visibility:public"],
-)
-
-cc_library(
-    name = "cuda_headers",
-    hdrs = glob([
-        "**/*.h",
-    ]),
-    includes = [
-        ".",
-        "include",
-    ],
-    visibility = ["//visibility:public"],
-)
-
-cc_library(
-    name = "cudart_static",
-    srcs = [
-        cuda_static_library_path("cudart"),
-    ],
-    includes = ["include/"],
-    linkopts = [
-        "-ldl",
-        "-lpthread",
-    ] + select({
-        "//tensorflow:darwin": [],
-        "//conditions:default": ["-lrt"],
-    }),
-    visibility = ["//visibility:public"],
-)
-
-cc_library(
-    name = "cudart",
-    srcs = [
-        cuda_library_path("cudart"),
-    ],
-    data = [
-        cuda_library_path("cudart"),
-    ],
-    includes = ["include/"],
-    linkstatic = 1,
-    visibility = ["//visibility:public"],
-)
-
-cc_library(
-    name = "cublas",
-    srcs = [
-        cuda_library_path("cublas"),
-    ],
-    data = [
-        cuda_library_path("cublas"),
-    ],
-    includes = ["include/"],
-    linkstatic = 1,
-    visibility = ["//visibility:public"],
-)
-
-cc_library(
-    name = "cudnn",
-    srcs = [
-        cudnn_library_path(),
-    ],
-    data = [
-        cudnn_library_path(),
-    ],
-    includes = ["include/"],
-    linkstatic = 1,
-    visibility = ["//visibility:public"],
-)
-
-cc_library(
-    name = "cufft",
-    srcs = [
-        cuda_library_path("cufft"),
-    ],
-    data = [
-        cuda_library_path("cufft"),
-    ],
-    includes = ["include/"],
-    linkstatic = 1,
-    visibility = ["//visibility:public"],
-)
-
-cc_library(
-    name = "cuda",
-    visibility = ["//visibility:public"],
-    deps = [
-        ":cublas",
-        ":cuda_headers",
-        ":cudart",
-        ":cudnn",
-        ":cufft",
-    ],
-)
-
-cc_library(
-    name = "cupti_headers",
-    hdrs = glob([
-        "**/*.h",
-    ]),
-    includes = [
-        ".",
-        "extras/CUPTI/include/",
-    ],
-    visibility = ["//visibility:public"],
-)
-
-cc_library(
-    name = "cupti_dsos",
-    data = [
-        cupti_library_path(),
-    ],
-    visibility = ["//visibility:public"],
-)
-
-# TODO(opensource): for now, we have to invoke the cuda_config.sh manually in the source tree.
-# This rule checks if Cuda libraries in the source tree has been properly configured.
-# The output list makes bazel runs this rule first if the Cuda files are missing.
-# This gives us an opportunity to check and print a meaningful error message.
-# But we will need to create the output file list to make bazel happy in a successful run.
-genrule(
-    name = "cuda_check",
-    srcs = [
-        "cuda.config",
-        "cuda_config.sh",
-    ],
-    outs = [
-        "include/cuda.h",
-        "include/cublas.h",
-        "include/cudnn.h",
-        "extras/CUPTI/include/cupti.h",
-        cuda_static_library_path("cudart"),
-        cuda_library_path("cublas"),
-        cudnn_library_path(),
-        cuda_library_path("cudart"),
-        cuda_library_path("cufft"),
-        cupti_library_path(),
-    ],
-    cmd = if_cuda(
-        # Under cuda config, create all the symbolic links to the actual cuda files
-        "OUTPUTDIR=`{} -f $(@D)/../../..`; cd `dirname $(location :cuda_config.sh)`; OUTPUTDIR=$$OUTPUTDIR ./cuda_config.sh --check;".format(readlink_command()),
-
-        # Under non-cuda config, create all dummy files to make the build go through
-        ";".join([
-            "mkdir -p $(@D)/include",
-            "mkdir -p $(@D)/lib64",
-            "mkdir -p $(@D)/extras/CUPTI/include",
-            "mkdir -p $(@D)/extras/CUPTI/lib64",
-            "touch $(@D)/include/cuda.h",
-            "touch $(@D)/include/cublas.h",
-            "touch $(@D)/include/cudnn.h",
-            "touch $(@D)/extras/CUPTI/include/cupti.h",
-            "touch $(@D)/{}".format(cuda_static_library_path("cudart")),
-            "touch $(@D)/{}".format(cuda_library_path("cublas")),
-            "touch $(@D)/{}".format(cudnn_library_path()),
-            "touch $(@D)/{}".format(cuda_library_path("cudart")),
-            "touch $(@D)/{}".format(cuda_library_path("cufft")),
-            "touch $(@D)/{}".format(cupti_library_path()),
-        ]),
-    ),
-    local = 1,
-)
-
-genrule(
-    name = "cuda_config_check",
-    outs = [
-        "cuda.config",
-    ],
-    cmd = if_cuda(
-        # Under cuda config, create the symbolic link to the actual cuda.config
-        "configfile=$(location :cuda.config); ln -sf `{} -f $${{configfile#*/*/*/}}` $(@D)/;".format(readlink_command()),
-
-        # Under non-cuda config, create the dummy file
-        ";".join([
-            "touch $(@D)/cuda.config",
-        ]),
-    ),
-    local = 1,
-)
diff --git a/third_party/gpus/cuda/BUILD.tpl b/third_party/gpus/cuda/BUILD.tpl
new file mode 100644
index 0000000000..ab98c2d528
--- /dev/null
+++ b/third_party/gpus/cuda/BUILD.tpl
@@ -0,0 +1,172 @@
+licenses(["restricted"])  # MPL2, portions GPL v3, LGPL v3, BSD-like
+
+load("@local_config_cuda//cuda:platform.bzl", "cuda_library_path")
+load("@local_config_cuda//cuda:platform.bzl", "cuda_static_library_path")
+load("@local_config_cuda//cuda:platform.bzl", "cudnn_library_path")
+load("@local_config_cuda//cuda:platform.bzl", "cupti_library_path")
+load("@local_config_cuda//cuda:platform.bzl", "readlink_command")
+
+package(default_visibility = ["//visibility:public"])
+
+config_setting(
+    name = "using_gcudacc",
+    values = {
+        "define": "using_cuda_gcudacc=true",
+    },
+    visibility = ["//visibility:public"],
+)
+
+config_setting(
+    name = "using_nvcc",
+    values = {
+        "define": "using_cuda_nvcc=true",
+    },
+)
+
+config_setting(
+    name = "using_clang",
+    values = {
+        "define": "using_cuda_clang=true",
+    },
+)
+
+# Equivalent to using_clang && -c opt.
+config_setting(
+    name = "using_clang_opt",
+    values = {
+        "define": "using_cuda_clang=true",
+        "compilation_mode": "opt",
+    },
+)
+
+config_setting(
+    name = "darwin",
+    values = {"cpu": "darwin"},
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cuda_headers",
+    hdrs = glob([
+        "**/*.h",
+    ]),
+    includes = [
+        ".",
+        "include",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cudart_static",
+    srcs = [
+        cuda_static_library_path("cudart"),
+    ],
+    includes = ["include/"],
+    linkopts = [
+        "-ldl",
+        "-lpthread",
+    ] + select({
+        "@//tensorflow:darwin": [],
+        "//conditions:default": ["-lrt"],
+    }),
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cudart",
+    srcs = [
+        cuda_library_path("cudart"),
+    ],
+    data = [
+        cuda_library_path("cudart"),
+    ],
+    includes = ["include/"],
+    linkstatic = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cublas",
+    srcs = [
+        cuda_library_path("cublas"),
+    ],
+    data = [
+        cuda_library_path("cublas"),
+    ],
+    includes = ["include/"],
+    linkstatic = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cudnn",
+    srcs = [
+        cudnn_library_path(),
+    ],
+    data = [
+        cudnn_library_path(),
+    ],
+    includes = ["include/"],
+    linkstatic = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cufft",
+    srcs = [
+        cuda_library_path("cufft"),
+    ],
+    data = [
+        cuda_library_path("cufft"),
+    ],
+    includes = ["include/"],
+    linkstatic = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "curand",
+    srcs = [
+        cuda_library_path("curand"),
+    ],
+    data = [
+        cuda_library_path("curand"),
+    ],
+    includes = ["include/"],
+    linkstatic = 1,
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cuda",
+    deps = [
+        ":cuda_headers",
+        ":cudart",
+        ":cublas",
+        ":cudnn",
+        ":cufft",
+        ":curand",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cupti_headers",
+    hdrs = glob([
+        "**/*.h",
+    ]),
+    includes = [
+        ".",
+        "extras/CUPTI/include/",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "cupti_dsos",
+    data = [
+        cupti_library_path(),
+    ],
+    visibility = ["//visibility:public"],
+)
\ No newline at end of file
diff --git a/third_party/gpus/cuda/build_defs.bzl.tpl b/third_party/gpus/cuda/build_defs.bzl.tpl
new file mode 100644
index 0000000000..8f7dc00630
--- /dev/null
+++ b/third_party/gpus/cuda/build_defs.bzl.tpl
@@ -0,0 +1,14 @@
+# Macros for building CUDA code.
+
+def if_cuda(if_true, if_false = []):
+    """Shorthand for select()'ing on whether we're building with CUDA.
+
+    Returns a select statement which evaluates to if_true if we're building
+    with CUDA enabled.  Otherwise, the select statement evaluates to if_false.
+
+    """
+    return select({
+        "@local_config_cuda//cuda:using_nvcc": if_true,
+        "@local_config_cuda//cuda:using_gcudacc": if_true,
+        "//conditions:default": if_false
+    })
diff --git a/third_party/gpus/cuda/cuda_config.h.tpl b/third_party/gpus/cuda/cuda_config.h.tpl
new file mode 100644
index 0000000000..ea51fbb26f
--- /dev/null
+++ b/third_party/gpus/cuda/cuda_config.h.tpl
@@ -0,0 +1,24 @@
+/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef CUDA_CUDA_CONFIG_H_
+#define CUDA_CUDA_CONFIG_H_
+
+#define TF_CUDA_CAPABILITIES %{cuda_compute_capabilities}
+
+#define TF_CUDA_VERSION "%{cuda_version}"
+#define TF_CUDNN_VERSION "%{cudnn_version}"
+
+#endif  // CUDA_CUDA_CONFIG_H_
diff --git a/third_party/gpus/cuda/platform.bzl.tpl b/third_party/gpus/cuda/platform.bzl.tpl
new file mode 100644
index 0000000000..7565dfc129
--- /dev/null
+++ b/third_party/gpus/cuda/platform.bzl.tpl
@@ -0,0 +1,57 @@
+CUDA_VERSION = "%{cuda_version}"
+CUDNN_VERSION = "%{cudnn_version}"
+PLATFORM = "%{platform}"
+
+def cuda_sdk_version():
+  return CUDA_VERSION
+
+def cudnn_sdk_version():
+  return CUDNN_VERSION
+
+def cuda_library_path(name, version = cuda_sdk_version()):
+  if PLATFORM == "Darwin":
+    if not version:
+      return "lib/lib{}.dylib".format(name)
+    else:
+      return "lib/lib{}.{}.dylib".format(name, version)
+  else:
+    if not version:
+      return "lib64/lib{}.so".format(name)
+    else:
+      return "lib64/lib{}.so.{}".format(name, version)
+
+def cuda_static_library_path(name):
+  if PLATFORM == "Darwin":
+    return "lib/lib{}_static.a".format(name)
+  else:
+    return "lib64/lib{}_static.a".format(name)
+
+def cudnn_library_path(version = cudnn_sdk_version()):
+  if PLATFORM == "Darwin":
+    if not version:
+      return "lib/libcudnn.dylib"
+    else:
+      return "lib/libcudnn.{}.dylib".format(version)
+  else:
+    if not version:
+      return "lib64/libcudnn.so"
+    else:
+      return "lib64/libcudnn.so.{}".format(version)
+
+def cupti_library_path(version = cuda_sdk_version()):
+  if PLATFORM == "Darwin":
+    if not version:
+      return "extras/CUPTI/lib/libcupti.dylib"
+    else:
+      return "extras/CUPTI/lib/libcupti.{}.dylib".format(version)
+  else:
+    if not version:
+      return "extras/CUPTI/lib64/libcupti.so"
+    else:
+      return "extras/CUPTI/lib64/libcupti.so.{}".format(version)
+
+def readlink_command():
+  if PLATFORM == "Darwin":
+    return "greadlink"
+  else:
+    return "readlink"
diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl
new file mode 100644
index 0000000000..3682cb305d
--- /dev/null
+++ b/third_party/gpus/cuda_configure.bzl
@@ -0,0 +1,423 @@
+# -*- Python -*-
+"""Repository rule for CUDA autoconfiguration.
+
+`cuda_configure` depends on the following environment variables:
+
+  * `ENABLE_CUDA`: Whether to enable building with CUDA.
+  * `CC`: The GCC host compiler path
+  * `CUDA_TOOLKIT_PATH`: The path to the CUDA toolkit. Default is
+    `/usr/local/cuda`.
+  * `CUDA_VERSION`: The version of the CUDA toolkit. If this is blank, then
+    use the system default.
+  * `CUDNN_VERSION`: The version of the cuDNN library.
+  * `CUDNN_INSTALL_PATH`: The path to the cuDNN library. Default is
+    `/usr/local/cuda`.
+  * `CUDA_COMPUTE_CAPABILITIES`: The CUDA compute capabilities. Default is
+    `3.5,5.2`.
+"""
+
+
+_DEFAULT_CUDA_VERSION = ""
+_DEFAULT_CUDNN_VERSION = ""
+_DEFAULT_CUDA_TOOLKIT_PATH = "/usr/local/cuda"
+_DEFAULT_CUDNN_INSTALL_PATH = "/usr/local/cuda"
+_DEFAULT_CUDA_COMPUTE_CAPABILITIES = ["3.5", "5.2"]
+
+
+# TODO(dzc): Once these functions have been factored out of Bazel's
+# cc_configure.bzl, load them from @bazel_tools instead.
+# BEGIN cc_configure common functions.
+def find_cc(repository_ctx):
+  """Find the C++ compiler."""
+  cc_name = "gcc"
+  if "CC" in repository_ctx.os.environ:
+    cc_name = repository_ctx.os.environ["CC"].strip()
+    if not cc_name:
+      cc_name = "gcc"
+  if cc_name.startswith("/"):
+    # Absolute path, maybe we should make this suported by our which function.
+    return cc_name
+  cc = repository_ctx.which(cc_name)
+  if cc == None:
+    fail(
+        "Cannot find gcc, either correct your path or set the CC" +
+        " environment variable")
+  return cc
+
+
+_INC_DIR_MARKER_BEGIN = "#include <...>"
+
+
+# OSX add " (framework directory)" at the end of line, strip it.
+_OSX_FRAMEWORK_SUFFIX = " (framework directory)"
+_OSX_FRAMEWORK_SUFFIX_LEN =  len(_OSX_FRAMEWORK_SUFFIX)
+def _cxx_inc_convert(path):
+  """Convert path returned by cc -E xc++ in a complete path."""
+  path = path.strip()
+  if path.endswith(_OSX_FRAMEWORK_SUFFIX):
+    path = path[:-_OSX_FRAMEWORK_SUFFIX_LEN].strip()
+  return path
+
+
+def get_cxx_inc_directories(repository_ctx, cc):
+  """Compute the list of default C++ include directories."""
+  result = repository_ctx.execute([cc, "-E", "-xc++", "-", "-v"])
+  index1 = result.stderr.find(_INC_DIR_MARKER_BEGIN)
+  if index1 == -1:
+    return []
+  index1 = result.stderr.find("\n", index1)
+  if index1 == -1:
+    return []
+  index2 = result.stderr.rfind("\n ")
+  if index2 == -1 or index2 < index1:
+    return []
+  index2 = result.stderr.find("\n", index2 + 1)
+  if index2 == -1:
+    inc_dirs = result.stderr[index1 + 1:]
+  else:
+    inc_dirs = result.stderr[index1 + 1:index2].strip()
+
+  return [repository_ctx.path(_cxx_inc_convert(p))
+          for p in inc_dirs.split("\n")]
+
+# END cc_configure common functions (see TODO above).
+
+
+def _enable_cuda(repository_ctx):
+  if "TF_NEED_CUDA" in repository_ctx.os.environ:
+    enable_cuda = repository_ctx.os.environ["TF_NEED_CUDA"].strip()
+    return enable_cuda == "1"
+  return False
+
+
+def _cuda_toolkit_path(repository_ctx):
+  """Finds the cuda toolkit directory."""
+  cuda_toolkit_path = _DEFAULT_CUDA_TOOLKIT_PATH
+  if "CUDA_TOOLKIT_PATH" in repository_ctx.os.environ:
+    cuda_toolkit_path = repository_ctx.os.environ["CUDA_TOOLKIT_PATH"].strip()
+  if not repository_ctx.path(cuda_toolkit_path).exists:
+    fail("Cannot find cuda toolkit path.")
+  return cuda_toolkit_path
+
+
+def _cudnn_install_basedir(repository_ctx):
+  """Finds the cudnn install directory."""
+  cudnn_install_path = _DEFAULT_CUDNN_INSTALL_PATH
+  if "CUDNN_INSTALL_PATH" in repository_ctx.os.environ:
+    cudnn_install_path = repository_ctx.os.environ["CUDNN_INSTALL_PATH"].strip()
+  if not repository_ctx.path(cudnn_install_path).exists:
+    fail("Cannot find cudnn install path.")
+  return cudnn_install_path
+
+
+def _cuda_version(repository_ctx):
+  """Detects the cuda version."""
+  if "CUDA_VERSION" in repository_ctx.os.environ:
+    return repository_ctx.os.environ["CUDA_VERSION"].strip()
+  else:
+    return ""
+
+
+def _cudnn_version(repository_ctx):
+  """Detects the cudnn version."""
+  if "CUDNN_VERSION" in repository_ctx.os.environ:
+    return repository_ctx.os.environ["CUDNN_VERSION"].strip()
+  else:
+    return ""
+
+
+def _compute_capabilities(repository_ctx):
+  """Returns a list of strings representing cuda compute capabilities."""
+  if "CUDA_COMPUTE_CAPABILITIES" not in repository_ctx.os.environ:
+    return _DEFAULT_CUDA_COMPUTE_CAPABILITIES
+  capabilities_str = repository_ctx.os.environ["CUDA_COMPUTE_CAPABILITIES"]
+  capabilities = capabilities_str.split(",")
+  for capability in capabilities:
+    # Workaround for Skylark's lack of support for regex. This check should
+    # be equivalent to checking:
+    #     if re.match("[0-9]+.[0-9]+", capability) == None:
+    parts = capability.split(".")
+    if len(parts) != 2 or not parts[0].isdigit() or not parts[1].isdigit():
+      fail("Invalid compute capability: %s" % capability)
+  return capabilities
+
+
+def _cpu_value(repository_ctx):
+  result = repository_ctx.execute(["uname", "-s"])
+  return result.stdout.strip()
+
+
+def _cuda_symlink_files(cpu_value, cuda_version, cudnn_version):
+  """Returns a struct containing platform-specific paths.
+
+  Args:
+    cpu_value: The string representing the host OS.
+    cuda_version: The cuda version as returned by _cuda_version
+    cudnn_version: The cudnn version as returned by _cudnn_version
+  """
+  cuda_ext = ".%s" % cuda_version if cuda_version else ""
+  cudnn_ext = ".%s" % cudnn_version if cudnn_version else ""
+  if cpu_value == "Linux":
+    return struct(
+        cuda_lib_path = "lib64",
+        cuda_rt_lib = "lib64/libcudart.so%s" % cuda_ext,
+        cuda_rt_lib_static = "lib64/libcudart_static.a",
+        cuda_blas_lib = "lib64/libcublas.so%s" % cuda_ext,
+        cuda_dnn_lib = "lib64/libcudnn.so%s" % cudnn_ext,
+        cuda_dnn_lib_alt = "libcudnn.so%s" % cudnn_ext,
+        cuda_rand_lib = "lib64/libcurand.so%s" % cuda_ext,
+        cuda_fft_lib = "lib64/libcufft.so%s" % cuda_ext,
+        cuda_cupti_lib = "extras/CUPTI/lib64/libcupti.so%s" % cuda_ext)
+  elif cpu_value == "Darwin":
+    return struct(
+        cuda_lib_path = "lib",
+        cuda_rt_lib = "lib/libcudart%s.dylib" % cuda_ext,
+        cuda_rt_lib_static = "lib/libcudart_static.a",
+        cuda_blas_lib = "lib/libcublas%s.dylib" % cuda_ext,
+        cuda_dnn_lib = "lib/libcudnn%s.dylib" % cudnn_ext,
+        cuda_dnn_lib_alt = "libcudnn%s.dylib" % cudnn_ext,
+        cuda_rand_lib = "lib/libcurand%s.dylib" % cuda_ext,
+        cuda_fft_lib = "lib/libcufft%s.dylib" % cuda_ext,
+        cuda_cupti_lib = "extras/CUPTI/lib/libcupti%s.dylib" % cuda_ext)
+  else:
+    fail("Not supported CPU value %s" % cpu_value)
+
+
+def _check_lib(repository_ctx, cuda_toolkit_path, cuda_lib):
+  """Checks if cuda_lib exists under cuda_toolkit_path or fail if it doesn't.
+
+  Args:
+    repository_ctx: The repository context.
+    cuda_toolkit_path: The cuda toolkit directory containing the cuda libraries.
+    cuda_lib: The library to look for under cuda_toolkit_path.
+  """
+  lib_path = cuda_toolkit_path + "/" + cuda_lib
+  if not repository_ctx.path(lib_path).exists:
+    fail("Cannot find %s" % lib_path)
+
+
+def _check_dir(repository_ctx, directory):
+  """Checks whether the directory exists and fail if it does not.
+
+  Args:
+    repository_ctx: The repository context.
+    directory: The directory to check the existence of.
+  """
+  if not repository_ctx.path(directory).exists:
+    fail("Cannot find dir: %s" % directory)
+
+
+def _find_cudnn_header_dir(repository_ctx, cudnn_install_basedir):
+  """Returns the path to the directory containing cudnn.h
+
+  Args:
+    repository_ctx: The repository context.
+    cudnn_install_basedir: The cudnn install directory as returned by
+      _cudnn_install_basedir.
+
+  Returns:
+    The path of the directory containing the cudnn header.
+  """
+  if repository_ctx.path(cudnn_install_basedir + "/cudnn.h").exists:
+    return cudnn_install_basedir
+  if repository_ctx.path(cudnn_install_basedir + "/include/cudnn.h").exists:
+    return cudnn_install_basedir + "/include"
+  if repository_ctx.path("/usr/include/cudnn.h").exists:
+    return "/usr/include"
+  fail("Cannot find cudnn.h under %s" % cudnn_install_basedir)
+
+
+def _find_cudnn_lib_path(repository_ctx, cudnn_install_basedir, symlink_files):
+  """Returns the path to the directory containing libcudnn
+
+  Args:
+    repository_ctx: The repository context.
+    cudnn_install_basedir: The cudnn install dir as returned by
+      _cudnn_install_basedir.
+    symlink_files: The symlink files as returned by _cuda_symlink_files.
+
+  Returns:
+    The path of the directory containing the cudnn libraries.
+  """
+  lib_dir = cudnn_install_basedir + "/" + symlink_files.cuda_dnn_lib
+  if repository_ctx.path(lib_dir).exists:
+    return lib_dir
+  alt_lib_dir = cudnn_install_basedir + "/" + symlink_files.cuda_dnn_lib_alt
+  if repository_ctx.path(alt_lib_dir).exists:
+    return alt_lib_dir
+
+  fail("Cannot find %s or %s under %s" %
+       (symlink_files.cuda_dnn_lib, symlink_files.cuda_dnn_lib_alt,
+        cudnn_install_basedir))
+
+
+def _tpl(repository_ctx, tpl, substitutions={}, out=None):
+  if not out:
+    out = tpl.replace(":", "/")
+  repository_ctx.template(
+      out,
+      Label("//third_party/gpus/%s.tpl" % tpl),
+      substitutions)
+
+
+def _file(repository_ctx, label):
+  repository_ctx.template(
+      label.replace(":", "/"),
+      Label("//third_party/gpus/%s.tpl" % label),
+      {})
+
+
+def _create_dummy_repository(repository_ctx):
+  cpu_value = _cpu_value(repository_ctx)
+  symlink_files = _cuda_symlink_files(cpu_value, _DEFAULT_CUDA_VERSION,
+                                      _DEFAULT_CUDNN_VERSION)
+
+  # Set up BUILD file for cuda/.
+  _file(repository_ctx, "cuda:BUILD")
+  _file(repository_ctx, "cuda:build_defs.bzl")
+  _tpl(repository_ctx, "cuda:platform.bzl",
+       {
+           "%{cuda_version}": _DEFAULT_CUDA_VERSION,
+           "%{cudnn_version}": _DEFAULT_CUDNN_VERSION,
+           "%{platform}": cpu_value,
+       })
+
+  # Create dummy files for the CUDA toolkit since they are still required by
+  # tensorflow/core/platform/default/build_config:cuda.
+  repository_ctx.file("cuda/include/cuda.h", "")
+  repository_ctx.file("cuda/include/cublas.h", "")
+  repository_ctx.file("cuda/include/cudnn.h", "")
+  repository_ctx.file("cuda/extras/CUPTI/include/cupti.h", "")
+  repository_ctx.file("cuda/%s" % symlink_files.cuda_rt_lib, "")
+  repository_ctx.file("cuda/%s" % symlink_files.cuda_rt_lib_static, "")
+  repository_ctx.file("cuda/%s" % symlink_files.cuda_blas_lib, "")
+  repository_ctx.file("cuda/%s" % symlink_files.cuda_dnn_lib, "")
+  repository_ctx.file("cuda/%s" % symlink_files.cuda_rand_lib, "")
+  repository_ctx.file("cuda/%s" % symlink_files.cuda_fft_lib, "")
+  repository_ctx.file("cuda/%s" % symlink_files.cuda_cupti_lib, "")
+
+  # Set up cuda_config.h, which is used by
+  # tensorflow/stream_executor/dso_loader.cc.
+  _tpl(repository_ctx, "cuda:cuda_config.h",
+       {
+           "%{cuda_version}": _DEFAULT_CUDA_VERSION,
+           "%{cudnn_version}": _DEFAULT_CUDNN_VERSION,
+           "%{cuda_compute_capabilities}": ",".join([
+               "CudaVersion(\"%s\")" % c
+               for c in _DEFAULT_CUDA_COMPUTE_CAPABILITIES]),
+       })
+
+
+def _symlink_dir(repository_ctx, src_dir, dest_dir):
+  """Symlinks all the files in a directory.
+
+  Args:
+    repository_ctx: The repository context.
+    src_dir: The source directory.
+    dest_dir: The destination directory to create the symlinks in.
+  """
+  files = repository_ctx.path(src_dir).readdir()
+  for src_file in files:
+    repository_ctx.symlink(src_file, dest_dir + "/" + src_file.basename)
+
+
+def _create_cuda_repository(repository_ctx):
+  """Creates the repository containing files set up to build with CUDA."""
+  cuda_toolkit_path = _cuda_toolkit_path(repository_ctx)
+  cuda_version = _cuda_version(repository_ctx)
+  cudnn_install_basedir = _cudnn_install_basedir(repository_ctx)
+  cudnn_version = _cudnn_version(repository_ctx)
+  compute_capabilities = _compute_capabilities(repository_ctx)
+
+  cpu_value = _cpu_value(repository_ctx)
+  symlink_files = _cuda_symlink_files(cpu_value, cuda_version, cudnn_version)
+  _check_lib(repository_ctx, cuda_toolkit_path, symlink_files.cuda_rt_lib)
+  _check_lib(repository_ctx, cuda_toolkit_path, symlink_files.cuda_cupti_lib)
+  _check_dir(repository_ctx, cudnn_install_basedir)
+
+  cudnn_header_dir = _find_cudnn_header_dir(repository_ctx,
+                                            cudnn_install_basedir)
+  cudnn_lib_path = _find_cudnn_lib_path(repository_ctx, cudnn_install_basedir,
+                                        symlink_files)
+
+  # Set up symbolic links for the cuda toolkit. We link at the individual file
+  # level not at the directory level. This is because the external library may
+  # have a different file layout from our desired structure.
+  _symlink_dir(repository_ctx, cuda_toolkit_path + "/include", "cuda/include")
+  _symlink_dir(repository_ctx,
+               cuda_toolkit_path + "/" + symlink_files.cuda_lib_path,
+               "cuda/" + symlink_files.cuda_lib_path)
+  _symlink_dir(repository_ctx, cuda_toolkit_path + "/bin", "cuda/bin")
+  _symlink_dir(repository_ctx, cuda_toolkit_path + "/nvvm", "cuda/nvvm")
+  _symlink_dir(repository_ctx, cuda_toolkit_path + "/extras/CUPTI/include",
+               "cuda/extras/CUPTI/include")
+  repository_ctx.symlink(cuda_toolkit_path + "/" + symlink_files.cuda_cupti_lib,
+                         "cuda/" + symlink_files.cuda_cupti_lib)
+
+  # Set up the symbolic links for cudnn if cudnn was was not installed to
+  # CUDA_TOOLKIT_PATH.
+  if not repository_ctx.path("cuda/include/cudnn.h").exists:
+    repository_ctx.symlink(cudnn_header_dir + "/cudnn.h",
+                           "cuda/include/cudnn.h")
+  if not repository_ctx.path("cuda/" + symlink_files.cuda_dnn_lib).exists:
+    repository_ctx.symlink(cudnn_lib_path, "cuda/" + symlink_files.cuda_dnn_lib)
+
+  # Set up BUILD file for cuda/
+  _file(repository_ctx, "cuda:BUILD")
+  _file(repository_ctx, "cuda:build_defs.bzl")
+  _tpl(repository_ctx, "cuda:platform.bzl",
+       {
+           "%{cuda_version}": cuda_version,
+           "%{cudnn_version}": cudnn_version,
+           "%{platform}": cpu_value,
+       })
+
+  # Set up crosstool/
+  _file(repository_ctx, "crosstool:BUILD")
+  _tpl(repository_ctx, "crosstool:CROSSTOOL",
+       {
+           "%{cuda_version}": ("-%s" % cuda_version) if cuda_version else "",
+       })
+  _tpl(repository_ctx,
+       "crosstool:clang/bin/crosstool_wrapper_driver_is_not_gcc",
+       {
+           "%{cpu_compiler}": str(find_cc(repository_ctx)),
+           "%{gcc_host_compiler_path}": str(find_cc(repository_ctx)),
+           "%{cuda_compute_capabilities}": ", ".join(
+               ["\"%s\"" % c for c in compute_capabilities]),
+       })
+
+  # Set up cuda_config.h, which is used by
+  # tensorflow/stream_executor/dso_loader.cc.
+  _tpl(repository_ctx, "cuda:cuda_config.h",
+       {
+           "%{cuda_version}": cuda_version,
+           "%{cudnn_version}": cudnn_version,
+           "%{cuda_compute_capabilities}": ",".join(
+               ["CudaVersion(\"%s\")" % c for c in compute_capabilities]),
+       })
+
+
+def _cuda_autoconf_impl(repository_ctx):
+  """Implementation of the cuda_autoconf repository rule."""
+  if not _enable_cuda(repository_ctx):
+    _create_dummy_repository(repository_ctx)
+  else:
+    _create_cuda_repository(repository_ctx)
+
+
+cuda_configure = repository_rule(
+    implementation = _cuda_autoconf_impl,
+    local = True,
+)
+"""Detects and configures the local CUDA toolchain.
+
+Add the following to your WORKSPACE FILE:
+
+```python
+cuda_configure(name = "local_config_cuda")
+```
+
+Args:
+  name: A unique name for this workspace rule.
+"""
diff --git a/tools/bazel.rc.template b/tools/bazel.rc.template
index 02856822c9..9a69cac1f6 100644
--- a/tools/bazel.rc.template
+++ b/tools/bazel.rc.template
@@ -1,4 +1,4 @@
-build:cuda --crosstool_top=//third_party/gpus/crosstool
+build:cuda --crosstool_top=@local_config_cuda//crosstool
 build:cuda --define=using_cuda=true --define=using_cuda_nvcc=true
 
 build --force_python=py$PYTHON_MAJOR_VERSION
-- 
GitLab


From cd898d06feeff09a9fb9a0c5cbf8b42d810b55e1 Mon Sep 17 00:00:00 2001
From: Yuan Yu <yuanbyu@google.com>
Date: Thu, 25 Aug 2016 10:25:43 -0800
Subject: [PATCH 142/223] Fix a synchronization bug in the gradient computation
 of nested loops. The stack push/pop operations of one inner loop must be
 ordered before the next one. Change: 131311192

---
 .../kernel_tests/control_flow_ops_py_test.py  | 20 +++++++
 tensorflow/python/ops/control_flow_ops.py     | 57 +++++++++++++++----
 2 files changed, 66 insertions(+), 11 deletions(-)

diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
index c4a99d5d9a..33a07c4807 100644
--- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
+++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
@@ -1405,6 +1405,26 @@ class ControlFlowTest(tf.test.TestCase):
       r = tf.gradients(r, v)[0]
       self.assertAllClose(512.0, r.eval())
 
+  def testNestedWhileGrad_ParallelIterations(self):
+    # Make sure the stack pushes and pops of an inner loop are executed in
+    # the sequential order of the iterations of its outer loop.
+    with self.test_session() as sess:
+      def inner_loop(t):
+        fn = lambda n: n + tf.square(var)
+        return tf.map_fn(fn=fn, elems=t, parallel_iterations=10)
+
+      def outer_loop(inp):
+        return tf.map_fn(fn=inner_loop, elems=inp, parallel_iterations=10)
+
+      var = tf.Variable(tf.constant(3.0))
+      inp = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
+      res = outer_loop(inp)
+      optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
+      train_op = optimizer.minimize(tf.reduce_mean(tf.square(res)))
+      sess.run(tf.initialize_all_variables())
+      sess.run(train_op)
+      self.assertAllClose(2.999, var.eval())
+
   def _testWhileCondGrad_Simple(self, use_gpu):
     with self.test_session(use_gpu=use_gpu):
       v = tf.convert_to_tensor(2.0, name="v")
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index 2af82cf9de..afeab05cd9 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -504,7 +504,7 @@ class GradLoopState(object):
     # The while loop context for forward.
     self._forward_context = None
 
-    # The loop counter added by AddForwardCounter. It is the value
+    # The loop counter added by AddForwardLoopCounter. It is the value
     # of the loop counter for the next iteration.
     self._forward_index = None
 
@@ -514,7 +514,7 @@ class GradLoopState(object):
     # The while loop context for backprop.
     self._grad_context = None
 
-    # The loop counter added by AddBackPropCounter. It is the value
+    # The loop counter added by AddBackPropLoopCounter. It is the value
     # of the loop counter for the current iteration.
     self._grad_index = None
 
@@ -533,7 +533,7 @@ class GradLoopState(object):
 
     # Add the forward loop counter.
     if outer_forward_ctxt: outer_forward_ctxt.Enter()
-    cnt, forward_index = forward_ctxt.AddForwardCounter()
+    cnt, forward_index = forward_ctxt.AddForwardLoopCounter(outer_grad_state)
     if outer_forward_ctxt: outer_forward_ctxt.Exit()
     self._forward_context = forward_ctxt
     self._forward_index = forward_index
@@ -553,7 +553,8 @@ class GradLoopState(object):
                                         forward_ctxt.name,
                                         self)
       real_cnt = outer_grad_state.AddBackPropAccumulatedValue(history_cnt, cnt)
-      self._grad_index = self._grad_context.AddBackPropCounter(real_cnt)
+      self._grad_index = self._grad_context.AddBackPropLoopCounter(
+          real_cnt, outer_grad_state)
       outer_grad_ctxt.Exit()
     else:
       if outer_forward_ctxt: outer_forward_ctxt.Enter()
@@ -562,7 +563,8 @@ class GradLoopState(object):
                                         forward_ctxt.swap_memory,
                                         forward_ctxt.name,
                                         self)
-      self._grad_index = self._grad_context.AddBackPropCounter(cnt)
+      self._grad_index = self._grad_context.AddBackPropLoopCounter(
+          cnt, outer_grad_state)
       if outer_forward_ctxt: outer_forward_ctxt.Exit()
 
   @property
@@ -1142,6 +1144,15 @@ class ControlFlowContext(object):
       return self._outer_context.GetWhileContext()
     return None
 
+  def _IsInOuterContext(self, op):
+    op_ctxt = _GetOutputContext(op)
+    outer_ctxt = self.outer_context
+    while outer_ctxt != op_ctxt:
+      if outer_ctxt is None:
+        return False
+      outer_ctxt = outer_ctxt.outer_context
+    return True
+
   def _MaybeAddToWhileContext(self, op):
     """Add a control dependency to the containing WhileContext.
 
@@ -1364,6 +1375,7 @@ def cond(pred, fn1, fn2, name=None):
     pivot_1 = array_ops.identity(p_1, name="switch_t")
     pivot_2 = array_ops.identity(p_2, name="switch_f")
     pred = array_ops.identity(pred, name="pred_id")
+    # Disable the fetching of tensors that are only on one branch of cond.
     for tensor in [p_1, p_2, pivot_1, pivot_2, pred]:
       tensor.op.graph.prevent_fetching(tensor.op)
 
@@ -1583,7 +1595,7 @@ class WhileContext(ControlFlowContext):
       op._add_control_input(self.GetControlPivot().op)
       # pylint: enable=protected-access
 
-  def AddForwardCounter(self):
+  def AddForwardLoopCounter(self, outer_grad_state):
     """Adds a loop that counts the number of iterations.
 
     This is added to the forward loop at the time when we start to
@@ -1593,11 +1605,21 @@ class WhileContext(ControlFlowContext):
     The pseudocode is:
       `n = 0; while (_pivot) { n++; }`
 
+    Note that a control dependency is added to `n` to ensure the correct
+    execution order of stack push ops.
+
+    Args:
+      outer_grad_state: The outer grad state. None if not nested.
+
     Returns:
       The number of iterations taken by the forward loop and the loop index.
     """
     n = constant_op.constant(0, name="f_count")
-    assert n.op._get_control_flow_context() == self.outer_context
+    if outer_grad_state is not None:
+      # Force the stack pushes of i-th execution of an inner loop to be ordered
+      # before the pushes of (i+1)-th execution of the same inner loop.
+      outer_add_op = outer_grad_state.forward_index.op.inputs[0].op
+      n.op._add_control_input(outer_add_op)  # pylint: disable=protected-access
 
     self.Enter()
     self.AddName(n.name)
@@ -1616,7 +1638,7 @@ class WhileContext(ControlFlowContext):
     self.Exit()
     return total_iterations, next_n
 
-  def AddBackPropCounter(self, count):
+  def AddBackPropLoopCounter(self, count, outer_grad_state):
     """Add the backprop loop that controls the iterations.
 
     This is added to the backprop loop. It is used to control the loop
@@ -1626,8 +1648,12 @@ class WhileContext(ControlFlowContext):
     The pseudocode is:
       `n = count; while (n >= 1) { n--; }`
 
+    Note that a control dependency is added to `final_zero` to ensure the
+    correct execution order of stack pop ops.
+
     Args:
       count: The number of iterations for backprop.
+      outer_grad_state: The outer grad state. None if not nested.
 
     Returns:
       The loop index.
@@ -1651,6 +1677,15 @@ class WhileContext(ControlFlowContext):
     next_count = _NextIteration(index)
     merge_count.op._update_input(1, next_count)
 
+    final_zero = exit(switch_count[0], name="b_count")
+    if outer_grad_state is not None:
+      # Force the stack pops of i-th execution of an inner loop to be ordered
+      # before the pops of (i+1)-th execution of the same inner loop.
+      # pylint: disable=protected-access
+      outer_grad_state.grad_sync._add_control_input(final_zero.op)
+      # pylint: enable=protected-access
+
+    self.ExitResult([final_zero])
     self.Exit()
     return next_count
 
@@ -1938,10 +1973,10 @@ class WhileContext(ControlFlowContext):
       for x in xs:
         inp_op = x.op.inputs[0]
         control_inputs = graph._control_dependencies_for_inputs([inp_op])
-        control_inputs = [op for op in control_inputs
-                          if op._get_control_flow_context() != self]
+        outer_control_inputs = [op for op in control_inputs
+                                if self._IsInOuterContext(op)]
         x.op._set_control_flow_context(self)
-        x.op._add_control_inputs(control_inputs)
+        x.op._add_control_inputs(outer_control_inputs)
         graph._record_op_seen_by_control_dependencies(x.op)
     # pylint: enable=protected-access
 
-- 
GitLab


From e2f551ce6de4740bc5726bbd7bca011a3726e776 Mon Sep 17 00:00:00 2001
From: Rasmus Munk Larsen <rmlarsen@google.com>
Date: Thu, 25 Aug 2016 11:46:20 -0700
Subject: [PATCH 143/223] Add absolute tolerance so test with expected zero
 outputs do not fail. (#4042)

---
 tensorflow/contrib/layers/python/layers/layers_test.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index 2cf47467b5..8e5bc916dd 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -202,7 +202,7 @@ class Convolution2dTest(tf.test.TestCase):
       output = tf.contrib.layers.convolution2d(images, 32, [3, 3],
                                                scope='conv1')
       self.assertEquals(output.op.name, 'conv1/Relu')
-  
+
   def testCreateConvWithCollection(self):
     height, width = 3, 3
     images = tf.random_uniform((5, height, width, 3), seed=1)
@@ -998,7 +998,7 @@ class FCTest(tf.test.TestCase):
       inputs = tf.random_uniform((5, height * width * 3), seed=1)
       output = tf.contrib.layers.fully_connected(inputs, 32, scope='fc1')
       self.assertEquals(output.op.name, 'fc1/Relu')
-      
+
   def testCreateFCWithCollection(self):
     height, width = 3, 3
     inputs = tf.random_uniform((5, height * width * 3), seed=1)
@@ -1585,8 +1585,9 @@ class LayerNormTest(tf.test.TestCase):
       expected_var = np.ones(input_shape[0])
       mean = np.mean(outputs, axis=moments_axis)
       var = np.var(outputs, axis=moments_axis)
-      self.assertAllClose(mean, expected_mean, rtol=1e-5)
-      self.assertAllClose(var, expected_var, rtol=1e-5)
+      tol = 1e-5
+      self.assertAllClose(mean, expected_mean, rtol=tol, atol=tol)
+      self.assertAllClose(var, expected_var, rtol=tol, atol=tol)
 
   def testOutput2DInput(self):
     self.doOutputTest((10, 300))
-- 
GitLab


From 62dcfe9788b1e447e8c8a3f4e1feeef7b5486ccf Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 10:33:02 -0800
Subject: [PATCH 144/223] Update ops-related pbtxt files. Change: 131312123

---
 tensorflow/core/ops/ops.pbtxt | 36 -----------------------------------
 1 file changed, 36 deletions(-)

diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index bd5593a6a2..2583128d8d 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -4680,42 +4680,6 @@ op {
   summary: "Decode a PNG-encoded image to a uint8 or uint16 tensor."
   description: "The attr `channels` indicates the desired number of color channels for the\ndecoded image.\n\nAccepted values are:\n\n*   0: Use the number of channels in the PNG-encoded image.\n*   1: output a grayscale image.\n*   3: output an RGB image.\n*   4: output an RGBA image.\n\nIf needed, the PNG-encoded image is transformed to match the requested number\nof color channels."
 }
-op {
-  name: "DecodeGif"
-  input_arg {
-    name: "contents"
-    description: "0-D.  The GIF-encoded image."
-    type: DT_STRING
-  }
-  output_arg {
-    name: "image"
-    description: "3-D with shape `[height, width, channels]`."
-    type_attr: "dtype"
-  }
-  attr {
-    name: "channels"
-    type: "int"
-    default_value {
-      i: 0
-    }
-    description: "Number of color channels for the decoded image."
-  }
-  attr {
-    name: "dtype"
-    type: "type"
-    default_value {
-      type: DT_UINT8
-    }
-    allowed_values {
-      list {
-        type: DT_UINT8
-        type: DT_UINT16
-      }
-    }
-  }
-  summary: "Decode a GIF-encoded image to a uint8 or uint16 tensor."
-  description: "The attr `channels` indicates the desired number of color channels for the\ndecoded image.\n\nAccepted values are:\n\n*   0: Use the number of channels in the GIF-encoded image.\n*   1: output a grayscale image.\n*   3: output an RGB image.\n*   4: output an RGBA image.\n\nIf needed, the GIF-encoded image is transformed to match the requested number\nof color channels."
-}
 op {
   name: "DecodeRaw"
   input_arg {
-- 
GitLab


From b987f966757f09009eddadc3884ed5714c42f0ad Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 10:36:03 -0800
Subject: [PATCH 145/223] Add unittest coverage for TensorArrayUnpack applied
 to 0-element matrix. Change: 131312466

---
 .../python/kernel_tests/tensor_array_ops_test.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/tensorflow/python/kernel_tests/tensor_array_ops_test.py b/tensorflow/python/kernel_tests/tensor_array_ops_test.py
index e21a199595..49250eb42a 100644
--- a/tensorflow/python/kernel_tests/tensor_array_ops_test.py
+++ b/tensorflow/python/kernel_tests/tensor_array_ops_test.py
@@ -171,6 +171,22 @@ class TensorArrayCPUTest(tf.test.TestCase):
       self.assertAllEqual(convert([2.0, 2.1]), d1)
       self.assertAllEqual(convert([3.0, 3.1]), d2)
 
+      # Reset ta because we're going to change the shape, else shape
+      # inference will throw an error.
+      ta = tensor_array_ops.TensorArray(
+          dtype=tf_dtype, tensor_array_name="foo", size=3)
+
+      # Try unpacking an empty matrix, which should not cause an error.
+      w2 = ta.unpack(convert([[], [], []]))
+      r0 = w2.read(0)
+      r1 = w2.read(1)
+      r2 = w2.read(2)
+
+      d0, d1, d2 = session.run([r0, r1, r2])
+      self.assertAllEqual(convert([]), d0)
+      self.assertAllEqual(convert([]), d1)
+      self.assertAllEqual(convert([]), d2)
+
   def testTensorArrayUnpackRead(self):
     self._testTensorArrayUnpackRead(tf.float32)
     self._testTensorArrayUnpackRead(tf.float64)
-- 
GitLab


From 99ce233191920fb0dde2d823cf493e28b89618c7 Mon Sep 17 00:00:00 2001
From: HW-ZZ <dingzezhen@huawei.com>
Date: Fri, 26 Aug 2016 02:47:18 +0800
Subject: [PATCH 146/223] Change device_setter.py for choose ps device (#4008)

* Change device_setter.py for choose ps device

Add arguments "ps_devices" for replica_device_setter(), in order to choose ps decive .

* Change device_setter.py for choose ps device

Add arguments "ps_device_name" for replica_device_setter(), in order to choose ps decive.

* Update ps_job_name in replica_device_setter()

* Update ps_job_name in replica_device_setter()

* Add testPS2TasksWithCPUConstraint()

Add testPS2TasksWithCPUConstraint() to test if ps_decive="/job:ps/cpu:0" can put all the variables on the CPU by default

* Modify code indentation
---
 tensorflow/python/training/device_setter.py     |  2 +-
 .../python/training/device_setter_test.py       | 17 +++++++++++++++++
 2 files changed, 18 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/training/device_setter.py b/tensorflow/python/training/device_setter.py
index c7a8af85d1..a414116a74 100644
--- a/tensorflow/python/training/device_setter.py
+++ b/tensorflow/python/training/device_setter.py
@@ -150,7 +150,7 @@ def replica_device_setter(ps_tasks=0, ps_device="/job:ps",
     else:
       cluster_spec = server_lib.ClusterSpec(cluster).as_dict()
     # Get ps_job_name from ps_device by striping "/job:".
-    ps_job_name = ps_device.lstrip("/job:")
+    ps_job_name = pydev.DeviceSpec.from_string(ps_device).job
     if ps_job_name not in cluster_spec or cluster_spec[ps_job_name] is None:
       return None
     ps_tasks = len(cluster_spec[ps_job_name])
diff --git a/tensorflow/python/training/device_setter_test.py b/tensorflow/python/training/device_setter_test.py
index 8fb2768c5f..607cf2d20d 100644
--- a/tensorflow/python/training/device_setter_test.py
+++ b/tensorflow/python/training/device_setter_test.py
@@ -77,6 +77,23 @@ class DeviceSetterTest(tf.test.TestCase):
       self.assertDeviceEqual("/job:moon/task:1", w.device)
       self.assertDeviceEqual("/job:moon/task:1", w.initializer.device)
       self.assertDeviceEqual("/job:sun", a.device)
+  
+  def testPS2TasksWithCPUConstraint(self):
+    cluster_spec = tf.train.ClusterSpec({
+        "sun": ["sun0:2222", "sun1:2222", "sun2:2222"],
+        "moon": ["moon0:2222", "moon1:2222"]})
+
+    with tf.device(tf.train.replica_device_setter(
+        ps_device="/job:moon/cpu:0", worker_device="/job:sun",
+        cluster=cluster_spec.as_cluster_def())):
+      v = tf.Variable([1, 2])
+      w = tf.Variable([2, 1])
+      a = v + w
+      self.assertDeviceEqual("/job:moon/task:0/cpu:0", v.device)
+      self.assertDeviceEqual("/job:moon/task:0/cpu:0", v.initializer.device)
+      self.assertDeviceEqual("/job:moon/task:1/cpu:0", w.device)
+      self.assertDeviceEqual("/job:moon/task:1/cpu:0", w.initializer.device)
+      self.assertDeviceEqual("/job:sun", a.device)
 
 
 if __name__ == "__main__":
-- 
GitLab


From 3cc4fb680a8869e8c6b8e892f96b03ff75584b3b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 10:39:22 -0800
Subject: [PATCH 147/223] Update generated Python Op docs. Change: 131312892

---
 .../shard0/tf.nn.ctc_loss.md                   | 18 ++++++++++++++++--
 .../tf.report_uninitialized_variables.md       |  3 +--
 .../shard5/tf.train.Optimizer.md               |  5 +++--
 tensorflow/g3doc/api_docs/python/nn.md         | 18 ++++++++++++++++--
 tensorflow/g3doc/api_docs/python/state_ops.md  |  3 +--
 tensorflow/g3doc/api_docs/python/train.md      |  5 +++--
 6 files changed, 40 insertions(+), 12 deletions(-)

diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.ctc_loss.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.ctc_loss.md
index 229df8bdbd..7cc14a877a 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.ctc_loss.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.nn.ctc_loss.md
@@ -19,6 +19,18 @@ max(labels.indices(labels.indices[:, 1] == b, 2))
   <= sequence_length(b) for all b.
 ```
 
+Notes:
+
+This class performs the softmax operation for you, so inputs should
+be e.g. linear projections of outputs by an LSTM.
+
+The `inputs` Tensor's innermost dimension size, `num_classes`, represents
+`num_labels + 1` classes, where num_labels is the number of true labels, and
+the largest value `(num_classes - 1)` is reserved for the blank label.
+
+For example, for a vocabulary containing 3 labels `[a, b, c]`,
+`num_classes = 4` and the labels indexing is `{a: 0, b: 1, c: 2, blank: 3}`.
+
 Regarding the arguments `preprocess_collapse_repeated` and
 `ctc_merge_repeated`:
 
@@ -57,10 +69,12 @@ Here is a table of the (roughly) expected first order behavior:
 
 
 *  <b>`inputs`</b>: 3-D `float` `Tensor` sized
-    `[max_time x batch_size x num_classes]`.  The logits.
+    `[max_time x batch_size x num_classes]`. The logits.
 *  <b>`labels`</b>: An `int32` `SparseTensor`.
     `labels.indices[i, :] == [b, t]` means `labels.values[i]` stores
-    the id for (batch b, time t).  See `core/ops/ctc_ops.cc` for more details.
+    the id for (batch b, time t).
+    `labels.values[i]` must take on values in `[0, num_labels)`.
+    See `core/ops/ctc_ops.cc` for more details.
 *  <b>`sequence_length`</b>: 1-D `int32` vector, size `[batch_size]`.
     The sequence lengths.
 *  <b>`preprocess_collapse_repeated`</b>: Boolean.  Default: False.
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.report_uninitialized_variables.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.report_uninitialized_variables.md
index 5ff0341d83..59c1394a4a 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.report_uninitialized_variables.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.report_uninitialized_variables.md
@@ -15,6 +15,5 @@ variables if there are any, or an empty array if there are none.
 ##### Returns:
 
   A 1-D tensor containing names of the uninitialized variables, or an empty
-  1-D
-  tensor if there are no variables or no uninitialized variables.
+  1-D tensor if there are no variables or no uninitialized variables.
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Optimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Optimizer.md
index ff14086bb4..cc3b53d119 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Optimizer.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Optimizer.md
@@ -184,8 +184,9 @@ applies gradients.
 
 ### Gating Gradients
 
-Both `minimize()` and `compute_gradients()` accept a `gate_gradients` argument
-that controls the degree of parallelism during the application of the gradients.
+Both `minimize()` and `compute_gradients()` accept a `gate_gradients`
+argument that controls the degree of parallelism during the application of
+the gradients.
 
 The possible values are: `GATE_NONE`, `GATE_OP`, and `GATE_GRAPH`.
 
diff --git a/tensorflow/g3doc/api_docs/python/nn.md b/tensorflow/g3doc/api_docs/python/nn.md
index 2e4a58e417..85cf91a4fa 100644
--- a/tensorflow/g3doc/api_docs/python/nn.md
+++ b/tensorflow/g3doc/api_docs/python/nn.md
@@ -2107,6 +2107,18 @@ max(labels.indices(labels.indices[:, 1] == b, 2))
   <= sequence_length(b) for all b.
 ```
 
+Notes:
+
+This class performs the softmax operation for you, so inputs should
+be e.g. linear projections of outputs by an LSTM.
+
+The `inputs` Tensor's innermost dimension size, `num_classes`, represents
+`num_labels + 1` classes, where num_labels is the number of true labels, and
+the largest value `(num_classes - 1)` is reserved for the blank label.
+
+For example, for a vocabulary containing 3 labels `[a, b, c]`,
+`num_classes = 4` and the labels indexing is `{a: 0, b: 1, c: 2, blank: 3}`.
+
 Regarding the arguments `preprocess_collapse_repeated` and
 `ctc_merge_repeated`:
 
@@ -2145,10 +2157,12 @@ Here is a table of the (roughly) expected first order behavior:
 
 
 *  <b>`inputs`</b>: 3-D `float` `Tensor` sized
-    `[max_time x batch_size x num_classes]`.  The logits.
+    `[max_time x batch_size x num_classes]`. The logits.
 *  <b>`labels`</b>: An `int32` `SparseTensor`.
     `labels.indices[i, :] == [b, t]` means `labels.values[i]` stores
-    the id for (batch b, time t).  See `core/ops/ctc_ops.cc` for more details.
+    the id for (batch b, time t).
+    `labels.values[i]` must take on values in `[0, num_labels)`.
+    See `core/ops/ctc_ops.cc` for more details.
 *  <b>`sequence_length`</b>: 1-D `int32` vector, size `[batch_size]`.
     The sequence lengths.
 *  <b>`preprocess_collapse_repeated`</b>: Boolean.  Default: False.
diff --git a/tensorflow/g3doc/api_docs/python/state_ops.md b/tensorflow/g3doc/api_docs/python/state_ops.md
index 39574c4cb0..52735d36b0 100644
--- a/tensorflow/g3doc/api_docs/python/state_ops.md
+++ b/tensorflow/g3doc/api_docs/python/state_ops.md
@@ -624,8 +624,7 @@ variables if there are any, or an empty array if there are none.
 ##### Returns:
 
   A 1-D tensor containing names of the uninitialized variables, or an empty
-  1-D
-  tensor if there are no variables or no uninitialized variables.
+  1-D tensor if there are no variables or no uninitialized variables.
 
 
 - - -
diff --git a/tensorflow/g3doc/api_docs/python/train.md b/tensorflow/g3doc/api_docs/python/train.md
index 3d0329ad3f..e9c9ad63e3 100644
--- a/tensorflow/g3doc/api_docs/python/train.md
+++ b/tensorflow/g3doc/api_docs/python/train.md
@@ -204,8 +204,9 @@ applies gradients.
 
 ### Gating Gradients
 
-Both `minimize()` and `compute_gradients()` accept a `gate_gradients` argument
-that controls the degree of parallelism during the application of the gradients.
+Both `minimize()` and `compute_gradients()` accept a `gate_gradients`
+argument that controls the degree of parallelism during the application of
+the gradients.
 
 The possible values are: `GATE_NONE`, `GATE_OP`, and `GATE_GRAPH`.
 
-- 
GitLab


From 6abea4ff1c8f873db3e091365e6ccb44ac5370d6 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 10:41:26 -0800
Subject: [PATCH 148/223] enable creating shared embedding columns Change:
 131313156

---
 .../layers/python/layers/feature_column.py    | 247 ++++++++++++++++--
 .../python/layers/feature_column_test.py      |  49 ++++
 2 files changed, 272 insertions(+), 24 deletions(-)

diff --git a/tensorflow/contrib/layers/python/layers/feature_column.py b/tensorflow/contrib/layers/python/layers/feature_column.py
index 9f68498bc6..35c6d1a2d7 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column.py
@@ -91,6 +91,7 @@ from tensorflow.python.ops import parsing_ops
 from tensorflow.python.ops import string_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
+from tensorflow.python.platform import tf_logging as logging
 
 
 class _EmbeddingLookupArguments(
@@ -360,6 +361,18 @@ class _SparseColumn(_FeatureColumn,
         initializer=init_ops.zeros_initializer,
         combiner=self.combiner)
 
+  def is_compatible(self, other_column):
+    """Check compatability of two sparse columns."""
+    if self.lookup_config and other_column.lookup_config:
+      return self.lookup_config == other_column.lookup_config
+    compatible = (self.length == other_column.length and
+                  (self.dtype == other_column.dtype or
+                   (self.dtype.is_integer and other_column.dtype.is_integer)))
+    if compatible:
+      logging.warn("Column {} and {} may not have the same vocabulary.".
+                   format(self.name, other_column.name))
+    return compatible
+
 
 class _SparseColumnIntegerized(_SparseColumn):
   """See `sparse_column_with_integerized_feature`."""
@@ -619,8 +632,8 @@ def weighted_sparse_column(sparse_id_column,
   """Creates a _SparseColumn by combining sparse_id_column with a weight column.
 
   Args:
-    sparse_id_column: A _SparseColumn which is created by `sparse_column_with_*`
-      functions.
+    sparse_id_column: A `_SparseColumn` which is created by
+      `sparse_column_with_*` functions.
     weight_column_name: A string defining a sparse column name which represents
       weight or value of the corresponding sparse id feature.
     dtype: Type of weights, such as `tf.float32`
@@ -657,12 +670,13 @@ def weighted_sparse_column(sparse_id_column,
 class _EmbeddingColumn(_FeatureColumn, collections.namedtuple(
     "_EmbeddingColumn",
     ["sparse_id_column", "dimension", "combiner", "initializer",
-     "ckpt_to_load_from", "tensor_name_in_ckpt"])):
+     "ckpt_to_load_from", "tensor_name_in_ckpt", "shared_embedding_name",
+     "shared_vocab_size"])):
   """Represents an embedding column.
 
   Args:
-    sparse_id_column: A _SparseColumn which is created by `sparse_column_with_*`
-      or `weighted_sparse_column` functions.
+    sparse_id_column: A `_SparseColumn` which is created by
+      `sparse_column_with_*` or `weighted_sparse_column` functions.
     dimension: An integer specifying dimension of the embedding.
     combiner: A string specifying how to reduce if there are multiple entries
       in a single row. Currently "mean", "sqrtn" and "sum" are supported. Each
@@ -681,6 +695,9 @@ class _EmbeddingColumn(_FeatureColumn, collections.namedtuple(
     tensor_name_in_ckpt: (Optional). Name of the `Tensor` in the provided
       checkpoint from which to restore the column weights. Required if
       `ckpt_to_load_from` is not None.
+    shared_embedding_name: (Optional). The common name for shared embedding.
+    shared_vocab_size: (Optional). The common vocab_size used for shared
+      embedding space.
 
   Raises:
     ValueError: if `initializer` is specified and is not callable. Also,
@@ -693,7 +710,9 @@ class _EmbeddingColumn(_FeatureColumn, collections.namedtuple(
               combiner="mean",
               initializer=None,
               ckpt_to_load_from=None,
-              tensor_name_in_ckpt=None):
+              tensor_name_in_ckpt=None,
+              shared_embedding_name=None,
+              shared_vocab_size=None):
     if initializer is not None and not callable(initializer):
       raise ValueError("initializer must be callable if specified. "
                        "Embedding of column_name: {}".format(
@@ -710,16 +729,24 @@ class _EmbeddingColumn(_FeatureColumn, collections.namedtuple(
     return super(_EmbeddingColumn, cls).__new__(cls, sparse_id_column,
                                                 dimension, combiner,
                                                 initializer, ckpt_to_load_from,
-                                                tensor_name_in_ckpt)
+                                                tensor_name_in_ckpt,
+                                                shared_embedding_name,
+                                                shared_vocab_size)
 
   @property
   def name(self):
-    return "{}_embedding".format(self.sparse_id_column.name)
+    if self.shared_embedding_name is None:
+      return "{}_embedding".format(self.sparse_id_column.name)
+    else:
+      return "{}_shared_embedding".format(self.sparse_id_column.name)
 
   @property
   def length(self):
     """Returns id size."""
-    return self.sparse_id_column.length
+    if self.shared_vocab_size is None:
+      return self.sparse_id_column.length
+    else:
+      return self.shared_vocab_size
 
   @property
   def config(self):
@@ -738,6 +765,7 @@ class _EmbeddingColumn(_FeatureColumn, collections.namedtuple(
                          input_tensor,
                          weight_collections=None,
                          trainable=True):
+    is_shared_embedding = self.shared_embedding_name is not None
     output, embedding_weights = _create_embedding_lookup(
         input_tensor=self.sparse_id_column.id_tensor(input_tensor),
         weight_tensor=self.sparse_id_column.weight_tensor(input_tensor),
@@ -746,7 +774,10 @@ class _EmbeddingColumn(_FeatureColumn, collections.namedtuple(
         weight_collections=_add_variable_collection(weight_collections),
         initializer=self.initializer,
         combiner=self.combiner,
-        trainable=trainable)
+        trainable=trainable,
+        name=self.shared_embedding_name,
+        is_shared_embedding=is_shared_embedding)
+
     if self.ckpt_to_load_from is not None:
       weights_to_restore = embedding_weights
       if len(embedding_weights) == 1:
@@ -782,16 +813,16 @@ def embedding_column(sparse_id_column,
                      initializer=None,
                      ckpt_to_load_from=None,
                      tensor_name_in_ckpt=None):
-  """Creates an _EmbeddingColumn.
+  """Creates an `_EmbeddingColumn`.
 
   Args:
-    sparse_id_column: A _SparseColumn which is created by `sparse_column_with_*`
-      or crossed_column functions. Note that `combiner` defined in
-      `sparse_id_column` is ignored.
+    sparse_id_column: A `_SparseColumn` which is created by for example
+      `sparse_column_with_*` or crossed_column functions. Note that `combiner`
+      defined in `sparse_id_column` is ignored.
     dimension: An integer specifying dimension of the embedding.
     combiner: A string specifying how to reduce if there are multiple entries
       in a single row. Currently "mean", "sqrtn" and "sum" are supported. Each
-      of this can be thought as example level normalizations on the column:
+      of this can be considered an example level normalization on the column:
         * "sum": do not normalize
         * "mean": do l1 normalization
         * "sqrtn": do l2 normalization
@@ -808,12 +839,100 @@ def embedding_column(sparse_id_column,
       `ckpt_to_load_from` is not None.
 
   Returns:
-    An _EmbeddingColumn.
+    An `_EmbeddingColumn`.
   """
   return _EmbeddingColumn(sparse_id_column, dimension, combiner, initializer,
                           ckpt_to_load_from, tensor_name_in_ckpt)
 
 
+def shared_embedding_columns(sparse_id_columns,
+                             dimension,
+                             combiner="mean",
+                             shared_embedding_name=None,
+                             initializer=None,
+                             ckpt_to_load_from=None,
+                             tensor_name_in_ckpt=None):
+  """Creates a list of `_EmbeddingColumn` sharing the same embedding.
+
+  Args:
+    sparse_id_columns: An iterable of `_SparseColumn`, such as those created by
+      `sparse_column_with_*` or crossed_column functions. Note that `combiner`
+      defined in each sparse_id_column is ignored.
+    dimension: An integer specifying dimension of the embedding.
+    combiner: A string specifying how to reduce if there are multiple entries
+      in a single row. Currently "mean", "sqrtn" and "sum" are supported. Each
+      of this can be considered an example level normalization on the column:
+        * "sum": do not normalize
+        * "mean": do l1 normalization
+        * "sqrtn": do l2 normalization
+      For more information: `tf.embedding_lookup_sparse`.
+    shared_embedding_name: (Optional). A string specifying the name of shared
+      embedding weights. This will be needed if you want to reference the shared
+      embedding separately from the generated `_EmbeddingColumn`.
+    initializer: A variable initializer function to be used in embedding
+      variable initialization. If not specified, defaults to
+      `tf.truncated_normal_initializer` with mean 0.0 and standard deviation
+      1/sqrt(sparse_id_columns[0].length).
+    ckpt_to_load_from: (Optional). String representing checkpoint name/pattern
+      to restore the column weights. Required if `tensor_name_in_ckpt` is not
+      None.
+    tensor_name_in_ckpt: (Optional). Name of the `Tensor` in the provided
+      checkpoint from which to restore the column weights. Required if
+      `ckpt_to_load_from` is not None.
+
+  Returns:
+    A tuple of `_EmbeddingColumn` with shared embedding space.
+
+  Raises:
+    ValueError: if sparse_id_columns is empty, or its elements are not
+      compatible with each other.
+    TypeError: if at least one element of sparse_id_columns is not a
+      `SparseTensor`.
+  """
+  if len(sparse_id_columns) < 1:
+    raise ValueError("The input sparse_id_columns should have at least one "
+                     "element.")
+  for sparse_id_column in sparse_id_columns:
+    if not isinstance(sparse_id_column, _SparseColumn):
+      raise TypeError("Elements of sparse_id_columns must be _SparseColumn, but"
+                      "{} is not.".format(sparse_id_column))
+
+  if not isinstance(sparse_id_columns, list):
+    sparse_id_columns = list(sparse_id_columns)
+  if len(sparse_id_columns) == 1:
+    return [
+        _EmbeddingColumn(sparse_id_columns[0], dimension, combiner, initializer,
+                         ckpt_to_load_from, tensor_name_in_ckpt,
+                         shared_embedding_name)]
+  else:
+    # check compatibility of sparse_id_columns
+    compatible = True
+    for column in sparse_id_columns[1:]:
+      compatible = compatible and column.is_compatible(sparse_id_columns[0])
+    if not compatible:
+      raise ValueError("The input sparse id columns are not compatible.")
+    # Construct the shared name and size for shared embedding space.
+    if not shared_embedding_name:
+      if len(sparse_id_columns) <= 3:
+        shared_embedding_name = "_".join([column.name
+                                          for column in sparse_id_columns])
+      else:
+        shared_embedding_name = "_".join([column.name
+                                          for column in sparse_id_columns[0:3]])
+        shared_embedding_name += (
+            "_plus_{}_others".format(len(sparse_id_columns)-3))
+      shared_embedding_name += "_shared_embedding"
+    shared_vocab_size = sparse_id_columns[0].length
+
+    embedded_columns = []
+    for column in sparse_id_columns:
+      embedded_columns.append(
+          _EmbeddingColumn(column, dimension, combiner, initializer,
+                           ckpt_to_load_from, tensor_name_in_ckpt,
+                           shared_embedding_name, shared_vocab_size))
+    return tuple(embedded_columns)
+
+
 class _HashedEmbeddingColumn(collections.namedtuple(
     "_HashedEmbeddingColumn", ["column_name", "size", "dimension", "combiner",
                                "initializer"]), _EmbeddingColumn):
@@ -1723,7 +1842,7 @@ def _add_variable_collection(weight_collections):
 
 
 def _create_embeddings(shape, dtype, initializer, trainable, weight_collections,
-                       name="weights"):
+                       name=None):
   """Creates embedding variable.
 
   If called within the scope of a partitioner, will partition the variable and
@@ -1748,6 +1867,8 @@ def _create_embeddings(shape, dtype, initializer, trainable, weight_collections,
   Raises:
     ValueError: If initializer is None or not callable.
   """
+  if name is None:
+    name = "weights"
   if not initializer:
     raise ValueError("initializer must be defined.")
   if not callable(initializer):
@@ -1764,9 +1885,76 @@ def _create_embeddings(shape, dtype, initializer, trainable, weight_collections,
     return embeddings._get_variable_list()  # pylint: disable=protected-access
 
 
+def _create_shared_embeddings(name, shape, dtype, initializer, trainable,
+                              weight_collections):
+  """Creates or reuse shared embedding variable.
+
+  If called within the scope of a partitioner, will partition the variable and
+  return a list of `tf.Variable`. If no partitioner is specified, returns a list
+  with just one variable.
+
+  Args:
+    name: A string specifying the name of the embedding variable.
+    shape: shape of the embeddding. Note this is not the shape of partitioned
+      variables.
+    dtype: type of the embedding. Also the shape of each partitioned variable.
+    initializer: A variable initializer function to be used in embedding
+      variable initialization.
+    trainable: If `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
+    weight_collections: List of graph collections to which embedding variables
+      are added.
+
+  Returns:
+    A list of `tf.Variable` containing the partitioned embeddings.
+
+  Raises:
+    ValueError: If initializer is None or not callable, or shape of existing
+      embedding does not match required shape.
+  """
+  if not initializer:
+    raise ValueError("initializer must be defined.")
+  if not callable(initializer):
+    raise ValueError("initializer must be callable.")
+
+  shared_embedding_collection_name = (
+      "SHARED_EMBEDDING_COLLECTION_" + name.upper())
+  graph = ops.get_default_graph()
+  shared_embedding_collection = (
+      graph.get_collection_ref(shared_embedding_collection_name))
+  if shared_embedding_collection:
+    if len(shared_embedding_collection) > 1:
+      raise ValueError("Collection %s can only contain one "
+                       "(partitioned) variable."
+                       % shared_embedding_collection_name)
+    else:
+      embeddings = shared_embedding_collection[0]
+      if embeddings.get_shape() != shape:
+        raise ValueError("The embedding variable with name {} already exists, "
+                         "but its shape does not match required embedding shape"
+                         " here. Please make sure to use different "
+                         "shared_embedding_name for different shared "
+                         "embeddings.".format(name))
+  else:
+    embeddings = contrib_variables.model_variable(
+        name=name,
+        shape=shape,
+        dtype=dtype,
+        initializer=initializer,
+        trainable=trainable,
+        collections=weight_collections)
+    graph.add_to_collection(shared_embedding_collection_name, embeddings)
+
+  if isinstance(embeddings, variables.Variable):
+    return [embeddings]
+  else:  # Else it should be of type `_PartitionedVariable`.
+    return embeddings._get_variable_list()  # pylint: disable=protected-access
+
+
 def _create_embedding_lookup(input_tensor, weight_tensor, vocab_size, dimension,
                              weight_collections, initializer, combiner,
-                             trainable, name="weights"):
+                             trainable, name="weights",
+                             is_shared_embedding=False):
   """Creates embedding variable and does a lookup.
 
   Args:
@@ -1788,17 +1976,28 @@ def _create_embedding_lookup(input_tensor, weight_tensor, vocab_size, dimension,
     trainable: If `True` also add variables to the graph collection
       `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
     name: A string specifying the name of the embedding variable.
+    is_shared_embedding: An bool indicating if this is creating shared embedding
+      variable.
 
   Returns:
     A Tensor with shape [batch_size, dimension] and embedding Variable.
   """
 
-  embeddings = _create_embeddings(name=name,
-                                  shape=[vocab_size, dimension],
-                                  dtype=dtypes.float32,
-                                  initializer=initializer,
-                                  trainable=trainable,
-                                  weight_collections=weight_collections)
+  if is_shared_embedding:
+    embeddings = _create_shared_embeddings(
+        name=name,
+        shape=[vocab_size, dimension],
+        dtype=dtypes.float32,
+        initializer=initializer,
+        trainable=trainable,
+        weight_collections=weight_collections)
+  else:
+    embeddings = _create_embeddings(name=name,
+                                    shape=[vocab_size, dimension],
+                                    dtype=dtypes.float32,
+                                    initializer=initializer,
+                                    trainable=trainable,
+                                    weight_collections=weight_collections)
   return embedding_ops.safe_embedding_lookup_sparse(
       embeddings,
       input_tensor,
diff --git a/tensorflow/contrib/layers/python/layers/feature_column_test.py b/tensorflow/contrib/layers/python/layers/feature_column_test.py
index 91ff677b1b..87aebb466c 100644
--- a/tensorflow/contrib/layers/python/layers/feature_column_test.py
+++ b/tensorflow/contrib/layers/python/layers/feature_column_test.py
@@ -52,6 +52,55 @@ class FeatureColumnTest(tf.test.TestCase):
     self.assertEqual(b.dimension, 4)
     self.assertEqual(b.combiner, "mean")
 
+  def testSharedEmbeddingColumn(self):
+    a1 = tf.contrib.layers.sparse_column_with_keys(
+        "a1", ["marlo", "omar", "stringer"])
+    a2 = tf.contrib.layers.sparse_column_with_keys(
+        "a2", ["marlo", "omar", "stringer"])
+    b = tf.contrib.layers.shared_embedding_columns(
+        [a1, a2], dimension=4, combiner="mean")
+    self.assertEqual(len(b), 2)
+    self.assertEqual(b[0].shared_embedding_name, "a1_a2_shared_embedding")
+    self.assertEqual(b[1].shared_embedding_name, "a1_a2_shared_embedding")
+
+    # Create a sparse id tensor for a1.
+    input_tensor_c1 = tf.SparseTensor(indices=[[0, 0], [1, 1], [2, 2]],
+                                      values=[0, 1, 2], shape=[3, 3])
+    # Create a sparse id tensor for a2.
+    input_tensor_c2 = tf.SparseTensor(indices=[[0, 0], [1, 1], [2, 2]],
+                                      values=[0, 1, 2], shape=[3, 3])
+    with tf.variable_scope("run_1"):
+      b1 = b[0].to_dnn_input_layer(input_tensor_c1)
+      b2 = b[1].to_dnn_input_layer(input_tensor_c2)
+    with self.test_session() as sess:
+      sess.run(tf.initialize_all_variables())
+      b1_value = b1.eval()
+      b2_value = b2.eval()
+    for i in range(len(b1_value)):
+      self.assertAllClose(b1_value[i], b2_value[i])
+
+    # Test the case when a shared_embedding_name is explictly specified.
+    d = tf.contrib.layers.shared_embedding_columns(
+        [a1, a2], dimension=4, combiner="mean",
+        shared_embedding_name="my_shared_embedding")
+    # a3 is a completely different sparse column with a1 and a2, but since the
+    # same shared_embedding_name is passed in, a3 will have the same embedding
+    # as a1 and a2
+    a3 = tf.contrib.layers.sparse_column_with_keys(
+        "a3", ["cathy", "tom", "anderson"])
+    e = tf.contrib.layers.shared_embedding_columns(
+        [a3], dimension=4, combiner="mean",
+        shared_embedding_name="my_shared_embedding")
+    with tf.variable_scope("run_2"):
+      d1 = d[0].to_dnn_input_layer(input_tensor_c1)
+      e1 = e[0].to_dnn_input_layer(input_tensor_c1)
+    with self.test_session() as sess:
+      sess.run(tf.initialize_all_variables())
+      d1_value = d1.eval()
+      e1_value = e1.eval()
+    for i in range(len(d1_value)):
+      self.assertAllClose(d1_value[i], e1_value[i])
+
   def testRealValuedColumn(self):
     a = tf.contrib.layers.real_valued_column("aaa")
     self.assertEqual(a.name, "aaa")
-- 
GitLab


From 406e819d0c1b73295b22f5c348c7e1e3ee3f6280 Mon Sep 17 00:00:00 2001
From: cwhipkey <cwhipkey@google.com>
Date: Thu, 25 Aug 2016 14:04:50 -0700
Subject: [PATCH 149/223] Switch 'friend class DimensionOrConstant' to use
 struct, to match actual declaration. (#4045)

---
 tensorflow/core/framework/shape_inference.h | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/framework/shape_inference.h b/tensorflow/core/framework/shape_inference.h
index f65dac1686..74cfbba72e 100644
--- a/tensorflow/core/framework/shape_inference.h
+++ b/tensorflow/core/framework/shape_inference.h
@@ -57,7 +57,7 @@ class DimensionHandle {
 
   const Dimension* ptr_ = nullptr;
 
-  friend class DimensionOrConstant;
+  friend struct DimensionOrConstant;
   friend class InferenceContext;
   friend class ShapeInferenceTest;
   friend class ShapeInferenceTestutil;
-- 
GitLab


From 0cdde9b59489eba6ed40285d64f6326e5cd7b4a0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Dan=20Man=C3=A9?= <danmane@google.com>
Date: Thu, 25 Aug 2016 10:51:39 -0800
Subject: [PATCH 150/223] Add tf.summary.scalar, a replacement for
 tf.scalar_summary implemented via TensorSummary.

It is very similar to tf.scalar_summary (almost identical in usage), but as it is implemented via TensorSummary it has some advantages, e.g. labels may be attached and the summaries always include the node_name.
This CL tests that it works; however, integration into the EventAccumulator (and thus full usability via TensorBoard) is saved for another CL.
Change: 131314340
---
 tensorflow/core/kernels/summary_op.cc     | 10 +--
 tensorflow/python/BUILD                   |  1 +
 tensorflow/python/ops/summary_ops.py      | 17 +++--
 tensorflow/python/summary/summary.py      | 58 ++++++++++++++-
 tensorflow/python/summary/summary_test.py | 88 +++++++++++++++++++++++
 5 files changed, 160 insertions(+), 14 deletions(-)
 create mode 100644 tensorflow/python/summary/summary_test.py

diff --git a/tensorflow/core/kernels/summary_op.cc b/tensorflow/core/kernels/summary_op.cc
index 7cbced2590..af75fe92c9 100644
--- a/tensorflow/core/kernels/summary_op.cc
+++ b/tensorflow/core/kernels/summary_op.cc
@@ -146,10 +146,12 @@ class SummaryMergeOp : public OpKernel {
         }
 
         for (int v = 0; v < summary_in.value_size(); v++) {
-          if (!tags.insert(summary_in.value(v).tag()).second) {
-            c->SetStatus(errors::InvalidArgument(
-                strings::StrCat("Duplicate tag ", summary_in.value(v).tag(),
-                                " found in summary inputs")));
+          const string& tag = summary_in.value(v).tag();
+          // The tag is unused by the TensorSummary op, so no need to check
+          // for duplicates.
+          if ((tag != "") && !tags.insert(tag).second) {
+            c->SetStatus(errors::InvalidArgument(strings::StrCat(
+                "Duplicate tag ", tag, " found in summary inputs")));
             return;
           }
           *s.add_value() = summary_in.value(v);
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index 56fd552330..e1f62a81f9 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -1233,6 +1233,7 @@ py_tests(
         "summary/impl/event_file_loader_test.py",
         "summary/impl/gcs_file_loader_test.py",
         "summary/impl/reservoir_test.py",
+        "summary/summary_test.py",
     ],
     additional_deps = [
         ":summary",
diff --git a/tensorflow/python/ops/summary_ops.py b/tensorflow/python/ops/summary_ops.py
index 1d34b8a47e..81d57a6c20 100644
--- a/tensorflow/python/ops/summary_ops.py
+++ b/tensorflow/python/ops/summary_ops.py
@@ -24,6 +24,7 @@ from tensorflow.python.ops import gen_logging_ops
 # go/tf-wildcard-import
 # pylint: disable=wildcard-import
 from tensorflow.python.ops.gen_logging_ops import *
+
 # pylint: enable=wildcard-import
 
 
@@ -45,7 +46,7 @@ def tensor_summary(display_name,  # pylint: disable=invalid-name
 
   The generated
   [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
-  has one summary value containing input_tensor.
+  has one summary value containing the input tensor.
 
   Args:
     display_name: A name to associate with the data series. Will be used to
@@ -60,7 +61,7 @@ def tensor_summary(display_name,  # pylint: disable=invalid-name
          other tensors that are all in a group. (e.g. bounding boxes and images)
     collections: Optional list of graph collections keys. The new summary op is
       added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
-    name: A name for the operation (optional).
+    name: An optional name for the generated node (optional).
 
   Returns:
     A scalar `Tensor` of type `string`. The serialized `Summary` protocol
@@ -69,14 +70,16 @@ def tensor_summary(display_name,  # pylint: disable=invalid-name
   # pylint: enable=line-too-long
 
   with ops.name_scope(name, "TensorSummary", [tensor]) as scope:
-    val = gen_logging_ops._tensor_summary(display_name=display_name,
-                                          tensor=tensor,
-                                          description=description,
-                                          labels=labels,
-                                          name=scope)
+    val = gen_logging_ops._tensor_summary(
+        display_name=display_name,
+        tensor=tensor,
+        description=description,
+        labels=labels,
+        name=scope)
     _Collect(val, collections, [ops.GraphKeys.SUMMARIES])
   return val
 
+
 ops.NoGradient("TensorSummary")
 
 
diff --git a/tensorflow/python/summary/summary.py b/tensorflow/python/summary/summary.py
index 90e3f009a8..46e33aae27 100644
--- a/tensorflow/python/summary/summary.py
+++ b/tensorflow/python/summary/summary.py
@@ -18,18 +18,70 @@
 
 ## Summary Ops
 @@tensor_summary
+@@scalar
 
 """
 # pylint: enable=line-too-long
 
-# Optimizers.
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-# pylint: disable=g-bad-import-order,unused-import
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.framework.dtypes import as_dtype
 from tensorflow.python.ops.summary_ops import tensor_summary
-
 from tensorflow.python.util.all_util import make_all
 
+SCALAR_SUMMARY_LABEL = "tf_summary_type:scalar"
+
+
+def scalar(display_name,
+           tensor,
+           description="",
+           labels=None,
+           collections=None,
+           name=None):
+  """Outputs a `Summary` protocol buffer containing a single scalar value.
+
+  The generated Summary has a Tensor.proto containing the input Tensor.
+
+  Args:
+    display_name: A name to associate with the data series. Will be used to
+      organize output data and as a name in visualizers.
+    tensor: A tensor containing a single floating point or integer value.
+    description: An optional long description of the data being output.
+    labels: a list of strings used to attach metadata.
+    collections: Optional list of graph collections keys. The new summary op is
+      added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
+    name: An optional name for the generated node (optional).
+
+  Returns:
+    A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf.
+
+  Raises:
+    ValueError: If tensor has the wrong shape or type.
+  """
+
+  dtype = as_dtype(tensor.dtype)
+  if dtype.is_quantized or not (dtype.is_integer or dtype.is_floating):
+    raise ValueError("Can't create scalar summary for type %s." % dtype)
+
+  shape = tensor.get_shape()
+  if not shape.is_compatible_with(tensor_shape.scalar()):
+    raise ValueError("Can't create scalar summary for shape %s." % shape)
+
+  if labels is None:
+    labels = []
+  else:
+    labels = labels[:]  # Otherwise we would mutate the input argument
+
+  labels.append(SCALAR_SUMMARY_LABEL)
+
+  with ops.name_scope(name, "ScalarSummary", [tensor]):
+    tensor = ops.convert_to_tensor(tensor)
+    return tensor_summary(display_name, tensor, description, labels,
+                          collections, name)
+
+
 __all__ = make_all(__name__)
diff --git a/tensorflow/python/summary/summary_test.py b/tensorflow/python/summary/summary_test.py
new file mode 100644
index 0000000000..fb49759ba0
--- /dev/null
+++ b/tensorflow/python/summary/summary_test.py
@@ -0,0 +1,88 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import six
+import tensorflow as tf
+
+from tensorflow.core.framework import types_pb2
+
+
+class ScalarSummaryTest(tf.test.TestCase):
+
+  def testDtypeErrors(self):
+    def _TryMakingScalarSummary(dtype):
+      base = dtype.base_dtype
+      if base == tf.bool:
+        v = False
+      elif base == tf.string:
+        v = ''
+      elif base.is_complex:
+        v = complex(0, 0)
+      else:
+        v = base.min
+      c = tf.constant(v, dtype)
+      return tf.summary.scalar('name', c)
+
+    for datatype_enum in types_pb2.DataType.values():
+      if datatype_enum == types_pb2.DT_INVALID:
+        continue
+      dtype = tf.as_dtype(datatype_enum)
+      if dtype.is_quantized:
+        # Quantized ops are funky, and not expected to work.
+        continue
+      if dtype.is_integer or dtype.is_floating:
+        _TryMakingScalarSummary(dtype)
+        # No exception should be thrown
+      else:
+        with self.assertRaises(ValueError):
+          _TryMakingScalarSummary(dtype)
+
+  def testShapeErrors(self):
+    c1 = tf.constant(0)
+    c2 = tf.zeros(5)
+    c3 = tf.zeros(5, 5)
+
+    tf.summary.scalar('1', c1)
+    with self.assertRaises(ValueError):
+      tf.summary.scalar('2', c2)
+    with self.assertRaises(ValueError):
+      tf.summary.scalar('3', c3)
+
+  def testLabelsAdded(self):
+    c = tf.constant(0)
+
+    no_labels = tf.summary.scalar('2', c)
+    labels = tf.summary.scalar('1', c, labels=['foo'])
+
+    def _GetLabels(n):
+      return n.op.get_attr('labels')
+
+    expected_label = six.b(tf.summary.SCALAR_SUMMARY_LABEL)
+    self.assertEquals(_GetLabels(no_labels), [expected_label])
+    self.assertEquals(_GetLabels(labels), [six.b('foo'), expected_label])
+
+  def testTensorSummaryOpCreated(self):
+    c = tf.constant(0)
+    s = tf.summary.scalar('', c)
+    self.assertEquals(s.op.type, 'TensorSummary')
+    self.assertEquals(s.op.inputs[0], c)
+
+
+if __name__ == '__main__':
+  tf.test.main()
-- 
GitLab


From 64ab6600147ad02b328bede9bb28b232ca47fc70 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 10:59:37 -0800
Subject: [PATCH 151/223] Companion code for tf.learn tutorial on input_fn.
 Change: 131315226

---
 .../examples/tutorials/input_fn/boston.py     |  70 +++
 .../tutorials/input_fn/boston_predict.csv     |   7 +
 .../tutorials/input_fn/boston_test.csv        | 101 +++++
 .../tutorials/input_fn/boston_train.csv       | 401 ++++++++++++++++++
 4 files changed, 579 insertions(+)
 create mode 100644 tensorflow/examples/tutorials/input_fn/boston.py
 create mode 100644 tensorflow/examples/tutorials/input_fn/boston_predict.csv
 create mode 100644 tensorflow/examples/tutorials/input_fn/boston_test.csv
 create mode 100644 tensorflow/examples/tutorials/input_fn/boston_train.csv

diff --git a/tensorflow/examples/tutorials/input_fn/boston.py b/tensorflow/examples/tutorials/input_fn/boston.py
new file mode 100644
index 0000000000..9d18bfa9e0
--- /dev/null
+++ b/tensorflow/examples/tutorials/input_fn/boston.py
@@ -0,0 +1,70 @@
+#  Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+"""DNNRegressor with custom input_fn for Housing dataset."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import pandas as pd
+import tensorflow as tf
+
+tf.logging.set_verbosity(tf.logging.INFO)
+
+COLUMNS = ["crim", "zn", "indus", "nox", "rm", "age",
+           "dis", "tax", "ptratio", "medv"]
+FEATURES = ["crim", "zn", "indus", "nox", "rm",
+            "age", "dis", "tax", "ptratio"]
+LABEL = "medv"
+
+
+def input_fn(data_set):
+  feature_cols = {k: tf.constant(data_set[k].values) for k in FEATURES}
+  labels = tf.constant(data_set[LABEL].values)
+  return feature_cols, labels
+
+
+def main(unused_argv):
+  # Load datasets
+  training_set = pd.read_csv("boston_train.csv", skipinitialspace=True,
+                             skiprows=1, names=COLUMNS)
+  test_set = pd.read_csv("boston_test.csv", skipinitialspace=True,
+                         skiprows=1, names=COLUMNS)
+
+  # Set of 6 examples for which to predict median house values
+  prediction_set = pd.read_csv("boston_predict.csv", skipinitialspace=True,
+                               skiprows=1, names=COLUMNS)
+
+  # Feature cols
+  feature_cols = [tf.contrib.layers.real_valued_column(k)
+                  for k in FEATURES]
+
+  # Build 2 layer fully connected DNN with 10, 10 units respectively.
+  regressor = tf.contrib.learn.DNNRegressor(
+      feature_columns=feature_cols, hidden_units=[10, 10])
+
+  # Fit
+  regressor.fit(input_fn=lambda: input_fn(training_set), steps=5000)
+
+  # Score accuracy
+  ev = regressor.evaluate(input_fn=lambda: input_fn(test_set), steps=1)
+  loss_score = ev["loss"]
+  print("Loss: {0:f}".format(loss_score))
+
+  # Print out predictions
+  y = regressor.predict(input_fn=lambda: input_fn(prediction_set))
+  print ("Predictions: {}".format(str(y)))
+
+if __name__ == "__main__":
+  tf.app.run()
diff --git a/tensorflow/examples/tutorials/input_fn/boston_predict.csv b/tensorflow/examples/tutorials/input_fn/boston_predict.csv
new file mode 100644
index 0000000000..27b017155c
--- /dev/null
+++ b/tensorflow/examples/tutorials/input_fn/boston_predict.csv
@@ -0,0 +1,7 @@
+6,9,CRIM,ZN,INDUS,NOX,RM,AGE,DIS,TAX,PTRATIO
+0.03359,75.0,2.95,0.428,7.024,15.8,5.4011,252,18.3
+5.09017,0.0,18.1,0.713,6.297,91.8,2.3682,666,20.2
+0.1265,25.0,5.13,0.453,6.762,43.4,7.9809,284,19.7
+0.05515,33.0,2.18,0.472,7.236,41.1,4.022,222,18.4
+8.15174,0.0,18.1,0.7,5.39,98.9,1.7281,666,20.2
+0.24522,0.0,9.9,0.544,5.782,71.7,4.0317,304,18.4
diff --git a/tensorflow/examples/tutorials/input_fn/boston_test.csv b/tensorflow/examples/tutorials/input_fn/boston_test.csv
new file mode 100644
index 0000000000..00cd0c6bb3
--- /dev/null
+++ b/tensorflow/examples/tutorials/input_fn/boston_test.csv
@@ -0,0 +1,101 @@
+100,9,CRIM,ZN,INDUS,NOX,RM,AGE,DIS,TAX,PTRATIO,MEDV
+0.13587,0.0,10.59,0.489,6.064,59.1,4.2392,277,18.6,24.4
+0.08664,45.0,3.44,0.437,7.178,26.3,6.4798,398,15.2,36.4
+0.26938,0.0,9.9,0.544,6.266,82.8,3.2628,304,18.4,21.6
+0.05302,0.0,3.41,0.489,7.079,63.1,3.4145,270,17.8,28.7
+0.0686,0.0,2.89,0.445,7.416,62.5,3.4952,276,18.0,33.2
+0.14231,0.0,10.01,0.547,6.254,84.2,2.2565,432,17.8,18.5
+0.1676,0.0,7.38,0.493,6.426,52.3,4.5404,287,19.6,23.8
+0.04301,80.0,1.91,0.413,5.663,21.9,10.5857,334,22.0,18.2
+0.06905,0.0,2.18,0.458,7.147,54.2,6.0622,222,18.7,36.2
+0.21719,0.0,10.59,0.489,5.807,53.8,3.6526,277,18.6,22.4
+0.06076,0.0,11.93,0.573,6.976,91.0,2.1675,273,21.0,23.9
+4.03841,0.0,18.1,0.532,6.229,90.7,3.0993,666,20.2,19.6
+0.16902,0.0,25.65,0.581,5.986,88.4,1.9929,188,19.1,21.4
+0.52693,0.0,6.2,0.504,8.725,83.0,2.8944,307,17.4,50.0
+12.0482,0.0,18.1,0.614,5.648,87.6,1.9512,666,20.2,20.8
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diff --git a/tensorflow/examples/tutorials/input_fn/boston_train.csv b/tensorflow/examples/tutorials/input_fn/boston_train.csv
new file mode 100644
index 0000000000..5d30ebd3a6
--- /dev/null
+++ b/tensorflow/examples/tutorials/input_fn/boston_train.csv
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+0.1029,30.0,4.93,0.428,6.358,52.9,7.0355,300,16.6,22.2
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+8.49213,0.0,18.1,0.584,6.348,86.1,2.0527,666,20.2,14.5
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+67.9208,0.0,18.1,0.693,5.683,100.0,1.4254,666,20.2,5.0
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+0.21038,20.0,3.33,0.4429,6.812,32.2,4.1007,216,14.9,35.1
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+0.28392,0.0,7.38,0.493,5.708,74.3,4.7211,287,19.6,18.5
+0.19133,22.0,5.86,0.431,5.605,70.2,7.9549,330,19.1,18.5
+0.06162,0.0,4.39,0.442,5.898,52.3,8.0136,352,18.8,17.2
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+0.04666,80.0,1.52,0.404,7.107,36.6,7.309,329,12.6,30.3
+5.44114,0.0,18.1,0.713,6.655,98.2,2.3552,666,20.2,15.2
+0.21161,0.0,8.56,0.52,6.137,87.4,2.7147,384,20.9,19.3
+0.38735,0.0,25.65,0.581,5.613,95.6,1.7572,188,19.1,15.7
+3.69695,0.0,18.1,0.718,4.963,91.4,1.7523,666,20.2,21.9
+0.0837,45.0,3.44,0.437,7.185,38.9,4.5667,398,15.2,34.9
+0.02055,85.0,0.74,0.41,6.383,35.7,9.1876,313,17.3,24.7
+0.0459,52.5,5.32,0.405,6.315,45.6,7.3172,293,16.6,22.3
+0.06127,40.0,6.41,0.447,6.826,27.6,4.8628,254,17.6,33.1
+0.19186,0.0,7.38,0.493,6.431,14.7,5.4159,287,19.6,24.6
+0.1712,0.0,8.56,0.52,5.836,91.9,2.211,384,20.9,19.5
+1.6566,0.0,19.58,0.871,6.122,97.3,1.618,403,14.7,21.5
+0.00632,18.0,2.31,0.538,6.575,65.2,4.09,296,15.3,24.0
+0.88125,0.0,21.89,0.624,5.637,94.7,1.9799,437,21.2,14.3
+0.98843,0.0,8.14,0.538,5.813,100.0,4.0952,307,21.0,14.5
+0.01538,90.0,3.75,0.394,7.454,34.2,6.3361,244,15.9,44.0
+0.05561,70.0,2.24,0.4,7.041,10.0,7.8278,358,14.8,29.0
+0.3692,0.0,9.9,0.544,6.567,87.3,3.6023,304,18.4,23.8
+0.0315,95.0,1.47,0.403,6.975,15.3,7.6534,402,17.0,34.9
+0.05789,12.5,6.07,0.409,5.878,21.4,6.498,345,18.9,22.0
+0.01778,95.0,1.47,0.403,7.135,13.9,7.6534,402,17.0,32.9
+0.57529,0.0,6.2,0.507,8.337,73.3,3.8384,307,17.4,41.7
+20.0849,0.0,18.1,0.7,4.368,91.2,1.4395,666,20.2,8.8
+18.811,0.0,18.1,0.597,4.628,100.0,1.5539,666,20.2,17.9
+14.3337,0.0,18.1,0.614,6.229,88.0,1.9512,666,20.2,21.4
+0.07896,0.0,12.83,0.437,6.273,6.0,4.2515,398,18.7,24.1
+0.03578,20.0,3.33,0.4429,7.82,64.5,4.6947,216,14.9,45.4
+0.10612,30.0,4.93,0.428,6.095,65.1,6.3361,300,16.6,20.1
+0.97617,0.0,21.89,0.624,5.757,98.4,2.346,437,21.2,15.6
+18.4982,0.0,18.1,0.668,4.138,100.0,1.137,666,20.2,13.8
+4.0974,0.0,19.58,0.871,5.468,100.0,1.4118,403,14.7,15.6
+0.15445,25.0,5.13,0.453,6.145,29.2,7.8148,284,19.7,23.3
+0.23912,0.0,9.69,0.585,6.019,65.3,2.4091,391,19.2,21.2
+0.0456,0.0,13.89,0.55,5.888,56.0,3.1121,276,16.4,23.3
+1.38799,0.0,8.14,0.538,5.95,82.0,3.99,307,21.0,13.2
+7.36711,0.0,18.1,0.679,6.193,78.1,1.9356,666,20.2,11.0
+0.1415,0.0,6.91,0.448,6.169,6.6,5.7209,233,17.9,25.3
-- 
GitLab


From 7bf9fa38b6744049b617e722b3f03f2deed1d51f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 11:08:48 -0800
Subject: [PATCH 152/223] Fix interfaces incompatibilities between Classifier
 and Estimator. Change: 131316326

---
 .../python/learn/estimators/classifier.py     | 23 ++++++++++--
 .../learn/estimators/classifier_test.py       | 37 ++++++++++++++-----
 2 files changed, 47 insertions(+), 13 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/estimators/classifier.py b/tensorflow/contrib/learn/python/learn/estimators/classifier.py
index 61b2c5e181..d9d88bd15b 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/classifier.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/classifier.py
@@ -42,7 +42,8 @@ class Classifier(estimator.Estimator):
   CLASS_OUTPUT = 'classes'
   PROBABILITY_OUTPUT = 'probabilities'
 
-  def __init__(self, model_fn, n_classes, model_dir=None, config=None):
+  def __init__(self, model_fn, n_classes, model_dir=None, config=None,
+               params=None):
     """Constructor for Classifier.
 
     Args:
@@ -52,11 +53,17 @@ class Classifier(estimator.Estimator):
         also be used to load checkpoints from the directory into a estimator to
         continue training a previously saved model.
       config: Configuration object (optional)
+      params: `dict` of hyper parameters that will be passed into `model_fn`.
     """
     self._n_classes = n_classes
     self._logits_fn = model_fn
-    super(Classifier, self).__init__(model_fn=self._classifier_model,
-                                     model_dir=model_dir, config=config)
+    if params:
+      model_fn = self._classifier_model_with_params
+    else:
+      model_fn = self._classifier_model
+    super(Classifier, self).__init__(model_fn=model_fn,
+                                     model_dir=model_dir, config=config,
+                                     params=params)
 
   def evaluate(self,
                x=None,
@@ -161,7 +168,15 @@ class Classifier(estimator.Estimator):
       return predictions[self.PROBABILITY_OUTPUT]
 
   def _classifier_model(self, features, targets, mode):
-    logits, loss, train_op = self._logits_fn(features, targets, mode)
+    return self._convert_to_estimator_model_result(
+        self._logits_fn(features, targets, mode))
+
+  def _classifier_model_with_params(self, features, targets, mode, params):
+    return self._convert_to_estimator_model_result(
+        self._logits_fn(features, targets, mode, params))
+
+  def _convert_to_estimator_model_result(self, logits_fn_result):
+    logits, loss, train_op = logits_fn_result
     return {
         'classes': math_ops.argmax(logits, len(logits.get_shape()) - 1),
         'probabilities': nn.softmax(logits)
diff --git a/tensorflow/contrib/learn/python/learn/estimators/classifier_test.py b/tensorflow/contrib/learn/python/learn/estimators/classifier_test.py
index fd6618561e..f14de75252 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/classifier_test.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/classifier_test.py
@@ -46,19 +46,27 @@ def logistic_model_fn(features, target, unused_mode):
   return prediction, loss, train_op
 
 
+def logistic_model_params_fn(features, target, unused_mode, params):
+  target = tf.one_hot(target, 3, 1, 0)
+  prediction, loss = tf.contrib.learn.models.logistic_regression_zero_init(
+      features, target)
+  train_op = tf.contrib.layers.optimize_loss(
+      loss, tf.contrib.framework.get_global_step(), optimizer='Adagrad',
+      learning_rate=params['learning_rate'])
+  return prediction, loss, train_op
+
+
 class ClassifierTest(tf.test.TestCase):
 
   def testIrisAll(self):
-    iris = tf.contrib.learn.datasets.load_iris()
     est = tf.contrib.learn.Classifier(model_fn=logistic_model_fn, n_classes=3)
-    est.fit(iris.data, iris.target, steps=100)
-    scores = est.evaluate(x=iris.data, y=iris.target, name='eval')
-    predictions = est.predict(x=iris.data)
-    predictions_proba = est.predict_proba(x=iris.data)
-    self.assertEqual(predictions.shape[0], iris.target.shape[0])
-    self.assertAllEqual(predictions, np.argmax(predictions_proba, axis=1))
-    other_score = _sklearn.accuracy_score(iris.target, predictions)
-    self.assertAllClose(other_score, scores['accuracy'])
+    self._runIrisAll(est)
+
+  def testIrisAllWithParams(self):
+    est = tf.contrib.learn.Classifier(model_fn=logistic_model_params_fn,
+                                      n_classes=3,
+                                      params={'learning_rate': 0.01})
+    self._runIrisAll(est)
 
   def testIrisPredictAsIterable(self):
     iris = tf.contrib.learn.datasets.load_iris()
@@ -89,6 +97,17 @@ class ClassifierTest(tf.test.TestCase):
     predictions = list(est.predict(input_fn=predict_input_fn, as_iterable=True))
     self.assertEqual(len(predictions), iris.target.shape[0])
 
+  def _runIrisAll(self, est):
+    iris = tf.contrib.learn.datasets.load_iris()
+    est.fit(iris.data, iris.target, steps=100)
+    scores = est.evaluate(x=iris.data, y=iris.target, name='eval')
+    predictions = est.predict(x=iris.data)
+    predictions_proba = est.predict_proba(x=iris.data)
+    self.assertEqual(predictions.shape[0], iris.target.shape[0])
+    self.assertAllEqual(predictions, np.argmax(predictions_proba, axis=1))
+    other_score = _sklearn.accuracy_score(iris.target, predictions)
+    self.assertAllClose(other_score, scores['accuracy'])
+
 
 if __name__ == '__main__':
   tf.test.main()
-- 
GitLab


From 8555555f1a48b3219e3bb73c8b8ac71405736203 Mon Sep 17 00:00:00 2001
From: David Soergel <soergel@google.com>
Date: Thu, 25 Aug 2016 11:21:58 -0800
Subject: [PATCH 153/223] Allow removing columns from DataFrame, both in place
 and in new copy. Change: 131317731

---
 .../learn/python/learn/dataframe/dataframe.py | 40 +++++++++++++------
 .../learn/tests/dataframe/dataframe_test.py   | 12 ++++++
 2 files changed, 40 insertions(+), 12 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/dataframe/dataframe.py b/tensorflow/contrib/learn/python/learn/dataframe/dataframe.py
index 6e03f08642..dd836c1dec 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/dataframe.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/dataframe.py
@@ -59,21 +59,16 @@ class DataFrame(object):
       if not isinstance(k, str):
         raise TypeError("The only supported type for keys is string; got %s" %
                         type(k))
-      if isinstance(v, Series):
-        s = v
+      if v is None:
+        del self._columns[k]
+      elif isinstance(v, Series):
+        self._columns[k] = v
       elif isinstance(v, Transform) and v.input_valency() == 0:
-        s = v()
-      # TODO(jamieas): hook up these special cases again
-      # TODO(soergel): can these special cases be generalized?
-      # elif isinstance(v, pd.Series):
-      #   s = series.NumpySeries(v.values)
-      # elif isinstance(v, np.ndarray):
-      #   s = series.NumpySeries(v)
+        self._columns[k] = v()
       else:
         raise TypeError(
-            "Column in assignment must be an inflow.Series, pandas.Series or a"
-            " numpy array; got type '%s'." % type(v).__name__)
-      self._columns[k] = s
+            "Column in assignment must be an inflow.Series, inflow.Transform,"
+            " or None; got type '%s'." % type(v).__name__)
 
   def select_columns(self, keys):
     """Returns a new DataFrame with a subset of columns.
@@ -89,6 +84,21 @@ class DataFrame(object):
       result[key] = self._columns[key]
     return result
 
+  def exclude_columns(self, exclude_keys):
+    """Returns a new DataFrame with all columns not excluded via exclude_keys.
+
+    Args:
+      exclude_keys: A list of strings. Each should be the name of a column in
+        the DataFrame.  These columns will be excluded from the result.
+    Returns:
+      A new DataFrame containing all columns except those specified.
+    """
+    result = type(self)()
+    for key, value in self._columns.items():
+      if key not in exclude_keys:
+        result[key] = value
+    return result
+
   def __getitem__(self, key):
     """Indexing functionality for DataFrames.
 
@@ -117,6 +127,12 @@ class DataFrame(object):
       value = [value]
     self.assign(**dict(zip(key, value)))
 
+  def __delitem__(self, key):
+    if isinstance(key, str):
+      key = [key]
+    value = [None for _ in key]
+    self.assign(**dict(zip(key, value)))
+
   def build(self, **kwargs):
     # We do not allow passing a cache here, because that would encourage
     # working around the rule that DataFrames cannot be expected to be
diff --git a/tensorflow/contrib/learn/python/learn/tests/dataframe/dataframe_test.py b/tensorflow/contrib/learn/python/learn/tests/dataframe/dataframe_test.py
index 780c884078..c5da6244f4 100644
--- a/tensorflow/contrib/learn/python/learn/tests/dataframe/dataframe_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/dataframe/dataframe_test.py
@@ -51,11 +51,23 @@ class DataFrameTest(tf.test.TestCase):
     df2 = df.select_columns(["a", "c"])
     self.assertEqual(df2.columns(), frozenset(["a", "c"]))
 
+  def test_exclude_columns(self):
+    df = setup_test_df()
+    df2 = df.exclude_columns(["a", "c"])
+    self.assertEqual(df2.columns(), frozenset(["b"]))
+
   def test_get_item(self):
     df = setup_test_df()
     c1 = df["b"]
     self.assertEqual(mocks.MockTensor("Mock Tensor 2", tf.int32), c1.build())
 
+  def test_del_item_column(self):
+    df = setup_test_df()
+    self.assertEqual(3, len(df))
+    del df["b"]
+    self.assertEqual(2, len(df))
+    self.assertEqual(df.columns(), frozenset(["a", "c"]))
+
   def test_set_item_column(self):
     df = setup_test_df()
     self.assertEqual(3, len(df))
-- 
GitLab


From 61f6590807dfda9414e76d328a7c6bea44555df3 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 11:22:16 -0800
Subject: [PATCH 154/223] Update generated Python Op docs. Change: 131317756

---
 .../shard2/tf.summary.tensor_summary.md       |  4 +--
 .../shard6/tf.summary.scalar.md               | 27 +++++++++++++++
 tensorflow/g3doc/api_docs/python/index.md     |  1 +
 tensorflow/g3doc/api_docs/python/summary.md   | 34 +++++++++++++++++--
 4 files changed, 62 insertions(+), 4 deletions(-)
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.summary.scalar.md

diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.summary.tensor_summary.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.summary.tensor_summary.md
index ea842cde60..61f16181b5 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.summary.tensor_summary.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.summary.tensor_summary.md
@@ -4,7 +4,7 @@ Outputs a `Summary` protocol buffer with a serialized tensor.proto.
 
 The generated
 [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
-has one summary value containing input_tensor.
+has one summary value containing the input tensor.
 
 ##### Args:
 
@@ -21,7 +21,7 @@ has one summary value containing input_tensor.
        other tensors that are all in a group. (e.g. bounding boxes and images)
 *  <b>`collections`</b>: Optional list of graph collections keys. The new summary op is
     added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
-*  <b>`name`</b>: A name for the operation (optional).
+*  <b>`name`</b>: An optional name for the generated node (optional).
 
 ##### Returns:
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.summary.scalar.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.summary.scalar.md
new file mode 100644
index 0000000000..020c9c060d
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.summary.scalar.md
@@ -0,0 +1,27 @@
+### `tf.summary.scalar(display_name, tensor, description='', labels=None, collections=None, name=None)` {#scalar}
+
+Outputs a `Summary` protocol buffer containing a single scalar value.
+
+The generated Summary has a Tensor.proto containing the input Tensor.
+
+##### Args:
+
+
+*  <b>`display_name`</b>: A name to associate with the data series. Will be used to
+    organize output data and as a name in visualizers.
+*  <b>`tensor`</b>: A tensor containing a single floating point or integer value.
+*  <b>`description`</b>: An optional long description of the data being output.
+*  <b>`labels`</b>: a list of strings used to attach metadata.
+*  <b>`collections`</b>: Optional list of graph collections keys. The new summary op is
+    added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
+*  <b>`name`</b>: An optional name for the generated node (optional).
+
+##### Returns:
+
+  A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If tensor has the wrong shape or type.
+
diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md
index 12aa7c6a03..7a32d5e55e 100644
--- a/tensorflow/g3doc/api_docs/python/index.md
+++ b/tensorflow/g3doc/api_docs/python/index.md
@@ -585,6 +585,7 @@
   * [`py_func`](../../api_docs/python/script_ops.md#py_func)
 
 * **[Summary Operations](../../api_docs/python/summary.md)**:
+  * [`scalar`](../../api_docs/python/summary.md#scalar)
   * [`tensor_summary`](../../api_docs/python/summary.md#tensor_summary)
 
 * **[Testing](../../api_docs/python/test.md)**:
diff --git a/tensorflow/g3doc/api_docs/python/summary.md b/tensorflow/g3doc/api_docs/python/summary.md
index 8f19460e21..14dac8117f 100644
--- a/tensorflow/g3doc/api_docs/python/summary.md
+++ b/tensorflow/g3doc/api_docs/python/summary.md
@@ -14,7 +14,7 @@ Outputs a `Summary` protocol buffer with a serialized tensor.proto.
 
 The generated
 [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto)
-has one summary value containing input_tensor.
+has one summary value containing the input tensor.
 
 ##### Args:
 
@@ -31,7 +31,7 @@ has one summary value containing input_tensor.
        other tensors that are all in a group. (e.g. bounding boxes and images)
 *  <b>`collections`</b>: Optional list of graph collections keys. The new summary op is
     added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
-*  <b>`name`</b>: A name for the operation (optional).
+*  <b>`name`</b>: An optional name for the generated node (optional).
 
 ##### Returns:
 
@@ -39,3 +39,33 @@ has one summary value containing input_tensor.
   buffer.
 
 
+- - -
+
+### `tf.summary.scalar(display_name, tensor, description='', labels=None, collections=None, name=None)` {#scalar}
+
+Outputs a `Summary` protocol buffer containing a single scalar value.
+
+The generated Summary has a Tensor.proto containing the input Tensor.
+
+##### Args:
+
+
+*  <b>`display_name`</b>: A name to associate with the data series. Will be used to
+    organize output data and as a name in visualizers.
+*  <b>`tensor`</b>: A tensor containing a single floating point or integer value.
+*  <b>`description`</b>: An optional long description of the data being output.
+*  <b>`labels`</b>: a list of strings used to attach metadata.
+*  <b>`collections`</b>: Optional list of graph collections keys. The new summary op is
+    added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
+*  <b>`name`</b>: An optional name for the generated node (optional).
+
+##### Returns:
+
+  A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If tensor has the wrong shape or type.
+
+
-- 
GitLab


From cdf4b2f52b53a7d6f68934e76a4c1239700cb954 Mon Sep 17 00:00:00 2001
From: Robin Nabel <rnabel@google.com>
Date: Thu, 25 Aug 2016 11:41:42 -0800
Subject: [PATCH 155/223] Fix vz-heatmap in catalog. Change: 131319524

---
 .../components/vz-heatmap/vz-heatmap.html     | 22 +++++++++++++++++++
 1 file changed, 22 insertions(+)

diff --git a/tensorflow/tensorboard/components/vz-heatmap/vz-heatmap.html b/tensorflow/tensorboard/components/vz-heatmap/vz-heatmap.html
index b1108051f4..7acba47d5f 100644
--- a/tensorflow/tensorboard/components/vz-heatmap/vz-heatmap.html
+++ b/tensorflow/tensorboard/components/vz-heatmap/vz-heatmap.html
@@ -37,20 +37,39 @@ fits itself to the width of the container it is placed in.
     Polymer({
       is: 'vz-heatmap',
       properties: {
+        /**
+         * An 2-element array which defines the lower and upper bound of the
+         * automatically created color scale for the heatmap.
+         */
         colors: {
           type: Array,
           value: ['white', 'steelblue'],
           observer: '_onColorsChange'
         },
+        /**
+         * A function which returns a hex-formatted color string
+         * given a number type.
+         * i.e. (input: number) => string
+         *
+         * Passing a colorFunction deactivates the color
+         * function generated by the `colors` and `values` fields.
+         */
         colorFunction: {
           type: Object,
           observer: '_onColorFunctionChange'
         },
+        /**
+         * A 2D data array containing the data to be visualized.
+         */
         data: {
           type: Array,
           value: [],
           observer: '_onDataChange'
         },
+        /**
+         * A 2-element array containing the domain of values which
+         * maps to the color range defined by the `colors` array.
+         */
         values: {
           type: Array,
           observer: '_onValuesChange'
@@ -326,6 +345,9 @@ fits itself to the width of the container it is placed in.
         canvasElement.setAttribute('height', rows);
         canvasElement.setAttribute('width', columns);
       },
+      /**
+       * Function which clears the canvas.
+       */
       resetCanvas: function () {
         // Reset the canvas.
         var canvas = this.$.heatmap;
-- 
GitLab


From 0e5d49d362a5ef72179e385d1f71ec29ab0392f6 Mon Sep 17 00:00:00 2001
From: Jonathan Hseu <jhseu@google.com>
Date: Thu, 25 Aug 2016 11:59:12 -0800
Subject: [PATCH 156/223] Rename rank to dimensions to follow our style guide.
 Change: 131321270

---
 tensorflow/go/tensor.go | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/tensorflow/go/tensor.go b/tensorflow/go/tensor.go
index e364e80f86..76a4615a7b 100644
--- a/tensorflow/go/tensor.go
+++ b/tensorflow/go/tensor.go
@@ -50,11 +50,11 @@ type Tensor struct {
 // that the resulting Tensor has a valid shape.
 func NewTensor(value interface{}) (*Tensor, error) {
 	val := reflect.ValueOf(value)
-	rank, dataType, err := rankAndDataTypeOf(val.Type())
+	dims, dataType, err := dimsAndDataTypeOf(val.Type())
 	if err != nil {
 		return nil, err
 	}
-	t := &Tensor{buf: bytes.NewBuffer(nil), dt: dataType, shape: make([]int64, rank)}
+	t := &Tensor{buf: bytes.NewBuffer(nil), dt: dataType, shape: make([]int64, dims)}
 	if err = encodeTensor(t.buf, t.shape, val); err != nil {
 		return nil, err
 	}
@@ -90,7 +90,8 @@ func (t *Tensor) Shape() []int64 {
 // supported, and this function may panic if it encounters an unsupported
 // DataType.
 //
-// The type of the output depends on the Tensor type and rank. For example:
+// The type of the output depends on the Tensor type and dimensions.
+// For example:
 // Tensor(int64, 0): int64
 // Tensor(float64, 3): [][][]float64
 func (t *Tensor) Value() interface{} {
@@ -145,18 +146,17 @@ var types = []struct {
 	{reflect.TypeOf(complex(float64(0), float64(0))), C.TF_COMPLEX128},
 }
 
-// rankAndDataTypeOf returns the data type and rank of a Go type for use when
-// encoding. We fetch them separately from encoding to support 0-sized
-// dimensions.
-func rankAndDataTypeOf(typ reflect.Type) (int, DataType, error) {
-	rank := 0
+// dimsAndDataTypeOf returns the data type and dimensions of a Go type for use
+// when encoding. We fetch them separately from encoding to support 0-D tensors.
+func dimsAndDataTypeOf(typ reflect.Type) (int, DataType, error) {
+	dims := 0
 	elem := typ
 	for ; elem.Kind() == reflect.Array || elem.Kind() == reflect.Slice; elem = elem.Elem() {
-		rank++
+		dims++
 	}
 	for _, t := range types {
 		if elem.Kind() == t.typ.Kind() {
-			return rank, DataType(t.dataType), nil
+			return dims, DataType(t.dataType), nil
 		}
 	}
 	return 0, DataType(0), fmt.Errorf("unsupported type %v", typ)
-- 
GitLab


From 8b4c16de8882c7213eed0a529109c03747fc7449 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 12:00:42 -0800
Subject: [PATCH 157/223] Move from std::fabs to std::abs, which is more
 standards compliant for complex types Change: 131321424

---
 tensorflow/core/framework/tensor_testutil.cc | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tensorflow/core/framework/tensor_testutil.cc b/tensorflow/core/framework/tensor_testutil.cc
index cc117ad19e..a8d1412300 100644
--- a/tensorflow/core/framework/tensor_testutil.cc
+++ b/tensorflow/core/framework/tensor_testutil.cc
@@ -34,7 +34,7 @@ void ExpectClose(const Tensor& x, const Tensor& y, double atol, double rtol) {
       LOG(ERROR) << "x = " << x.DebugString();
       LOG(ERROR) << "y = " << y.DebugString();
       LOG(ERROR) << "atol = " << atol << " rtol = " << rtol
-                 << " tol = " << atol + rtol * std::fabs(Tx(i));
+                 << " tol = " << atol + rtol * std::abs(Tx(i));
       EXPECT_TRUE(false) << i << "-th element is not close " << Tx(i) << " vs. "
                          << Ty(i);
     }
-- 
GitLab


From ac997d8b4b7d24008b7979cb58581105925c20b6 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 25 Aug 2016 12:10:46 -0800
Subject: [PATCH 158/223] Import order fix Change: 131322587

---
 tensorflow/examples/learn/wide_n_deep_tutorial.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tensorflow/examples/learn/wide_n_deep_tutorial.py b/tensorflow/examples/learn/wide_n_deep_tutorial.py
index 5a23087b5a..a9f84cec60 100644
--- a/tensorflow/examples/learn/wide_n_deep_tutorial.py
+++ b/tensorflow/examples/learn/wide_n_deep_tutorial.py
@@ -20,9 +20,9 @@ from __future__ import print_function
 import tempfile
 import urllib
 
-import pandas as pd
 import tensorflow as tf
 
+
 flags = tf.app.flags
 FLAGS = flags.FLAGS
 
@@ -179,6 +179,8 @@ def input_fn(df):
 def train_and_eval():
   """Train and evaluate the model."""
   train_file_name, test_file_name = maybe_download()
+
+  import pandas as pd  # pylint: disable=g-import-not-at-top
   df_train = pd.read_csv(
       tf.gfile.Open(train_file_name),
       names=COLUMNS,
-- 
GitLab


From c46abae176717ef6c6649ba0ca1099c35b90194e Mon Sep 17 00:00:00 2001
From: Yuan Yu <yuanbyu@google.com>
Date: Thu, 25 Aug 2016 12:26:28 -0800
Subject: [PATCH 159/223] Strictly enforce shape invariance for while loop. TF
 Python frontend has increasingly been taking advantage of shape information
 in graph construction. This could lead to incorrect graph construction for
 while loop if the shapes of the loop variables are not loop invariants. This
 CL implements the following solution.

We add a new optional argument `shape_invariants` to tf.while_loop. If `shape_invariants` is None, we consider the initial shapes of `loop_vars` as the shape invariants for the loop. We statically check and raise an error if the shapes of `loop_vars` are not shape invariants. In that case, users are required to provide shape invariants, which we also check statically. We expect that in most cases the initial shapes of loop_vars are loop invariants and therefore there is no need to provide shape_invariants.

NOTE. This can be a *breaking* change. There are unfortunately cases that we have to provide the shape invariants.
NOTE. The deprecated `While` is removed. Use `tf.while_loop`.
Change: 131324247
---
 .../kernel_tests/control_flow_ops_py_test.py  | 111 +++++++-
 .../python/kernel_tests/stack_ops_test.py     |   4 +-
 .../kernel_tests/tensor_array_ops_test.py     |   3 +
 tensorflow/python/ops/control_flow_ops.py     | 247 +++++++++++++++---
 .../python/ops/control_flow_ops_test.py       |   4 +-
 5 files changed, 320 insertions(+), 49 deletions(-)

diff --git a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
index 33a07c4807..5cb9591f88 100644
--- a/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
+++ b/tensorflow/python/kernel_tests/control_flow_ops_py_test.py
@@ -27,6 +27,7 @@ from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
 
 from tensorflow.python.framework import function
+from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import gen_array_ops
 from tensorflow.python.ops import gen_data_flow_ops
@@ -585,12 +586,13 @@ class ControlFlowTest(tf.test.TestCase):
         return [i, c, o]
 
       i = tf.convert_to_tensor(0)
-      c = tf.convert_to_tensor(0)
+      c = tf.convert_to_tensor([0])
       o = tf.convert_to_tensor([0])
       x = tf.convert_to_tensor([1, 2, 3, 4, 5, 6])
       s = tf.size(x)
       r = tf.while_loop(
-          lambda i, c, o: tf.less(i, s), compute, [i, c, o])
+          lambda i, c, o: tf.less(i, s), compute, [i, c, o],
+          [i.get_shape(), c.get_shape(), tensor_shape.unknown_shape()])
       result = r[2].eval()
     self.assertTrue(check_op_order(i.graph))
     self.assertAllEqual(np.array([0, 1, 2, 3, 4, 5, 6]), result)
@@ -630,7 +632,8 @@ class ControlFlowTest(tf.test.TestCase):
         new_i = tf.add(i, 1)
         new_j = tf.tile(j, [2, 2])
         return [new_i, new_j]
-      r = tf.while_loop(c, _b, [i, m])
+      r = tf.while_loop(c, _b, [i, m],
+                        [i.get_shape(), tensor_shape.unknown_shape()])
       r = r[1] * tf.ones([8, 8])
       self.assertAllEqual(np.ones((8, 8)), r.eval())
 
@@ -655,14 +658,69 @@ class ControlFlowTest(tf.test.TestCase):
       i = tf.constant(0)
       m = tf.ones([2, 2])
       c = lambda i, j: tf.less(i, 2)
-      def _b(i, j):
+      def b(i, j):
         new_i = tf.add(i, 1)
         new_j = tf.concat(0, [j, j])
         return [new_i, new_j]
-      r = tf.while_loop(c, _b, [i, m])
+      r = tf.while_loop(c, b, [i, m],
+                        [i.get_shape(), tensor_shape.TensorShape([None, 2])])
       self.assertTrue(r[1].get_shape()[0].value is None)
       self.assertEqual(r[1].get_shape()[1], tf.Dimension(2))
 
+      with self.assertRaisesRegexp(ValueError, "not an invariant for"):
+        r = tf.while_loop(c, b, [i, m])
+
+  def testWhileShapeInferenceSparseTensor(self):
+    with self.test_session():
+      values = tf.constant([2.0, 4.0], name="values")
+      indices = tf.constant([[0], [3]], dtype=tf.int64, name="indices")
+      shape = tf.constant([10], dtype=tf.int64, name="dense_shape")
+      i = tf.constant(0)
+      x = tf.SparseTensor(indices, values, shape=shape)
+      def c(i, _):
+        return i < 10
+      def b(i, x):
+        return [i + 1, tf.SparseTensor(x.indices, x.values * 2.0,
+                                       x.shape)]
+      _, r = tf.while_loop(c, b, [i, x])
+      self.assertEqual(r.shape.get_shape()[0].value, 1)
+
+      _, r = tf.while_loop(c, b, [i, x],
+                           [i.get_shape(), tensor_shape.TensorShape([None])])
+      self.assertTrue(r.shape.get_shape()[0].value is None)
+
+      with self.assertRaisesRegexp(ValueError, "is not compatible with"):
+        _, r = tf.while_loop(c, b, [i, x],
+                             [i.get_shape(), tensor_shape.TensorShape([5])])
+
+  def testWhileShapeInferenceIndexedSlices(self):
+    with self.test_session():
+      values = tf.constant([[2.0, 4.0], [3.0, 5.0]], name="values")
+      indices = tf.constant([0, 3], name="indices")
+      shape = tf.constant([10, 2], name="dense_shape")
+      i = tf.constant(0)
+      x = tf.IndexedSlices(values, indices, dense_shape=shape)
+      def c(i, _):
+        return i < 10
+      def b(i, x):
+        return [i + 1, tf.IndexedSlices(x.values * 2.0, x.indices,
+                                        x.dense_shape)]
+      _, r = tf.while_loop(c, b, [i, x])
+      self.assertEqual(r.dense_shape.get_shape()[0].value, 2)
+      self.assertEqual(r.values.get_shape(), tensor_shape.TensorShape([2, 2]))
+
+      _, r = tf.while_loop(c, b, [i, x],
+                           [i.get_shape(), tensor_shape.TensorShape([None, 2])])
+      self.assertEqual(r.dense_shape.get_shape()[0].value, 2)
+      self.assertTrue(r.values.get_shape()[0].value is None)
+      self.assertEqual(r.values.get_shape()[1].value, 2)
+
+      with self.assertRaisesRegexp(ValueError, "is not compatible with"):
+        _, r = tf.while_loop(c, b, [i, x],
+                             [i.get_shape(),
+                              tensor_shape.TensorShape([None, 5])])
+
+
   def _testNestedWhile_1(self, use_gpu):
     with self.test_session(use_gpu=use_gpu):
       n = tf.constant(0)
@@ -982,7 +1040,8 @@ class ControlFlowTest(tf.test.TestCase):
           inc_b = tf.identity(var_b)
         return inc_b
 
-      lpa = tf.while_loop(pred, loop_body, [var_b], 1, name="loop")
+      lpa = tf.while_loop(pred, loop_body, [var_b], parallel_iterations=1,
+                          name="loop")
 
       self.assertEqual(0, var_b.eval())
       lpa.eval()  # Run the loop
@@ -1011,7 +1070,8 @@ class ControlFlowTest(tf.test.TestCase):
           ni = tf.add(i, 1, name="i_add")
           return ni
 
-      lpa = tf.while_loop(pred, loop_body, [c], 1, name="loop")
+      lpa = tf.while_loop(pred, loop_body, [c], parallel_iterations=1,
+                          name="loop")
 
       self.assertEqual(0, var_b.eval())
       lpa.eval()  # Run the loop
@@ -1057,7 +1117,9 @@ class ControlFlowTest(tf.test.TestCase):
         ni = tf.sub(i, 1)
         nx = x + gen_data_flow_ops._stack_pop(s, tf.int32)
         return [ni, nx]
-      _, rx = tf.while_loop(c1, b1, [r, x], parallel_iterations=1)
+      _, rx = tf.while_loop(c1, b1, [r, x],
+                            [r.get_shape(), tensor_shape.unknown_shape()],
+                            parallel_iterations=1)
       self.assertEqual(45, rx.eval())
 
   def _testWhileGrad_ColocateGradients(self, colocate):
@@ -1107,7 +1169,9 @@ class ControlFlowTest(tf.test.TestCase):
       n = tf.constant(0, name="n")
       c = lambda i, v: tf.less(i, 5)
       b = lambda i, v: [i + 1, tf.mul(x, v)]
-      r = tf.while_loop(c, b, [n, v], parallel_iterations=1)
+      r = tf.while_loop(c, b, [n, v],
+                        [n.get_shape(), tensor_shape.unknown_shape()],
+                        parallel_iterations=1)
 
       r = tf.gradients(r[1], x)[0]
       self.assertEqual([None], r.get_shape().as_list())
@@ -1462,6 +1526,29 @@ class ControlFlowTest(tf.test.TestCase):
       r = sess.run(r, feed_dict={v: 2.0})
       self.assertAllClose(1024.0, r)
 
+  def testWhileGrad_Concat(self):
+    with self.test_session() as sess:
+      x = tf.get_variable("x", initializer=[[1., 2.]])
+      i0 = tf.constant(0)
+      h0 = tf.zeros([0, 2])
+
+      def condition(i, _):
+        return i < 2
+
+      def body(i, h):
+        return i+1, tf.concat(0, [h, x])
+
+      _, h = tf.while_loop(
+          condition, body, [i0, h0],
+          [i0.get_shape(), tensor_shape.TensorShape([None, 2])])
+      s = tf.reduce_sum(h)
+
+      sess.run(tf.initialize_all_variables())
+      optimizer = tf.train.GradientDescentOptimizer(0.01)
+      op = optimizer.minimize(s)
+      sess.run(op)
+      self.assertAllClose([[0.98000002, 1.98000002]], sess.run(x))
+
   def testWhileWithRefsWithGradients_1(self):
     with self.test_session() as sess:
       x = tf.Variable(0).ref()
@@ -2029,7 +2116,8 @@ class TupleTest(tf.test.TestCase):
       r = tf.while_loop(
           lambda i, v: i < 4,
           lambda i, v: [i + 1, tf.py_func(func, [v], [tf.float32])[0]],
-          [tf.constant(0), tf.constant(2.0, tf.float32)])
+          [tf.constant(0), tf.constant(2.0, tf.float32)],
+          [tensor_shape.unknown_shape(), tensor_shape.unknown_shape()])
       self.assertEqual(r[1].eval(), 65536.0)
 
   def testWhileFuncBasic(self):
@@ -2042,7 +2130,8 @@ class TupleTest(tf.test.TestCase):
       r = tf.while_loop(
           lambda i, v: i < 2,
           lambda i, v: [i + 1, func(v)],
-          [tf.constant(0), x])
+          [tf.constant(0), x],
+          [tensor_shape.unknown_shape(), tensor_shape.unknown_shape()])
       self.assertEqual(r[1].eval(), 65536.0)
 
       r = tf.gradients(r, x)[0]
diff --git a/tensorflow/python/kernel_tests/stack_ops_test.py b/tensorflow/python/kernel_tests/stack_ops_test.py
index 352a0e0f1c..bad141e7a8 100644
--- a/tensorflow/python/kernel_tests/stack_ops_test.py
+++ b/tensorflow/python/kernel_tests/stack_ops_test.py
@@ -22,6 +22,7 @@ import numpy as np
 import tensorflow as tf
 
 from tensorflow.python.framework import errors
+from tensorflow.python.framework import tensor_shape
 from tensorflow.python.ops import gen_data_flow_ops
 
 
@@ -75,7 +76,8 @@ class StackOpTest(tf.test.TestCase):
         nx = tf.sub(x, 1)
         ny = y + gen_data_flow_ops._stack_pop(h, tf.float32)
         return [nx, ny]
-      rx, ry = tf.while_loop(c1, b1, [r, v])
+      rx, ry = tf.while_loop(c1, b1, [r, v],
+                             [r.get_shape(), tensor_shape.unknown_shape()])
       self.assertAllClose(np.ones(2000) * 10.0, ry.eval())
 
   def testStackWhileSwap(self):
diff --git a/tensorflow/python/kernel_tests/tensor_array_ops_test.py b/tensorflow/python/kernel_tests/tensor_array_ops_test.py
index 49250eb42a..532e619d59 100644
--- a/tensorflow/python/kernel_tests/tensor_array_ops_test.py
+++ b/tensorflow/python/kernel_tests/tensor_array_ops_test.py
@@ -736,6 +736,9 @@ class TensorArrayCPUTest(tf.test.TestCase):
           cond=lambda time, unused_1, unused_2: time < 3,
           body=body,
           loop_vars=(time_0, ta, state0),
+          shape_invariants=(time_0.get_shape(),
+                            tensor_shape.unknown_shape(),
+                            tensor_shape.unknown_shape()),
           parallel_iterations=3)
       vout = h_final.pack()
 
diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py
index afeab05cd9..be263d1bbe 100644
--- a/tensorflow/python/ops/control_flow_ops.py
+++ b/tensorflow/python/ops/control_flow_ops.py
@@ -155,7 +155,7 @@ def _NextIteration(data, name=None):
 
 
 def _Enter(data, frame_name, is_constant=False, parallel_iterations=10,
-           use_ref=True, name=None):
+           use_ref=True, use_input_shape=True, name=None):
   """Creates or finds a child frame, and makes `data` available to it.
 
   The unique `frame_name` is used by the `Executor` to identify frames. If
@@ -177,27 +177,37 @@ def _Enter(data, frame_name, is_constant=False, parallel_iterations=10,
   data = ops.convert_to_tensor_or_indexed_slices(data, as_ref=True)
   if isinstance(data, ops.Tensor):
     if data.dtype.is_ref_dtype and use_ref:
-      return ref_enter(data, frame_name, is_constant, parallel_iterations,
-                       name=name)
+      result = ref_enter(data, frame_name, is_constant, parallel_iterations,
+                         name=name)
     else:
-      return enter(data, frame_name, is_constant, parallel_iterations,
-                   name=name)
+      result = enter(data, frame_name, is_constant, parallel_iterations,
+                     name=name)
+    if use_input_shape:
+      result.set_shape(data.get_shape())
+    return result
   else:
     if not isinstance(data, (ops.IndexedSlices, ops.SparseTensor)):
       raise TypeError("Type %s not supported" % type(data))
     values = _Enter(data.values, frame_name, is_constant,
-                    parallel_iterations, name=name)
+                    parallel_iterations=parallel_iterations,
+                    use_input_shape=use_input_shape, name=name)
     indices = enter(data.indices, frame_name, is_constant,
                     parallel_iterations, name="indices")
+    if use_input_shape:
+      indices.set_shape(data.indices.get_shape())
     if isinstance(data, ops.IndexedSlices):
       dense_shape = data.dense_shape
       if dense_shape is not None:
         dense_shape = enter(dense_shape, frame_name, is_constant,
                             parallel_iterations, name="dense_shape")
+        if use_input_shape:
+          dense_shape.set_shape(data.dense_shape.get_shape())
       return ops.IndexedSlices(values, indices, dense_shape)
     else:
       dense_shape = enter(data.shape, frame_name, is_constant,
                           parallel_iterations, name="dense_shape")
+      if use_input_shape:
+        dense_shape.set_shape(data.shape.get_shape())
       return ops.SparseTensor(indices, values, dense_shape)
 
 
@@ -440,16 +450,139 @@ def _GetOutputContext(op):
   return ctxt
 
 
-def _ShapeIntersection(shape1, shape2):
-  if shape1.dims is None or shape1.ndims != shape2.ndims:
-    return tensor_shape.unknown_shape()
-  rdims = []
+def _ShapeLessThanOrEqual(shape1, shape2):
+  if shape2.dims is None:
+    return True
+  if shape1.ndims != shape2.ndims:
+    return False
   for dim1, dim2 in zip(shape1.dims, shape2.dims):
-    if dim1 == dim2:
-      rdims.append(dim1)
+    if dim2.value is not None and dim1.value != dim2.value:
+      return False
+  return True
+
+
+def _SetShapeInvariants(input_vars, enter_vars, shapes):
+  """Set the shapes of the tensors in `enter_vars` to `shapes`.
+
+  Args:
+    input_vars: A list of tensors that are inputs to `enter_vars`.
+    enter_vars: A list of tensors whose shapes will be set.
+    shapes: A (possibly nested) list of shapes.
+
+  Raises:
+    ValueError: If any tensor in `enter_vars` has a less specific shape
+      than its corresponding shape in `shapes`.
+  """
+  if shapes is None:
+    return
+  flat_shapes = nest.flatten(shapes)
+  if not all([isinstance(s, tensor_shape.TensorShape) for s in flat_shapes]):
+    raise ValueError("`shapes` must be a (possibly nested) list of shapes.")
+  # Check that the shapes of the inputs are less than the shape invariants,
+  # and set the shapes of `enter_vars` to the shape invariants.
+  for inp, var, shape in zip(input_vars, enter_vars, flat_shapes):
+    if isinstance(var, ops.Tensor):
+      if not _ShapeLessThanOrEqual(inp.get_shape(), shape):
+        raise ValueError(
+            "The shape invariant specified for %s is not compatible with "
+            "the initial shape of the loop variable. It enters the loop "
+            "with shape %s, but the specified shape invariant is %s."
+            % (inp.name, inp.get_shape(), shape))
+      var.set_shape(shape)
+    else:
+      if not isinstance(var, (ops.IndexedSlices, ops.SparseTensor)):
+        raise TypeError("Type %s not supported" % type(var))
+      if isinstance(var, ops.IndexedSlices):
+        if not _ShapeLessThanOrEqual(inp.values.get_shape(), shape):
+          raise ValueError(
+              "The shape invariant specified for %s is not compatible with "
+              "the initial shape of the values tensor of this IndexedSlices. "
+              "It enters the loop with shape %s, but the specified shape "
+              "invariant is %s."
+              % (inp.values.name, inp.values.get_shape(), shape))
+        var.values.set_shape(shape)
+        var.indices.set_shape(tensor_shape.TensorShape([shape[0]]))
+        if var.dense_shape is not None:
+          var.dense_shape.set_shape(tensor_shape.TensorShape([shape.ndims]))
+      else:
+        if not _ShapeLessThanOrEqual(inp.shape.get_shape(), shape):
+          raise ValueError(
+              "The shape invariant specified for %s is not compatible with "
+              "the initial shape of the shape tensor of this SparseTensor. "
+              "It enters the loop with shape %s, but the specified shape "
+              "invariant is %s."
+              % (inp.shape.name, inp.shape.get_shape(), shape))
+        var.values.set_shape(tensor_shape.TensorShape([None]))
+        var.indices.set_shape(tensor_shape.TensorShape([None, shape.ndims]))
+        var.shape.set_shape(shape)
+
+
+def _EnforceShapeInvariant(merge_var, next_var):
+  """Check if the shapes of the loops variables are invariants.
+
+  Args:
+    merge_vars: The list of tensors representing the initial values of the
+      loop variables.
+    next_vars: The list of tensors representing the values of the loop
+      variables after one loop iteration.
+
+  Raises:
+    ValueError: If any tensor in `merge_vars` has a more specific shape than
+      its correspnding tensor in `next_var`.
+  """
+  if isinstance(merge_var, ops.Tensor):
+    m_shape = merge_var.get_shape()
+    n_shape = next_var.get_shape()
+    if not _ShapeLessThanOrEqual(n_shape, m_shape):
+      raise ValueError(
+          "The shape for %s is not an invariant for the loop. It enters "
+          "the loop with shape %s, but has shape %s after one iteration. "
+          "Provide shape invariants using either the `shape_invariants` "
+          "argument of tf.while_loop or set_shape() on the loop variables."
+          % (merge_var.name, m_shape, n_shape))
+  else:
+    if not isinstance(var, (ops.IndexedSlices, ops.SparseTensor)):
+      raise TypeError("Type %s not supported" % type(var))
+    if isinstance(var, ops.IndexedSlices):
+      m_values_shape = merge_var.values.get_shape()
+      m_indices_shape = merge_var.indices.get_shape()
+      m_shape_shape = tensor_shape.TensorShape(None)
+      if merge_var.dense_shape is not None:
+        m_shape_shape = merge_var.dense_shape.get_shape()
+      n_values_shape = next_var.values.get_shape()
+      n_indices_shape = next_var.indices.get_shape()
+      n_shape_shape = tensor_shape.TensorShape(None)
+      if next_var.dense_shape is not None:
+        n_shape_shape = next_var.dense_shape.get_shape()
+      if (not _ShapeLessThanOrEqual(n_values_shape, m_values_shape) or
+          not _ShapeLessThanOrEqual(n_indices_shape, m_indices_shape)):
+        if not _ShapeLessThanOrEqual(n_values_shape, m_values_shape):
+          raise ValueError(
+              "The shape for %s is not an invariant for the loop. It enters "
+              "the loop with shape (%s, %s, %s), but has shape (%s, %s, %s) "
+              "after one iteration. Provide shape invariants using either the "
+              "`shape_invariants` argument of tf.while_loop or set_shape() "
+              "on the loop variables."
+              % (merge_var.name, m_values_shape, m_indices_shape, m_shape_shape,
+                 n_values_shape, n_indices_shape, n_shape_shape))
     else:
-      rdims.append(tensor_shape.Dimension(None))
-  return tensor_shape.TensorShape(rdims)
+      m_values_shape = merge_var.values.get_shape()
+      m_indices_shape = merge_var.indices.get_shape()
+      m_shape_shape = merge_var.shape.get_shape()
+      n_values_shape = next_var.values.get_shape()
+      n_indices_shape = next_var.indices.get_shape()
+      n_shape_shape = next_var.shape.get_shape()
+      if (not _ShapeLessThanOrEqual(n_values_shape, m_values_shape) or
+          not _ShapeLessThanOrEqual(n_indices_shape, m_indices_shape) or
+          not _ShapeLessThanOrEqual(n_shape_shape, m_shape_shape)):
+        raise ValueError(
+          "The shape for %s is not an invariant for the loop. It enters "
+          "the loop with shape (%s, %s, %s), but has shape (%s, %s, %s) "
+          "after one iteration. Provide shape invariants using either "
+          "the `shape_invariants` argument of tf.while_loop or set_shape() "
+          "on the loop variables."
+          % (merge_var.name, m_values_shape, m_indices_shape, m_shape_shape,
+             n_values_shape, n_indices_shape, n_shape_shape))
 
 
 def _AddNextAndBackEdge(m, v):
@@ -1018,12 +1151,13 @@ class ControlFlowState(object):
             shape = None
           grad_state.grad_context.Enter()
           grad_val = constant_op.constant(0, dtype=dtype, shape=shape)
-          grad_val = _NextIteration(grad_val)
+          next_grad_val = _NextIteration(grad_val)
           grad_state.grad_context.Exit()
           # pylint: disable=protected-access
           if not shape:
             grad_val._shape = b_merge.op.inputs[0].get_shape()
-          b_merge.op._update_input(1, grad_val)
+            next_grad_val.set_shape(grad_val.get_shape())
+          b_merge.op._update_input(1, next_grad_val)
           # pylint: enable=protected-access
 
 
@@ -1835,6 +1969,7 @@ class WhileContext(ControlFlowContext):
         dense_shape=acc_exits[2] if shape_acc is not None else None)
 
   def _InitializeValues(self, values):
+    """Makes the values known to this context."""
     self._values = set()
     for x in values:
       if isinstance(x, ops.Tensor):
@@ -1851,7 +1986,8 @@ class WhileContext(ControlFlowContext):
         if dense_shape is not None:
           self._values.add(dense_shape.name)
 
-  def _BuildLoop(self, pred, body, original_loop_vars, loop_vars):
+  def _BuildLoop(self, pred, body, original_loop_vars, loop_vars,
+                 shape_invariants):
     """Core: Add the loop termination condition and body to the graph."""
     flat_loop_vars = nest.flatten(original_loop_vars)
 
@@ -1863,8 +1999,11 @@ class WhileContext(ControlFlowContext):
       real_vars = [self._outer_context.AddValue(x) for x in loop_vars]
     with ops.control_dependencies(None):
       enter_vars = [_Enter(x, self._name, is_constant=False,
-                           parallel_iterations=self._parallel_iterations)
+                           parallel_iterations=self._parallel_iterations,
+                           use_input_shape=(shape_invariants is None))
                     for x in real_vars]
+    _SetShapeInvariants(real_vars, enter_vars, shape_invariants)
+
     # Fix the control inputs and control flow context of these enter ops.
     self._FixControlInputsAndContext(enter_vars)
     self._InitializeValues(enter_vars)
@@ -1921,16 +2060,16 @@ class WhileContext(ControlFlowContext):
     self._loop_exits = exit_vars
 
     # Make sure the shapes of loop outputs are correct.
-    for m_var, n_var, e_var in zip(merge_vars, next_vars, exit_vars):
+    for m_var, n_var in zip(merge_vars, next_vars):
       if isinstance(m_var, ops.Tensor):
-        e_var._shape = _ShapeIntersection(m_var.get_shape(), n_var.get_shape())
+        _EnforceShapeInvariant(m_var, n_var)
 
     # Exit the loop.
     self.ExitResult(exit_vars)
 
     return original_body_result, exit_vars
 
-  def BuildLoop(self, pred, body, loop_vars):
+  def BuildLoop(self, pred, body, loop_vars, shape_invariants):
     """Add the loop termination condition and body to the graph."""
 
     # Keep original_loop_vars to identify which are TensorArrays
@@ -1942,7 +2081,7 @@ class WhileContext(ControlFlowContext):
     try:
       self.Enter()
       original_body_result, exit_vars = self._BuildLoop(
-          pred, body, original_loop_vars, loop_vars)
+          pred, body, original_loop_vars, loop_vars, shape_invariants)
     finally:
       self.Exit()
 
@@ -1981,8 +2120,9 @@ class WhileContext(ControlFlowContext):
     # pylint: enable=protected-access
 
 
-def while_loop(cond, body, loop_vars, parallel_iterations=10, back_prop=True,
-               swap_memory=False, name=None):
+def while_loop(cond, body, loop_vars, shape_invariants=None,
+               parallel_iterations=10, back_prop=True, swap_memory=False,
+               name=None):
   """Repeat `body` while the condition `cond` is true.
 
   `cond` is a callable returning a boolean scalar tensor. `body` is a callable
@@ -1992,11 +2132,39 @@ def while_loop(cond, body, loop_vars, parallel_iterations=10, back_prop=True,
   and `body`. `cond` and `body` both take as many arguments as there are
   `loop_vars`.
 
+  While `cond` evaluates to true, `body` is executed.
+
   In addition to regular Tensors or IndexedSlices, the body may accept and
   return TensorArray objects.  The flows of the TensorArray objects will
   be appropriately forwarded between loops and during gradient calculations.
 
-  While `cond` evaluates to true, `body` is executed.
+  For correctness, `tf.while_loop()` strictly enforces shape invariants for
+  the loop variables. A shape invariant is a (possibly partial) shape that
+  is unchanged across the iterations of the loop. An error will be raised
+  if the shape of a loop variable after an iteration is determined to be more
+  general than or incompatible with its shape invariant. For example, a shape
+  of [11, None] is more general than a shape of [11, 17], and [11, 21] is not
+  compatible with [11, 17]. By default (if the argument `shape_invariants` is
+  not specified), it is assumed that the initial shape of each tensor in
+  `loop_vars` is the same in every iteration. The `shape_invariants` argument
+  allows the caller to specify a less specific shape invariant for each loop
+  variable, which is needed if the shape varies between iterations. The
+  [`Tensor.set_shape()`](../../api_docs/python/framework.md#Tensor.set_shape)
+  function may also be used in the `body` function to indicate that
+  the output loop variable has a particular shape. The shape invariant for
+  SparseTensor and IndexedSlices are treated specially as follows:
+
+  a) If a loop variable is a SparseTensor, the shape invariant must be
+  TensorShape([r]) where r is the rank of the dense tensor represented
+  by the sparse tensor. It means the shapes of the three tensors of the
+  SparseTensor are ([None], [None, r], [r]). NOTE: The shape invariant here
+  is the shape of the SparseTensor.shape property. It must be the shape of
+  a vector.
+
+  b) If a loop variable is an IndexedSlices, the shape invariant must be
+  a shape invariant of the values tensor of the IndexedSlices. It means
+  the shapes of the three tensors of the IndexedSlices are (shape, [shape[0]],
+  [shape.ndims]).
 
   `while_loop` implements non-strict semantics, enabling multiple iterations
   to run in parallel. The maximum number of parallel iterations can be
@@ -2016,6 +2184,7 @@ def while_loop(cond, body, loop_vars, parallel_iterations=10, back_prop=True,
     body: A callable that represents the loop body.
     loop_vars: A (possibly nested) tuple or list of numpy array, `Tensor`,
       and `TensorArray` objects.
+    shape_invariants: The shape invariants for the loop variables.
     parallel_iterations: The number of iterations allowed to run in parallel.
     back_prop: Whether backprop is enabled for this while loop.
     swap_memory: Whether GPU-CPU memory swap is enabled for this loop.
@@ -2047,6 +2216,19 @@ def while_loop(cond, body, loop_vars, parallel_iterations=10, back_prop=True,
     b = lambda i, (j, k): (i + 1, ((j + k), (j - k)))
     ijk_final = tf.while_loop(c, b, ijk_0)
     ```
+
+  Example using shape_invariants:
+
+    ```python
+    i0 = tf.constant(0)
+    m0 = tf.ones([2, 2])
+    c = lambda i, m: i < 10
+    b = lambda i, m: [i+1, tf.concat(0, [m, m])]
+    tf.while_loop(
+        c, b, loop_vars=[i0, m0],
+        shape_invariants=[i0.get_shape(), tensor_shape.TensorShape([None, 2])])
+    ```
+
   """
   with ops.name_scope(name, "while", loop_vars) as name:
     if not loop_vars:
@@ -2056,19 +2238,14 @@ def while_loop(cond, body, loop_vars, parallel_iterations=10, back_prop=True,
     if not callable(body):
       raise TypeError("body must be callable.")
 
+    if shape_invariants is not None:
+      nest.assert_same_structure(loop_vars, shape_invariants)
+
     context = WhileContext(parallel_iterations, back_prop, swap_memory, name)
-    result = context.BuildLoop(cond, body, loop_vars)
+    result = context.BuildLoop(cond, body, loop_vars, shape_invariants)
     return result
 
 
-def While(cond, body, loop_vars, parallel_iterations=10, back_prop=True,
-          swap_memory=False, name=None):
-  """DEPRECATED: Use `while_loop`."""
-  return while_loop(cond=cond, body=body, loop_vars=loop_vars,
-                    parallel_iterations=parallel_iterations,
-                    back_prop=back_prop, swap_memory=swap_memory, name=name)
-
-
 def _AsTensorList(x, p):
   """Return x as a list of Tensors or IndexedSlices.
 
@@ -2440,7 +2617,7 @@ def case(pred_fn_pairs, default, exclusive=False, name="case"):
     return case_seq
 
 
-ops.RegisterShape("Enter")(common_shapes.unchanged_shape)
+ops.RegisterShape("Enter")(common_shapes.unknown_shape)
 ops.RegisterShape("Exit")(common_shapes.unchanged_shape)
 ops.RegisterShape("NextIteration")(common_shapes.unchanged_shape)
 ops.RegisterShape("RefEnter")(common_shapes.unchanged_shape)
diff --git a/tensorflow/python/ops/control_flow_ops_test.py b/tensorflow/python/ops/control_flow_ops_test.py
index 8c80318054..23813c4001 100644
--- a/tensorflow/python/ops/control_flow_ops_test.py
+++ b/tensorflow/python/ops/control_flow_ops_test.py
@@ -129,7 +129,7 @@ class SwitchTestCase(TensorFlowTestCase):
         embedding = embedding_ops.embedding_lookup(embedding_matrix + 0.0, [0])
         cost += tf.reduce_sum(embedding)
         return it + 1, cost
-      _, cost = control_flow_ops.While(
+      _, cost = control_flow_ops.while_loop(
           Cond, Body, [tf.constant(0), tf.constant(0.0)])
       optimizer = momentum.MomentumOptimizer(0.1, 0.9)
       train_op = optimizer.minimize(cost)
@@ -152,7 +152,7 @@ class SwitchTestCase(TensorFlowTestCase):
                        lambda: tf.square(cost),
                        lambda: cost + tf.reduce_sum(embedding))
         return it + 1, cost
-      _, cost = control_flow_ops.While(
+      _, cost = control_flow_ops.while_loop(
           Cond, Body, [tf.constant(0), tf.constant(0.0)])
 
       dynamic_grads = tf.gradients(cost, [embedding_matrix])[0]
-- 
GitLab


From 7749e64a999ea5418fae08677c1eb1326402bbca Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 12:52:26 -0800
Subject: [PATCH 160/223] Update generated Python Op docs. Change: 131327090

---
 .../g3doc/api_docs/python/control_flow_ops.md | 45 ++++++++++++++++++-
 .../shard4/tf.while_loop.md                   | 45 ++++++++++++++++++-
 2 files changed, 86 insertions(+), 4 deletions(-)

diff --git a/tensorflow/g3doc/api_docs/python/control_flow_ops.md b/tensorflow/g3doc/api_docs/python/control_flow_ops.md
index 365c6a821a..3eeddebf7f 100644
--- a/tensorflow/g3doc/api_docs/python/control_flow_ops.md
+++ b/tensorflow/g3doc/api_docs/python/control_flow_ops.md
@@ -275,7 +275,7 @@ Example 2:
 
 - - -
 
-### `tf.while_loop(cond, body, loop_vars, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#while_loop}
+### `tf.while_loop(cond, body, loop_vars, shape_invariants=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#while_loop}
 
 Repeat `body` while the condition `cond` is true.
 
@@ -286,11 +286,39 @@ arity (length and structure) and types as `loop_vars`. `loop_vars` is a
 and `body`. `cond` and `body` both take as many arguments as there are
 `loop_vars`.
 
+While `cond` evaluates to true, `body` is executed.
+
 In addition to regular Tensors or IndexedSlices, the body may accept and
 return TensorArray objects.  The flows of the TensorArray objects will
 be appropriately forwarded between loops and during gradient calculations.
 
-While `cond` evaluates to true, `body` is executed.
+For correctness, `tf.while_loop()` strictly enforces shape invariants for
+the loop variables. A shape invariant is a (possibly partial) shape that
+is unchanged across the iterations of the loop. An error will be raised
+if the shape of a loop variable after an iteration is determined to be more
+general than or incompatible with its shape invariant. For example, a shape
+of [11, None] is more general than a shape of [11, 17], and [11, 21] is not
+compatible with [11, 17]. By default (if the argument `shape_invariants` is
+not specified), it is assumed that the initial shape of each tensor in
+`loop_vars` is the same in every iteration. The `shape_invariants` argument
+allows the caller to specify a less specific shape invariant for each loop
+variable, which is needed if the shape varies between iterations. The
+[`Tensor.set_shape()`](../../api_docs/python/framework.md#Tensor.set_shape)
+function may also be used in the `body` function to indicate that
+the output loop variable has a particular shape. The shape invariant for
+SparseTensor and IndexedSlices are treated specially as follows:
+
+a) If a loop variable is a SparseTensor, the shape invariant must be
+TensorShape([r]) where r is the rank of the dense tensor represented
+by the sparse tensor. It means the shapes of the three tensors of the
+SparseTensor are ([None], [None, r], [r]). NOTE: The shape invariant here
+is the shape of the SparseTensor.shape property. It must be the shape of
+a vector.
+
+b) If a loop variable is an IndexedSlices, the shape invariant must be
+a shape invariant of the values tensor of the IndexedSlices. It means
+the shapes of the three tensors of the IndexedSlices are (shape, [shape[0]],
+[shape.ndims]).
 
 `while_loop` implements non-strict semantics, enabling multiple iterations
 to run in parallel. The maximum number of parallel iterations can be
@@ -312,6 +340,7 @@ sequences and large batches.
 *  <b>`body`</b>: A callable that represents the loop body.
 *  <b>`loop_vars`</b>: A (possibly nested) tuple or list of numpy array, `Tensor`,
     and `TensorArray` objects.
+*  <b>`shape_invariants`</b>: The shape invariants for the loop variables.
 *  <b>`parallel_iterations`</b>: The number of iterations allowed to run in parallel.
 *  <b>`back_prop`</b>: Whether backprop is enabled for this while loop.
 *  <b>`swap_memory`</b>: Whether GPU-CPU memory swap is enabled for this loop.
@@ -348,6 +377,18 @@ Example with nesting:
   ijk_final = tf.while_loop(c, b, ijk_0)
   ```
 
+Example using shape_invariants:
+
+  ```python
+  i0 = tf.constant(0)
+  m0 = tf.ones([2, 2])
+  c = lambda i, m: i < 10
+  b = lambda i, m: [i+1, tf.concat(0, [m, m])]
+  tf.while_loop(
+      c, b, loop_vars=[i0, m0],
+      shape_invariants=[i0.get_shape(), tensor_shape.TensorShape([None, 2])])
+  ```
+
 
 
 ## Logical Operators
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.while_loop.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.while_loop.md
index 8d8320962b..2574c0be69 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.while_loop.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.while_loop.md
@@ -1,4 +1,4 @@
-### `tf.while_loop(cond, body, loop_vars, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#while_loop}
+### `tf.while_loop(cond, body, loop_vars, shape_invariants=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None)` {#while_loop}
 
 Repeat `body` while the condition `cond` is true.
 
@@ -9,11 +9,39 @@ arity (length and structure) and types as `loop_vars`. `loop_vars` is a
 and `body`. `cond` and `body` both take as many arguments as there are
 `loop_vars`.
 
+While `cond` evaluates to true, `body` is executed.
+
 In addition to regular Tensors or IndexedSlices, the body may accept and
 return TensorArray objects.  The flows of the TensorArray objects will
 be appropriately forwarded between loops and during gradient calculations.
 
-While `cond` evaluates to true, `body` is executed.
+For correctness, `tf.while_loop()` strictly enforces shape invariants for
+the loop variables. A shape invariant is a (possibly partial) shape that
+is unchanged across the iterations of the loop. An error will be raised
+if the shape of a loop variable after an iteration is determined to be more
+general than or incompatible with its shape invariant. For example, a shape
+of [11, None] is more general than a shape of [11, 17], and [11, 21] is not
+compatible with [11, 17]. By default (if the argument `shape_invariants` is
+not specified), it is assumed that the initial shape of each tensor in
+`loop_vars` is the same in every iteration. The `shape_invariants` argument
+allows the caller to specify a less specific shape invariant for each loop
+variable, which is needed if the shape varies between iterations. The
+[`Tensor.set_shape()`](../../api_docs/python/framework.md#Tensor.set_shape)
+function may also be used in the `body` function to indicate that
+the output loop variable has a particular shape. The shape invariant for
+SparseTensor and IndexedSlices are treated specially as follows:
+
+a) If a loop variable is a SparseTensor, the shape invariant must be
+TensorShape([r]) where r is the rank of the dense tensor represented
+by the sparse tensor. It means the shapes of the three tensors of the
+SparseTensor are ([None], [None, r], [r]). NOTE: The shape invariant here
+is the shape of the SparseTensor.shape property. It must be the shape of
+a vector.
+
+b) If a loop variable is an IndexedSlices, the shape invariant must be
+a shape invariant of the values tensor of the IndexedSlices. It means
+the shapes of the three tensors of the IndexedSlices are (shape, [shape[0]],
+[shape.ndims]).
 
 `while_loop` implements non-strict semantics, enabling multiple iterations
 to run in parallel. The maximum number of parallel iterations can be
@@ -35,6 +63,7 @@ sequences and large batches.
 *  <b>`body`</b>: A callable that represents the loop body.
 *  <b>`loop_vars`</b>: A (possibly nested) tuple or list of numpy array, `Tensor`,
     and `TensorArray` objects.
+*  <b>`shape_invariants`</b>: The shape invariants for the loop variables.
 *  <b>`parallel_iterations`</b>: The number of iterations allowed to run in parallel.
 *  <b>`back_prop`</b>: Whether backprop is enabled for this while loop.
 *  <b>`swap_memory`</b>: Whether GPU-CPU memory swap is enabled for this loop.
@@ -71,3 +100,15 @@ Example with nesting:
   ijk_final = tf.while_loop(c, b, ijk_0)
   ```
 
+Example using shape_invariants:
+
+  ```python
+  i0 = tf.constant(0)
+  m0 = tf.ones([2, 2])
+  c = lambda i, m: i < 10
+  b = lambda i, m: [i+1, tf.concat(0, [m, m])]
+  tf.while_loop(
+      c, b, loop_vars=[i0, m0],
+      shape_invariants=[i0.get_shape(), tensor_shape.TensorShape([None, 2])])
+  ```
+
-- 
GitLab


From 6cf3f2aa584a4b69bb0626a1b402b00b190f69d4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 13:06:51 -0800
Subject: [PATCH 161/223] Get rid of SupportsBatchOperation template parameter
 from linear algebra ops, to reduce code size and compilation time. Change:
 131328798

---
 tensorflow/core/kernels/cholesky_grad.cc      | 15 +++--
 tensorflow/core/kernels/cholesky_op.cc        | 15 +++--
 tensorflow/core/kernels/determinant_op.cc     | 16 ++---
 tensorflow/core/kernels/linalg_ops_common.cc  | 66 ++++++++-----------
 tensorflow/core/kernels/linalg_ops_common.h   |  8 +--
 tensorflow/core/kernels/matrix_inverse_op.cc  | 15 ++---
 tensorflow/core/kernels/matrix_solve_ls_op.cc | 15 ++---
 tensorflow/core/kernels/matrix_solve_op.cc    | 14 ++--
 .../kernels/matrix_triangular_solve_op.cc     | 19 +++---
 .../core/kernels/self_adjoint_eig_op.cc       | 17 ++---
 .../core/kernels/self_adjoint_eig_v2_op.cc    | 18 ++---
 tensorflow/core/kernels/svd_op.cc             | 14 ++--
 12 files changed, 104 insertions(+), 128 deletions(-)

diff --git a/tensorflow/core/kernels/cholesky_grad.cc b/tensorflow/core/kernels/cholesky_grad.cc
index 027897a419..9d33845c2f 100644
--- a/tensorflow/core/kernels/cholesky_grad.cc
+++ b/tensorflow/core/kernels/cholesky_grad.cc
@@ -22,10 +22,10 @@ limitations under the License.
 
 namespace tensorflow {
 
-template <typename Scalar, bool SupportsBatchOperation>
-class CholeskyGrad : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
+template <typename Scalar>
+class CholeskyGrad : public LinearAlgebraOp<Scalar> {
  public:
-  typedef LinearAlgebraOp<Scalar, SupportsBatchOperation> Base;
+  typedef LinearAlgebraOp<Scalar> Base;
 
   explicit CholeskyGrad(OpKernelConstruction* context) : Base(context) {}
 
@@ -156,8 +156,9 @@ class CholeskyGrad : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
   }
 };
 
-REGISTER_LINALG_OP("CholeskyGrad", (CholeskyGrad<float, false>), float);
-REGISTER_LINALG_OP("CholeskyGrad", (CholeskyGrad<double, false>), double);
-REGISTER_LINALG_OP("BatchCholeskyGrad", (CholeskyGrad<float, true>), float);
-REGISTER_LINALG_OP("BatchCholeskyGrad", (CholeskyGrad<double, true>), double);
+REGISTER_LINALG_OP("CholeskyGrad", (CholeskyGrad<float>), float);
+REGISTER_LINALG_OP("CholeskyGrad", (CholeskyGrad<double>), double);
+REGISTER_LINALG_OP("BatchCholeskyGrad", (CholeskyGrad<float>), float);
+REGISTER_LINALG_OP("BatchCholeskyGrad", (CholeskyGrad<double>), double);
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/cholesky_op.cc b/tensorflow/core/kernels/cholesky_op.cc
index f124ce2cef..e5bf164cfa 100644
--- a/tensorflow/core/kernels/cholesky_op.cc
+++ b/tensorflow/core/kernels/cholesky_op.cc
@@ -29,10 +29,10 @@ limitations under the License.
 
 namespace tensorflow {
 
-template <class Scalar, bool SupportsBatchOperation>
-class CholeskyOp : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
+template <class Scalar>
+class CholeskyOp : public LinearAlgebraOp<Scalar> {
  public:
-  typedef LinearAlgebraOp<Scalar, SupportsBatchOperation> Base;
+  typedef LinearAlgebraOp<Scalar> Base;
 
   explicit CholeskyOp(OpKernelConstruction* context) : Base(context) {}
 
@@ -65,8 +65,9 @@ class CholeskyOp : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
   }
 };
 
-REGISTER_LINALG_OP("Cholesky", (CholeskyOp<float, false>), float);
-REGISTER_LINALG_OP("Cholesky", (CholeskyOp<double, false>), double);
-REGISTER_LINALG_OP("BatchCholesky", (CholeskyOp<float, true>), float);
-REGISTER_LINALG_OP("BatchCholesky", (CholeskyOp<double, true>), double);
+REGISTER_LINALG_OP("Cholesky", (CholeskyOp<float>), float);
+REGISTER_LINALG_OP("Cholesky", (CholeskyOp<double>), double);
+REGISTER_LINALG_OP("BatchCholesky", (CholeskyOp<float>), float);
+REGISTER_LINALG_OP("BatchCholesky", (CholeskyOp<double>), double);
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/determinant_op.cc b/tensorflow/core/kernels/determinant_op.cc
index b2e69a8df5..d51563580b 100644
--- a/tensorflow/core/kernels/determinant_op.cc
+++ b/tensorflow/core/kernels/determinant_op.cc
@@ -27,10 +27,10 @@ limitations under the License.
 
 namespace tensorflow {
 
-template <class Scalar, bool SupportsBatchOperation>
-class DeterminantOp : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
+template <class Scalar>
+class DeterminantOp : public LinearAlgebraOp<Scalar> {
  public:
-  typedef LinearAlgebraOp<Scalar, SupportsBatchOperation> Base;
+  typedef LinearAlgebraOp<Scalar> Base;
 
   explicit DeterminantOp(OpKernelConstruction* context) : Base(context) {}
 
@@ -60,11 +60,9 @@ class DeterminantOp : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
   }
 };
 
-REGISTER_LINALG_OP("MatrixDeterminant", (DeterminantOp<float, false>), float);
-REGISTER_LINALG_OP("MatrixDeterminant", (DeterminantOp<double, false>), double);
-REGISTER_LINALG_OP("BatchMatrixDeterminant", (DeterminantOp<float, true>),
-                   float);
-REGISTER_LINALG_OP("BatchMatrixDeterminant", (DeterminantOp<double, true>),
-                   double);
+REGISTER_LINALG_OP("MatrixDeterminant", (DeterminantOp<float>), float);
+REGISTER_LINALG_OP("MatrixDeterminant", (DeterminantOp<double>), double);
+REGISTER_LINALG_OP("BatchMatrixDeterminant", (DeterminantOp<float>), float);
+REGISTER_LINALG_OP("BatchMatrixDeterminant", (DeterminantOp<double>), double);
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/linalg_ops_common.cc b/tensorflow/core/kernels/linalg_ops_common.cc
index 575c7e2e7c..2bf476b4de 100644
--- a/tensorflow/core/kernels/linalg_ops_common.cc
+++ b/tensorflow/core/kernels/linalg_ops_common.cc
@@ -27,8 +27,8 @@ limitations under the License.
 namespace tensorflow {
 
 // static
-template <typename Scalar, bool SupportsBatchOperation>
-void LinearAlgebraOp<Scalar, SupportsBatchOperation>::ValidateSingleMatrix(
+template <typename Scalar>
+void LinearAlgebraOp<Scalar>::ValidateSingleMatrix(
     OpKernelContext* context, const TensorShapes& input_matrix_shapes) {
   OP_REQUIRES(context, input_matrix_shapes.size() == 1,
               errors::InvalidArgument("Expected a single input matrix, got %d.",
@@ -38,10 +38,9 @@ void LinearAlgebraOp<Scalar, SupportsBatchOperation>::ValidateSingleMatrix(
 }
 
 // static
-template <typename Scalar, bool SupportsBatchOperation>
-void LinearAlgebraOp<Scalar, SupportsBatchOperation>::
-    ValidateSingleSquareMatrix(OpKernelContext* context,
-                               const TensorShapes& input_matrix_shapes) {
+template <typename Scalar>
+void LinearAlgebraOp<Scalar>::ValidateSingleSquareMatrix(
+    OpKernelContext* context, const TensorShapes& input_matrix_shapes) {
   OP_REQUIRES(context, input_matrix_shapes.size() == 1,
               errors::InvalidArgument("Expected a single input matrix, got %d.",
                                       input_matrix_shapes.size()));
@@ -50,8 +49,8 @@ void LinearAlgebraOp<Scalar, SupportsBatchOperation>::
 }
 
 // static
-template <typename Scalar, bool SupportsBatchOperation>
-void LinearAlgebraOp<Scalar, SupportsBatchOperation>::ValidateSolver(
+template <typename Scalar>
+void LinearAlgebraOp<Scalar>::ValidateSolver(
     OpKernelContext* context, const TensorShapes& input_matrix_shapes) {
   OP_REQUIRES(context, input_matrix_shapes.size() == 2,
               errors::InvalidArgument("Expected two input matrices, got %d.",
@@ -67,8 +66,8 @@ void LinearAlgebraOp<Scalar, SupportsBatchOperation>::ValidateSolver(
 }
 
 // static
-template <typename Scalar, bool SupportsBatchOperation>
-void LinearAlgebraOp<Scalar, SupportsBatchOperation>::ValidateSquareSolver(
+template <typename Scalar>
+void LinearAlgebraOp<Scalar>::ValidateSquareSolver(
     OpKernelContext* context, const TensorShapes& input_matrix_shapes) {
   OP_REQUIRES(context, input_matrix_shapes.size() == 2,
               errors::InvalidArgument("Expected two input matrices, got %d.",
@@ -84,9 +83,8 @@ void LinearAlgebraOp<Scalar, SupportsBatchOperation>::ValidateSquareSolver(
       errors::InvalidArgument("Input matrix and rhs are incompatible."));
 }
 
-template <typename Scalar, bool SupportsBatchOperation>
-void LinearAlgebraOp<Scalar, SupportsBatchOperation>::Compute(
-    OpKernelContext* context) {
+template <typename Scalar>
+void LinearAlgebraOp<Scalar>::Compute(OpKernelContext* context) {
   TensorInputs inputs;
   TensorShapes input_matrix_shapes;
   TensorShape batch_shape;
@@ -110,27 +108,20 @@ void LinearAlgebraOp<Scalar, SupportsBatchOperation>::Compute(
         batch_shape.num_elements(), GetCostPerUnit(input_matrix_shapes), shard);
 }
 
-template <typename Scalar, bool SupportsBatchOperation>
-void LinearAlgebraOp<Scalar, SupportsBatchOperation>::AnalyzeInputs(
-    OpKernelContext* context, TensorInputs* inputs,
-    TensorShapes* input_matrix_shapes, TensorShape* batch_shape) {
+template <typename Scalar>
+void LinearAlgebraOp<Scalar>::AnalyzeInputs(OpKernelContext* context,
+                                            TensorInputs* inputs,
+                                            TensorShapes* input_matrix_shapes,
+                                            TensorShape* batch_shape) {
   int input_rank = -1;
   for (int i = 0; i < NumMatrixInputs(context); ++i) {
     const Tensor& in = context->input(i);
     if (i == 0) {
       input_rank = in.dims();
-      if (SupportsBatchOperation) {
-        OP_REQUIRES(
-            context, input_rank >= 2,
-            errors::InvalidArgument("Input tensor ", i,
-                                    " must have rank >= 2, got", input_rank));
-      } else {
-        OP_REQUIRES(
-            context, input_rank == 2,
-            errors::InvalidArgument("Input tensor ", i,
-                                    " must have rank == 2, got", input_rank));
-      }
-
+      OP_REQUIRES(
+          context, input_rank >= 2,
+          errors::InvalidArgument("Input tensor ", i,
+                                  " must have rank >= 2, got", input_rank));
       // If the tensor rank is greater than 2, we consider the inner-most
       // dimensions as matrices, and loop over all the other outer ("batch")
       // dimensions to compute the results.
@@ -163,8 +154,8 @@ void LinearAlgebraOp<Scalar, SupportsBatchOperation>::AnalyzeInputs(
   ValidateInputMatrixShapes(context, *input_matrix_shapes);
 }
 
-template <typename Scalar, bool SupportsBatchOperation>
-void LinearAlgebraOp<Scalar, SupportsBatchOperation>::PrepareOutputs(
+template <typename Scalar>
+void LinearAlgebraOp<Scalar>::PrepareOutputs(
     OpKernelContext* context, const TensorShapes& input_matrix_shapes,
     const TensorShape& batch_shape, TensorOutputs* outputs,
     TensorShapes* output_matrix_shapes) {
@@ -205,8 +196,8 @@ void LinearAlgebraOp<Scalar, SupportsBatchOperation>::PrepareOutputs(
   }
 }
 
-template <typename Scalar, bool SupportsBatchOperation>
-void LinearAlgebraOp<Scalar, SupportsBatchOperation>::ComputeTensorSlice(
+template <typename Scalar>
+void LinearAlgebraOp<Scalar>::ComputeTensorSlice(
     OpKernelContext* context, int64 matrix_index, const TensorInputs& inputs,
     const TensorShapes& input_matrix_shapes, const TensorOutputs& outputs,
     const TensorShapes& output_matrix_shapes) {
@@ -238,11 +229,8 @@ void LinearAlgebraOp<Scalar, SupportsBatchOperation>::ComputeTensorSlice(
   ComputeMatrix(context, matrix_inputs, &matrix_outputs);
 }
 
-// Explicitly instantiate LinearAlgebraOp for the scalar types we expect to
-// use.
-template class LinearAlgebraOp<float, false>;
-template class LinearAlgebraOp<float, true>;
-template class LinearAlgebraOp<double, false>;
-template class LinearAlgebraOp<double, true>;
+// Explicitly instantiate LinearAlgebraOp for the scalar types we expect to use.
+template class LinearAlgebraOp<float>;
+template class LinearAlgebraOp<double>;
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/linalg_ops_common.h b/tensorflow/core/kernels/linalg_ops_common.h
index 3be9853c6c..95940554f5 100644
--- a/tensorflow/core/kernels/linalg_ops_common.h
+++ b/tensorflow/core/kernels/linalg_ops_common.h
@@ -39,7 +39,7 @@ limitations under the License.
 namespace tensorflow {
 
 // Base class for linear algebra operators.
-template <typename Scalar, bool SupportsBatchOperationT>
+template <typename Scalar>
 class LinearAlgebraOp : public OpKernel {
  public:
   explicit LinearAlgebraOp(OpKernelConstruction* context) : OpKernel(context) {}
@@ -164,10 +164,8 @@ class LinearAlgebraOp : public OpKernel {
 
 // Declare that LinearAlgebraOp is explicitly instantiated in
 // linalg_ops_common.cc for float and double.
-extern template class LinearAlgebraOp<float, false>;
-extern template class LinearAlgebraOp<float, true>;
-extern template class LinearAlgebraOp<double, false>;
-extern template class LinearAlgebraOp<double, true>;
+extern template class LinearAlgebraOp<float>;
+extern template class LinearAlgebraOp<double>;
 
 }  // namespace tensorflow
 
diff --git a/tensorflow/core/kernels/matrix_inverse_op.cc b/tensorflow/core/kernels/matrix_inverse_op.cc
index d548e3f65b..884e3d61a4 100644
--- a/tensorflow/core/kernels/matrix_inverse_op.cc
+++ b/tensorflow/core/kernels/matrix_inverse_op.cc
@@ -28,10 +28,10 @@ limitations under the License.
 
 namespace tensorflow {
 
-template <class Scalar, bool SupportsBatchOperation>
-class MatrixInverseOp : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
+template <class Scalar>
+class MatrixInverseOp : public LinearAlgebraOp<Scalar> {
  public:
-  typedef LinearAlgebraOp<Scalar, SupportsBatchOperation> Base;
+  typedef LinearAlgebraOp<Scalar> Base;
 
   explicit MatrixInverseOp(OpKernelConstruction* context) : Base(context) {
     OP_REQUIRES_OK(context, context->GetAttr("adjoint", &adjoint_));
@@ -77,10 +77,9 @@ class MatrixInverseOp : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
   TF_DISALLOW_COPY_AND_ASSIGN(MatrixInverseOp);
 };
 
-REGISTER_LINALG_OP("MatrixInverse", (MatrixInverseOp<float, false>), float);
-REGISTER_LINALG_OP("MatrixInverse", (MatrixInverseOp<double, false>), double);
-REGISTER_LINALG_OP("BatchMatrixInverse", (MatrixInverseOp<float, true>), float);
-REGISTER_LINALG_OP("BatchMatrixInverse", (MatrixInverseOp<double, true>),
-                   double);
+REGISTER_LINALG_OP("MatrixInverse", (MatrixInverseOp<float>), float);
+REGISTER_LINALG_OP("MatrixInverse", (MatrixInverseOp<double>), double);
+REGISTER_LINALG_OP("BatchMatrixInverse", (MatrixInverseOp<float>), float);
+REGISTER_LINALG_OP("BatchMatrixInverse", (MatrixInverseOp<double>), double);
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/matrix_solve_ls_op.cc b/tensorflow/core/kernels/matrix_solve_ls_op.cc
index 9ee3f2c924..716015e7de 100644
--- a/tensorflow/core/kernels/matrix_solve_ls_op.cc
+++ b/tensorflow/core/kernels/matrix_solve_ls_op.cc
@@ -28,10 +28,10 @@ limitations under the License.
 
 namespace tensorflow {
 
-template <class Scalar, bool SupportsBatchOperation>
-class MatrixSolveLsOp : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
+template <class Scalar>
+class MatrixSolveLsOp : public LinearAlgebraOp<Scalar> {
  public:
-  typedef LinearAlgebraOp<Scalar, SupportsBatchOperation> Base;
+  typedef LinearAlgebraOp<Scalar> Base;
 
   explicit MatrixSolveLsOp(OpKernelConstruction* context) : Base(context) {
     OP_REQUIRES_OK(context, context->GetAttr("fast", &fast_));
@@ -155,10 +155,9 @@ class MatrixSolveLsOp : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
   bool fast_;
 };
 
-REGISTER_LINALG_OP("MatrixSolveLs", (MatrixSolveLsOp<float, false>), float);
-REGISTER_LINALG_OP("MatrixSolveLs", (MatrixSolveLsOp<double, false>), double);
-REGISTER_LINALG_OP("BatchMatrixSolveLs", (MatrixSolveLsOp<float, true>), float);
-REGISTER_LINALG_OP("BatchMatrixSolveLs", (MatrixSolveLsOp<double, true>),
-                   double);
+REGISTER_LINALG_OP("MatrixSolveLs", (MatrixSolveLsOp<float>), float);
+REGISTER_LINALG_OP("MatrixSolveLs", (MatrixSolveLsOp<double>), double);
+REGISTER_LINALG_OP("BatchMatrixSolveLs", (MatrixSolveLsOp<float>), float);
+REGISTER_LINALG_OP("BatchMatrixSolveLs", (MatrixSolveLsOp<double>), double);
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/matrix_solve_op.cc b/tensorflow/core/kernels/matrix_solve_op.cc
index 32f3bd32c1..1c881783e1 100644
--- a/tensorflow/core/kernels/matrix_solve_op.cc
+++ b/tensorflow/core/kernels/matrix_solve_op.cc
@@ -28,10 +28,10 @@ limitations under the License.
 
 namespace tensorflow {
 
-template <class Scalar, bool SupportsBatchOperation>
-class MatrixSolveOp : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
+template <class Scalar>
+class MatrixSolveOp : public LinearAlgebraOp<Scalar> {
  public:
-  typedef LinearAlgebraOp<Scalar, SupportsBatchOperation> Base;
+  typedef LinearAlgebraOp<Scalar> Base;
 
   explicit MatrixSolveOp(OpKernelConstruction* context) : Base(context) {
     OP_REQUIRES_OK(context, context->GetAttr("adjoint", &adjoint_));
@@ -105,9 +105,9 @@ class MatrixSolveOp : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
   TF_DISALLOW_COPY_AND_ASSIGN(MatrixSolveOp);
 };
 
-REGISTER_LINALG_OP("MatrixSolve", (MatrixSolveOp<float, false>), float);
-REGISTER_LINALG_OP("MatrixSolve", (MatrixSolveOp<double, false>), double);
-REGISTER_LINALG_OP("BatchMatrixSolve", (MatrixSolveOp<float, true>), float);
-REGISTER_LINALG_OP("BatchMatrixSolve", (MatrixSolveOp<double, true>), double);
+REGISTER_LINALG_OP("MatrixSolve", (MatrixSolveOp<float>), float);
+REGISTER_LINALG_OP("MatrixSolve", (MatrixSolveOp<double>), double);
+REGISTER_LINALG_OP("BatchMatrixSolve", (MatrixSolveOp<float>), float);
+REGISTER_LINALG_OP("BatchMatrixSolve", (MatrixSolveOp<double>), double);
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/matrix_triangular_solve_op.cc b/tensorflow/core/kernels/matrix_triangular_solve_op.cc
index 50cab2b84e..09f75f2d5f 100644
--- a/tensorflow/core/kernels/matrix_triangular_solve_op.cc
+++ b/tensorflow/core/kernels/matrix_triangular_solve_op.cc
@@ -27,11 +27,10 @@ limitations under the License.
 
 namespace tensorflow {
 
-template <class Scalar, bool SupportsBatchOperation>
-class MatrixTriangularSolveOp
-    : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
+template <class Scalar>
+class MatrixTriangularSolveOp : public LinearAlgebraOp<Scalar> {
  public:
-  typedef LinearAlgebraOp<Scalar, SupportsBatchOperation> Base;
+  typedef LinearAlgebraOp<Scalar> Base;
 
   explicit MatrixTriangularSolveOp(OpKernelConstruction* context)
       : Base(context), lower_(true), adjoint_(false) {
@@ -104,13 +103,13 @@ class MatrixTriangularSolveOp
   TF_DISALLOW_COPY_AND_ASSIGN(MatrixTriangularSolveOp);
 };
 
-REGISTER_LINALG_OP("MatrixTriangularSolve",
-                   (MatrixTriangularSolveOp<float, false>), float);
-REGISTER_LINALG_OP("MatrixTriangularSolve",
-                   (MatrixTriangularSolveOp<double, false>), double);
+REGISTER_LINALG_OP("MatrixTriangularSolve", (MatrixTriangularSolveOp<float>),
+                   float);
+REGISTER_LINALG_OP("MatrixTriangularSolve", (MatrixTriangularSolveOp<double>),
+                   double);
 REGISTER_LINALG_OP("BatchMatrixTriangularSolve",
-                   (MatrixTriangularSolveOp<float, true>), float);
+                   (MatrixTriangularSolveOp<float>), float);
 REGISTER_LINALG_OP("BatchMatrixTriangularSolve",
-                   (MatrixTriangularSolveOp<double, true>), double);
+                   (MatrixTriangularSolveOp<double>), double);
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/self_adjoint_eig_op.cc b/tensorflow/core/kernels/self_adjoint_eig_op.cc
index 9d3a411f3b..9765780726 100644
--- a/tensorflow/core/kernels/self_adjoint_eig_op.cc
+++ b/tensorflow/core/kernels/self_adjoint_eig_op.cc
@@ -28,11 +28,10 @@ limitations under the License.
 
 namespace tensorflow {
 
-template <class Scalar, bool SupportsBatchOperation>
-class SelfAdjointEigOp
-    : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
+template <class Scalar>
+class SelfAdjointEigOp : public LinearAlgebraOp<Scalar> {
  public:
-  typedef LinearAlgebraOp<Scalar, SupportsBatchOperation> Base;
+  typedef LinearAlgebraOp<Scalar> Base;
 
   explicit SelfAdjointEigOp(OpKernelConstruction* context) : Base(context) {}
 
@@ -69,10 +68,8 @@ class SelfAdjointEigOp
   }
 };
 
-REGISTER_LINALG_OP("SelfAdjointEig", (SelfAdjointEigOp<float, false>), float);
-REGISTER_LINALG_OP("SelfAdjointEig", (SelfAdjointEigOp<double, false>), double);
-REGISTER_LINALG_OP("BatchSelfAdjointEig", (SelfAdjointEigOp<float, true>),
-                   float);
-REGISTER_LINALG_OP("BatchSelfAdjointEig", (SelfAdjointEigOp<double, true>),
-                   double);
+REGISTER_LINALG_OP("SelfAdjointEig", (SelfAdjointEigOp<float>), float);
+REGISTER_LINALG_OP("SelfAdjointEig", (SelfAdjointEigOp<double>), double);
+REGISTER_LINALG_OP("BatchSelfAdjointEig", (SelfAdjointEigOp<float>), float);
+REGISTER_LINALG_OP("BatchSelfAdjointEig", (SelfAdjointEigOp<double>), double);
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/self_adjoint_eig_v2_op.cc b/tensorflow/core/kernels/self_adjoint_eig_v2_op.cc
index 1b457ebe9e..c647d3aaac 100644
--- a/tensorflow/core/kernels/self_adjoint_eig_v2_op.cc
+++ b/tensorflow/core/kernels/self_adjoint_eig_v2_op.cc
@@ -27,11 +27,10 @@ limitations under the License.
 
 namespace tensorflow {
 
-template <class Scalar, bool SupportsBatchOperation>
-class SelfAdjointEigV2Op
-    : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
+template <class Scalar>
+class SelfAdjointEigV2Op : public LinearAlgebraOp<Scalar> {
  public:
-  typedef LinearAlgebraOp<Scalar, SupportsBatchOperation> Base;
+  typedef LinearAlgebraOp<Scalar> Base;
 
   explicit SelfAdjointEigV2Op(OpKernelConstruction* context) : Base(context) {
     OP_REQUIRES_OK(context, context->GetAttr("compute_v", &compute_v_));
@@ -80,12 +79,9 @@ class SelfAdjointEigV2Op
   bool compute_v_;
 };
 
-REGISTER_LINALG_OP("SelfAdjointEigV2", (SelfAdjointEigV2Op<float, false>),
-                   float);
-REGISTER_LINALG_OP("SelfAdjointEigV2", (SelfAdjointEigV2Op<double, false>),
-                   double);
-REGISTER_LINALG_OP("BatchSelfAdjointEigV2", (SelfAdjointEigV2Op<float, true>),
-                   float);
-REGISTER_LINALG_OP("BatchSelfAdjointEigV2", (SelfAdjointEigV2Op<double, true>),
+REGISTER_LINALG_OP("SelfAdjointEigV2", (SelfAdjointEigV2Op<float>), float);
+REGISTER_LINALG_OP("SelfAdjointEigV2", (SelfAdjointEigV2Op<double>), double);
+REGISTER_LINALG_OP("BatchSelfAdjointEigV2", (SelfAdjointEigV2Op<float>), float);
+REGISTER_LINALG_OP("BatchSelfAdjointEigV2", (SelfAdjointEigV2Op<double>),
                    double);
 }  // namespace tensorflow
diff --git a/tensorflow/core/kernels/svd_op.cc b/tensorflow/core/kernels/svd_op.cc
index 04925cc42a..c941b8c70e 100644
--- a/tensorflow/core/kernels/svd_op.cc
+++ b/tensorflow/core/kernels/svd_op.cc
@@ -28,10 +28,10 @@ limitations under the License.
 
 namespace tensorflow {
 
-template <class Scalar, bool SupportsBatchOperation>
-class SvdOp : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
+template <class Scalar>
+class SvdOp : public LinearAlgebraOp<Scalar> {
  public:
-  typedef LinearAlgebraOp<Scalar, SupportsBatchOperation> Base;
+  typedef LinearAlgebraOp<Scalar> Base;
 
   explicit SvdOp(OpKernelConstruction* context) : Base(context) {
     OP_REQUIRES_OK(context, context->GetAttr("compute_uv", &compute_uv_));
@@ -97,9 +97,9 @@ class SvdOp : public LinearAlgebraOp<Scalar, SupportsBatchOperation> {
   TF_DISALLOW_COPY_AND_ASSIGN(SvdOp);
 };
 
-REGISTER_LINALG_OP("Svd", (SvdOp<float, false>), float);
-REGISTER_LINALG_OP("Svd", (SvdOp<double, false>), double);
-REGISTER_LINALG_OP("BatchSvd", (SvdOp<float, true>), float);
-REGISTER_LINALG_OP("BatchSvd", (SvdOp<double, true>), double);
+REGISTER_LINALG_OP("Svd", (SvdOp<float>), float);
+REGISTER_LINALG_OP("Svd", (SvdOp<double>), double);
+REGISTER_LINALG_OP("BatchSvd", (SvdOp<float>), float);
+REGISTER_LINALG_OP("BatchSvd", (SvdOp<double>), double);
 
 }  // namespace tensorflow
-- 
GitLab


From 001dd202ec3e4b6b29965fcbd45483e5d1649495 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 13:09:52 -0800
Subject: [PATCH 162/223] The moving average of the variables is done in
 parallel causing benign data races, disable tsan for now. Change: 131329184

---
 tensorflow/contrib/opt/BUILD | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/tensorflow/contrib/opt/BUILD b/tensorflow/contrib/opt/BUILD
index a0ca0fabd2..55314613c2 100644
--- a/tensorflow/contrib/opt/BUILD
+++ b/tensorflow/contrib/opt/BUILD
@@ -33,6 +33,9 @@ py_test(
     name = "moving_average_optimizer_test",
     srcs = ["python/training/moving_average_optimizer_test.py"],
     srcs_version = "PY2AND3",
+    tags = [
+        "notsan",  # b/31055119
+    ],
     deps = [
         ":opt_py",
         "//tensorflow:tensorflow_py",
-- 
GitLab


From 3815d9ee3795464c147057ef55330114cde1bb73 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 13:11:31 -0800
Subject: [PATCH 163/223] Open source the image-to-text model. Change:
 131329383

---
 tensorflow/contrib/slim/python/slim/nets/BUILD | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/slim/python/slim/nets/BUILD b/tensorflow/contrib/slim/python/slim/nets/BUILD
index 94410c2967..9d5b95c6b1 100644
--- a/tensorflow/contrib/slim/python/slim/nets/BUILD
+++ b/tensorflow/contrib/slim/python/slim/nets/BUILD
@@ -6,7 +6,10 @@ licenses(["notice"])  # Apache 2.0
 exports_files(["LICENSE"])
 
 package(
-    default_visibility = ["//tensorflow:__subpackages__"],
+    default_visibility = [
+        "//tensorflow:__subpackages__",
+        "//tensorflow_models:__subpackages__",
+    ],
 )
 
 py_library(
-- 
GitLab


From 4c89fa441926e834ac057af78bbfa6ffec8f5d54 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 25 Aug 2016 13:15:57 -0800
Subject: [PATCH 164/223] Automated rollback of change 131322587 Change:
 131329968

---
 tensorflow/examples/learn/wide_n_deep_tutorial.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/tensorflow/examples/learn/wide_n_deep_tutorial.py b/tensorflow/examples/learn/wide_n_deep_tutorial.py
index a9f84cec60..5a23087b5a 100644
--- a/tensorflow/examples/learn/wide_n_deep_tutorial.py
+++ b/tensorflow/examples/learn/wide_n_deep_tutorial.py
@@ -20,9 +20,9 @@ from __future__ import print_function
 import tempfile
 import urllib
 
+import pandas as pd
 import tensorflow as tf
 
-
 flags = tf.app.flags
 FLAGS = flags.FLAGS
 
@@ -179,8 +179,6 @@ def input_fn(df):
 def train_and_eval():
   """Train and evaluate the model."""
   train_file_name, test_file_name = maybe_download()
-
-  import pandas as pd  # pylint: disable=g-import-not-at-top
   df_train = pd.read_csv(
       tf.gfile.Open(train_file_name),
       names=COLUMNS,
-- 
GitLab


From ecc291750b4752842fe784748085cb5553fbd995 Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Thu, 25 Aug 2016 13:19:07 -0800
Subject: [PATCH 165/223] Implement C++ ConstantValue for more nodes (Shape,
 Size, Rank).

Range/Cast/Concat/Pack will be done in a separate CL, since they
are all recursive calls on ConstantValue and I want to write
those separately.
Change: 131330339
---
 tensorflow/core/graph/shape_inferer.cc      | 127 +++++++++++++++++---
 tensorflow/core/graph/shape_inferer.h       |  17 ++-
 tensorflow/core/graph/shape_inferer_test.cc |  88 ++++++++++++++
 3 files changed, 216 insertions(+), 16 deletions(-)

diff --git a/tensorflow/core/graph/shape_inferer.cc b/tensorflow/core/graph/shape_inferer.cc
index 49f0bf297f..30066ce187 100644
--- a/tensorflow/core/graph/shape_inferer.cc
+++ b/tensorflow/core/graph/shape_inferer.cc
@@ -94,21 +94,8 @@ Status ShapeInferer::AddNode(const Node* node) {
       if (c->requested_input_tensor(i) && !attempted_materialization[i]) {
         rerun_shape_fn = true;
         attempted_materialization[i] = true;
-
-        // For now, we do a simple static analysis of the graph to
-        // materialize those tensors, but in the future, we should try to do
-        // a partial evaluation of the graph.
-        const Node* input_node = input_nodes[i];
-
-        // TODO(vrv): Handle other types of nodes, like we do in python:
-        // Shape, Size, Rank, Range, Cast, Concat, Pack.  Some of these
-        // require recursively accessing the input's inputs, while some
-        // can be computed using local information.
-        if (input_node->IsConstant()) {
-          TF_RETURN_IF_ERROR(
-              GetNodeAttr(input_node->def(), "value", &real_tensors[i]));
-          input_tensors[i] = &real_tensors[i];
-        }
+        TF_RETURN_IF_ERROR(
+            ConstantValue(input_nodes[i], &real_tensors[i], &input_tensors[i]));
       }
     }
 
@@ -126,4 +113,114 @@ Status ShapeInferer::AddNode(const Node* node) {
   return Status::OK();
 }
 
+Status ShapeInferer::ConstantValue(const Node* node, Tensor* tensor_storage,
+                                   const Tensor** input_tensor) const {
+  *input_tensor = nullptr;
+  // For now, we do a simple static analysis of the graph to
+  // materialize those tensors, but in the future, we should try to do
+  // a partial evaluation of the graph.
+
+  // TODO(vrv): Handle other types of nodes, like we do in python:
+  // Size, Rank, Range, Cast, Concat, Pack.  Some of these
+  // require recursively accessing the input's inputs, while some
+  // can be computed using local information.
+  if (node->IsConstant()) {
+    return Constant(node, tensor_storage, input_tensor);
+  }
+  if (node->type_string() == "Shape") {
+    return Shape(node, tensor_storage, input_tensor);
+  }
+  if (node->type_string() == "Size") {
+    return Size(node, tensor_storage, input_tensor);
+  }
+  if (node->type_string() == "Rank") {
+    return Rank(node, tensor_storage, input_tensor);
+  }
+
+  return Status::OK();
+}
+
+Status ShapeInferer::Constant(const Node* node, Tensor* tensor_storage,
+                              const Tensor** input_tensor) const {
+  TF_RETURN_IF_ERROR(GetNodeAttr(node->def(), "value", tensor_storage));
+  *input_tensor = tensor_storage;
+  return Status::OK();
+}
+
+Status ShapeInferer::Shape(const Node* node, Tensor* tensor_storage,
+                           const Tensor** input_tensor) const {
+  // Get the input to the node.
+  const Node* shape_node;
+  TF_RETURN_IF_ERROR(node->input_node(0, &shape_node));
+  auto ic = GetContext(shape_node);
+  if (!ic) {
+    return errors::Internal("Could not find InferenceContext for ",
+                            shape_node->name());
+  }
+  shape_inference::ShapeHandle input_shape = ic->output(0);
+  if (ic->FullyDefined(input_shape)) {
+    *tensor_storage = Tensor(DT_INT32, {ic->Rank(input_shape)});
+    for (int i = 0; i < ic->Rank(input_shape); ++i) {
+      int64 dim = ic->Value(ic->Dim(input_shape, i));
+      if (dim > std::numeric_limits<int32>::max()) {
+        // The output of Shape is 32-bits, so we cannot fill in anything
+        // here.  See b/28119922.
+        return Status::OK();
+      }
+
+      tensor_storage->vec<int32>()(i) = dim;
+    }
+    *input_tensor = tensor_storage;
+  }
+
+  return Status::OK();
+}
+
+Status ShapeInferer::Size(const Node* node, Tensor* tensor_storage,
+                          const Tensor** input_tensor) const {
+  // Get the input to the node.
+  const Node* size_node;
+  TF_RETURN_IF_ERROR(node->input_node(0, &size_node));
+  auto ic = GetContext(size_node);
+  if (!ic) {
+    return errors::Internal("Could not find InferenceContext for ",
+                            size_node->name());
+  }
+  auto num_elements = ic->NumElements(ic->output(0));
+  if (ic->ValueKnown(num_elements)) {
+    *tensor_storage = Tensor(DT_INT32, {});
+    int64 ne = ic->Value(num_elements);
+    if (ne > std::numeric_limits<int32>::max()) {
+      // The output of Size is 32-bits, so we cannot fill in anything
+      // here.  See b/28119922.
+      return Status::OK();
+    }
+    tensor_storage->scalar<int32>()() = ic->Value(num_elements);
+    *input_tensor = tensor_storage;
+  }
+
+  return Status::OK();
+}
+
+Status ShapeInferer::Rank(const Node* node, Tensor* tensor_storage,
+                          const Tensor** input_tensor) const {
+  // Get the input to the node.
+  const Node* rank_node;
+  TF_RETURN_IF_ERROR(node->input_node(0, &rank_node));
+  auto ic = GetContext(rank_node);
+  if (!ic) {
+    return errors::Internal("Could not find InferenceContext for ",
+                            rank_node->name());
+  }
+
+  if (ic->RankKnown(ic->output(0))) {
+    int32 rank = ic->Rank(ic->output(0));
+    *tensor_storage = Tensor(DT_INT32, {});
+    tensor_storage->scalar<int32>()() = rank;
+    *input_tensor = tensor_storage;
+  }
+
+  return Status::OK();
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/graph/shape_inferer.h b/tensorflow/core/graph/shape_inferer.h
index 6f2df6aa74..51f1a5f44a 100644
--- a/tensorflow/core/graph/shape_inferer.h
+++ b/tensorflow/core/graph/shape_inferer.h
@@ -47,7 +47,7 @@ class ShapeInferer {
   Status AddNode(const Node* node);
 
   // Returns the InferenceContext for 'node', if present.
-  shape_inference::InferenceContext* GetContext(const Node* node) {
+  shape_inference::InferenceContext* GetContext(const Node* node) const {
     auto it = node_to_context_.find(node);
     if (it == node_to_context_.end()) {
       return nullptr;
@@ -56,6 +56,21 @@ class ShapeInferer {
   }
 
  private:
+  // Extracts the 'constant_value' of 'input_node' if possible.  Uses
+  // 'tensor_storage' for storage and sets '*input_tensor' to
+  // 'tensor_storage' if a constant value could be extracted.
+  Status ConstantValue(const Node* node, Tensor* tensor_storage,
+                       const Tensor** input_tensor) const;
+
+  // Helper functions to extract the Tensor associated with 'node'.
+  Status Constant(const Node* node, Tensor* tensor_storage,
+                  const Tensor** input_tensor) const;
+  Status Shape(const Node* node, Tensor* tensor_storage,
+               const Tensor** input_tensor) const;
+  Status Size(const Node* node, Tensor* tensor_storage,
+              const Tensor** input_tensor) const;
+  Status Rank(const Node* node, Tensor* tensor_storage,
+              const Tensor** input_tensor) const;
   // Stores a map from a node to its InferenceContext.
   //
   // Owns values.
diff --git a/tensorflow/core/graph/shape_inferer_test.cc b/tensorflow/core/graph/shape_inferer_test.cc
index 86fc46d9c1..7484244c5b 100644
--- a/tensorflow/core/graph/shape_inferer_test.cc
+++ b/tensorflow/core/graph/shape_inferer_test.cc
@@ -190,5 +190,93 @@ TEST(ShapeInfererTest, InputTensorDependencies) {
   EXPECT_EQ("[10,10]", ctx->DebugString(ctx->output(0)));
 }
 
+namespace {
+
+// An op with a shape function that looks at its input tensor
+// data and makes a Shape out of it.
+REGISTER_OP("ShapeData")
+    .Input("a: int32")
+    .Output("o: int32")
+    .SetShapeFn([](shape_inference::InferenceContext* c) {
+      const Tensor* shape_data = c->input_tensor(0);
+      if (shape_data == nullptr) {
+        return shape_inference::UnknownShape(c);
+      }
+
+      std::vector<shape_inference::DimensionHandle> dims;
+      for (int i = 0; i < shape_data->NumElements(); ++i) {
+        dims.emplace_back(c->MakeDim(shape_data->flat<int32>()(i)));
+      }
+
+      c->set_output(0, c->MakeShape(dims));
+      return Status::OK();
+    });
+
+}  // namespace
+
+TEST(ShapeInfererTest, PropagateShape) {
+  Scope root = Scope::NewRootScope();
+  // 3x2 input
+  auto input = ops::Const(root, {{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}});
+
+  // Shape is a vector of 2 elements (3,2)
+  auto shape = ops::Shape(root, input);
+
+  Node* shape_data;
+  TF_ASSERT_OK(NodeBuilder("Test", "ShapeData")
+                   .Input(shape.node())
+                   .Finalize(root.graph(), &shape_data));
+
+  ShapeInferer m;
+  TF_ASSERT_OK(m.AddNode(input.node()));
+  TF_ASSERT_OK(m.AddNode(shape.node()));
+  TF_ASSERT_OK(m.AddNode(shape_data));
+
+  shape_inference::InferenceContext* ctx = m.GetContext(shape_data);
+  EXPECT_EQ("[3,2]", ctx->DebugString(ctx->output(0)));
+}
+
+TEST(ShapeInfererTest, PropagateSize) {
+  Scope root = Scope::NewRootScope();
+  // 3x2 input
+  auto input = ops::Const(root, {{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}});
+
+  auto size = ops::Size(root, input);
+
+  Node* shape_data;
+  TF_ASSERT_OK(NodeBuilder("Test", "ShapeData")
+                   .Input(size.node())
+                   .Finalize(root.graph(), &shape_data));
+
+  ShapeInferer m;
+  TF_ASSERT_OK(m.AddNode(input.node()));
+  TF_ASSERT_OK(m.AddNode(size.node()));
+  TF_ASSERT_OK(m.AddNode(shape_data));
+
+  shape_inference::InferenceContext* ctx = m.GetContext(shape_data);
+  EXPECT_EQ("[6]", ctx->DebugString(ctx->output(0)));
+}
+
+TEST(ShapeInfererTest, PropagateRank) {
+  Scope root = Scope::NewRootScope();
+  // 3x2 input
+  auto input = ops::Const(root, {{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}});
+
+  auto rank = ops::Rank(root, input);
+
+  Node* shape_data;
+  TF_ASSERT_OK(NodeBuilder("Test", "ShapeData")
+                   .Input(rank.node())
+                   .Finalize(root.graph(), &shape_data));
+
+  ShapeInferer m;
+  TF_ASSERT_OK(m.AddNode(input.node()));
+  TF_ASSERT_OK(m.AddNode(rank.node()));
+  TF_ASSERT_OK(m.AddNode(shape_data));
+
+  shape_inference::InferenceContext* ctx = m.GetContext(shape_data);
+  EXPECT_EQ("[2]", ctx->DebugString(ctx->output(0)));
+}
+
 }  // namespace
 }  // namespace tensorflow
-- 
GitLab


From b27a4db481ab094b501078fc86bb5d864c1085c8 Mon Sep 17 00:00:00 2001
From: Geoffrey Irving <geoffreyi@google.com>
Date: Thu, 25 Aug 2016 13:20:24 -0800
Subject: [PATCH 166/223] Teach tf.ones and tf.zeros about bool Change:
 131330482

---
 .../python/kernel_tests/constant_op_test.py   | 12 ++++++++++--
 tensorflow/python/ops/array_ops.py            | 19 ++++++++++++-------
 2 files changed, 22 insertions(+), 9 deletions(-)

diff --git a/tensorflow/python/kernel_tests/constant_op_test.py b/tensorflow/python/kernel_tests/constant_op_test.py
index adfedbed64..71ffe8c61d 100644
--- a/tensorflow/python/kernel_tests/constant_op_test.py
+++ b/tensorflow/python/kernel_tests/constant_op_test.py
@@ -298,19 +298,23 @@ class ZerosTest(tf.test.TestCase):
       z = tf.zeros([2, 3])
       self.assertEqual(z.dtype, tf.float32)
       self.assertEqual([2, 3], z.get_shape())
+      self.assertAllEqual(z.eval(), np.zeros([2, 3]))
       z = tf.zeros(tf.shape(d))
       self.assertEqual(z.dtype, tf.float32)
       self.assertEqual([2, 3], z.get_shape())
+      self.assertAllEqual(z.eval(), np.zeros([2, 3]))
       # Test explicit type control
       for dtype in [tf.float32, tf.float64, tf.int32,
                     tf.uint8, tf.int16, tf.int8,
-                    tf.complex64, tf.complex128, tf.int64]:
+                    tf.complex64, tf.complex128, tf.int64, tf.bool]:
         z = tf.zeros([2, 3], dtype=dtype)
         self.assertEqual(z.dtype, dtype)
         self.assertEqual([2, 3], z.get_shape())
+        self.assertAllEqual(z.eval(), np.zeros([2, 3]))
         z = tf.zeros(tf.shape(d), dtype=dtype)
         self.assertEqual(z.dtype, dtype)
         self.assertEqual([2, 3], z.get_shape())
+        self.assertAllEqual(z.eval(), np.zeros([2, 3]))
 
 
 class ZerosLikeTest(tf.test.TestCase):
@@ -404,19 +408,23 @@ class OnesTest(tf.test.TestCase):
       z = tf.ones([2, 3])
       self.assertEqual(z.dtype, tf.float32)
       self.assertEqual([2, 3], z.get_shape())
+      self.assertAllEqual(z.eval(), np.ones([2, 3]))
       z = tf.ones(tf.shape(d))
       self.assertEqual(z.dtype, tf.float32)
       self.assertEqual([2, 3], z.get_shape())
+      self.assertAllEqual(z.eval(), np.ones([2, 3]))
       # Test explicit type control
       for dtype in (tf.float32, tf.float64, tf.int32,
                     tf.uint8, tf.int16, tf.int8,
-                    tf.complex64, tf.complex128, tf.int64):
+                    tf.complex64, tf.complex128, tf.int64, tf.bool):
         z = tf.ones([2, 3], dtype=dtype)
         self.assertEqual(z.dtype, dtype)
         self.assertEqual([2, 3], z.get_shape())
+        self.assertAllEqual(z.eval(), np.ones([2, 3]))
         z = tf.ones(tf.shape(d), dtype=dtype)
         self.assertEqual(z.dtype, dtype)
         self.assertEqual([2, 3], z.get_shape())
+        self.assertAllEqual(z.eval(), np.ones([2, 3]))
 
 
 class OnesLikeTest(tf.test.TestCase):
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index 16267fd481..ebc1687dc5 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -1121,14 +1121,16 @@ def zeros(shape, dtype=dtypes.float32, name=None):
   Returns:
     A `Tensor` with all elements set to zero.
   """
+  dtype = dtypes.as_dtype(dtype).base_dtype
   with ops.name_scope(name, "zeros", [shape]) as name:
+    zero = False if dtype == dtypes.bool else 0
     try:
       shape = tensor_shape.as_shape(shape)
-      output = constant(0, shape=shape, dtype=dtype, name=name)
+      output = constant(zero, shape=shape, dtype=dtype, name=name)
     except (TypeError, ValueError):
       shape = ops.convert_to_tensor(shape, dtype=dtypes.int32, name="shape")
-      output = fill(shape, constant(0, dtype=dtype), name=name)
-  assert output.dtype.base_dtype == dtypes.as_dtype(dtype).base_dtype
+      output = fill(shape, constant(zero, dtype=dtype), name=name)
+  assert output.dtype.base_dtype == dtype
   return output
 
 
@@ -1184,7 +1186,8 @@ def ones_like(tensor, dtype=None, name=None, optimize=True):
   Args:
     tensor: A `Tensor`.
     dtype: A type for the returned `Tensor`. Must be `float32`, `float64`,
-    `int8`, `int16`, `int32`, `int64`, `uint8`, `complex64`, or `complex128`.
+      `int8`, `int16`, `int32`, `int64`, `uint8`, `complex64`, `complex128` or
+      `bool`.
     name: A name for the operation (optional).
     optimize: if true, attempt to statically determine the shape of 'tensor'
     and encode it as a constant.
@@ -1222,14 +1225,16 @@ def ones(shape, dtype=dtypes.float32, name=None):
   Returns:
     A `Tensor` with all elements set to 1.
   """
+  dtype = dtypes.as_dtype(dtype).base_dtype
   with ops.name_scope(name, "ones", [shape]) as name:
+    one = True if dtype == dtypes.bool else 1
     try:
       shape = tensor_shape.as_shape(shape)
-      output = constant(1, shape=shape, dtype=dtype, name=name)
+      output = constant(one, shape=shape, dtype=dtype, name=name)
     except (TypeError, ValueError):
       shape = ops.convert_to_tensor(shape, dtype=dtypes.int32, name="shape")
-      output = fill(shape, constant(1, dtype=dtype), name=name)
-  assert output.dtype.base_dtype == dtypes.as_dtype(dtype).base_dtype
+      output = fill(shape, constant(one, dtype=dtype), name=name)
+  assert output.dtype.base_dtype == dtype
   return output
 
 
-- 
GitLab


From 6d82b46435b6dde34a5eb5a926eec20dbfaede85 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 13:35:39 -0800
Subject: [PATCH 167/223] Update generated Python Op docs. Change: 131332394

---
 tensorflow/g3doc/api_docs/python/constant_op.md               | 4 ++--
 .../python/functions_and_classes/shard7/tf.ones_like.md       | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/g3doc/api_docs/python/constant_op.md b/tensorflow/g3doc/api_docs/python/constant_op.md
index cfd488428a..eb9388f4ee 100644
--- a/tensorflow/g3doc/api_docs/python/constant_op.md
+++ b/tensorflow/g3doc/api_docs/python/constant_op.md
@@ -121,8 +121,8 @@ tf.ones_like(tensor) ==> [[1, 1, 1], [1, 1, 1]]
 
 *  <b>`tensor`</b>: A `Tensor`.
 *  <b>`dtype`</b>: A type for the returned `Tensor`. Must be `float32`, `float64`,
-  `int8`, `int16`, `int32`, `int64`, `uint8`, `complex64`, or `complex128`.
-
+    `int8`, `int16`, `int32`, `int64`, `uint8`, `complex64`, `complex128` or
+    `bool`.
 *  <b>`name`</b>: A name for the operation (optional).
 *  <b>`optimize`</b>: if true, attempt to statically determine the shape of 'tensor'
   and encode it as a constant.
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.ones_like.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.ones_like.md
index dac818fdfe..5ca57f52a5 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.ones_like.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.ones_like.md
@@ -18,8 +18,8 @@ tf.ones_like(tensor) ==> [[1, 1, 1], [1, 1, 1]]
 
 *  <b>`tensor`</b>: A `Tensor`.
 *  <b>`dtype`</b>: A type for the returned `Tensor`. Must be `float32`, `float64`,
-  `int8`, `int16`, `int32`, `int64`, `uint8`, `complex64`, or `complex128`.
-
+    `int8`, `int16`, `int32`, `int64`, `uint8`, `complex64`, `complex128` or
+    `bool`.
 *  <b>`name`</b>: A name for the operation (optional).
 *  <b>`optimize`</b>: if true, attempt to statically determine the shape of 'tensor'
   and encode it as a constant.
-- 
GitLab


From 67fdaa461c8d0234cc4a8f39d63074fa9bfeef50 Mon Sep 17 00:00:00 2001
From: Derek Murray <mrry@google.com>
Date: Thu, 25 Aug 2016 13:38:16 -0800
Subject: [PATCH 168/223] Load the `tf.contrib` module on demand only.

This change makes it so that `tf.contrib` and the libraries it
contains are not loaded until their first use. The rationale for this
change is that `tf.contrib` has a lower code quality than the other
`tf.*` libraries, and it frequently breaks users due to undeclared
dependencies or other churn.

See e.g. https://github.com/tensorflow/tensorflow/issues/2388 for an
example of where `import tensorflow as tf` fails due to a breakage in
`tf.contrib`. This issue
(https://github.com/tensorflow/tensorflow/issues/2154) shows a
dependency on a particular version of pandas.
Change: 131332657
---
 tensorflow/__init__.py                      | 15 +++++++++++++++
 tensorflow/python/__init__.py               |  6 ++----
 tensorflow/python/framework/contrib_test.py |  1 +
 3 files changed, 18 insertions(+), 4 deletions(-)

diff --git a/tensorflow/__init__.py b/tensorflow/__init__.py
index 4fc5d8e816..ec7cd91e7e 100644
--- a/tensorflow/__init__.py
+++ b/tensorflow/__init__.py
@@ -21,3 +21,18 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.python import *
+
+
+# Lazily import the `tf.contrib` module. This avoids loading all of the
+# dependencies of `tf.contrib` at `import tensorflow` time.
+class _LazyContribLoader(object):
+
+  def __getattr__(self, item):
+    global contrib
+    # Replace the lazy loader with the imported module itself.
+    import importlib  # pylint: disable=g-import-not-at-top
+    contrib = importlib.import_module('tensorflow.contrib')
+    return getattr(contrib, item)
+
+
+contrib = _LazyContribLoader()
diff --git a/tensorflow/python/__init__.py b/tensorflow/python/__init__.py
index 78da74268b..974d3c2457 100644
--- a/tensorflow/python/__init__.py
+++ b/tensorflow/python/__init__.py
@@ -26,6 +26,7 @@ import tensorflow as tf
 """
 
 import ctypes
+import importlib
 import inspect
 import sys
 import traceback
@@ -62,8 +63,6 @@ from tensorflow.core.framework.summary_pb2 import *
 from tensorflow.core.framework.attr_value_pb2 import *
 from tensorflow.core.protobuf.config_pb2 import *
 from tensorflow.core.util.event_pb2 import *
-# Import things out of contrib
-import tensorflow.contrib as contrib
 
 # Framework
 from tensorflow.python.framework.framework_lib import *
@@ -119,7 +118,7 @@ from tensorflow.python.ops import string_ops
 from tensorflow.python.ops import tensor_array_ops
 
 # Don't export modules except for the few we really want
-_whitelist = set([app, compat, contrib, errors, flags, gfile, image, logging,
+_whitelist = set([app, compat, errors, flags, gfile, image, logging,
                   nn, python_io, resource_loader, sysconfig, test, train,
                   user_ops])
 
@@ -231,7 +230,6 @@ __all__.extend([
 # Export modules and constants.
 __all__.extend([
     'app',
-    'contrib',
     'errors',
     'flags',
     'gfile',
diff --git a/tensorflow/python/framework/contrib_test.py b/tensorflow/python/framework/contrib_test.py
index 5ca43b3849..db6d7d0a7c 100644
--- a/tensorflow/python/framework/contrib_test.py
+++ b/tensorflow/python/framework/contrib_test.py
@@ -27,6 +27,7 @@ class ContribTest(googletest.TestCase):
   def testContrib(self):
     # pylint: disable=g-import-not-at-top
     import tensorflow as tf
+    _ = tf.contrib.layers  # `tf.contrib` is loaded lazily on first use.
     assert inspect.ismodule(tf.contrib)
 
   def testLayers(self):
-- 
GitLab


From 4501365d9a5bee11bbdc66154acecaf6897db946 Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Thu, 25 Aug 2016 13:52:14 -0800
Subject: [PATCH 169/223] Rename ShapeInferer to ShapeRefiner -- Inferer isn't
 a word, and Refiner is an anagram.

Co-author: mrry@
Change: 131334227
---
 tensorflow/core/BUILD                         |  4 +-
 .../{shape_inferer.cc => shape_refiner.cc}    | 20 ++++-----
 .../{shape_inferer.h => shape_refiner.h}      | 18 ++++----
 ..._inferer_test.cc => shape_refiner_test.cc} | 44 +++++++++----------
 4 files changed, 43 insertions(+), 43 deletions(-)
 rename tensorflow/core/graph/{shape_inferer.cc => shape_refiner.cc} (93%)
 rename tensorflow/core/graph/{shape_inferer.h => shape_refiner.h} (87%)
 rename tensorflow/core/graph/{shape_inferer_test.cc => shape_refiner_test.cc} (92%)

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index d30a94b928..7aa39ce5c3 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -427,7 +427,7 @@ tf_cuda_library(
         "graph/graph_constructor.h",
         "graph/graph_def_builder.h",
         "graph/node_builder.h",
-        "graph/shape_inferer.h",
+        "graph/shape_refiner.h",
         "graph/validate.h",
         "public/session.h",
         "public/session_options.h",
@@ -1468,7 +1468,7 @@ tf_cc_tests(
         "graph/node_builder_test.cc",
         "graph/optimizer_cse_test.cc",
         "graph/quantize_training_test.cc",
-        "graph/shape_inferer_test.cc",
+        "graph/shape_refiner_test.cc",
         "graph/subgraph_test.cc",
         "graph/tensor_id_test.cc",
         "graph/validate_test.cc",
diff --git a/tensorflow/core/graph/shape_inferer.cc b/tensorflow/core/graph/shape_refiner.cc
similarity index 93%
rename from tensorflow/core/graph/shape_inferer.cc
rename to tensorflow/core/graph/shape_refiner.cc
index 30066ce187..abc80937c1 100644
--- a/tensorflow/core/graph/shape_inferer.cc
+++ b/tensorflow/core/graph/shape_refiner.cc
@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
-#include "tensorflow/core/graph/shape_inferer.h"
+#include "tensorflow/core/graph/shape_refiner.h"
 
 #include <memory>
 #include <vector>
@@ -23,10 +23,10 @@ limitations under the License.
 
 namespace tensorflow {
 
-ShapeInferer::ShapeInferer() {}
-ShapeInferer::~ShapeInferer() { gtl::STLDeleteValues(&node_to_context_); }
+ShapeRefiner::ShapeRefiner() {}
+ShapeRefiner::~ShapeRefiner() { gtl::STLDeleteValues(&node_to_context_); }
 
-Status ShapeInferer::AddNode(const Node* node) {
+Status ShapeRefiner::AddNode(const Node* node) {
   // For each 'input' of this node, fetch the corresponding shape
   // from 'input's InferenceContext, and store into a vector
   // indexed by 'node's input.
@@ -40,7 +40,7 @@ Status ShapeInferer::AddNode(const Node* node) {
     if (it == node_to_context_.end()) {
       return errors::FailedPrecondition(
           "Input ", e->dst_input(), " ('", input->name(), "') for '",
-          node->name(), "' was not previously added to ShapeInferer.");
+          node->name(), "' was not previously added to ShapeRefiner.");
     }
 
     shape_inference::InferenceContext* c = it->second;
@@ -113,7 +113,7 @@ Status ShapeInferer::AddNode(const Node* node) {
   return Status::OK();
 }
 
-Status ShapeInferer::ConstantValue(const Node* node, Tensor* tensor_storage,
+Status ShapeRefiner::ConstantValue(const Node* node, Tensor* tensor_storage,
                                    const Tensor** input_tensor) const {
   *input_tensor = nullptr;
   // For now, we do a simple static analysis of the graph to
@@ -140,14 +140,14 @@ Status ShapeInferer::ConstantValue(const Node* node, Tensor* tensor_storage,
   return Status::OK();
 }
 
-Status ShapeInferer::Constant(const Node* node, Tensor* tensor_storage,
+Status ShapeRefiner::Constant(const Node* node, Tensor* tensor_storage,
                               const Tensor** input_tensor) const {
   TF_RETURN_IF_ERROR(GetNodeAttr(node->def(), "value", tensor_storage));
   *input_tensor = tensor_storage;
   return Status::OK();
 }
 
-Status ShapeInferer::Shape(const Node* node, Tensor* tensor_storage,
+Status ShapeRefiner::Shape(const Node* node, Tensor* tensor_storage,
                            const Tensor** input_tensor) const {
   // Get the input to the node.
   const Node* shape_node;
@@ -176,7 +176,7 @@ Status ShapeInferer::Shape(const Node* node, Tensor* tensor_storage,
   return Status::OK();
 }
 
-Status ShapeInferer::Size(const Node* node, Tensor* tensor_storage,
+Status ShapeRefiner::Size(const Node* node, Tensor* tensor_storage,
                           const Tensor** input_tensor) const {
   // Get the input to the node.
   const Node* size_node;
@@ -202,7 +202,7 @@ Status ShapeInferer::Size(const Node* node, Tensor* tensor_storage,
   return Status::OK();
 }
 
-Status ShapeInferer::Rank(const Node* node, Tensor* tensor_storage,
+Status ShapeRefiner::Rank(const Node* node, Tensor* tensor_storage,
                           const Tensor** input_tensor) const {
   // Get the input to the node.
   const Node* rank_node;
diff --git a/tensorflow/core/graph/shape_inferer.h b/tensorflow/core/graph/shape_refiner.h
similarity index 87%
rename from tensorflow/core/graph/shape_inferer.h
rename to tensorflow/core/graph/shape_refiner.h
index 51f1a5f44a..468d1dd014 100644
--- a/tensorflow/core/graph/shape_inferer.h
+++ b/tensorflow/core/graph/shape_refiner.h
@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
-#ifndef THIRD_PARTY_TENSORFLOW_CORE_GRAPH_SHAPE_INFERER_H_
-#define THIRD_PARTY_TENSORFLOW_CORE_GRAPH_SHAPE_INFERER_H_
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_GRAPH_SHAPE_REFINER_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_GRAPH_SHAPE_REFINER_H_
 
 #include <vector>
 
@@ -24,20 +24,20 @@ limitations under the License.
 
 namespace tensorflow {
 
-// ShapeInferer performs shape inference for TensorFlow Graphs.  It is
+// ShapeRefiner performs shape inference for TensorFlow Graphs.  It is
 // responsible for instantiating InferenceContext objects for each
 // Node in the Graph, and providing/storing the 'input_tensor' Tensors
 // used by Shape Inference functions, when available at graph
 // construction time.
-class ShapeInferer {
+class ShapeRefiner {
  public:
-  ShapeInferer();
-  ~ShapeInferer();
+  ShapeRefiner();
+  ~ShapeRefiner();
 
   // Performs validation of 'node' and runs 'node's shape function,
   // storing its shape outputs.
   //
-  // All inputs of 'node' must be added to ShapeInferer prior to
+  // All inputs of 'node' must be added to ShapeRefiner prior to
   // adding 'node'.
   //
   // Returns an error if:
@@ -77,9 +77,9 @@ class ShapeInferer {
   std::unordered_map<const Node*, shape_inference::InferenceContext*>
       node_to_context_;
 
-  TF_DISALLOW_COPY_AND_ASSIGN(ShapeInferer);
+  TF_DISALLOW_COPY_AND_ASSIGN(ShapeRefiner);
 };
 
 }  // namespace tensorflow
 
-#endif  // THIRD_PARTY_TENSORFLOW_CORE_GRAPH_SHAPE_INFERER_H_
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_GRAPH_SHAPE_REFINER_H_
diff --git a/tensorflow/core/graph/shape_inferer_test.cc b/tensorflow/core/graph/shape_refiner_test.cc
similarity index 92%
rename from tensorflow/core/graph/shape_inferer_test.cc
rename to tensorflow/core/graph/shape_refiner_test.cc
index 7484244c5b..ca0676e891 100644
--- a/tensorflow/core/graph/shape_inferer_test.cc
+++ b/tensorflow/core/graph/shape_refiner_test.cc
@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/core/graph/shape_inferer.h"
+#include "tensorflow/core/graph/shape_refiner.h"
 
 #include "tensorflow/cc/framework/scope.h"
 #include "tensorflow/cc/ops/standard_ops.h"
@@ -33,19 +33,19 @@ namespace {
     EXPECT_EQ(EXPECTED, ctx->DebugString(ctx->output(IDX)));          \
   } while (0);
 
-TEST(ShapeInfererTest, Constant) {
+TEST(ShapeRefinerTest, Constant) {
   // Create a constant node and validate that adding it is successful
   // and that its shape is correct.
   Scope root = Scope::NewRootScope();
   auto c = ops::Const(root, 42.0f);
-  ShapeInferer m;
+  ShapeRefiner m;
   TF_ASSERT_OK(m.AddNode(c.node()));
 
   EXPECT_SHAPE("[]", m, c, 0);
 }
 
-TEST(ShapeInfererTest, MatMul) {
-  ShapeInferer m;
+TEST(ShapeRefinerTest, MatMul) {
+  ShapeRefiner m;
 
   Scope root = Scope::NewRootScope();
   auto a = ops::Const(root, {{1.0f}, {2.0f}});
@@ -61,8 +61,8 @@ TEST(ShapeInfererTest, MatMul) {
   EXPECT_SHAPE("[2,2]", m, mm, 0);
 }
 
-TEST(ShapeInfererTest, InvalidOrder) {
-  ShapeInferer m;
+TEST(ShapeRefinerTest, InvalidOrder) {
+  ShapeRefiner m;
   Scope root = Scope::NewRootScope();
   auto a = ops::Const(root, {{1.0f}, {2.0f}});
   auto b = ops::Const(root, {{1.0f, 2.0f}});
@@ -72,12 +72,12 @@ TEST(ShapeInfererTest, InvalidOrder) {
   ASSERT_FALSE(s.ok());
   ASSERT_EQ(
       "Input 0 ('Const') for 'MatMul' was not previously added to "
-      "ShapeInferer.",
+      "ShapeRefiner.",
       s.error_message());
 }
 
-TEST(ShapeInfererTest, BadShapes) {
-  ShapeInferer m;
+TEST(ShapeRefinerTest, BadShapes) {
+  ShapeRefiner m;
   Scope root = Scope::NewRootScope();
   auto a = ops::Const(root, {{1.0f}, {2.0f}});
   auto b = ops::Const(root, {{1.0f}, {2.0f}});
@@ -92,7 +92,7 @@ TEST(ShapeInfererTest, BadShapes) {
   ASSERT_EQ("Dimensions must be equal, but are 1 and 2", s.error_message());
 }
 
-TEST(ShapeInfererTest, PropagateConstants) {
+TEST(ShapeRefinerTest, PropagateConstants) {
   // Reduction dimension is a variable, so we don't know its value.
   // So the output shape value is unknown (though its rank is known).
   {
@@ -103,7 +103,7 @@ TEST(ShapeInfererTest, PropagateConstants) {
     auto dim = ops::Variable(root, {}, DT_INT32);
 
     auto am = ops::ArgMax(root, input, dim);
-    ShapeInferer m;
+    ShapeRefiner m;
     TF_ASSERT_OK(m.AddNode(input.node()));
     TF_ASSERT_OK(m.AddNode(dim.node()));
     TF_ASSERT_OK(m.AddNode(am.node()));
@@ -120,7 +120,7 @@ TEST(ShapeInfererTest, PropagateConstants) {
     auto dim = ops::Const(root, 1);
 
     auto am = ops::ArgMax(root, input, dim);
-    ShapeInferer m;
+    ShapeRefiner m;
     TF_ASSERT_OK(m.AddNode(input.node()));
     TF_ASSERT_OK(m.AddNode(dim.node()));
     TF_ASSERT_OK(m.AddNode(am.node()));
@@ -136,7 +136,7 @@ TEST(ShapeInfererTest, PropagateConstants) {
     auto dim = ops::Const(root, 0);
 
     auto am = ops::ArgMax(root, input, dim);
-    ShapeInferer m;
+    ShapeRefiner m;
     TF_ASSERT_OK(m.AddNode(input.node()));
     TF_ASSERT_OK(m.AddNode(dim.node()));
     TF_ASSERT_OK(m.AddNode(am.node()));
@@ -165,8 +165,8 @@ REGISTER_OP("TestOp")
 
 }  // namespace
 
-TEST(ShapeInfererTest, InputTensorDependencies) {
-  ShapeInferer m;
+TEST(ShapeRefinerTest, InputTensorDependencies) {
+  ShapeRefiner m;
   Graph graph(OpRegistry::Global());
   Node* node;
 
@@ -214,7 +214,7 @@ REGISTER_OP("ShapeData")
 
 }  // namespace
 
-TEST(ShapeInfererTest, PropagateShape) {
+TEST(ShapeRefinerTest, PropagateShape) {
   Scope root = Scope::NewRootScope();
   // 3x2 input
   auto input = ops::Const(root, {{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}});
@@ -227,7 +227,7 @@ TEST(ShapeInfererTest, PropagateShape) {
                    .Input(shape.node())
                    .Finalize(root.graph(), &shape_data));
 
-  ShapeInferer m;
+  ShapeRefiner m;
   TF_ASSERT_OK(m.AddNode(input.node()));
   TF_ASSERT_OK(m.AddNode(shape.node()));
   TF_ASSERT_OK(m.AddNode(shape_data));
@@ -236,7 +236,7 @@ TEST(ShapeInfererTest, PropagateShape) {
   EXPECT_EQ("[3,2]", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeInfererTest, PropagateSize) {
+TEST(ShapeRefinerTest, PropagateSize) {
   Scope root = Scope::NewRootScope();
   // 3x2 input
   auto input = ops::Const(root, {{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}});
@@ -248,7 +248,7 @@ TEST(ShapeInfererTest, PropagateSize) {
                    .Input(size.node())
                    .Finalize(root.graph(), &shape_data));
 
-  ShapeInferer m;
+  ShapeRefiner m;
   TF_ASSERT_OK(m.AddNode(input.node()));
   TF_ASSERT_OK(m.AddNode(size.node()));
   TF_ASSERT_OK(m.AddNode(shape_data));
@@ -257,7 +257,7 @@ TEST(ShapeInfererTest, PropagateSize) {
   EXPECT_EQ("[6]", ctx->DebugString(ctx->output(0)));
 }
 
-TEST(ShapeInfererTest, PropagateRank) {
+TEST(ShapeRefinerTest, PropagateRank) {
   Scope root = Scope::NewRootScope();
   // 3x2 input
   auto input = ops::Const(root, {{1.0, 2.0}, {3.0, 4.0}, {5.0, 6.0}});
@@ -269,7 +269,7 @@ TEST(ShapeInfererTest, PropagateRank) {
                    .Input(rank.node())
                    .Finalize(root.graph(), &shape_data));
 
-  ShapeInferer m;
+  ShapeRefiner m;
   TF_ASSERT_OK(m.AddNode(input.node()));
   TF_ASSERT_OK(m.AddNode(rank.node()));
   TF_ASSERT_OK(m.AddNode(shape_data));
-- 
GitLab


From db7bdab6e586e02051556d9f36a7887500378cf9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 14:28:26 -0800
Subject: [PATCH 170/223] Implement fast ParseExample. Change: 131338565

---
 tensorflow/core/BUILD                         |   2 +
 .../core/kernels/example_parsing_ops.cc       | 151 +---
 .../core/kernels/example_parsing_ops_test.cc  |  31 +-
 .../core/util/example_proto_fast_parsing.cc   | 761 ++++++++++++++++++
 .../core/util/example_proto_fast_parsing.h    |  88 ++
 .../util/example_proto_fast_parsing_test.cc   | 184 +++++
 tensorflow/core/util/presized_cuckoo_map.h    |  14 +-
 .../core/util/presized_cuckoo_map_test.cc     |  18 +-
 8 files changed, 1106 insertions(+), 143 deletions(-)
 create mode 100644 tensorflow/core/util/example_proto_fast_parsing.cc
 create mode 100644 tensorflow/core/util/example_proto_fast_parsing.h
 create mode 100644 tensorflow/core/util/example_proto_fast_parsing_test.cc

diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD
index 7aa39ce5c3..fedddac671 100644
--- a/tensorflow/core/BUILD
+++ b/tensorflow/core/BUILD
@@ -266,6 +266,7 @@ tf_cuda_library(
         "util/cuda_kernel_helper.h",
         "util/device_name_utils.h",
         "util/events_writer.h",
+        "util/example_proto_fast_parsing.h",
         "util/example_proto_helper.h",
         "util/guarded_philox_random.h",
         "util/memmapped_file_system.h",
@@ -1476,6 +1477,7 @@ tf_cc_tests(
         "util/command_line_flags_test.cc",
         "util/device_name_utils_test.cc",
         "util/events_writer_test.cc",
+        "util/example_proto_fast_parsing_test.cc",
         "util/example_proto_helper_test.cc",
         "util/memmapped_file_system_test.cc",
         "util/presized_cuckoo_map_test.cc",
diff --git a/tensorflow/core/kernels/example_parsing_ops.cc b/tensorflow/core/kernels/example_parsing_ops.cc
index a7091645fa..6338638a3b 100644
--- a/tensorflow/core/kernels/example_parsing_ops.cc
+++ b/tensorflow/core/kernels/example_parsing_ops.cc
@@ -24,8 +24,10 @@ limitations under the License.
 #include "tensorflow/core/framework/common_shape_fns.h"
 #include "tensorflow/core/framework/numeric_op.h"
 #include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/lib/gtl/array_slice.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/protobuf.h"
+#include "tensorflow/core/util/example_proto_fast_parsing.h"
 #include "tensorflow/core/util/example_proto_helper.h"
 #include "tensorflow/core/util/sparse/sparse_tensor.h"
 #include "tensorflow/core/util/work_sharder.h"
@@ -67,8 +69,7 @@ class ExampleParserOp : public OpKernel {
       sparse_keys_t[di] = sparse_keys[di].scalar<string>()();
     }
 
-    bool has_names = (names->NumElements() > 0);
-    if (has_names) {
+    if (names->NumElements() > 0) {
       OP_REQUIRES(
           ctx, TensorShapeUtils::IsVector(names->shape()),
           errors::InvalidArgument("Expected names to be a vector, got shape: ",
@@ -79,7 +80,6 @@ class ExampleParserOp : public OpKernel {
               "Expected len(names) == len(serialized), but got: ",
               names->NumElements(), " vs. ", serialized->NumElements()));
     }
-    auto names_t = names->flat<string>();
 
     OP_REQUIRES(ctx, TensorShapeUtils::IsVector(serialized->shape()),
                 errors::InvalidArgument(
@@ -106,136 +106,43 @@ class ExampleParserOp : public OpKernel {
       }
     }
 
-    auto serialized_t = serialized->vec<string>();
+    example::Result result;
+
+    example::FastParseExampleConfig config;
+    for (int d = 0; d < attrs_.num_dense; ++d) {
+      config.dense.push_back({dense_keys_t[d], attrs_.dense_types[d],
+                              attrs_.dense_shapes[d], dense_defaults[d]});
+    }
+    for (int d = 0; d < attrs_.num_sparse; ++d) {
+      config.sparse.push_back({sparse_keys_t[d], attrs_.sparse_types[d]});
+    }
+
+    auto serialized_t = serialized->flat<string>();
+    auto names_t = names->flat<string>();
+    gtl::ArraySlice<string> slice(serialized_t.data(), serialized_t.size());
+    gtl::ArraySlice<string> names_slice(names_t.data(), names_t.size());
 
-    const int64 batch_size = serialized_t.size();
+    OP_REQUIRES_OK(
+        ctx,
+        FastParseExample(
+            config, slice, names_slice,
+            ctx->device()->tensorflow_cpu_worker_threads()->workers, &result));
 
+    OpOutputList dense_values;
     OpOutputList sparse_indices;
     OpOutputList sparse_values;
     OpOutputList sparse_shapes;
-    OpOutputList dense_values;
-
+    OP_REQUIRES_OK(ctx, ctx->output_list("dense_values", &dense_values));
     OP_REQUIRES_OK(ctx, ctx->output_list("sparse_indices", &sparse_indices));
     OP_REQUIRES_OK(ctx, ctx->output_list("sparse_values", &sparse_values));
     OP_REQUIRES_OK(ctx, ctx->output_list("sparse_shapes", &sparse_shapes));
-    OP_REQUIRES_OK(ctx, ctx->output_list("dense_values", &dense_values));
-
-    // Setup Dense features and the output_dense_values Tensor* vector.
-    std::vector<FixedLenFeature> fixed_len_features(attrs_.num_dense);
-    std::vector<Tensor*> output_dense_values(attrs_.num_dense);
-
     for (int d = 0; d < attrs_.num_dense; ++d) {
-      // Preallocate dense_values, since we know their sizes
-      TensorShape out_shape;
-      out_shape.AddDim(batch_size);
-      for (const int64 dim : attrs_.dense_shapes[d].dim_sizes()) {
-        out_shape.AddDim(dim);
-      }
-      Tensor* out = nullptr;
-      dense_values.allocate(d, out_shape, &out);
-
-      FixedLenFeature config;
-      config.key = dense_keys_t[d];
-      config.dtype = attrs_.dense_types[d];
-      config.shape = attrs_.dense_shapes[d];
-      config.default_value = dense_defaults[d];
-      fixed_len_features[d] = config;
-      output_dense_values[d] = dense_values[d];
+      dense_values.set(d, result.dense_values[d]);
     }
-
-    // sparse_values_tmp will be attrs_.num_sparse size map of batch_size length
-    // tensor vector's, containing the sparse values from the input layer.
-    // After these are all stored, we can allocate properly sized outputs
-    // and copy data over. Doing it this way saves us the trouble of either
-    // performing deserialization twice, or alternatively storing all copies of
-    // the full Example protos.
-    std::vector<std::vector<Tensor>> sparse_values_tmp(
-        attrs_.num_sparse, std::vector<Tensor>(batch_size));
-
-    // Setup Sparse features.
-    std::vector<VarLenFeature> var_len_features(attrs_.num_sparse);
     for (int d = 0; d < attrs_.num_sparse; ++d) {
-      VarLenFeature config;
-      config.key = sparse_keys_t[d];
-      config.dtype = attrs_.sparse_types[d];
-      var_len_features[d] = config;
-    }
-
-    auto worker_threads = *(ctx->device()->tensorflow_cpu_worker_threads());
-
-    // Estimate the cost of parsing each batch element.
-    int64 work_unit_size = 1000 + 100 * attrs_.num_sparse;
-    for (int d = 0; d < attrs_.num_dense; ++d) {
-      work_unit_size += 100 + attrs_.dense_shapes[d].num_elements();
-    }
-
-    mutex mu;
-
-    auto DoWork = [&ctx, &mu, &serialized_t, has_names, &names_t,
-                   &fixed_len_features, &var_len_features, &output_dense_values,
-                   &sparse_values_tmp](int64 start, int64 limit) {
-      // Processing each Example in the batch starts here.
-      for (std::size_t b = static_cast<size_t>(start);
-           b < static_cast<size_t>(limit); ++b) {
-        // Benchmarks indicate that a tight Arena+Example is most performant.
-        protobuf::Arena arena;
-        // ex is owned by the arena.
-        Example* ex = protobuf::Arena::CreateMessage<Example>(&arena);
-        bool parse_success = ParseProtoUnlimited(ex, serialized_t(b));
-        if (!TF_PREDICT_TRUE(parse_success)) {
-          mutex_lock l(mu);
-          ctx->CtxFailure(errors::InvalidArgument(
-              "Could not parse example input, value: '", serialized_t(b), "'"));
-          return;
-        }
-        const string& example_name = (has_names) ? names_t(b) : "<unknown>";
-        Status s = SingleExampleProtoToTensors(
-            *ex, example_name, b, fixed_len_features, var_len_features,
-            &output_dense_values, &sparse_values_tmp);
-        if (!TF_PREDICT_TRUE(s.ok())) {
-          mutex_lock l(mu);
-          ctx->CtxFailureWithWarning(s);
-        }
-      }
-    };
-
-    Shard(worker_threads.num_threads, worker_threads.workers, batch_size,
-          work_unit_size, DoWork);
-
-    if (!TF_PREDICT_TRUE(ctx->status().ok())) {
-      return;
-    }
-
-    // Copy from sparse_values_tmp into final resting Tensors
-    // -------------------------
-    for (int d = 0; d < attrs_.num_sparse; ++d) {
-      const VarLenFeature& feature_config = var_len_features[d];
-      const std::vector<Tensor>& sparse_values_tmp_tensors =
-          sparse_values_tmp[d];
-      VarLenFeatureBatchShapes sparse_tensor_batch_shapes;
-      GetSparseTensorShapes(feature_config, sparse_values_tmp_tensors,
-                            batch_size, &sparse_tensor_batch_shapes);
-
-      Tensor* sp_indices_d = nullptr;
-      Tensor* sp_values_d = nullptr;
-      Tensor* sp_shape_d = nullptr;
-
-      sparse_indices.allocate(d, sparse_tensor_batch_shapes.indices_shape,
-                              &sp_indices_d);
-      sparse_values.allocate(d, sparse_tensor_batch_shapes.values_shape,
-                             &sp_values_d);
-      sparse_shapes.allocate(d, TensorShape({2}), &sp_shape_d);
-
-      auto shape_t = sp_shape_d->vec<int64>();
-      shape_t(0) = batch_size;
-      shape_t(1) = sparse_tensor_batch_shapes.max_num_features;
-
-      int64 offset = 0;
-      for (int b = 0; b < batch_size; ++b) {
-        const int64 num_elements = CopyIntoSparseTensor(
-            sparse_values_tmp_tensors[b], b, offset, sp_indices_d, sp_values_d);
-        offset += num_elements;
-      }
+      sparse_indices.set(d, result.sparse_indices[d]);
+      sparse_values.set(d, result.sparse_values[d]);
+      sparse_shapes.set(d, result.sparse_shapes[d]);
     }
   }
 
diff --git a/tensorflow/core/kernels/example_parsing_ops_test.cc b/tensorflow/core/kernels/example_parsing_ops_test.cc
index e58cecff14..187d72685e 100644
--- a/tensorflow/core/kernels/example_parsing_ops_test.cc
+++ b/tensorflow/core/kernels/example_parsing_ops_test.cc
@@ -161,7 +161,7 @@ static Graph* ParseExample(int batch_size, int num_keys) {
 // Benchmark settings (Sparse, Dense) X (Bytes, Int64, Float)
 typedef BenchmarkOptions<ExampleStore<BytesFiller>, false> SparseString;
 typedef BenchmarkOptions<ExampleStore<BytesFiller>, true> DenseString;
-typedef BenchmarkOptions<ExampleStore<Int64Filler>, false> SparseIn64;
+typedef BenchmarkOptions<ExampleStore<Int64Filler>, false> SparseInt64;
 typedef BenchmarkOptions<ExampleStore<Int64Filler>, true> DenseInt64;
 typedef BenchmarkOptions<ExampleStore<FloatFiller>, false> SparseFloat;
 typedef BenchmarkOptions<ExampleStore<FloatFiller>, true> DenseFloat;
@@ -176,20 +176,19 @@ typedef BenchmarkOptions<ExampleStore<FloatFiller>, true> DenseFloat;
   }                                                                      \
   BENCHMARK(BM_ParseExample##_##TYPE##_##B##_##K);
 
-#define BM_AllParseExample(B, K)       \
-  BM_ParseExample(SparseString, B, K); \
-  BM_ParseExample(DenseString, B, K);  \
-  BM_ParseExample(SparseIn64, B, K);   \
-  BM_ParseExample(DenseInt64, B, K);   \
-  BM_ParseExample(SparseFloat, B, K);  \
-  BM_ParseExample(DenseFloat, B, K);
-
-BM_AllParseExample(128, 10);
-BM_AllParseExample(128, 100);
-BM_AllParseExample(128, 1000);
-
-BM_AllParseExample(512, 10);
-BM_AllParseExample(512, 100);
-BM_AllParseExample(512, 1000);
+#define BM_AllParseExample(Type)    \
+  BM_ParseExample(Type, 128, 10);   \
+  BM_ParseExample(Type, 512, 10);   \
+  BM_ParseExample(Type, 128, 100);  \
+  BM_ParseExample(Type, 512, 100);  \
+  BM_ParseExample(Type, 128, 1000); \
+  BM_ParseExample(Type, 512, 1000);
+
+BM_AllParseExample(SparseString);
+BM_AllParseExample(DenseString);
+BM_AllParseExample(SparseInt64);
+BM_AllParseExample(DenseInt64);
+BM_AllParseExample(SparseFloat);
+BM_AllParseExample(DenseFloat);
 
 }  // end namespace tensorflow
diff --git a/tensorflow/core/util/example_proto_fast_parsing.cc b/tensorflow/core/util/example_proto_fast_parsing.cc
new file mode 100644
index 0000000000..fee6b8885f
--- /dev/null
+++ b/tensorflow/core/util/example_proto_fast_parsing.cc
@@ -0,0 +1,761 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/core/util/example_proto_fast_parsing.h"
+
+#include <vector>
+
+#include "tensorflow/core/example/example.pb.h"
+#include "tensorflow/core/example/feature.pb_text.h"
+#include "tensorflow/core/framework/numeric_op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/lib/core/blocking_counter.h"
+#include "tensorflow/core/lib/core/casts.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/lib/core/threadpool.h"
+#include "tensorflow/core/platform/logging.h"
+#include "tensorflow/core/platform/protobuf.h"
+#include "tensorflow/core/util/presized_cuckoo_map.h"
+#include "tensorflow/core/util/sparse/sparse_tensor.h"
+
+namespace tensorflow {
+namespace example {
+
+namespace {
+template <typename A>
+auto EnableAliasing(A* a) -> decltype(a->EnableAliasing(true), void()) {
+  a->EnableAliasing(true);
+}
+
+template <typename A>
+void EnableAliasing(A&& a) {}
+
+uint8 PeekTag(protobuf::io::CodedInputStream* stream) {
+  DCHECK(stream != nullptr);
+  const void* ptr;
+  int size;
+  if (!stream->GetDirectBufferPointer(&ptr, &size)) return 0;
+  return *static_cast<const uint8*>(ptr);
+}
+
+constexpr uint8 kVarintTag(uint tag) { return (tag << 3) | 0; }
+constexpr uint8 kDelimitedTag(uint tag) { return (tag << 3) | 2; }
+constexpr uint8 kFixed32Tag(uint tag) { return (tag << 3) | 5; }
+
+namespace parsed {
+
+// ParseDataType has to be called first, then appropriate ParseZzzzList.
+class Feature {
+ public:
+  Feature() {}
+  Feature(StringPiece serialized) : serialized_(serialized) {}
+
+  Status ParseDataType(DataType* dtype) {
+    DCHECK(dtype != nullptr);
+    if (serialized_.empty()) {
+      *dtype = DT_INVALID;
+      return Status::OK();
+    }
+    uint8 oneof_tag = static_cast<uint8>(*serialized_.data());
+    serialized_.remove_prefix(1);
+    switch (oneof_tag) {
+      case kDelimitedTag(1):
+        *dtype = DT_STRING;
+        break;
+      case kDelimitedTag(2):
+        *dtype = DT_FLOAT;
+        break;
+      case kDelimitedTag(3):
+        *dtype = DT_INT64;
+        break;
+      default:
+        return errors::InvalidArgument("Unsuported datatype.");
+    }
+    return Status::OK();
+  }
+
+  bool ParseBytesList(std::vector<string>* bytes_list) {
+    DCHECK(bytes_list != nullptr);
+    protobuf::io::CodedInputStream stream(
+        reinterpret_cast<const uint8*>(serialized_.data()), serialized_.size());
+
+    EnableAliasing(&stream);
+
+    uint32 length;
+    if (!stream.ReadVarint32(&length)) return false;
+    auto limit = stream.PushLimit(length);
+
+    while (!stream.ExpectAtEnd()) {
+      if (!stream.ExpectTag(kDelimitedTag(1))) return false;
+      // parse string
+      uint32 bytes_length;
+      if (!stream.ReadVarint32(&bytes_length)) return false;
+      string bytes;
+      if (!stream.ReadString(&bytes, bytes_length)) return false;
+      bytes_list->push_back(std::move(bytes));
+    }
+    stream.PopLimit(limit);
+    return true;
+  }
+
+  bool ParseFloatList(std::vector<float>* float_list) {
+    DCHECK(float_list != nullptr);
+    protobuf::io::CodedInputStream stream(
+        reinterpret_cast<const uint8*>(serialized_.data()), serialized_.size());
+    EnableAliasing(&stream);
+    uint32 length;
+    if (!stream.ReadVarint32(&length)) return false;
+    auto limit = stream.PushLimit(length);
+
+    if (!stream.ExpectAtEnd()) {
+      uint8 peek_tag = PeekTag(&stream);
+      if (peek_tag != kDelimitedTag(1) && peek_tag != kFixed32Tag(1)) {
+        return false;
+      }
+
+      if (peek_tag == kDelimitedTag(1)) {                       // packed
+        if (!stream.ExpectTag(kDelimitedTag(1))) return false;  // packed tag
+        uint32 packed_length;
+        if (!stream.ReadVarint32(&packed_length)) return false;
+        auto packed_limit = stream.PushLimit(packed_length);
+
+        while (!stream.ExpectAtEnd()) {
+          uint32 buffer32;
+          if (!stream.ReadLittleEndian32(&buffer32)) return false;
+          float_list->push_back(bit_cast<float>(buffer32));
+        }
+
+        stream.PopLimit(packed_limit);
+      } else {  // non-packed
+        while (!stream.ExpectAtEnd()) {
+          if (!stream.ExpectTag(kFixed32Tag(1))) return false;
+          uint32 buffer32;
+          if (!stream.ReadLittleEndian32(&buffer32)) return false;
+          float_list->push_back(bit_cast<float>(buffer32));
+        }
+      }
+    }
+
+    stream.PopLimit(limit);
+    return true;
+  }
+
+  bool ParseInt64List(std::vector<int64>* int64_list) {
+    DCHECK(int64_list != nullptr);
+    protobuf::io::CodedInputStream stream(
+        reinterpret_cast<const uint8*>(serialized_.data()), serialized_.size());
+    EnableAliasing(&stream);
+    uint32 length;
+    if (!stream.ReadVarint32(&length)) return false;
+    auto limit = stream.PushLimit(length);
+
+    if (!stream.ExpectAtEnd()) {
+      uint8 peek_tag = PeekTag(&stream);
+      if (peek_tag != kDelimitedTag(1) && peek_tag != kVarintTag(1)) {
+        return false;
+      }
+      if (peek_tag == kDelimitedTag(1)) {                       // packed
+        if (!stream.ExpectTag(kDelimitedTag(1))) return false;  // packed tag
+        uint32 packed_length;
+        if (!stream.ReadVarint32(&packed_length)) return false;
+        auto packed_limit = stream.PushLimit(packed_length);
+
+        while (!stream.ExpectAtEnd()) {
+          uint64 n;  // There is no API for int64
+          if (!stream.ReadVarint64(&n)) return false;
+          int64_list->push_back(n);
+        }
+
+        stream.PopLimit(packed_limit);
+      } else {  // non-packed
+        while (!stream.ExpectAtEnd()) {
+          if (!stream.ExpectTag(kVarintTag(1))) return false;
+          uint64 n;  // There is no API for int64
+          if (!stream.ReadVarint64(&n)) return false;
+          int64_list->push_back(n);
+        }
+      }
+    }
+    stream.PopLimit(limit);
+    return true;
+  }
+
+  StringPiece GetSerialized() const { return serialized_; }
+
+ private:
+  // TODO(lew): Pair of uint8* would be more natural.
+  StringPiece serialized_;
+};
+
+using FeatureMapEntry = std::pair<StringPiece, Feature>;
+using Example = std::vector<FeatureMapEntry>;
+
+}  // namespace parsed
+
+bool ParseString(protobuf::io::CodedInputStream* stream, StringPiece* result) {
+  DCHECK(stream != nullptr);
+  DCHECK(result != nullptr);
+  uint32 length;
+  if (!stream->ReadVarint32(&length)) return false;
+  if (length == 0) {
+    *result = StringPiece(nullptr, 0);
+    return true;
+  }
+  const void* stream_alias;
+  int stream_size;
+  if (!stream->GetDirectBufferPointer(&stream_alias, &stream_size)) {
+    return false;
+  }
+  if (static_cast<uint32>(stream_size) < length) return false;
+  *result = StringPiece(static_cast<const char*>(stream_alias), length);
+  stream->Skip(length);
+  return true;
+}
+
+bool ParseFeatureMapEntry(protobuf::io::CodedInputStream* stream,
+                          parsed::FeatureMapEntry* feature_map_entry) {
+  DCHECK(stream != nullptr);
+  DCHECK(feature_map_entry != nullptr);
+  uint32 length;
+  if (!stream->ReadVarint32(&length)) return false;
+  auto limit = stream->PushLimit(length);
+  if (!stream->ExpectTag(kDelimitedTag(1))) return false;
+  if (!ParseString(stream, &feature_map_entry->first)) return false;
+  if (!stream->ExpectTag(kDelimitedTag(2))) return false;
+  StringPiece feature_string_piece;
+  if (!ParseString(stream, &feature_string_piece)) return false;
+  feature_map_entry->second = parsed::Feature(feature_string_piece);
+  if (!stream->ExpectAtEnd()) return false;
+  stream->PopLimit(limit);
+  return true;
+}
+
+bool ParseFeatures(protobuf::io::CodedInputStream* stream,
+                   parsed::Example* example) {
+  DCHECK(stream != nullptr);
+  DCHECK(example != nullptr);
+  uint32 length;
+  if (!stream->ReadVarint32(&length)) return false;
+  auto limit = stream->PushLimit(length);
+  while (!stream->ExpectAtEnd()) {
+    parsed::FeatureMapEntry feature_map_entry;
+    if (!stream->ExpectTag(kDelimitedTag(1))) return false;
+    if (!ParseFeatureMapEntry(stream, &feature_map_entry)) return false;
+    example->push_back(std::move(feature_map_entry));
+  }
+  stream->PopLimit(limit);
+  return true;
+}
+
+bool ParseExample(protobuf::io::CodedInputStream* stream,
+                  parsed::Example* example) {
+  DCHECK(stream != nullptr);
+  DCHECK(example != nullptr);
+  if (stream->ExpectTag(kDelimitedTag(1))) {
+    if (!ParseFeatures(stream, example)) return false;
+  }
+  if (!stream->ExpectAtEnd()) return false;
+  return true;
+}
+
+bool ParseExample(StringPiece serialized, parsed::Example* example) {
+  DCHECK(example != nullptr);
+  protobuf::io::CodedInputStream stream(
+      reinterpret_cast<const uint8*>(serialized.data()), serialized.size());
+  EnableAliasing(&stream);
+  return ParseExample(&stream, example);
+}
+
+}  // namespace
+
+bool TestFastParse(const string& serialized, Example* example) {
+  DCHECK(example != nullptr);
+  parsed::Example parsed_example;
+  if (!ParseExample(serialized, &parsed_example)) return false;
+  auto& features = *example->mutable_features();
+  for (parsed::FeatureMapEntry& entry : parsed_example) {
+    auto& value = (*features.mutable_feature())[entry.first.ToString()];
+    DataType dtype;
+    if (!entry.second.ParseDataType(&dtype).ok()) return false;
+    switch (dtype) {
+      case DT_INVALID:
+        break;
+      case DT_STRING: {
+        std::vector<string> list;
+        if (!entry.second.ParseBytesList(&list)) return false;
+        auto* result_list = value.mutable_bytes_list();
+        for (auto& bytes : list) {
+          result_list->add_value(std::move(bytes));
+        }
+        break;
+      }
+      case DT_FLOAT: {
+        std::vector<float> list;
+        if (!entry.second.ParseFloatList(&list)) return false;
+        auto* result_list = value.mutable_float_list();
+        for (float f : list) {
+          result_list->add_value(f);
+        }
+        break;
+      }
+      case DT_INT64: {
+        std::vector<int64> list;
+        if (!entry.second.ParseInt64List(&list)) return false;
+        auto* result_list = value.mutable_int64_list();
+        for (int64 i : list) {
+          result_list->add_value(i);
+        }
+        break;
+      }
+      default:
+        CHECK(false) << "Should not happen.";
+    }
+  }
+  return true;
+}
+
+// -----------------------------------------------------------------------------
+
+namespace {
+
+using Config = FastParseExampleConfig;
+
+void ParallelFor(const std::function<void(size_t)>& f, size_t n,
+                 thread::ThreadPool* thread_pool) {
+  DCHECK(thread_pool != nullptr);
+  if (n == 0) return;
+  BlockingCounter counter(n - 1);
+  for (size_t i = 1; i < n; ++i) {
+    thread_pool->Schedule([i, &f, &counter] {
+      f(i);
+      counter.DecrementCount();
+    });
+  }
+  f(0);
+  counter.Wait();
+}
+
+enum class Type { Sparse, Dense };
+
+struct SparseBuffer {
+  // TODO(lew): Use InlinedVector.
+  // Features are in one of the 3 vectors below depending on config's dtype.
+  // Other 2 vectors remain empty.
+  std::vector<string> bytes_list;
+  std::vector<float> float_list;
+  std::vector<int64> int64_list;
+
+  // Features of example i are elements with indices
+  // from example_end_indices[i-1] to example_end_indices[i]-1 on the
+  // appropriate xxxxx_list
+  std::vector<size_t> example_end_indices;
+};
+
+struct SeededHasher {
+  uint64 operator()(StringPiece s) const {
+    return Hash64(s.data(), s.size(), seed);
+  }
+  uint64 seed{0xDECAFCAFFE};
+};
+
+Status FastParseSerializedExample(
+    const string& serialized_example, const string& example_name,
+    const size_t example_index, const Config& config,
+    const PresizedCuckooMap<std::pair<size_t, Type>>& config_index,
+    SeededHasher hasher, std::vector<Tensor>* output_dense,
+    std::vector<SparseBuffer>* output_sparse) {
+  DCHECK(output_dense != nullptr);
+  DCHECK(output_sparse != nullptr);
+  parsed::Example parsed_example;
+  if (!ParseExample(serialized_example, &parsed_example)) {
+    return errors::InvalidArgument("Could not parse example input, value: '",
+                                   serialized_example, "'");
+  }
+  constexpr size_t kMax = std::numeric_limits<size_t>::max();
+  std::vector<size_t> sparse_features_found(config.sparse.size(), kMax);
+  std::vector<size_t> dense_features_found(config.dense.size(), kMax);
+
+  // Handle features present in the example.
+  for (parsed::FeatureMapEntry& name_and_feature : parsed_example) {
+    parsed::Feature& feature = name_and_feature.second;
+    std::pair<size_t, Type> d_and_type;
+    uint64 h = hasher(name_and_feature.first);
+    if (!config_index.Find(h, &d_and_type)) continue;
+    size_t d = d_and_type.first;
+
+    auto parse_error = [&](StringPiece feature_name) {
+      return errors::InvalidArgument("Name: ", example_name, ", Key: ",
+                                     feature_name, ", Index: ", example_index,
+                                     ". Can't parse serialized Example.");
+    };
+
+    if (d_and_type.second == Type::Dense) {
+      DataType example_dtype;
+      TF_RETURN_IF_ERROR(feature.ParseDataType(&example_dtype));
+      if (example_dtype == DT_INVALID) continue;
+
+      dense_features_found[d] = example_index;
+      if (example_dtype != config.dense[d].dtype) {
+        return errors::InvalidArgument(
+            "Name: ", example_name, ", Feature: ", config.dense[d].feature_name,
+            ".  Data types don't match. ", "Data type: ",
+            DataTypeString(example_dtype), "Expected type: ",
+            DataTypeString(config.dense[d].dtype));
+      }
+      const string& feature_name = config.dense[d].feature_name;
+      const TensorShape& shape = config.dense[d].shape;
+      Tensor& out = (*output_dense)[d];
+
+      const std::size_t num_elements = shape.num_elements();
+      const std::size_t offset = example_index * num_elements;
+
+      auto shape_error = [&](size_t size, StringPiece type_str) {
+        return errors::InvalidArgument(
+            "Name: ", example_name, ", Key: ", feature_name, ", Index: ",
+            example_index, ".  Number of ", type_str,
+            " values != expected.  "
+            "Values size: ",
+            size, " but output shape: ", shape.DebugString());
+      };
+
+      switch (config.dense[d].dtype) {
+        case DT_INT64: {
+          std::vector<int64> list;
+          if (!feature.ParseInt64List(&list)) return parse_error(feature_name);
+          if (list.size() != num_elements) {
+            return shape_error(list.size(), "int64");
+          }
+          auto out_p = out.flat<int64>().data() + offset;
+          std::copy_n(list.begin(), list.size(), out_p);
+          break;
+        }
+        case DT_FLOAT: {
+          std::vector<float> list;
+          if (!feature.ParseFloatList(&list)) return parse_error(feature_name);
+          if (list.size() != num_elements) {
+            return shape_error(list.size(), "float");
+          }
+          auto out_p = out.flat<float>().data() + offset;
+          std::copy_n(list.begin(), list.size(), out_p);
+          break;
+        }
+        case DT_STRING: {
+          std::vector<string> list;
+          if (!feature.ParseBytesList(&list)) return parse_error(feature_name);
+          if (list.size() != num_elements) {
+            return shape_error(list.size(), "bytes");
+          }
+          auto out_p = out.flat<string>().data() + offset;
+          for (size_t i = 0; i < list.size(); ++i) {
+            out_p[i] = std::move(list[i]);
+          }
+          break;
+        }
+        default:
+          CHECK(false) << "Should not happen.";
+      }
+    } else {
+      // Handle sparse features.
+      sparse_features_found[d] = example_index;
+      const string& feature_name = config.sparse[d].feature_name;
+      SparseBuffer& out = (*output_sparse)[d];
+      DataType example_dtype;
+      TF_RETURN_IF_ERROR(feature.ParseDataType(&example_dtype));
+      if (example_dtype != DT_INVALID &&
+          example_dtype != config.sparse[d].dtype) {
+        return errors::InvalidArgument(
+            "Name: ", example_name, ", Feature: ",
+            config.sparse[d].feature_name, ".  Data types don't match. ",
+            "Expected type: ", DataTypeString(config.sparse[d].dtype));
+      }
+
+      switch (config.sparse[d].dtype) {
+        case DT_INT64: {
+          if (example_dtype != DT_INVALID) {
+            if (!feature.ParseInt64List(&out.int64_list)) {
+              return parse_error(feature_name);
+            }
+          }
+          out.example_end_indices.push_back(out.int64_list.size());
+          break;
+        }
+        case DT_FLOAT: {
+          if (example_dtype != DT_INVALID) {
+            if (!feature.ParseFloatList(&out.float_list)) {
+              return parse_error(feature_name);
+            }
+          }
+          out.example_end_indices.push_back(out.float_list.size());
+          break;
+        }
+        case DT_STRING: {
+          if (example_dtype != DT_INVALID) {
+            if (!feature.ParseBytesList(&out.bytes_list)) {
+              return parse_error(feature_name);
+            }
+          }
+          out.example_end_indices.push_back(out.bytes_list.size());
+          break;
+        }
+        default:
+          CHECK(false) << "Should not happen.";
+      }
+    }
+  }
+
+  // Handle missing dense features.
+  for (size_t d = 0; d < config.dense.size(); ++d) {
+    if (dense_features_found[d] == example_index) continue;
+    if (config.dense[d].default_value.NumElements() == 0) {
+      return errors::InvalidArgument("Name: ", example_name, ", Feature: ",
+                                     config.dense[d].feature_name,
+                                     " is required but could not be found.");
+    }
+
+    const Tensor& in = config.dense[d].default_value;
+    Tensor& out = (*output_dense)[d];
+    const std::size_t num_elements = in.shape().num_elements();
+    const std::size_t offset = example_index * num_elements;
+
+    switch (config.dense[d].dtype) {
+      case DT_INT64: {
+        std::copy_n(in.flat<int64>().data(), num_elements,
+                    out.flat<int64>().data() + offset);
+        break;
+      }
+      case DT_FLOAT: {
+        std::copy_n(in.flat<float>().data(), num_elements,
+                    out.flat<float>().data() + offset);
+        break;
+      }
+      case DT_STRING: {
+        std::copy_n(in.flat<string>().data(), num_elements,
+                    out.flat<string>().data() + offset);
+        break;
+      }
+      default:
+        CHECK(false) << "Should not happen.";
+    }
+  }
+
+  // Handle missing sparse features.
+  for (size_t d = 0; d < config.sparse.size(); ++d) {
+    if (sparse_features_found[d] == example_index) continue;
+    SparseBuffer& out = (*output_sparse)[d];
+    size_t prev_example_end_index =
+        out.example_end_indices.empty() ? 0 : out.example_end_indices.back();
+    out.example_end_indices.push_back(prev_example_end_index);
+  }
+
+  return Status::OK();
+}
+
+Status CheckConfigDataType(DataType dtype) {
+  switch (dtype) {
+    case DT_INT64:
+    case DT_FLOAT:
+    case DT_STRING:
+      return Status::OK();
+    default:
+      return errors::InvalidArgument("Invalid config dtype: ",
+                                     DataTypeString(dtype));
+  }
+}
+
+}  // namespace
+
+Status FastParseExample(const Config& config,
+                        gtl::ArraySlice<string> serialized,
+                        gtl::ArraySlice<string> example_names,
+                        thread::ThreadPool* thread_pool, Result* result) {
+  DCHECK(thread_pool != nullptr);
+  DCHECK(result != nullptr);
+  // Check config so we can safely CHECK(false) in switches on config.*.dtype
+  for (auto& c : config.sparse) {
+    TF_RETURN_IF_ERROR(CheckConfigDataType(c.dtype));
+  }
+  for (auto& c : config.dense) {
+    TF_RETURN_IF_ERROR(CheckConfigDataType(c.dtype));
+  }
+
+  size_t config_size = config.dense.size() + config.sparse.size();
+  SeededHasher hasher;
+  // Build config index.
+  PresizedCuckooMap<std::pair<size_t, Type>> config_index(config_size);
+  bool ok = true;
+  for (size_t i = 0; i < 1000; ++i) {
+    for (size_t d = 0; d < config.dense.size(); ++d) {
+      ok &= config_index.InsertUnique(hasher(config.dense[d].feature_name),
+                                      {d, Type::Dense});
+    }
+    for (size_t d = 0; d < config.sparse.size(); ++d) {
+      ok &= config_index.InsertUnique(hasher(config.sparse[d].feature_name),
+                                      {d, Type::Sparse});
+    }
+    if (ok) break;
+    LOG(WARNING) << "Collision found. This should happen only if you have "
+                    "around 2^32 entries in your config.";
+    hasher.seed++;
+    config_index.Clear(config_size);
+  }
+  if (!ok) {
+    return errors::Internal(
+        "Could not avoid collision. This should not happen.");
+  }
+
+  // Allocate dense output (sparse have to be buffered).
+  for (size_t d = 0; d < config.dense.size(); ++d) {
+    TensorShape out_shape;
+    out_shape.AddDim(serialized.size());
+    for (const int64 dim : config.dense[d].shape.dim_sizes()) {
+      out_shape.AddDim(dim);
+    }
+    result->dense_values.emplace_back(config.dense[d].dtype, out_shape);
+  }
+
+  // This parameter affects performance in a big and data-dependent way.
+  const size_t kMiniBatchSizeBytes = 100000;
+
+  // Split examples into mini-batches for parallel processing.
+  auto first_example_of_minibatch = [&] {
+    std::vector<size_t> result;
+    size_t minibatch_bytes = 0;
+    for (size_t i = 0; i < serialized.size(); i++) {
+      if (minibatch_bytes == 0) {  // start minibatch
+        result.push_back(i);
+      }
+      minibatch_bytes += serialized[i].size() + 1;
+      if (minibatch_bytes > kMiniBatchSizeBytes) {
+        minibatch_bytes = 0;
+      }
+    }
+    return result;
+  }();
+
+  size_t num_minibatches = first_example_of_minibatch.size();
+
+  // Do minibatches in parallel.
+  std::vector<std::vector<SparseBuffer>> sparse_buffers(num_minibatches);
+  std::vector<Status> status_of_minibatch(num_minibatches);
+
+  auto ProcessMiniBatch = [&](size_t minibatch) {
+    sparse_buffers[minibatch].resize(config.sparse.size());
+    size_t start = first_example_of_minibatch[minibatch];
+    size_t end = minibatch + 1 < num_minibatches
+                     ? first_example_of_minibatch[minibatch + 1]
+                     : serialized.size();
+    for (size_t e = start; e < end; ++e) {
+      status_of_minibatch[minibatch] = FastParseSerializedExample(
+          serialized[e],
+          (example_names.size() > 0 ? example_names[e] : "<unknown>"), e,
+          config, config_index, hasher, &result->dense_values,
+          &sparse_buffers[minibatch]);
+      if (!status_of_minibatch[minibatch].ok()) break;
+    }
+  };
+
+  ParallelFor(ProcessMiniBatch, num_minibatches, thread_pool);
+
+  for (Status& status : status_of_minibatch) {
+    TF_RETURN_IF_ERROR(status);
+  }
+
+  // Merge SparseBuffers from all minibatches for every config.sparse.
+  auto MergeMinibatches = [&](size_t d) {
+    // Loop over minibatches
+    size_t total_num_features = 0;
+    size_t max_num_features = 0;
+    for (auto& sparse_values_tmp : sparse_buffers) {
+      std::vector<size_t>& end_indices =
+          sparse_values_tmp[d].example_end_indices;
+      total_num_features += end_indices.back();
+      max_num_features = std::max(max_num_features, end_indices[0]);
+      for (size_t i = 1; i < end_indices.size(); ++i) {
+        size_t example_size = end_indices[i] - end_indices[i - 1];
+        max_num_features = std::max(max_num_features, example_size);
+      }
+    }
+
+    TensorShape indices_shape;
+    indices_shape.AddDim(total_num_features);
+    indices_shape.AddDim(2);
+    result->sparse_indices.emplace_back(DT_INT64, indices_shape);
+    Tensor* indices = &result->sparse_indices.back();
+
+    TensorShape values_shape;
+    values_shape.AddDim(total_num_features);
+    result->sparse_values.emplace_back(config.sparse[d].dtype, values_shape);
+    Tensor* values = &result->sparse_values.back();
+
+    result->sparse_shapes.emplace_back(DT_INT64, TensorShape({2}));
+    auto shapes_shape_t = result->sparse_shapes.back().vec<int64>();
+    shapes_shape_t(0) = serialized.size();
+    shapes_shape_t(1) = max_num_features;
+
+    size_t offset = 0;
+    for (size_t i = 0; i < sparse_buffers.size(); ++i) {
+      const SparseBuffer& buffer = sparse_buffers[i][d];
+
+      // Update indices.
+      int64* ix_p = &indices->matrix<int64>()(offset, 0);
+      size_t delta = 0;
+      size_t example_index = first_example_of_minibatch[i];
+      for (size_t example_end_index : buffer.example_end_indices) {
+        size_t feature_index = 0;
+        for (; delta < example_end_index; ++delta) {
+          // Column 0: example index
+          *ix_p = example_index;
+          // Column 1: the feature index buffer example
+          *(ix_p + 1) = feature_index;
+          ix_p += 2;
+          ++feature_index;
+        }
+        ++example_index;
+      }
+
+      // Copy values over.
+      switch (config.sparse[d].dtype) {
+        case DT_INT64: {
+          std::copy(buffer.int64_list.begin(), buffer.int64_list.end(),
+                    values->flat<int64>().data() + offset);
+          break;
+        }
+        case DT_FLOAT: {
+          std::copy(buffer.float_list.begin(), buffer.float_list.end(),
+                    values->flat<float>().data() + offset);
+          break;
+        }
+        case DT_STRING: {
+          std::move(buffer.bytes_list.begin(), buffer.bytes_list.end(),
+                    values->flat<string>().data() + offset);
+          break;
+        }
+        default:
+          CHECK(false) << "Should not happen.";
+      }
+
+      offset += delta;
+    }
+  };
+
+  for (size_t d = 0; d < config.sparse.size(); ++d) {
+    MergeMinibatches(d);
+  }
+
+  return Status::OK();
+}
+
+}  // namespace example
+}  // namespace tensorflow
diff --git a/tensorflow/core/util/example_proto_fast_parsing.h b/tensorflow/core/util/example_proto_fast_parsing.h
new file mode 100644
index 0000000000..6ed9d57838
--- /dev/null
+++ b/tensorflow/core/util/example_proto_fast_parsing.h
@@ -0,0 +1,88 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_UTIL_EXAMPLE_PROTO_FAST_PARSING_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_UTIL_EXAMPLE_PROTO_FAST_PARSING_H_
+
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "tensorflow/core/example/example.pb.h"
+#include "tensorflow/core/framework/allocator.h"
+#include "tensorflow/core/framework/graph.pb.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/lib/gtl/array_slice.h"
+#include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/sparse/sparse_tensor.h"
+
+namespace tensorflow {
+namespace example {
+
+// FastParseExampleConfig defines how to parse features in Example.
+// Each sub-config is responsible for one feature identified with feautre_name.
+// FastParseExampleConfig can't have two sub-configs with the same feature_name.
+// dtype identifies the type of output vector and the kind of Feature expected
+// in Example.
+struct FastParseExampleConfig {
+  struct Dense {
+    string feature_name;
+    DataType dtype;
+    // These 2 fields correspond exactly to dense_shapes and dense_defaults in
+    // ParseExample op.
+    // Documentation is avaliable in: tensorflow/core/ops/parsing_ops.cc
+    TensorShape shape;
+    Tensor default_value;
+  };
+
+  struct Sparse {
+    string feature_name;
+    DataType dtype;
+  };
+
+  std::vector<Dense> dense;
+  std::vector<Sparse> sparse;
+};
+
+// This is exactly the output of TF's ParseExample Op.
+// Documentation is avaliable in: tensorflow/core/ops/parsing_ops.cc
+struct Result {
+  std::vector<Tensor> sparse_indices;
+  std::vector<Tensor> sparse_values;
+  std::vector<Tensor> sparse_shapes;
+  std::vector<Tensor> dense_values;
+};
+
+// Parses a batch of serialized Example protos and converts them into result
+// according to given config.
+// Given example names have to either be empty or the same size as serialized.
+// example_names are used only for error messages.
+Status FastParseExample(const FastParseExampleConfig& config,
+                        gtl::ArraySlice<string> serialized,
+                        gtl::ArraySlice<string> example_names,
+                        thread::ThreadPool* thread_pool, Result* result);
+
+// This function parses serialized Example and populates given example.
+// It uses the same specialized parser as FastParseExample which is efficient.
+// But then constructs Example which is relatively slow.
+// It is exported here as a convenient API to test parser part separately.
+bool TestFastParse(const string& serialized, Example* example);
+
+}  // namespace example
+}  // namespace tensorflow
+
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_UTIL_EXAMPLE_PROTO_FAST_PARSING_H_
diff --git a/tensorflow/core/util/example_proto_fast_parsing_test.cc b/tensorflow/core/util/example_proto_fast_parsing_test.cc
new file mode 100644
index 0000000000..6d3b548851
--- /dev/null
+++ b/tensorflow/core/util/example_proto_fast_parsing_test.cc
@@ -0,0 +1,184 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+#include "tensorflow/core/util/example_proto_fast_parsing.h"
+
+#include "tensorflow/core/example/example.pb.h"
+#include "tensorflow/core/platform/protobuf.h"
+#include "tensorflow/core/platform/test.h"
+#include "tensorflow/core/platform/test_benchmark.h"
+
+namespace tensorflow {
+namespace example {
+namespace {
+
+constexpr char kDenseInt64Key[] = "dense_int64";
+constexpr char kDenseFloatKey[] = "dense_float";
+constexpr char kDenseStringKey[] = "dense_string";
+
+constexpr char kSparseInt64Key[] = "sparse_int64";
+constexpr char kSparseFloatKey[] = "sparse_float";
+constexpr char kSparseStringKey[] = "sparse_string";
+
+string SerializedToReadable(string serialized) {
+  string result;
+  result += '"';
+  for (char c : serialized)
+    result += strings::StrCat("\\x", strings::Hex(c, strings::ZERO_PAD_2));
+  result += '"';
+  return result;
+}
+
+string Serialize(const Example& example) {
+  string serialized;
+  example.SerializeToString(&serialized);
+  return serialized;
+}
+
+void TestCorrectness(const string& serialized) {
+  Example example;
+  Example fast_example;
+  EXPECT_TRUE(example.ParseFromString(serialized));
+  EXPECT_TRUE(TestFastParse(serialized, &fast_example));
+  EXPECT_EQ(example.DebugString(), fast_example.DebugString());
+  if (example.DebugString() != fast_example.DebugString()) {
+    LOG(ERROR) << "Bad serialized: " << SerializedToReadable(serialized);
+  }
+}
+
+// Fast parsing does not differentiate between EmptyExample and EmptyFeatures
+// TEST(FastParse, EmptyExample) {
+//   Example example;
+//   TestCorrectness(example);
+// }
+
+TEST(FastParse, NonPacked) {
+  TestCorrectness(
+      "\x0a\x0e\x0a\x0c\x0a\x03\x61\x67\x65\x12\x05\x1a\x03\x0a\x01\x0d");
+}
+
+TEST(FastParse, Packed) {
+  TestCorrectness(
+      "\x0a\x0d\x0a\x0b\x0a\x03\x61\x67\x65\x12\x04\x1a\x02\x08\x0d");
+}
+
+TEST(FastParse, EmptyFeatures) {
+  Example example;
+  example.mutable_features();
+  TestCorrectness(Serialize(example));
+}
+
+void TestCorrectnessJson(const string& json) {
+  auto resolver = protobuf::util::NewTypeResolverForDescriptorPool(
+      "type.googleapis.com", protobuf::DescriptorPool::generated_pool());
+  string serialized;
+  auto s = protobuf::util::JsonToBinaryString(
+      resolver, "type.googleapis.com/tensorflow.Example", json, &serialized);
+  EXPECT_TRUE(s.ok()) << s;
+  delete resolver;
+  TestCorrectness(serialized);
+}
+
+TEST(FastParse, JsonUnivalent) {
+  TestCorrectnessJson(
+      "{'features': {"
+      "  'feature': {'age': {'int64_list': {'value': [0]} }}, "
+      "  'feature': {'flo': {'float_list': {'value': [1.1]} }}, "
+      "  'feature': {'byt': {'bytes_list': {'value': ['WW8='] }}}"
+      "}}");
+}
+
+TEST(FastParse, JsonMultivalent) {
+  TestCorrectnessJson(
+      "{'features': {"
+      "  'feature': {'age': {'int64_list': {'value': [0, 13, 23]} }}, "
+      "  'feature': {'flo': {'float_list': {'value': [1.1, 1.2, 1.3]} }}, "
+      "  'feature': {'byt': {'bytes_list': {'value': ['WW8=', 'WW8K'] }}}"
+      "}}");
+}
+
+TEST(FastParse, SingleInt64) {
+  Example example;
+  (*example.mutable_features()->mutable_feature())["age"]
+      .mutable_int64_list()
+      ->add_value(13);
+  TestCorrectness(Serialize(example));
+}
+
+TEST(FastParse, SomeFeatures) {
+  Example example;
+
+  (*example.mutable_features()->mutable_feature())[""];
+
+  (*example.mutable_features()->mutable_feature())["empty_bytes_list"]
+      .mutable_bytes_list();
+  (*example.mutable_features()->mutable_feature())["empty_float_list"]
+      .mutable_float_list();
+  (*example.mutable_features()->mutable_feature())["empty_int64_list"]
+      .mutable_int64_list();
+
+  BytesList* bytes_list =
+      (*example.mutable_features()->mutable_feature())["bytes_list"]
+          .mutable_bytes_list();
+  bytes_list->add_value("bytes1");
+  bytes_list->add_value("bytes2");
+
+  FloatList* float_list =
+      (*example.mutable_features()->mutable_feature())["float_list"]
+          .mutable_float_list();
+  float_list->add_value(1.0);
+  float_list->add_value(2.0);
+
+  Int64List* int64_list =
+      (*example.mutable_features()->mutable_feature())["int64_list"]
+          .mutable_int64_list();
+  int64_list->add_value(3);
+  int64_list->add_value(270);
+  int64_list->add_value(86942);
+
+  TestCorrectness(Serialize(example));
+}
+
+string MakeSerializedExample() {
+  Example example;
+  const int kFeatureNameLength = 10;
+  const int kFeatureValueLength = 20;
+  const int kBytesFeatureCount = 200;
+  const int kFloatFeatureCount = 200;
+  const int kInt64FeatureCount = 200;
+  auto& fmap = *example.mutable_features()->mutable_feature();
+  for (int i = 0; i < kBytesFeatureCount; ++i) {
+    fmap[strings::StrCat(string('b', kFeatureNameLength), i)]
+        .mutable_bytes_list()
+        ->add_value(string('v', kFeatureValueLength));
+  }
+  for (int i = 0; i < kFloatFeatureCount; ++i) {
+    fmap[strings::StrCat(string('f', kFeatureNameLength), i)]
+        .mutable_float_list()
+        ->add_value(123123123.123);
+  }
+  for (int i = 0; i < kInt64FeatureCount; ++i) {
+    fmap[strings::StrCat(string('i', kFeatureNameLength), i)]
+        .mutable_int64_list()
+        ->add_value(10 * i);
+  }
+  string serialized;
+  example.SerializeToString(&serialized);
+  return serialized;
+}
+
+}  // namespace
+
+}  // namespace example
+}  // namespace tensorflow
diff --git a/tensorflow/core/util/presized_cuckoo_map.h b/tensorflow/core/util/presized_cuckoo_map.h
index b488d32e03..a1ae3ec248 100644
--- a/tensorflow/core/util/presized_cuckoo_map.h
+++ b/tensorflow/core/util/presized_cuckoo_map.h
@@ -50,7 +50,10 @@ class PresizedCuckooMap {
   // The key type is fixed as a pre-hashed key for this specialized use.
   typedef uint64 key_type;
 
-  explicit PresizedCuckooMap(uint64 num_entries) : cpq_(new CuckooPathQueue) {
+  explicit PresizedCuckooMap(uint64 num_entries) { Clear(num_entries); }
+
+  void Clear(uint64 num_entries) {
+    cpq_.reset(new CuckooPathQueue());
     double n(num_entries);
     n /= kLoadFactor;
     num_buckets_ = (static_cast<uint64>(n) / kSlotsPerBucket);
@@ -62,8 +65,10 @@ class PresizedCuckooMap {
     for (int i = 0; i < kSlotsPerBucket; i++) {
       empty_bucket.keys[i] = kUnusedSlot;
     }
+    buckets_.clear();
     buckets_.resize(num_buckets_, empty_bucket);
-#if !defined(__GCUDACC__) && !defined(__GCUDACC_HOST__)
+#if !defined(__GCUDACC__) && !defined(__GCUDACC_HOST__) && \
+    !defined(IS_MOBILE_PLATFORM)
     buckets_divisor_ = Eigen::internal::TensorIntDivisor<uint64>(num_buckets_);
 #endif
   }
@@ -304,7 +309,8 @@ class PresizedCuckooMap {
   inline uint64 fast_mod_by_buckets(uint64 x) const {
 // Omitting the optimized bucket mod for CUDA platforms
 // until Eigen supports 2^63 divisors on GPU.
-#if !defined(__GCUDACC__) && !defined(__GCUDACC_HOST__)
+#if !defined(__GCUDACC__) && !defined(__GCUDACC_HOST__) && \
+    !defined(IS_MOBILE_PLATFORM)
     x &= ~(1ULL << 63);  // Fast div can only handle 2^63-1
     return x - num_buckets_ * (x / buckets_divisor_);
 #else
@@ -317,7 +323,7 @@ class PresizedCuckooMap {
   std::vector<Bucket> buckets_;
   Eigen::internal::TensorIntDivisor<uint64> buckets_divisor_;  // for fast mod
 
-  const std::unique_ptr<CuckooPathQueue> cpq_;
+  std::unique_ptr<CuckooPathQueue> cpq_;
   CuckooPathEntry visited_[kVisitedListSize];
 
   TF_DISALLOW_COPY_AND_ASSIGN(PresizedCuckooMap);
diff --git a/tensorflow/core/util/presized_cuckoo_map_test.cc b/tensorflow/core/util/presized_cuckoo_map_test.cc
index 64ad315518..fe8e5dcfbd 100644
--- a/tensorflow/core/util/presized_cuckoo_map_test.cc
+++ b/tensorflow/core/util/presized_cuckoo_map_test.cc
@@ -13,11 +13,11 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+#include "tensorflow/core/util/presized_cuckoo_map.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/fingerprint.h"
 #include "tensorflow/core/platform/test.h"
 #include "tensorflow/core/platform/test_benchmark.h"
-#include "tensorflow/core/util/presized_cuckoo_map.h"
 
 namespace tensorflow {
 namespace {
@@ -64,6 +64,22 @@ TEST(PresizedCuckooMapTest, ZeroSizeMap) {
   }
 }
 
+TEST(PresizedCuckooMapTest, RepeatedClear) {
+  PresizedCuckooMap<int> pscm(2);
+  int out;
+  for (int i = 0; i < 100; ++i) {
+    pscm.InsertUnique(0, 0);
+    pscm.InsertUnique(1, 1);
+    EXPECT_TRUE(pscm.Find(0, &out));
+    EXPECT_EQ(0, out);
+    EXPECT_TRUE(pscm.Find(1, &out));
+    EXPECT_EQ(1, out);
+    pscm.Clear(2);
+    EXPECT_FALSE(pscm.Find(0, &out));
+    EXPECT_FALSE(pscm.Find(1, &out));
+  }
+}
+
 void RunFill(int64 table_size) {
   PresizedCuckooMap<int> pscm(table_size);
   for (int64 i = 0; i < table_size; i++) {
-- 
GitLab


From fa042c7063b922f8048630de7084b2a28e59827c Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Thu, 25 Aug 2016 14:46:35 -0800
Subject: [PATCH 171/223] Add a 'visible to virtual' GPU device remapping to
 ConfigProto.

Allows one to remap the visible GPUs to virtual GPUs on
a per-session basis.

For example, if the visible devices are 0,1,2,3,4,5,6,8,
setting visible_device_list='5,3' means that visible device
5 gets mapped to /gpu:0 and visible device 3 maps to /gpu:1.

Tested manually on my local machine: our tests are single GPU
only so there's no good way to test this ongoing.

Fixes #1888.
Change: 131340536
---
 .../core/common_runtime/gpu/gpu_device.cc     | 61 +++++++++++++++----
 .../core/common_runtime/gpu/gpu_device.h      |  7 ++-
 tensorflow/core/protobuf/config.proto         | 17 ++++++
 3 files changed, 73 insertions(+), 12 deletions(-)

diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc
index c08abc5689..06c28b26d3 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc
@@ -597,7 +597,8 @@ Status BaseGPUDeviceFactory::CreateDevices(const SessionOptions& options,
     n = iter->second;
   }
   std::vector<int> valid_gpu_ids;
-  GetValidDeviceIds(&valid_gpu_ids);
+  TF_RETURN_IF_ERROR(GetValidDeviceIds(
+      options.config.gpu_options().visible_device_list(), &valid_gpu_ids));
   if (static_cast<size_t>(n) > valid_gpu_ids.size()) {
     n = valid_gpu_ids.size();
   }
@@ -810,21 +811,55 @@ std::vector<CudaVersion> GetSupportedCudaComputeCapabilities() {
 
 }  // namespace
 
-void BaseGPUDeviceFactory::GetValidDeviceIds(std::vector<int>* ids) {
+Status BaseGPUDeviceFactory::GetValidDeviceIds(
+    const string& visible_device_list, std::vector<int>* ids) {
   gpu::Platform* gpu_manager = GPUMachineManager();
   if (gpu_manager == nullptr) {
-    return;
+    return Status::OK();
   }
 
   auto cuda_supported_capabilities = GetSupportedCudaComputeCapabilities();
-  CHECK(!cuda_supported_capabilities.empty());
+  if (cuda_supported_capabilities.empty()) {
+    return errors::FailedPrecondition(
+        "No supported cuda capabilities in binary.");
+  }
   CudaVersion min_supported_capability = *std::min_element(
       cuda_supported_capabilities.begin(), cuda_supported_capabilities.end());
 
   int min_gpu_core_count = GetMinGPUMultiprocessorCount(gpu_manager);
 
-  for (int i = 0; i < gpu_manager->VisibleDeviceCount(); ++i) {
-    auto exec_status = gpu_manager->ExecutorForDevice(i);
+  // If the user wants to remap the visible to virtual GPU mapping,
+  // check for that here.
+  std::vector<int> visible_gpu_order;
+  if (visible_device_list.empty()) {
+    visible_gpu_order.resize(gpu_manager->VisibleDeviceCount());
+    // By default, visible to virtual mapping is unchanged.
+    std::iota(visible_gpu_order.begin(), visible_gpu_order.end(), 0);
+  } else {
+    std::vector<string> order_str = str_util::Split(visible_device_list, ',');
+    for (int i = 0; i < order_str.size(); ++i) {
+      const string& gpu_id_str = order_str[i];
+      int32 gpu_id;
+      if (!strings::safe_strto32(gpu_id_str, &gpu_id)) {
+        return errors::InvalidArgument(
+            "Could not parse entry in 'visible_device_list': '", gpu_id_str,
+            "'.  visible_device_list = ", visible_device_list);
+      }
+
+      if (gpu_id < 0 || gpu_id >= gpu_manager->VisibleDeviceCount()) {
+        return errors::InvalidArgument(
+            "'visible_device_list' listed an invalid GPU id '", gpu_id,
+            "' but visible device count is ",
+            gpu_manager->VisibleDeviceCount());
+      }
+
+      visible_gpu_order.push_back(gpu_id);
+    }
+  }
+
+  for (int i = 0; i < visible_gpu_order.size(); ++i) {
+    const int32 visible_gpu_id = visible_gpu_order[i];
+    auto exec_status = gpu_manager->ExecutorForDevice(visible_gpu_id);
     if (!exec_status.ok()) {
       continue;
     }
@@ -838,8 +873,9 @@ void BaseGPUDeviceFactory::GetValidDeviceIds(std::vector<int>* ids) {
     // Only GPUs with no less than the minimum supported compute capability is
     // accepted.
     if (device_capability < min_supported_capability) {
-      LOG(INFO) << "Ignoring gpu device "
-                << "(" << GetShortDeviceDescription(i, desc) << ") "
+      LOG(INFO) << "Ignoring physical gpu device "
+                << "(" << GetShortDeviceDescription(visible_gpu_id, desc)
+                << ") "
                 << "with Cuda compute capability " << device_capability
                 << ". The minimum required Cuda capability is "
                 << min_supported_capability << ".";
@@ -852,7 +888,8 @@ void BaseGPUDeviceFactory::GetValidDeviceIds(std::vector<int>* ids) {
     // variable is set, its value will be used to filter out GPUs.
     if (desc.core_count() < min_gpu_core_count) {
       LOG(INFO) << "Ignoring gpu device "
-                << "(" << GetShortDeviceDescription(i, desc) << ") "
+                << "(" << GetShortDeviceDescription(visible_gpu_id, desc)
+                << ") "
                 << "with Cuda multiprocessor count: " << desc.core_count()
                 << ". The minimum required count is " << min_gpu_core_count
                 << ". You can adjust this requirement with the env var "
@@ -861,11 +898,13 @@ void BaseGPUDeviceFactory::GetValidDeviceIds(std::vector<int>* ids) {
     }
 
     int new_id = ids->size();
-    ids->push_back(i);
+    ids->push_back(visible_gpu_id);
 
     LOG(INFO) << "Creating TensorFlow device (/gpu:" << new_id << ") -> "
-              << "(" << GetShortDeviceDescription(i, desc) << ")";
+              << "(" << GetShortDeviceDescription(visible_gpu_id, desc) << ")";
   }
+
+  return Status::OK();
 }
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.h b/tensorflow/core/common_runtime/gpu/gpu_device.h
index 03090aa537..abe7c0f687 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.h
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.h
@@ -123,7 +123,12 @@ class BaseGPUDeviceFactory : public DeviceFactory {
                                          Allocator* gpu_allocator,
                                          Allocator* cpu_allocator) = 0;
 
-  void GetValidDeviceIds(std::vector<int>* ids);
+  // Returns into 'ids' the list of valid GPU ids, in the order that
+  // they should map to logical gpu ids "/gpu:0", "/gpu:1", etc, based
+  // upon 'visible_device_list', a comma-separated list of 'visible
+  // gpu ids'.
+  Status GetValidDeviceIds(const string& visible_device_list,
+                           std::vector<int>* ids);
 };
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/protobuf/config.proto b/tensorflow/core/protobuf/config.proto
index cea68d43d4..f0ec90d781 100644
--- a/tensorflow/core/protobuf/config.proto
+++ b/tensorflow/core/protobuf/config.proto
@@ -35,6 +35,23 @@ message GPUOptions {
   // If true, the allocator does not pre-allocate the entire specified
   // GPU memory region, instead starting small and growing as needed.
   bool allow_growth = 4;
+
+  // A comma-separated list of GPU ids that determines the 'visible'
+  // to 'virtual' mapping of GPU devices.  For example, if TensorFlow
+  // can see 8 GPU devices in the process, and one wanted to map
+  // visible GPU devices 5 and 3 as "/gpu:0", and "/gpu:1", then one
+  // would specify this field as "5,3".  This field is similar in
+  // spirit to the CUDA_VISIBLE_DEVICES environment variable, except
+  // it applies to the visible GPU devices in the process.
+  //
+  // NOTE: The GPU driver provides the process with the visible GPUs
+  // in an order which is not guaranteed to have any correlation to
+  // the *physical* GPU id in the machine.  This field is used for
+  // remapping "visible" to "virtual", which means this operates only
+  // after the process starts.  Users are required to use vendor
+  // specific mechanisms (e.g., CUDA_VISIBLE_DEVICES) to control the
+  // physical to visible device mapping prior to invoking TensorFlow.
+  string visible_device_list = 5;
 };
 
 // Options passed to the graph optimizer
-- 
GitLab


From 0de021876cbfc65c3d55505f3f6e8ffd3aa80239 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 14:50:26 -0800
Subject: [PATCH 172/223] Remove some unnecessary work done during function
 construction (and fix a bad comment). Change: 131340944

---
 tensorflow/python/framework/function.py | 10 +++-------
 1 file changed, 3 insertions(+), 7 deletions(-)

diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py
index c7ccdf96c3..c31e5c9079 100644
--- a/tensorflow/python/framework/function.py
+++ b/tensorflow/python/framework/function.py
@@ -166,7 +166,6 @@ def _add_op_node(graph, op, func):
   func.node.extend([node])
 
 
-# pylint: disable=line-too-long
 def graph_to_function_def(graph, name, inputs, outputs):
   """Returns `graph` as a `FunctionDef` protocol buffer.
 
@@ -184,7 +183,7 @@ def graph_to_function_def(graph, name, inputs, outputs):
   different graph to make it available there.
 
   Args:
-    graph: GraphDef proto.
+    graph: Graph.
     name: string. The name to use for the function.
     inputs: List of tensors. Inputs to the function.
     outputs: List of tensors. Outputs of the function.
@@ -192,13 +191,10 @@ def graph_to_function_def(graph, name, inputs, outputs):
   Returns:
     A FunctionDef protocol buffer.
   """
-  # pylint: enable=line-too-long
   func = function_pb2.FunctionDef()
   func.signature.name = name
-  func.signature.input_arg.extend([_tensor_to_argdef(graph.get_tensor_by_name(
-      i.name)) for i in inputs])
-  func.signature.output_arg.extend([_tensor_to_argdef(graph.get_tensor_by_name(
-      o.name)) for o in outputs])
+  func.signature.input_arg.extend([_tensor_to_argdef(i) for i in inputs])
+  func.signature.output_arg.extend([_tensor_to_argdef(o) for o in outputs])
   func_arg_placeholders = set([i.name for i in inputs])
   g = ops.get_default_graph()
   for op in graph.get_operations():
-- 
GitLab


From 33847ebc8016b9d85bebba6ae8d3fe11d2a0a8a2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 15:04:49 -0800
Subject: [PATCH 173/223] Automated rollback of change 131315226 Change:
 131342536

---
 .../examples/learn/wide_n_deep_tutorial.py    |   3 +-
 .../examples/tutorials/input_fn/boston.py     |  70 ---
 .../tutorials/input_fn/boston_predict.csv     |   7 -
 .../tutorials/input_fn/boston_test.csv        | 101 -----
 .../tutorials/input_fn/boston_train.csv       | 401 ------------------
 5 files changed, 2 insertions(+), 580 deletions(-)
 delete mode 100644 tensorflow/examples/tutorials/input_fn/boston.py
 delete mode 100644 tensorflow/examples/tutorials/input_fn/boston_predict.csv
 delete mode 100644 tensorflow/examples/tutorials/input_fn/boston_test.csv
 delete mode 100644 tensorflow/examples/tutorials/input_fn/boston_train.csv

diff --git a/tensorflow/examples/learn/wide_n_deep_tutorial.py b/tensorflow/examples/learn/wide_n_deep_tutorial.py
index 5a23087b5a..cb51dc0287 100644
--- a/tensorflow/examples/learn/wide_n_deep_tutorial.py
+++ b/tensorflow/examples/learn/wide_n_deep_tutorial.py
@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
+
+# pylint: disable=g-bad-import-order
 """Example code for TensorFlow Wide & Deep Tutorial using TF.Learn API."""
 from __future__ import absolute_import
 from __future__ import division
@@ -19,7 +21,6 @@ from __future__ import print_function
 
 import tempfile
 import urllib
-
 import pandas as pd
 import tensorflow as tf
 
diff --git a/tensorflow/examples/tutorials/input_fn/boston.py b/tensorflow/examples/tutorials/input_fn/boston.py
deleted file mode 100644
index 9d18bfa9e0..0000000000
--- a/tensorflow/examples/tutorials/input_fn/boston.py
+++ /dev/null
@@ -1,70 +0,0 @@
-#  Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-"""DNNRegressor with custom input_fn for Housing dataset."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import pandas as pd
-import tensorflow as tf
-
-tf.logging.set_verbosity(tf.logging.INFO)
-
-COLUMNS = ["crim", "zn", "indus", "nox", "rm", "age",
-           "dis", "tax", "ptratio", "medv"]
-FEATURES = ["crim", "zn", "indus", "nox", "rm",
-            "age", "dis", "tax", "ptratio"]
-LABEL = "medv"
-
-
-def input_fn(data_set):
-  feature_cols = {k: tf.constant(data_set[k].values) for k in FEATURES}
-  labels = tf.constant(data_set[LABEL].values)
-  return feature_cols, labels
-
-
-def main(unused_argv):
-  # Load datasets
-  training_set = pd.read_csv("boston_train.csv", skipinitialspace=True,
-                             skiprows=1, names=COLUMNS)
-  test_set = pd.read_csv("boston_test.csv", skipinitialspace=True,
-                         skiprows=1, names=COLUMNS)
-
-  # Set of 6 examples for which to predict median house values
-  prediction_set = pd.read_csv("boston_predict.csv", skipinitialspace=True,
-                               skiprows=1, names=COLUMNS)
-
-  # Feature cols
-  feature_cols = [tf.contrib.layers.real_valued_column(k)
-                  for k in FEATURES]
-
-  # Build 2 layer fully connected DNN with 10, 10 units respectively.
-  regressor = tf.contrib.learn.DNNRegressor(
-      feature_columns=feature_cols, hidden_units=[10, 10])
-
-  # Fit
-  regressor.fit(input_fn=lambda: input_fn(training_set), steps=5000)
-
-  # Score accuracy
-  ev = regressor.evaluate(input_fn=lambda: input_fn(test_set), steps=1)
-  loss_score = ev["loss"]
-  print("Loss: {0:f}".format(loss_score))
-
-  # Print out predictions
-  y = regressor.predict(input_fn=lambda: input_fn(prediction_set))
-  print ("Predictions: {}".format(str(y)))
-
-if __name__ == "__main__":
-  tf.app.run()
diff --git a/tensorflow/examples/tutorials/input_fn/boston_predict.csv b/tensorflow/examples/tutorials/input_fn/boston_predict.csv
deleted file mode 100644
index 27b017155c..0000000000
--- a/tensorflow/examples/tutorials/input_fn/boston_predict.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-6,9,CRIM,ZN,INDUS,NOX,RM,AGE,DIS,TAX,PTRATIO
-0.03359,75.0,2.95,0.428,7.024,15.8,5.4011,252,18.3
-5.09017,0.0,18.1,0.713,6.297,91.8,2.3682,666,20.2
-0.1265,25.0,5.13,0.453,6.762,43.4,7.9809,284,19.7
-0.05515,33.0,2.18,0.472,7.236,41.1,4.022,222,18.4
-8.15174,0.0,18.1,0.7,5.39,98.9,1.7281,666,20.2
-0.24522,0.0,9.9,0.544,5.782,71.7,4.0317,304,18.4
diff --git a/tensorflow/examples/tutorials/input_fn/boston_test.csv b/tensorflow/examples/tutorials/input_fn/boston_test.csv
deleted file mode 100644
index 00cd0c6bb3..0000000000
--- a/tensorflow/examples/tutorials/input_fn/boston_test.csv
+++ /dev/null
@@ -1,101 +0,0 @@
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diff --git a/tensorflow/examples/tutorials/input_fn/boston_train.csv b/tensorflow/examples/tutorials/input_fn/boston_train.csv
deleted file mode 100644
index 5d30ebd3a6..0000000000
--- a/tensorflow/examples/tutorials/input_fn/boston_train.csv
+++ /dev/null
@@ -1,401 +0,0 @@
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-0.52058,0.0,6.2,0.507,6.631,76.5,4.148,307,17.4,25.1
-1.20742,0.0,19.58,0.605,5.875,94.6,2.4259,403,14.7,17.4
-0.17171,25.0,5.13,0.453,5.966,93.4,6.8185,284,19.7,16.0
-2.44953,0.0,19.58,0.605,6.402,95.2,2.2625,403,14.7,22.3
-0.04741,0.0,11.93,0.573,6.03,80.8,2.505,273,21.0,11.9
-0.04379,80.0,3.37,0.398,5.787,31.1,6.6115,337,16.1,19.4
-0.01432,100.0,1.32,0.411,6.816,40.5,8.3248,256,15.1,31.6
-0.03427,0.0,5.19,0.515,5.869,46.3,5.2311,224,20.2,19.5
-0.18159,0.0,7.38,0.493,6.376,54.3,4.5404,287,19.6,23.1
-5.58107,0.0,18.1,0.713,6.436,87.9,2.3158,666,20.2,14.3
-23.6482,0.0,18.1,0.671,6.38,96.2,1.3861,666,20.2,13.1
-2.14918,0.0,19.58,0.871,5.709,98.5,1.6232,403,14.7,19.4
-2.33099,0.0,19.58,0.871,5.186,93.8,1.5296,403,14.7,17.8
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-0.06466,70.0,2.24,0.4,6.345,20.1,7.8278,358,14.8,22.5
-0.01381,80.0,0.46,0.422,7.875,32.0,5.6484,255,14.4,50.0
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-0.35233,0.0,21.89,0.624,6.454,98.4,1.8498,437,21.2,17.1
-0.06724,0.0,3.24,0.46,6.333,17.2,5.2146,430,16.9,22.6
-0.04294,28.0,15.04,0.464,6.249,77.3,3.615,270,18.2,20.6
-4.55587,0.0,18.1,0.718,3.561,87.9,1.6132,666,20.2,27.5
-0.66351,20.0,3.97,0.647,7.333,100.0,1.8946,264,13.0,36.0
-7.67202,0.0,18.1,0.693,5.747,98.9,1.6334,666,20.2,8.5
-0.0136,75.0,4.0,0.41,5.888,47.6,7.3197,469,21.1,18.9
-0.17142,0.0,6.91,0.448,5.682,33.8,5.1004,233,17.9,19.3
-0.08221,22.0,5.86,0.431,6.957,6.8,8.9067,330,19.1,29.6
-0.31533,0.0,6.2,0.504,8.266,78.3,2.8944,307,17.4,44.8
-6.96215,0.0,18.1,0.7,5.713,97.0,1.9265,666,20.2,15.1
-20.7162,0.0,18.1,0.659,4.138,100.0,1.1781,666,20.2,11.9
-0.12083,0.0,2.89,0.445,8.069,76.0,3.4952,276,18.0,38.7
-0.01951,17.5,1.38,0.4161,7.104,59.5,9.2229,216,18.6,33.0
-0.09512,0.0,12.83,0.437,6.286,45.0,4.5026,398,18.7,21.4
-6.53876,0.0,18.1,0.631,7.016,97.5,1.2024,666,20.2,50.0
-0.02875,28.0,15.04,0.464,6.211,28.9,3.6659,270,18.2,25.0
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-0.02763,75.0,2.95,0.428,6.595,21.8,5.4011,252,18.3,30.8
-0.10153,0.0,12.83,0.437,6.279,74.5,4.0522,398,18.7,20.0
-0.06911,45.0,3.44,0.437,6.739,30.8,6.4798,398,15.2,30.5
-0.33045,0.0,6.2,0.507,6.086,61.5,3.6519,307,17.4,24.0
-2.77974,0.0,19.58,0.871,4.903,97.8,1.3459,403,14.7,11.8
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-0.10659,80.0,1.91,0.413,5.936,19.5,10.5857,334,22.0,20.6
-8.49213,0.0,18.1,0.584,6.348,86.1,2.0527,666,20.2,14.5
-0.25356,0.0,9.9,0.544,5.705,77.7,3.945,304,18.4,16.2
-0.59005,0.0,21.89,0.624,6.372,97.9,2.3274,437,21.2,23.0
-0.03502,80.0,4.95,0.411,6.861,27.9,5.1167,245,19.2,28.5
-13.9134,0.0,18.1,0.713,6.208,95.0,2.2222,666,20.2,11.7
-3.67367,0.0,18.1,0.583,6.312,51.9,3.9917,666,20.2,21.2
-67.9208,0.0,18.1,0.693,5.683,100.0,1.4254,666,20.2,5.0
-3.69311,0.0,18.1,0.713,6.376,88.4,2.5671,666,20.2,17.7
-0.21038,20.0,3.33,0.4429,6.812,32.2,4.1007,216,14.9,35.1
-0.04684,0.0,3.41,0.489,6.417,66.1,3.0923,270,17.8,22.6
-0.28392,0.0,7.38,0.493,5.708,74.3,4.7211,287,19.6,18.5
-0.19133,22.0,5.86,0.431,5.605,70.2,7.9549,330,19.1,18.5
-0.06162,0.0,4.39,0.442,5.898,52.3,8.0136,352,18.8,17.2
-13.0751,0.0,18.1,0.58,5.713,56.7,2.8237,666,20.2,20.1
-0.16439,22.0,5.86,0.431,6.433,49.1,7.8265,330,19.1,24.5
-0.13058,0.0,10.01,0.547,5.872,73.1,2.4775,432,17.8,20.4
-0.04666,80.0,1.52,0.404,7.107,36.6,7.309,329,12.6,30.3
-5.44114,0.0,18.1,0.713,6.655,98.2,2.3552,666,20.2,15.2
-0.21161,0.0,8.56,0.52,6.137,87.4,2.7147,384,20.9,19.3
-0.38735,0.0,25.65,0.581,5.613,95.6,1.7572,188,19.1,15.7
-3.69695,0.0,18.1,0.718,4.963,91.4,1.7523,666,20.2,21.9
-0.0837,45.0,3.44,0.437,7.185,38.9,4.5667,398,15.2,34.9
-0.02055,85.0,0.74,0.41,6.383,35.7,9.1876,313,17.3,24.7
-0.0459,52.5,5.32,0.405,6.315,45.6,7.3172,293,16.6,22.3
-0.06127,40.0,6.41,0.447,6.826,27.6,4.8628,254,17.6,33.1
-0.19186,0.0,7.38,0.493,6.431,14.7,5.4159,287,19.6,24.6
-0.1712,0.0,8.56,0.52,5.836,91.9,2.211,384,20.9,19.5
-1.6566,0.0,19.58,0.871,6.122,97.3,1.618,403,14.7,21.5
-0.00632,18.0,2.31,0.538,6.575,65.2,4.09,296,15.3,24.0
-0.88125,0.0,21.89,0.624,5.637,94.7,1.9799,437,21.2,14.3
-0.98843,0.0,8.14,0.538,5.813,100.0,4.0952,307,21.0,14.5
-0.01538,90.0,3.75,0.394,7.454,34.2,6.3361,244,15.9,44.0
-0.05561,70.0,2.24,0.4,7.041,10.0,7.8278,358,14.8,29.0
-0.3692,0.0,9.9,0.544,6.567,87.3,3.6023,304,18.4,23.8
-0.0315,95.0,1.47,0.403,6.975,15.3,7.6534,402,17.0,34.9
-0.05789,12.5,6.07,0.409,5.878,21.4,6.498,345,18.9,22.0
-0.01778,95.0,1.47,0.403,7.135,13.9,7.6534,402,17.0,32.9
-0.57529,0.0,6.2,0.507,8.337,73.3,3.8384,307,17.4,41.7
-20.0849,0.0,18.1,0.7,4.368,91.2,1.4395,666,20.2,8.8
-18.811,0.0,18.1,0.597,4.628,100.0,1.5539,666,20.2,17.9
-14.3337,0.0,18.1,0.614,6.229,88.0,1.9512,666,20.2,21.4
-0.07896,0.0,12.83,0.437,6.273,6.0,4.2515,398,18.7,24.1
-0.03578,20.0,3.33,0.4429,7.82,64.5,4.6947,216,14.9,45.4
-0.10612,30.0,4.93,0.428,6.095,65.1,6.3361,300,16.6,20.1
-0.97617,0.0,21.89,0.624,5.757,98.4,2.346,437,21.2,15.6
-18.4982,0.0,18.1,0.668,4.138,100.0,1.137,666,20.2,13.8
-4.0974,0.0,19.58,0.871,5.468,100.0,1.4118,403,14.7,15.6
-0.15445,25.0,5.13,0.453,6.145,29.2,7.8148,284,19.7,23.3
-0.23912,0.0,9.69,0.585,6.019,65.3,2.4091,391,19.2,21.2
-0.0456,0.0,13.89,0.55,5.888,56.0,3.1121,276,16.4,23.3
-1.38799,0.0,8.14,0.538,5.95,82.0,3.99,307,21.0,13.2
-7.36711,0.0,18.1,0.679,6.193,78.1,1.9356,666,20.2,11.0
-0.1415,0.0,6.91,0.448,6.169,6.6,5.7209,233,17.9,25.3
-- 
GitLab


From c630d53625c415ca7d792722811393b2a4e80336 Mon Sep 17 00:00:00 2001
From: Nathan Silberman <nsilberman@google.com>
Date: Thu, 25 Aug 2016 15:14:36 -0800
Subject: [PATCH 174/223] Changing ordering of evaluate_once arguments. Change:
 131343533

---
 tensorflow/contrib/slim/python/slim/evaluation.py      | 6 +++---
 tensorflow/contrib/slim/python/slim/evaluation_test.py | 3 ++-
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/slim/python/slim/evaluation.py b/tensorflow/contrib/slim/python/slim/evaluation.py
index c69365a150..6f2fae800c 100644
--- a/tensorflow/contrib/slim/python/slim/evaluation.py
+++ b/tensorflow/contrib/slim/python/slim/evaluation.py
@@ -246,9 +246,9 @@ def evaluation(sess,
 _USE_DEFAULT = 0
 
 
-def evaluate_once(checkpoint_path,
+def evaluate_once(master,
+                  checkpoint_path,
                   logdir,
-                  master='',
                   num_evals=1,
                   eval_op=None,
                   eval_op_feed_dict=None,
@@ -261,9 +261,9 @@ def evaluate_once(checkpoint_path,
   """Evaluates the model at the given checkpoint path.
 
   Args:
+    master: The BNS address of the TensorFlow master.
     checkpoint_path: The path to a checkpoint to use for evaluation.
     logdir: The directory where the TensorFlow summaries are written to.
-    master: The BNS address of the TensorFlow master.
     num_evals: The number of times to run `eval_op`.
     eval_op: A operation run `num_evals` times.
     eval_op_feed_dict: The feed dictionary to use when executing the `eval_op`.
diff --git a/tensorflow/contrib/slim/python/slim/evaluation_test.py b/tensorflow/contrib/slim/python/slim/evaluation_test.py
index 7cdd79a46b..5c8e7bf663 100644
--- a/tensorflow/contrib/slim/python/slim/evaluation_test.py
+++ b/tensorflow/contrib/slim/python/slim/evaluation_test.py
@@ -271,7 +271,7 @@ class SingleEvaluationTest(tf.test.TestCase):
                                    'this_file_doesnt_exist')
     log_dir = os.path.join(self.get_temp_dir(), 'error_raised')
     with self.assertRaises(ValueError):
-      slim.evaluation.evaluate_once(checkpoint_path, log_dir)
+      slim.evaluation.evaluate_once('', checkpoint_path, log_dir)
 
   def testRestoredModelPerformance(self):
     checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt')
@@ -291,6 +291,7 @@ class SingleEvaluationTest(tf.test.TestCase):
 
     # Run the evaluation and verify the results:
     accuracy_value = slim.evaluation.evaluate_once(
+        '',
         checkpoint_path,
         log_dir,
         eval_op=update_op,
-- 
GitLab


From f48f9a5be07ccd55b2589f9c3b870ad116f573d2 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 15:36:48 -0800
Subject: [PATCH 175/223] RunConfig now loads defaults from the TF_CONFIG
 environment variable if present. Change: 131345858

---
 .../python/learn/estimators/run_config.py     | 133 ++++++++++++++++--
 .../python/learn/tests/experiment_test.py     |  17 +--
 .../python/learn/tests/run_config_test.py     | 125 +++++++++++++++-
 3 files changed, 244 insertions(+), 31 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/estimators/run_config.py b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
index e5317b6a08..0e98596995 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/run_config.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
@@ -19,8 +19,12 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import json
+import os
+
 from tensorflow.python import ConfigProto
 from tensorflow.python import GPUOptions
+from tensorflow.python.training.server_lib import ClusterSpec
 
 
 class RunConfig(object):
@@ -33,9 +37,9 @@ class RunConfig(object):
 
   # TODO(wicke): Move options out once functionality is covered by monitors
   def __init__(self,
-               master='',
-               task=0,
-               num_ps_replicas=0,
+               master=None,
+               task=None,
+               num_ps_replicas=None,
                num_cores=4,
                log_device_placement=False,
                gpu_memory_fraction=1,
@@ -48,8 +52,46 @@ class RunConfig(object):
                job_name=None):
     """Constructor.
 
+    If set to None, `master`, `task`, `num_ps_replicas`, `cluster_spec`, and
+    `job_name` are set based on the TF_CONFIG environment variable, if the
+    pertinent information is present; otherwise, the defaults listed in the
+    Args section apply.
+
+    The TF_CONFIG environment variable is a JSON object with two relevant
+    attributes: `task` and `cluster_spec`. `cluster_spec` is a JSON serialized
+    version of the Python dict described in server_lib.py. `task` has two
+    attributes: `type` and `index`, where `type` can be any of the task types
+    in the cluster_spec. When TF_CONFIG contains said information, the
+    following properties are set on this class:
+
+      * `job_name` is set to [`task`][`type`]
+      * `task` is set to [`task`][`index`]
+      * `cluster_spec` is parsed from [`cluster`]
+      * 'master' is determined by looking up `job_name` and `task` in the
+        cluster_spec.
+      * `num_ps_replicas` is set by counting the number of nodes listed
+        in the `ps` job of `cluster_spec`.
+
+    Note that any of these values can be overridden by explicitly passing
+    their value to the constructor.
+
+    Example:
+    ```
+      cluster = {'ps': ['host1:2222', 'host2:2222'],
+                 'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
+      os.environ['TF_CONFIG'] = json.dumps({
+          {'cluster': cluster,
+           'task': {'type': 'worker', 'index': 1}}})
+      config = RunConfig()
+      assert config.master == 'host4:2222'
+      assert config.task == 1
+      assert config.num_ps_replicas == 2
+      assert config.cluster_spec == server_lib.ClusterSpec(cluster)
+      assert config.job_name == 'worker'
+    ```
+
     Args:
-      master: TensorFlow master. Empty string (the default) for local.
+      master: TensorFlow master. Defaults to empty string for local.
       task: Task id of the replica running the training (default: 0).
       num_ps_replicas: Number of parameter server tasks to use (default: 0).
       num_cores: Number of cores to be used (default: 4).
@@ -71,19 +113,43 @@ class RunConfig(object):
         to be saved. The default value of 10,000 hours effectively disables
         the feature.
       job_name: the type of task, e.g., 'ps', 'worker', etc. Must exist in
-        cluster_spec.jobs.
+        `cluster_spec.jobs`.
+
+    Raises:
+      ValueError: if num_ps_replicas and cluster_spec are set (cluster_spec
+        may fome from the TF_CONFIG environment variable).
     """
-    self.master = master
-    self.task = task
-    self._job_name = job_name
-    self.num_ps_replicas = num_ps_replicas
+    # If not explicitly specified in the constructor and the TF_CONFIG
+    # environment variable is present, load cluster_spec from TF_CONFIG.
+    config = json.loads(os.environ.get('TF_CONFIG') or '{}')
+    if not cluster_spec and 'cluster' in config:
+      cluster_spec = ClusterSpec(config['cluster'])
+    self.cluster_spec = cluster_spec
+
+    # Set job_name and task. If explicitly specified, use those values,
+    # otherwise, if the TF_CONFIG environment variable is present, use that.
+    # Otherwise, use the respective default (None / 0).
+    task_env = config.get('task', {})
+    self._job_name = job_name or task_env.get('type') or None
+    self.task = task or task_env.get('index') or 0
+
+    self.master = (master or _get_master(self.cluster_spec, self.job_name,
+                                         self.task) or '')
+
+    if num_ps_replicas is not None and self.cluster_spec:
+      raise ValueError('Cannot specify both num_ps_replicas and cluster_spec. '
+                       'Note: cluster_spec may have been set in the TF_CONFIG '
+                       'environment variable.')
+    self.num_ps_replicas = num_ps_replicas or _count_ps(self.cluster_spec) or 0
+
     gpu_options = GPUOptions(
         per_process_gpu_memory_fraction=gpu_memory_fraction)
-    self.tf_config = ConfigProto(log_device_placement=log_device_placement,
-                                 inter_op_parallelism_threads=num_cores,
-                                 intra_op_parallelism_threads=num_cores,
-                                 gpu_options=gpu_options)
-    self.cluster_spec = cluster_spec
+    self.tf_config = ConfigProto(
+        log_device_placement=log_device_placement,
+        inter_op_parallelism_threads=num_cores,
+        intra_op_parallelism_threads=num_cores,
+        gpu_options=gpu_options)
+
     self.tf_random_seed = tf_random_seed
     self.save_summary_steps = save_summary_steps
     self.save_checkpoints_secs = save_checkpoints_secs
@@ -93,3 +159,42 @@ class RunConfig(object):
   @property
   def job_name(self):
     return self._job_name
+
+
+def _count_ps(cluster_spec):
+  """Counts the number of parameter servers in cluster_spec."""
+  return len(cluster_spec.as_dict().get('ps', [])) if cluster_spec else 0
+
+
+def _get_master(cluster_spec, job_name, task_index):
+  """Returns the appropriate string for the TensorFlow master."""
+  if not cluster_spec:
+    return ''
+
+  # If there is only one node in the cluster, do things locally.
+  jobs = cluster_spec.jobs
+  if len(jobs) == 1 and len(cluster_spec.job_tasks(jobs[0])) == 1:
+    return ''
+
+  # Lookup the master in cluster_spec using job_name and task_index,
+  # if possible.
+  if job_name:
+    if job_name not in jobs:
+      raise ValueError(
+          '%s is not a valid task in the cluster_spec:\n'
+          '%s\n\n'
+          'Note that these values may be coming from the TF_CONFIG environment '
+          'variable.' % (job_name, cluster_spec))
+    addresses = cluster_spec.job_tasks(job_name)
+    if task_index >= len(addresses) or task_index < 0:
+      raise ValueError(
+          '%d is not a valid task index for task type %s in the '
+          'cluster_spec:\n'
+          '%s\n\n'
+          'Note that these value may be coming from the TF_CONFIG environment '
+          'variable.' % (task_index, job_name, cluster_spec))
+    return addresses[task_index]
+
+  # For backwards compatibility, we return empty string if job_name was
+  # not set (job_name did not previously exist).
+  return ''
diff --git a/tensorflow/contrib/learn/python/learn/tests/experiment_test.py b/tensorflow/contrib/learn/python/learn/tests/experiment_test.py
index 19cca2ac53..56401558d2 100644
--- a/tensorflow/contrib/learn/python/learn/tests/experiment_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/experiment_test.py
@@ -142,8 +142,7 @@ class ExperimentTest(tf.test.TestCase):
     # The server should not have started because there was no ClusterSpec.
     self.assertFalse(mock_server.called)
 
-  def test_train_raises_if_not_enough_info_to_start_server(self):
-    # Job name is missing.
+  def test_train_raises_if_job_name_is_missing(self):
     no_job_name = tf.contrib.learn.RunConfig(
         cluster_spec=tf.train.ClusterSpec(
             {'ps': ['host1:2222', 'host2:2222'],
@@ -157,20 +156,6 @@ class ExperimentTest(tf.test.TestCase):
                                        eval_input_fn='eval_input')
       ex.train()
 
-    # Task index is missing.
-    no_task = tf.contrib.learn.RunConfig(
-        cluster_spec=tf.train.ClusterSpec(
-            {'ps': ['host1:2222', 'host2:2222'],
-             'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
-        ),
-        job_name='worker',
-    )
-    with self.assertRaises(ValueError):
-      ex = tf.contrib.learn.Experiment(TestEstimator(no_task),
-                                       train_input_fn='train_input',
-                                       eval_input_fn='eval_input')
-      ex.train()
-
   def test_evaluate(self):
     est = TestEstimator()
     ex = tf.contrib.learn.Experiment(est,
diff --git a/tensorflow/contrib/learn/python/learn/tests/run_config_test.py b/tensorflow/contrib/learn/python/learn/tests/run_config_test.py
index 5086f00caf..edc7400bee 100644
--- a/tensorflow/contrib/learn/python/learn/tests/run_config_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/run_config_test.py
@@ -18,13 +18,17 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import json
+
 import tensorflow as tf
 from tensorflow.contrib.learn.python.learn import run_config
 
+patch = tf.test.mock.patch
+
 
 class RunConfigTest(tf.test.TestCase):
 
-  def test_defaults(self):
+  def test_defaults_with_no_tf_config(self):
     config = run_config.RunConfig()
     self.assertEquals(config.master, "")
     self.assertEquals(config.task, 0)
@@ -32,6 +36,20 @@ class RunConfigTest(tf.test.TestCase):
     self.assertEquals(config.cluster_spec, None)
     self.assertEquals(config.job_name, None)
 
+  def test_values_from_tf_config(self):
+    tf_config = {"cluster": {"ps": ["host1:1", "host2:2"],
+                             "worker": ["host3:3", "host4:4", "host5:5"]},
+                 "task": {"type": "worker",
+                          "index": 1}}
+    with patch.dict("os.environ", {"TF_CONFIG": json.dumps(tf_config)}):
+      config = run_config.RunConfig()
+
+    self.assertEquals(config.master, "host4:4")
+    self.assertEquals(config.task, 1)
+    self.assertEquals(config.num_ps_replicas, 2)
+    self.assertEquals(config.cluster_spec.as_dict(), tf_config["cluster"])
+    self.assertEquals(config.job_name, "worker")
+
   def test_explicitly_specified_values(self):
     cluster_spec = tf.train.ClusterSpec({
         "ps": ["localhost:9990"],
@@ -45,9 +63,114 @@ class RunConfigTest(tf.test.TestCase):
 
     self.assertEquals(config.master, "localhost:0")
     self.assertEquals(config.task, 2)
+    self.assertEquals(config.num_ps_replicas, 1)
     self.assertEquals(config.cluster_spec, cluster_spec)
     self.assertEquals(config.job_name, "my_job_name")
 
+  def test_tf_config_with_overrides(self):
+    # Purpose: to test the case where TF_CONFIG is set, but then
+    # values are overridden by manually passing them to the constructor.
+
+    # Setup the TF_CONFIG environment variable
+    tf_config = {"cluster": {"ps": ["host1:1", "host2:2"],
+                             "worker": ["host3:3", "host4:4", "host5:5"]},
+                 "task": {"type": "worker",
+                          "index": 1}}
+    with patch.dict("os.environ", {"TF_CONFIG": json.dumps(tf_config)}):
+      # Run, but override all of the values that would otherwise have been
+      # set by TF_CONFIG.
+      cluster_spec_override = tf.train.ClusterSpec({
+          "ps": ["my_host1:314"],
+          "my_job_name": ["my_host2:314", "my_host4:314", "my_host5:314"]
+      })
+      config = run_config.RunConfig(
+          master="my_master",
+          task=2,
+          job_name="my_job_name",
+          cluster_spec=cluster_spec_override,)
+
+    # To protect against changes to the test itself (either
+    # the TF_CONFIG variable or the manual overrides), we will assert
+    # that the overrides are in fact different than TF_CONFIG.
+    self.assertNotEquals(tf_config["cluster"], cluster_spec_override)
+    self.assertNotIn("my_job_name", tf_config["cluster"])
+
+    # Now we assert that the correct values were indeed returned.
+    self.assertEquals(config.master, "my_master")
+    self.assertEquals(config.task, 2)
+    self.assertEquals(config.num_ps_replicas, 1)
+    self.assertEquals(config.cluster_spec, cluster_spec_override)
+    self.assertEquals(config.job_name, "my_job_name")
+
+  def test_num_ps_replicas_and_cluster_spec_are_mutually_exclusive(self):
+    cluster_spec = tf.train.ClusterSpec(
+        {"ps": ["host1:1", "host2:2"],
+         "worker": ["host3:3", "host4:4", "host5:5"]})
+    expected_msg_regexp = "Cannot specify both num_ps_replicas and cluster_spec"
+    with self.assertRaisesRegexp(ValueError, expected_msg_regexp):
+      run_config.RunConfig(
+          num_ps_replicas=2,
+          cluster_spec=cluster_spec,)
+
+  def test_num_ps_replicas_from_tf_config(self):
+    tf_config = {"cluster": {"ps": ["host1:1", "host2:2"],
+                             "worker": ["host3:3", "host4:4", "host5:5"]},
+                 "task": {"type": "worker",
+                          "index": 1}}
+    with patch.dict("os.environ", {"TF_CONFIG": json.dumps(tf_config)}):
+      expected_msg_regexp = ("Cannot specify both num_ps_replicas and "
+                             "cluster_spec.*cluster_spec may have been set in "
+                             "the TF_CONFIG environment variable")
+      with self.assertRaisesRegexp(ValueError, expected_msg_regexp):
+        run_config.RunConfig(num_ps_replicas=2)
+
+  def test_no_cluster_spec_results_in_empty_master(self):
+    config = run_config.RunConfig()
+    self.assertEquals(config.master, "")
+
+  def test_single_node_in_cluster_spec_produces_empty_master(self):
+    cluster_spec = tf.train.ClusterSpec({"worker": ["host1:1"]})
+    config = run_config.RunConfig(cluster_spec=cluster_spec)
+    self.assertEquals(config.master, "")
+
+  def test_no_job_name_produces_empty_master(self):
+    cluster_spec = tf.train.ClusterSpec(
+        {"ps": ["host1:1", "host2:2"],
+         "worker": ["host3:3", "host4:4", "host5:5"]})
+    # NB: omitted job_name; better to omit than explictly set to None
+    # as this better mimics client behavior.
+    config = run_config.RunConfig(cluster_spec=cluster_spec)
+    self.assertEquals(config.master, "")
+
+  def test_invalid_job_name_raises(self):
+    cluster_spec = tf.train.ClusterSpec(
+        {"ps": ["host1:1", "host2:2"],
+         "worker": ["host3:3", "host4:4", "host5:5"]})
+    expected_msg_regexp = "not_in_cluster_spec is not a valid task"
+    with self.assertRaisesRegexp(ValueError, expected_msg_regexp):
+      run_config.RunConfig(
+          cluster_spec=cluster_spec, job_name="not_in_cluster_spec")
+
+  def test_illegal_task_index_raises(self):
+    cluster_spec = tf.train.ClusterSpec(
+        {"ps": ["host1:1", "host2:2"],
+         "worker": ["host3:3", "host4:4", "host5:5"]})
+    expected_msg_regexp = "3 is not a valid task index"
+    with self.assertRaisesRegexp(ValueError, expected_msg_regexp):
+      run_config.RunConfig(
+          cluster_spec=cluster_spec, job_name="worker", task=3)
+
+  def test_empty_cluster_spec(self):
+    config = run_config.RunConfig(cluster_spec=tf.train.ClusterSpec({}))
+    self.assertEquals(config.cluster_spec.as_dict(), {})
+
+  def test_num_ps_replicas_can_be_set_if_cluster_spec_is_empty(self):
+    config = run_config.RunConfig(
+        num_ps_replicas=2,
+        cluster_spec=tf.train.ClusterSpec({}))
+    # Basically, just make sure no exception is being raised.
+    self.assertEquals(config.num_ps_replicas, 2)
+
 
 if __name__ == "__main__":
   tf.test.main()
-- 
GitLab


From 261cf74d4fd724f34dc9ca549a4a523366b284e4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 15:49:31 -0800
Subject: [PATCH 176/223] Update generated Python Op docs. Change: 131347133

---
 .../g3doc/api_docs/python/contrib.learn.md    | 50 +++++++++++++++++--
 .../shard4/tf.contrib.learn.RunConfig.md      | 50 +++++++++++++++++--
 2 files changed, 94 insertions(+), 6 deletions(-)

diff --git a/tensorflow/g3doc/api_docs/python/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md
index 10df20c4e9..26758828ef 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.learn.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md
@@ -3573,14 +3573,52 @@ If you're a Google-internal user using command line flags with learn_runner.py
 probably want to use learn_runner.EstimatorConfig instead.
 - - -
 
-#### `tf.contrib.learn.RunConfig.__init__(master='', task=0, num_ps_replicas=0, num_cores=4, log_device_placement=False, gpu_memory_fraction=1, cluster_spec=None, tf_random_seed=None, save_summary_steps=100, save_checkpoints_secs=60, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000, job_name=None)` {#RunConfig.__init__}
+#### `tf.contrib.learn.RunConfig.__init__(master=None, task=None, num_ps_replicas=None, num_cores=4, log_device_placement=False, gpu_memory_fraction=1, cluster_spec=None, tf_random_seed=None, save_summary_steps=100, save_checkpoints_secs=60, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000, job_name=None)` {#RunConfig.__init__}
 
 Constructor.
 
+If set to None, `master`, `task`, `num_ps_replicas`, `cluster_spec`, and
+`job_name` are set based on the TF_CONFIG environment variable, if the
+pertinent information is present; otherwise, the defaults listed in the
+Args section apply.
+
+The TF_CONFIG environment variable is a JSON object with two relevant
+attributes: `task` and `cluster_spec`. `cluster_spec` is a JSON serialized
+version of the Python dict described in server_lib.py. `task` has two
+attributes: `type` and `index`, where `type` can be any of the task types
+in the cluster_spec. When TF_CONFIG contains said information, the
+following properties are set on this class:
+
+  * `job_name` is set to [`task`][`type`]
+  * `task` is set to [`task`][`index`]
+  * `cluster_spec` is parsed from [`cluster`]
+  * 'master' is determined by looking up `job_name` and `task` in the
+    cluster_spec.
+  * `num_ps_replicas` is set by counting the number of nodes listed
+    in the `ps` job of `cluster_spec`.
+
+Note that any of these values can be overridden by explicitly passing
+their value to the constructor.
+
+Example:
+```
+  cluster = {'ps': ['host1:2222', 'host2:2222'],
+             'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
+  os.environ['TF_CONFIG'] = json.dumps({
+      {'cluster': cluster,
+       'task': {'type': 'worker', 'index': 1}}})
+  config = RunConfig()
+  assert config.master == 'host4:2222'
+  assert config.task == 1
+  assert config.num_ps_replicas == 2
+  assert config.cluster_spec == server_lib.ClusterSpec(cluster)
+  assert config.job_name == 'worker'
+```
+
 ##### Args:
 
 
-*  <b>`master`</b>: TensorFlow master. Empty string (the default) for local.
+*  <b>`master`</b>: TensorFlow master. Defaults to empty string for local.
 *  <b>`task`</b>: Task id of the replica running the training (default: 0).
 *  <b>`num_ps_replicas`</b>: Number of parameter server tasks to use (default: 0).
 *  <b>`num_cores`</b>: Number of cores to be used (default: 4).
@@ -3602,7 +3640,13 @@ Constructor.
     to be saved. The default value of 10,000 hours effectively disables
     the feature.
 *  <b>`job_name`</b>: the type of task, e.g., 'ps', 'worker', etc. Must exist in
-    cluster_spec.jobs.
+    `cluster_spec.jobs`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if num_ps_replicas and cluster_spec are set (cluster_spec
+    may fome from the TF_CONFIG environment variable).
 
 
 - - -
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.RunConfig.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.RunConfig.md
index 61169a8511..4f02f8c723 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.RunConfig.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.RunConfig.md
@@ -5,14 +5,52 @@ If you're a Google-internal user using command line flags with learn_runner.py
 probably want to use learn_runner.EstimatorConfig instead.
 - - -
 
-#### `tf.contrib.learn.RunConfig.__init__(master='', task=0, num_ps_replicas=0, num_cores=4, log_device_placement=False, gpu_memory_fraction=1, cluster_spec=None, tf_random_seed=None, save_summary_steps=100, save_checkpoints_secs=60, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000, job_name=None)` {#RunConfig.__init__}
+#### `tf.contrib.learn.RunConfig.__init__(master=None, task=None, num_ps_replicas=None, num_cores=4, log_device_placement=False, gpu_memory_fraction=1, cluster_spec=None, tf_random_seed=None, save_summary_steps=100, save_checkpoints_secs=60, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000, job_name=None)` {#RunConfig.__init__}
 
 Constructor.
 
+If set to None, `master`, `task`, `num_ps_replicas`, `cluster_spec`, and
+`job_name` are set based on the TF_CONFIG environment variable, if the
+pertinent information is present; otherwise, the defaults listed in the
+Args section apply.
+
+The TF_CONFIG environment variable is a JSON object with two relevant
+attributes: `task` and `cluster_spec`. `cluster_spec` is a JSON serialized
+version of the Python dict described in server_lib.py. `task` has two
+attributes: `type` and `index`, where `type` can be any of the task types
+in the cluster_spec. When TF_CONFIG contains said information, the
+following properties are set on this class:
+
+  * `job_name` is set to [`task`][`type`]
+  * `task` is set to [`task`][`index`]
+  * `cluster_spec` is parsed from [`cluster`]
+  * 'master' is determined by looking up `job_name` and `task` in the
+    cluster_spec.
+  * `num_ps_replicas` is set by counting the number of nodes listed
+    in the `ps` job of `cluster_spec`.
+
+Note that any of these values can be overridden by explicitly passing
+their value to the constructor.
+
+Example:
+```
+  cluster = {'ps': ['host1:2222', 'host2:2222'],
+             'worker': ['host3:2222', 'host4:2222', 'host5:2222']}
+  os.environ['TF_CONFIG'] = json.dumps({
+      {'cluster': cluster,
+       'task': {'type': 'worker', 'index': 1}}})
+  config = RunConfig()
+  assert config.master == 'host4:2222'
+  assert config.task == 1
+  assert config.num_ps_replicas == 2
+  assert config.cluster_spec == server_lib.ClusterSpec(cluster)
+  assert config.job_name == 'worker'
+```
+
 ##### Args:
 
 
-*  <b>`master`</b>: TensorFlow master. Empty string (the default) for local.
+*  <b>`master`</b>: TensorFlow master. Defaults to empty string for local.
 *  <b>`task`</b>: Task id of the replica running the training (default: 0).
 *  <b>`num_ps_replicas`</b>: Number of parameter server tasks to use (default: 0).
 *  <b>`num_cores`</b>: Number of cores to be used (default: 4).
@@ -34,7 +72,13 @@ Constructor.
     to be saved. The default value of 10,000 hours effectively disables
     the feature.
 *  <b>`job_name`</b>: the type of task, e.g., 'ps', 'worker', etc. Must exist in
-    cluster_spec.jobs.
+    `cluster_spec.jobs`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if num_ps_replicas and cluster_spec are set (cluster_spec
+    may fome from the TF_CONFIG environment variable).
 
 
 - - -
-- 
GitLab


From e23c867bbc6d52e5231edadf7ccfbb292a2cd2b0 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 15:58:26 -0800
Subject: [PATCH 177/223] Added benchmark tests for GRUBlockCell vs GRUCell.
 Change: 131348187

---
 .../rnn/python/kernel_tests/gru_ops_test.py   | 260 ++++++++++++++++++
 1 file changed, 260 insertions(+)

diff --git a/tensorflow/contrib/rnn/python/kernel_tests/gru_ops_test.py b/tensorflow/contrib/rnn/python/kernel_tests/gru_ops_test.py
index 8a3fd75eab..f2ed962ab9 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/gru_ops_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/gru_ops_test.py
@@ -17,9 +17,11 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import time
 import numpy as np
 import tensorflow as tf
 from tensorflow.contrib.rnn.python.ops import gru_ops
+from tensorflow.python.ops import variable_scope as vs
 
 
 class GRUBlockCellTest(tf.test.TestCase):
@@ -314,6 +316,264 @@ class GRUBlockCellTest(tf.test.TestCase):
 class GRUBlockCellGpuTest(GRUBlockCellTest):
   _use_gpu = True
 
+#### Benchmarking GRUBlockCell vs GRUCell.
+
+
+def time_taken_by_op(op, sess, num_runs=50):
+  """Time taken by the Op."""
+  for _ in range(2):
+    sess.run([op])
+
+  start_time = time.time()
+  for _ in range(num_runs):
+    sess.run([op])
+
+  end_time = time.time()
+  time_taken = end_time - start_time
+  return time_taken
+
+
+def training_gru_block_vs_gru_cell(batch_size,
+                                   cell_size,
+                                   input_size,
+                                   time_steps,
+                                   use_gpu=False,
+                                   iters=30):
+  """Benchmark training speed between GRUBlockCell vs GRUCell."""
+  tf.reset_default_graph()
+  with tf.Session(graph=tf.Graph()) as sess:
+    # Specify the device which is been used.
+    with tf.device("/cpu:0" if not use_gpu else "/gpu:0"):
+
+      # Random initializers.
+      seed = 1994
+      initializer = tf.random_uniform_initializer(-1, 1, seed=seed)
+      np.random.seed(seed)
+
+      # Inputs
+      concat_x = vs.get_variable("concat_x",
+                                 [time_steps, batch_size, input_size])
+      h = vs.get_variable("h", [batch_size, cell_size])
+      y = vs.get_variable("y", [time_steps, batch_size, cell_size])
+
+      # Output from the basic GRU cell implementation.
+      with tf.variable_scope("basic", initializer=initializer):
+        cell = tf.nn.rnn_cell.GRUCell(cell_size)
+
+        outputs_dynamic, _ = tf.nn.dynamic_rnn(
+            cell,
+            inputs=concat_x,
+            initial_state=h,
+            time_major=True,
+            dtype=tf.float32)
+        sess.run([tf.initialize_all_variables()])
+        cost = tf.reduce_mean(tf.square(outputs_dynamic - y))
+        learning_rate = 0.01
+        optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(
+            cost)
+
+        # time for a training step.
+        basic_time_training = time_taken_by_op(optimizer, sess, iters)
+
+      # Output from the basic GRU cell implementation.
+      with tf.variable_scope("block", initializer=initializer):
+        cell = gru_ops.GRUBlockCell(cell_size)
+
+        outputs_dynamic, _ = tf.nn.dynamic_rnn(
+            cell,
+            inputs=concat_x,
+            initial_state=h,
+            time_major=True,
+            dtype=tf.float32)
+        sess.run([tf.initialize_all_variables()])
+        cost = tf.reduce_mean(tf.square(outputs_dynamic - y))
+        learning_rate = 0.01
+        optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(
+            cost)
+
+        # time for a training step.
+        block_time_training = time_taken_by_op(optimizer, sess, iters)
+
+    performance_training = (
+        basic_time_training - block_time_training) * 100 / basic_time_training
+
+    print(",".join([str(batch_size), str(cell_size), str(input_size), str(
+        time_steps), str(use_gpu), str(basic_time_training), str(
+            block_time_training), str(performance_training)]))
+
+    return basic_time_training, block_time_training
+
+
+def inference_gru_block_vs_gru_cell(batch_size,
+                                    cell_size,
+                                    input_size,
+                                    time_steps,
+                                    use_gpu=False,
+                                    iters=30):
+  """Benchmark inference speed between GRUBlockCell vs GRUCell."""
+  tf.reset_default_graph()
+  with tf.Session(graph=tf.Graph()) as sess:
+    with tf.device("/cpu:0" if not use_gpu else "/gpu:0"):
+
+      # Random initializers.
+      seed = 1994
+      initializer = tf.random_uniform_initializer(-1, 1, seed=seed)
+      np.random.seed(seed)
+
+      # Inputs
+      concat_x = vs.get_variable("concat_x",
+                                 [time_steps, batch_size, input_size])
+      h = vs.get_variable("h", [batch_size, cell_size])
+
+      # Output from the basic GRU cell implementation.
+      with tf.variable_scope("basic", initializer=initializer):
+        cell = tf.nn.rnn_cell.GRUCell(cell_size)
+        outputs_dynamic, _ = tf.nn.dynamic_rnn(
+            cell,
+            inputs=concat_x,
+            initial_state=h,
+            time_major=True,
+            dtype=tf.float32)
+        sess.run([tf.initialize_all_variables()])
+        basic_time_inference = time_taken_by_op(outputs_dynamic, sess, iters)
+
+      # Output from the block GRU cell implementation.
+      with tf.variable_scope("block", initializer=initializer):
+        cell = gru_ops.GRUBlockCell(cell_size)
+        outputs_dynamic, _ = tf.nn.dynamic_rnn(
+            cell,
+            inputs=concat_x,
+            initial_state=h,
+            time_major=True,
+            dtype=tf.float32)
+        sess.run([tf.initialize_all_variables()])
+        block_time_inference = time_taken_by_op(outputs_dynamic, sess, iters)
+
+    performance_inference = (basic_time_inference - block_time_inference
+                            ) * 100 / basic_time_inference
+    print(",".join([str(batch_size), str(cell_size), str(input_size), str(
+        time_steps), str(use_gpu), str(basic_time_inference), str(
+            block_time_inference), str(performance_inference)]))
+
+    return basic_time_inference, block_time_inference
+
+
+def single_bprop_step_gru_block_vs_gru_cell(batch_size,
+                                            cell_size,
+                                            input_size,
+                                            use_gpu=False,
+                                            iters=30):
+  """Benchmark single bprop step speed between GRUBlockCell vs GRUCell."""
+  tf.reset_default_graph()
+  with tf.Session(graph=tf.Graph()) as sess:
+    with tf.device("/cpu:0" if not use_gpu else "/gpu:0"):
+      initializer = tf.random_uniform_initializer(-1, 1, seed=1989)
+      # Inputs
+      x = vs.get_variable("x", [batch_size, input_size])
+      h = vs.get_variable("h", [batch_size, cell_size])
+
+      # Output from the basic GRU cell implementation.
+      with tf.variable_scope("basic", initializer=initializer):
+        output = tf.nn.rnn_cell.GRUCell(cell_size)(tf.identity(x),
+                                                   tf.identity(h))
+        sess.run([tf.initialize_all_variables()])
+        grad_output_wrt_input = tf.gradients([output], h)
+        basic_time_bprop = time_taken_by_op(grad_output_wrt_input, sess, iters)
+
+      # Output from the block GRU cell implementation.
+      with tf.variable_scope("block", initializer=initializer):
+        output = gru_ops.GRUBlockCell(cell_size)(tf.identity(x), tf.identity(h))
+        sess.run([tf.initialize_all_variables()])
+        grad_output_wrt_input = tf.gradients([output], h)
+        block_time_bprop = time_taken_by_op(grad_output_wrt_input, sess, iters)
+
+  performance_inference = (
+      basic_time_bprop - block_time_bprop) * 100 / basic_time_bprop
+
+  print(",".join([str(batch_size), str(cell_size), str(input_size), str(
+      use_gpu), str(basic_time_bprop), str(block_time_bprop), str(
+          performance_inference)]))
+
+  return basic_time_bprop, block_time_bprop
+
+
+class BenchmarkGRUBlock(tf.test.Benchmark):
+
+  def benchmarkTrainingBlockGRUVsGRUCell(self):
+    print("Comparison GRUBlockCell vs GRUCell")
+    print("--------------------------------------------------------------")
+    print("Training speed GRUBlockCell vs GRUCell")
+    print("batch_size, cell_size, input_size, time_steps, GPU, "
+          "basic_time_training, block_time_training, performance_training[%]")
+    iters = 10
+    for use_gpu in [True, False]:
+      for batch_size in [1, 32, 128]:
+        for cell_size in [128, 512]:
+          for input_size in [128, 512]:
+            for time_steps in [50]:
+              basic_time, block_time = training_gru_block_vs_gru_cell(
+                  batch_size, cell_size, input_size, time_steps, use_gpu, iters)
+              self.report_benchmark(
+                  name="GRUCell_training_time_BS%i_CS%i_IS%i_TS%i_gpu_%s" %
+                  (batch_size, cell_size, input_size, time_steps, use_gpu),
+                  iters=iters,
+                  wall_time=basic_time)
+              self.report_benchmark(
+                  name="GRUBlockCell_training_time_BS%i_CS%i_IS%i_TS%i_gpu_%s" %
+                  (batch_size, cell_size, input_size, time_steps, use_gpu),
+                  iters=iters,
+                  wall_time=block_time)
+
+  def benchmarkInferenceBlockGRUVsGRUCell(self):
+    print("--------------------------------------------------------------")
+    print("Inference speed GRUBlockCell vs GRUCell")
+    print(
+        "batch_size, cell_size, input_size, time_steps, GPU, "
+        "basic_time_inference, block_time_inference, performance_inference[%]")
+    iters = 10
+    for use_gpu in [True, False]:
+      for batch_size in [1, 32, 128]:
+        for cell_size in [128, 512]:
+          for input_size in [128, 512]:
+            for time_steps in [50]:
+              basic_time, block_time = inference_gru_block_vs_gru_cell(
+                  batch_size, cell_size, input_size, time_steps, use_gpu, iters)
+              self.report_benchmark(
+                  name="GRUCell_inference_time_BS%i_CS%i_IS%i_TS%i_gpu_%s" %
+                  (batch_size, cell_size, input_size, time_steps, use_gpu),
+                  iters=iters,
+                  wall_time=basic_time)
+              self.report_benchmark(
+                  name="GRUBlockCell_inference_time_BS%i_CS%i_IS%i_TS%i_gpu_%s"
+                  % (batch_size, cell_size, input_size, time_steps, use_gpu),
+                  iters=iters,
+                  wall_time=block_time)
+
+  def benchmarkSingleBpropStepBlockGRUVsGRUCell(self):
+    print("--------------------------------------------------------------")
+    print("Single bprop step speed GRUBlockCell vs GRUCell")
+    print("batch_size, cell_size, input_size, GPU, basic_time, "
+          "block_time, performance_inference[%]")
+    iters = 10
+    for use_gpu in [True, False]:
+      for batch_size in [1, 32, 128]:
+        for cell_size in [128, 512]:
+          for input_size in [128, 512]:
+            basic_time, block_time = single_bprop_step_gru_block_vs_gru_cell(
+                batch_size, cell_size, input_size, use_gpu, iters)
+            self.report_benchmark(
+                name="GRUCell_Bprop_single_step_time_BS%i_CS%i_IS%i_gpu_%s" %
+                (batch_size, cell_size, input_size, use_gpu),
+                iters=iters,
+                wall_time=basic_time)
+            self.report_benchmark(
+                name="GRUBlockCell_Bprop_single_step_time_BS%i_CS%i_IS%i_gpu_%s"
+                % (batch_size, cell_size, input_size, use_gpu),
+                iters=iters,
+                wall_time=block_time)
+
+    print("--------------------------------------------------------------")
+
 
 if __name__ == "__main__":
   tf.test.main()
-- 
GitLab


From 5b69f87613f51c3f70d8da60abb880046b09bab8 Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Thu, 25 Aug 2016 17:02:34 -0800
Subject: [PATCH 178/223] Implement ConstantValue optimization for Range.

This inlines the core kernel of Range op because it is simple.

We probably don't want to do this with Cast, Concat, or Pack, so in
another CL I might try to rip out the core algo in those kernels for
reuse, if I can.  Otherwise, I may just stop here and we'll get to
doing Cast/Concat/Pack when we actually evaluate the constant-folded
graph.
Change: 131354274
---
 tensorflow/core/graph/shape_refiner.cc      | 59 +++++++++++++++++++--
 tensorflow/core/graph/shape_refiner.h       |  2 +
 tensorflow/core/graph/shape_refiner_test.cc | 23 ++++++++
 3 files changed, 81 insertions(+), 3 deletions(-)

diff --git a/tensorflow/core/graph/shape_refiner.cc b/tensorflow/core/graph/shape_refiner.cc
index abc80937c1..e45e4e0d63 100644
--- a/tensorflow/core/graph/shape_refiner.cc
+++ b/tensorflow/core/graph/shape_refiner.cc
@@ -121,9 +121,9 @@ Status ShapeRefiner::ConstantValue(const Node* node, Tensor* tensor_storage,
   // a partial evaluation of the graph.
 
   // TODO(vrv): Handle other types of nodes, like we do in python:
-  // Size, Rank, Range, Cast, Concat, Pack.  Some of these
-  // require recursively accessing the input's inputs, while some
-  // can be computed using local information.
+  // Cast, Concat, Pack.  These require re-implementing the core
+  // kernels themselves, and we may want to switch this to partial
+  // evaluation instead of implementing these again.
   if (node->IsConstant()) {
     return Constant(node, tensor_storage, input_tensor);
   }
@@ -136,6 +136,9 @@ Status ShapeRefiner::ConstantValue(const Node* node, Tensor* tensor_storage,
   if (node->type_string() == "Rank") {
     return Rank(node, tensor_storage, input_tensor);
   }
+  if (node->type_string() == "Range") {
+    return Range(node, tensor_storage, input_tensor);
+  }
 
   return Status::OK();
 }
@@ -223,4 +226,54 @@ Status ShapeRefiner::Rank(const Node* node, Tensor* tensor_storage,
   return Status::OK();
 }
 
+Status ShapeRefiner::Range(const Node* node, Tensor* tensor_storage,
+                           const Tensor** input_tensor) const {
+  const Node* start_node;
+  TF_RETURN_IF_ERROR(node->input_node(0, &start_node));
+  const Node* limit_node;
+  TF_RETURN_IF_ERROR(node->input_node(1, &limit_node));
+  const Node* delta_node;
+  TF_RETURN_IF_ERROR(node->input_node(2, &delta_node));
+
+  const Tensor* start_node_tensor;
+  TF_RETURN_IF_ERROR(
+      ConstantValue(start_node, tensor_storage, &start_node_tensor));
+  if (start_node_tensor == nullptr) return Status::OK();
+  const int32 start = start_node_tensor->scalar<int32>()();
+
+  const Tensor* limit_node_tensor;
+  TF_RETURN_IF_ERROR(
+      ConstantValue(limit_node, tensor_storage, &limit_node_tensor));
+  if (limit_node_tensor == nullptr) return Status::OK();
+  const int32 limit = limit_node_tensor->scalar<int32>()();
+
+  const Tensor* delta_node_tensor;
+  TF_RETURN_IF_ERROR(
+      ConstantValue(delta_node, tensor_storage, &delta_node_tensor));
+  if (delta_node_tensor == nullptr) return Status::OK();
+  const int32 delta = delta_node_tensor->scalar<int32>()();
+
+  if (start > limit) {
+    return errors::InvalidArgument("Range requires start <= limit: ", start,
+                                   "/", limit);
+  }
+
+  if (delta <= 0) {
+    return errors::InvalidArgument("Range requires delta > 0: ", delta);
+  }
+
+  int32 size = (limit - start + delta - 1) / delta;
+  *tensor_storage = Tensor(DT_INT32, {size});
+
+  auto flat = tensor_storage->flat<int32>();
+  int32 val = start;
+  for (int32 i = 0; i < size; ++i) {
+    flat(i) = val;
+    val += delta;
+  }
+
+  *input_tensor = tensor_storage;
+  return Status::OK();
+}
+
 }  // namespace tensorflow
diff --git a/tensorflow/core/graph/shape_refiner.h b/tensorflow/core/graph/shape_refiner.h
index 468d1dd014..21551903c0 100644
--- a/tensorflow/core/graph/shape_refiner.h
+++ b/tensorflow/core/graph/shape_refiner.h
@@ -71,6 +71,8 @@ class ShapeRefiner {
               const Tensor** input_tensor) const;
   Status Rank(const Node* node, Tensor* tensor_storage,
               const Tensor** input_tensor) const;
+  Status Range(const Node* node, Tensor* tensor_storage,
+               const Tensor** input_tensor) const;
   // Stores a map from a node to its InferenceContext.
   //
   // Owns values.
diff --git a/tensorflow/core/graph/shape_refiner_test.cc b/tensorflow/core/graph/shape_refiner_test.cc
index ca0676e891..94cd6dc74a 100644
--- a/tensorflow/core/graph/shape_refiner_test.cc
+++ b/tensorflow/core/graph/shape_refiner_test.cc
@@ -278,5 +278,28 @@ TEST(ShapeRefinerTest, PropagateRank) {
   EXPECT_EQ("[2]", ctx->DebugString(ctx->output(0)));
 }
 
+TEST(ShapeRefinerTest, PropagateRange) {
+  Scope root = Scope::NewRootScope();
+  auto begin = ops::Const(root, 1);
+  auto limit = ops::Const(root, 11);
+  auto delta = ops::Const(root, 3);
+  auto range = ops::Range(root, begin, limit, delta);
+
+  Node* shape_data;
+  TF_ASSERT_OK(NodeBuilder("Test", "ShapeData")
+                   .Input(range.node())
+                   .Finalize(root.graph(), &shape_data));
+
+  ShapeRefiner m;
+  TF_ASSERT_OK(m.AddNode(begin.node()));
+  TF_ASSERT_OK(m.AddNode(limit.node()));
+  TF_ASSERT_OK(m.AddNode(delta.node()));
+  TF_ASSERT_OK(m.AddNode(range.node()));
+  TF_ASSERT_OK(m.AddNode(shape_data));
+
+  shape_inference::InferenceContext* ctx = m.GetContext(shape_data);
+  EXPECT_EQ("[1,4,7,10]", ctx->DebugString(ctx->output(0)));
+}
+
 }  // namespace
 }  // namespace tensorflow
-- 
GitLab


From 10d6f0eed8f4704589d036bcc09ba000c5c91e8a Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Thu, 25 Aug 2016 17:25:37 -0800
Subject: [PATCH 179/223] Automated rollback of change 131340536 Change:
 131356339

---
 .../core/common_runtime/gpu/gpu_device.cc     | 61 ++++---------------
 .../core/common_runtime/gpu/gpu_device.h      |  7 +--
 tensorflow/core/protobuf/config.proto         | 17 ------
 3 files changed, 12 insertions(+), 73 deletions(-)

diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc
index 06c28b26d3..c08abc5689 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc
@@ -597,8 +597,7 @@ Status BaseGPUDeviceFactory::CreateDevices(const SessionOptions& options,
     n = iter->second;
   }
   std::vector<int> valid_gpu_ids;
-  TF_RETURN_IF_ERROR(GetValidDeviceIds(
-      options.config.gpu_options().visible_device_list(), &valid_gpu_ids));
+  GetValidDeviceIds(&valid_gpu_ids);
   if (static_cast<size_t>(n) > valid_gpu_ids.size()) {
     n = valid_gpu_ids.size();
   }
@@ -811,55 +810,21 @@ std::vector<CudaVersion> GetSupportedCudaComputeCapabilities() {
 
 }  // namespace
 
-Status BaseGPUDeviceFactory::GetValidDeviceIds(
-    const string& visible_device_list, std::vector<int>* ids) {
+void BaseGPUDeviceFactory::GetValidDeviceIds(std::vector<int>* ids) {
   gpu::Platform* gpu_manager = GPUMachineManager();
   if (gpu_manager == nullptr) {
-    return Status::OK();
+    return;
   }
 
   auto cuda_supported_capabilities = GetSupportedCudaComputeCapabilities();
-  if (cuda_supported_capabilities.empty()) {
-    return errors::FailedPrecondition(
-        "No supported cuda capabilities in binary.");
-  }
+  CHECK(!cuda_supported_capabilities.empty());
   CudaVersion min_supported_capability = *std::min_element(
       cuda_supported_capabilities.begin(), cuda_supported_capabilities.end());
 
   int min_gpu_core_count = GetMinGPUMultiprocessorCount(gpu_manager);
 
-  // If the user wants to remap the visible to virtual GPU mapping,
-  // check for that here.
-  std::vector<int> visible_gpu_order;
-  if (visible_device_list.empty()) {
-    visible_gpu_order.resize(gpu_manager->VisibleDeviceCount());
-    // By default, visible to virtual mapping is unchanged.
-    std::iota(visible_gpu_order.begin(), visible_gpu_order.end(), 0);
-  } else {
-    std::vector<string> order_str = str_util::Split(visible_device_list, ',');
-    for (int i = 0; i < order_str.size(); ++i) {
-      const string& gpu_id_str = order_str[i];
-      int32 gpu_id;
-      if (!strings::safe_strto32(gpu_id_str, &gpu_id)) {
-        return errors::InvalidArgument(
-            "Could not parse entry in 'visible_device_list': '", gpu_id_str,
-            "'.  visible_device_list = ", visible_device_list);
-      }
-
-      if (gpu_id < 0 || gpu_id >= gpu_manager->VisibleDeviceCount()) {
-        return errors::InvalidArgument(
-            "'visible_device_list' listed an invalid GPU id '", gpu_id,
-            "' but visible device count is ",
-            gpu_manager->VisibleDeviceCount());
-      }
-
-      visible_gpu_order.push_back(gpu_id);
-    }
-  }
-
-  for (int i = 0; i < visible_gpu_order.size(); ++i) {
-    const int32 visible_gpu_id = visible_gpu_order[i];
-    auto exec_status = gpu_manager->ExecutorForDevice(visible_gpu_id);
+  for (int i = 0; i < gpu_manager->VisibleDeviceCount(); ++i) {
+    auto exec_status = gpu_manager->ExecutorForDevice(i);
     if (!exec_status.ok()) {
       continue;
     }
@@ -873,9 +838,8 @@ Status BaseGPUDeviceFactory::GetValidDeviceIds(
     // Only GPUs with no less than the minimum supported compute capability is
     // accepted.
     if (device_capability < min_supported_capability) {
-      LOG(INFO) << "Ignoring physical gpu device "
-                << "(" << GetShortDeviceDescription(visible_gpu_id, desc)
-                << ") "
+      LOG(INFO) << "Ignoring gpu device "
+                << "(" << GetShortDeviceDescription(i, desc) << ") "
                 << "with Cuda compute capability " << device_capability
                 << ". The minimum required Cuda capability is "
                 << min_supported_capability << ".";
@@ -888,8 +852,7 @@ Status BaseGPUDeviceFactory::GetValidDeviceIds(
     // variable is set, its value will be used to filter out GPUs.
     if (desc.core_count() < min_gpu_core_count) {
       LOG(INFO) << "Ignoring gpu device "
-                << "(" << GetShortDeviceDescription(visible_gpu_id, desc)
-                << ") "
+                << "(" << GetShortDeviceDescription(i, desc) << ") "
                 << "with Cuda multiprocessor count: " << desc.core_count()
                 << ". The minimum required count is " << min_gpu_core_count
                 << ". You can adjust this requirement with the env var "
@@ -898,13 +861,11 @@ Status BaseGPUDeviceFactory::GetValidDeviceIds(
     }
 
     int new_id = ids->size();
-    ids->push_back(visible_gpu_id);
+    ids->push_back(i);
 
     LOG(INFO) << "Creating TensorFlow device (/gpu:" << new_id << ") -> "
-              << "(" << GetShortDeviceDescription(visible_gpu_id, desc) << ")";
+              << "(" << GetShortDeviceDescription(i, desc) << ")";
   }
-
-  return Status::OK();
 }
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.h b/tensorflow/core/common_runtime/gpu/gpu_device.h
index abe7c0f687..03090aa537 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.h
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.h
@@ -123,12 +123,7 @@ class BaseGPUDeviceFactory : public DeviceFactory {
                                          Allocator* gpu_allocator,
                                          Allocator* cpu_allocator) = 0;
 
-  // Returns into 'ids' the list of valid GPU ids, in the order that
-  // they should map to logical gpu ids "/gpu:0", "/gpu:1", etc, based
-  // upon 'visible_device_list', a comma-separated list of 'visible
-  // gpu ids'.
-  Status GetValidDeviceIds(const string& visible_device_list,
-                           std::vector<int>* ids);
+  void GetValidDeviceIds(std::vector<int>* ids);
 };
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/protobuf/config.proto b/tensorflow/core/protobuf/config.proto
index f0ec90d781..cea68d43d4 100644
--- a/tensorflow/core/protobuf/config.proto
+++ b/tensorflow/core/protobuf/config.proto
@@ -35,23 +35,6 @@ message GPUOptions {
   // If true, the allocator does not pre-allocate the entire specified
   // GPU memory region, instead starting small and growing as needed.
   bool allow_growth = 4;
-
-  // A comma-separated list of GPU ids that determines the 'visible'
-  // to 'virtual' mapping of GPU devices.  For example, if TensorFlow
-  // can see 8 GPU devices in the process, and one wanted to map
-  // visible GPU devices 5 and 3 as "/gpu:0", and "/gpu:1", then one
-  // would specify this field as "5,3".  This field is similar in
-  // spirit to the CUDA_VISIBLE_DEVICES environment variable, except
-  // it applies to the visible GPU devices in the process.
-  //
-  // NOTE: The GPU driver provides the process with the visible GPUs
-  // in an order which is not guaranteed to have any correlation to
-  // the *physical* GPU id in the machine.  This field is used for
-  // remapping "visible" to "virtual", which means this operates only
-  // after the process starts.  Users are required to use vendor
-  // specific mechanisms (e.g., CUDA_VISIBLE_DEVICES) to control the
-  // physical to visible device mapping prior to invoking TensorFlow.
-  string visible_device_list = 5;
 };
 
 // Options passed to the graph optimizer
-- 
GitLab


From f5b307fc6e3596e69c6d29f8a30e719d323aaa35 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 17:26:19 -0800
Subject: [PATCH 180/223] Added an is_chief property to RunConfig for
 indicating whether or not the task should be the chief. Change: 131356393

---
 .../python/learn/estimators/run_config.py     | 61 ++++++++++++++-----
 .../python/learn/tests/run_config_test.py     | 55 ++++++++++++++---
 2 files changed, 93 insertions(+), 23 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/estimators/run_config.py b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
index 0e98596995..8803127deb 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/run_config.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/run_config.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
 """Run Config."""
 
 from __future__ import absolute_import
@@ -49,13 +48,14 @@ class RunConfig(object):
                save_checkpoints_secs=60,
                keep_checkpoint_max=5,
                keep_checkpoint_every_n_hours=10000,
-               job_name=None):
+               job_name=None,
+               is_chief=None):
     """Constructor.
 
-    If set to None, `master`, `task`, `num_ps_replicas`, `cluster_spec`, and
-    `job_name` are set based on the TF_CONFIG environment variable, if the
-    pertinent information is present; otherwise, the defaults listed in the
-    Args section apply.
+    If set to None, `master`, `task`, `num_ps_replicas`, `cluster_spec`,
+    `job_name`, and `is_chief` are set based on the TF_CONFIG environment
+    variable, if the pertinent information is present; otherwise, the defaults
+    listed in the Args section apply.
 
     The TF_CONFIG environment variable is a JSON object with two relevant
     attributes: `task` and `cluster_spec`. `cluster_spec` is a JSON serialized
@@ -71,9 +71,7 @@ class RunConfig(object):
         cluster_spec.
       * `num_ps_replicas` is set by counting the number of nodes listed
         in the `ps` job of `cluster_spec`.
-
-    Note that any of these values can be overridden by explicitly passing
-    their value to the constructor.
+      * `is_chief`: true when `job_name` == "master" and `task` == 0.
 
     Example:
     ```
@@ -88,6 +86,7 @@ class RunConfig(object):
       assert config.num_ps_replicas == 2
       assert config.cluster_spec == server_lib.ClusterSpec(cluster)
       assert config.job_name == 'worker'
+      assert not config.is_chief
     ```
 
     Args:
@@ -98,9 +97,9 @@ class RunConfig(object):
       log_device_placement: Log the op placement to devices (default: False).
       gpu_memory_fraction: Fraction of GPU memory used by the process on
         each GPU uniformly on the same machine.
-      cluster_spec: a tf.train.ClusterSpec object that describes the cluster in
-        the case of distributed computation. If missing, reasonable assumptions
-        are made for the addresses of jobs.
+      cluster_spec: a `tf.train.ClusterSpec` object that describes the cluster
+        in the case of distributed computation. If missing, reasonable
+        assumptions are made for the addresses of jobs.
       tf_random_seed: Random seed for TensorFlow initializers.
         Setting this value allows consistency between reruns.
       save_summary_steps: Save summaries every this many steps.
@@ -112,8 +111,10 @@ class RunConfig(object):
       keep_checkpoint_every_n_hours: Number of hours between each checkpoint
         to be saved. The default value of 10,000 hours effectively disables
         the feature.
-      job_name: the type of task, e.g., 'ps', 'worker', etc. Must exist in
-        `cluster_spec.jobs`.
+      job_name: the type of task, e.g., 'ps', 'worker', etc. The `job_name`
+        must exist in the `cluster_spec.jobs`.
+      is_chief: whether or not this task (as identified by the other parameters)
+        should be the chief task.
 
     Raises:
       ValueError: if num_ps_replicas and cluster_spec are set (cluster_spec
@@ -131,7 +132,7 @@ class RunConfig(object):
     # Otherwise, use the respective default (None / 0).
     task_env = config.get('task', {})
     self._job_name = job_name or task_env.get('type') or None
-    self.task = task or task_env.get('index') or 0
+    self.task = task if task is not None else task_env.get('index') or 0
 
     self.master = (master or _get_master(self.cluster_spec, self.job_name,
                                          self.task) or '')
@@ -142,6 +143,32 @@ class RunConfig(object):
                        'environment variable.')
     self.num_ps_replicas = num_ps_replicas or _count_ps(self.cluster_spec) or 0
 
+    # Set is_chief.
+    self._is_chief = is_chief
+    # When the TF_CONFIG environment variable is set, we can set the default
+    # of is_chief to 0 when job_name is "master" and task is 0.
+    if (self._is_chief is None) and config:
+      self._is_chief = (self._job_name == 'master' and self.task == 0)
+
+    # Enforce that is_chief is only applicable to workers or masters
+    # (Cloud ML) with task == 0.
+    if self._is_chief:
+      if self.task != 0:
+        raise ValueError(
+            'Task is %d, but only task 0 may be chief. Please check is_chief '
+            'and task, which may have been set in TF_CONFIG environment '
+            'variable.' % (self.task,))
+      if self._job_name not in (None, 'master', 'worker'):
+        raise ValueError(
+            'job_name is \'%s\', but only masters or workers may be chiefs. '
+            'Please check is_chief and job_name, which may have been set in '
+            'TF_CONFIG environment variable.' % (self._job_name,))
+    elif (self._is_chief is False and self._job_name == 'master' and
+          self.task == 0):
+      raise ValueError(
+          'Master task 0 must be chief. Please check is_chief, job_name, and '
+          'task, which may have been set in TF_CONFIG environment variable.')
+
     gpu_options = GPUOptions(
         per_process_gpu_memory_fraction=gpu_memory_fraction)
     self.tf_config = ConfigProto(
@@ -156,6 +183,10 @@ class RunConfig(object):
     self.keep_checkpoint_max = keep_checkpoint_max
     self.keep_checkpoint_every_n_hours = keep_checkpoint_every_n_hours
 
+  @property
+  def is_chief(self):
+    return self._is_chief
+
   @property
   def job_name(self):
     return self._job_name
diff --git a/tensorflow/contrib/learn/python/learn/tests/run_config_test.py b/tensorflow/contrib/learn/python/learn/tests/run_config_test.py
index edc7400bee..d421231667 100644
--- a/tensorflow/contrib/learn/python/learn/tests/run_config_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/run_config_test.py
@@ -33,8 +33,9 @@ class RunConfigTest(tf.test.TestCase):
     self.assertEquals(config.master, "")
     self.assertEquals(config.task, 0)
     self.assertEquals(config.num_ps_replicas, 0)
-    self.assertEquals(config.cluster_spec, None)
-    self.assertEquals(config.job_name, None)
+    self.assertIsNone(config.cluster_spec)
+    self.assertIsNone(config.job_name)
+    self.assertIsNone(config.is_chief)
 
   def test_values_from_tf_config(self):
     tf_config = {"cluster": {"ps": ["host1:1", "host2:2"],
@@ -49,6 +50,7 @@ class RunConfigTest(tf.test.TestCase):
     self.assertEquals(config.num_ps_replicas, 2)
     self.assertEquals(config.cluster_spec.as_dict(), tf_config["cluster"])
     self.assertEquals(config.job_name, "worker")
+    self.assertFalse(config.is_chief)
 
   def test_explicitly_specified_values(self):
     cluster_spec = tf.train.ClusterSpec({
@@ -66,6 +68,7 @@ class RunConfigTest(tf.test.TestCase):
     self.assertEquals(config.num_ps_replicas, 1)
     self.assertEquals(config.cluster_spec, cluster_spec)
     self.assertEquals(config.job_name, "my_job_name")
+    self.assertFalse(config.is_chief)
 
   def test_tf_config_with_overrides(self):
     # Purpose: to test the case where TF_CONFIG is set, but then
@@ -81,13 +84,15 @@ class RunConfigTest(tf.test.TestCase):
       # set by TF_CONFIG.
       cluster_spec_override = tf.train.ClusterSpec({
           "ps": ["my_host1:314"],
-          "my_job_name": ["my_host2:314", "my_host4:314", "my_host5:314"]
+          "my_job_name": ["my_host2:314", "my_host4:314", "my_host5:314"],
+          "master": ["my_host6:313"]
       })
       config = run_config.RunConfig(
           master="my_master",
-          task=2,
-          job_name="my_job_name",
-          cluster_spec=cluster_spec_override,)
+          task=0,
+          job_name="master",
+          cluster_spec=cluster_spec_override,
+          is_chief=True)
 
     # To protect against changes to the test itself (either
     # the TF_CONFIG variable or the manual overrides), we will assert
@@ -97,10 +102,18 @@ class RunConfigTest(tf.test.TestCase):
 
     # Now we assert that the correct values were indeed returned.
     self.assertEquals(config.master, "my_master")
-    self.assertEquals(config.task, 2)
+    self.assertEquals(config.task, 0)
     self.assertEquals(config.num_ps_replicas, 1)
     self.assertEquals(config.cluster_spec, cluster_spec_override)
-    self.assertEquals(config.job_name, "my_job_name")
+    self.assertEquals(config.job_name, "master")
+    self.assertTrue(config.is_chief)
+
+  def test_explicitly_setting_task_to_0_overrides_tf_config(self):
+    # Setup the TF_CONFIG environment variable
+    tf_config = {"task": {"index": 1}}
+    with patch.dict("os.environ", {"TF_CONFIG": json.dumps(tf_config)}):
+      config = run_config.RunConfig(task=0)
+    self.assertEquals(config.task, 0)
 
   def test_num_ps_replicas_and_cluster_spec_are_mutually_exclusive(self):
     cluster_spec = tf.train.ClusterSpec(
@@ -171,6 +184,32 @@ class RunConfigTest(tf.test.TestCase):
     # Basically, just make sure no exception is being raised.
     self.assertEquals(config.num_ps_replicas, 2)
 
+  def test_is_chief_from_tf_config(self):
+    # is_chief should be true when ["task"]["type"] == "master" and
+    # index == 0. Note that test_values_from_tf_config covers the
+    # non-master case.
+    tf_config = {"cluster": {"ps": ["host1:1", "host2:2"],
+                             "master": ["host3:3"],
+                             "worker": ["host4:4", "host5:5", "host6:6"]},
+                 "task": {"type": "master",
+                          "index": 0}}
+    with patch.dict("os.environ", {"TF_CONFIG": json.dumps(tf_config)}):
+      config = run_config.RunConfig()
+
+    self.assertTrue(config.is_chief)
+
+  def test_bad_is_chief_combinations_raise(self):
+    msg = "Task is 1, but only task 0 may be chief"
+    with self.assertRaisesRegexp(ValueError, msg):
+      run_config.RunConfig(is_chief=True, task=1)
+
+    msg = "job_name is \'ps\', but only masters or workers may be chiefs"
+    with self.assertRaisesRegexp(ValueError, msg):
+      run_config.RunConfig(is_chief=True, task=0, job_name="ps")
+
+    with self.assertRaisesRegexp(ValueError, "Master task 0 must be chief"):
+      run_config.RunConfig(is_chief=False, task=0, job_name="master")
+
 
 if __name__ == "__main__":
   tf.test.main()
-- 
GitLab


From 0d52fec4b23fa7c5e59d4c97c752cbd631f38d87 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 18:07:16 -0800
Subject: [PATCH 181/223] Update generated Python Op docs. Change: 131359121

---
 .../g3doc/api_docs/python/contrib.learn.md    | 34 ++++++++++++-------
 .../shard4/tf.contrib.learn.RunConfig.md      | 34 ++++++++++++-------
 2 files changed, 42 insertions(+), 26 deletions(-)

diff --git a/tensorflow/g3doc/api_docs/python/contrib.learn.md b/tensorflow/g3doc/api_docs/python/contrib.learn.md
index 26758828ef..abef96774e 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.learn.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.learn.md
@@ -3573,14 +3573,14 @@ If you're a Google-internal user using command line flags with learn_runner.py
 probably want to use learn_runner.EstimatorConfig instead.
 - - -
 
-#### `tf.contrib.learn.RunConfig.__init__(master=None, task=None, num_ps_replicas=None, num_cores=4, log_device_placement=False, gpu_memory_fraction=1, cluster_spec=None, tf_random_seed=None, save_summary_steps=100, save_checkpoints_secs=60, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000, job_name=None)` {#RunConfig.__init__}
+#### `tf.contrib.learn.RunConfig.__init__(master=None, task=None, num_ps_replicas=None, num_cores=4, log_device_placement=False, gpu_memory_fraction=1, cluster_spec=None, tf_random_seed=None, save_summary_steps=100, save_checkpoints_secs=60, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000, job_name=None, is_chief=None)` {#RunConfig.__init__}
 
 Constructor.
 
-If set to None, `master`, `task`, `num_ps_replicas`, `cluster_spec`, and
-`job_name` are set based on the TF_CONFIG environment variable, if the
-pertinent information is present; otherwise, the defaults listed in the
-Args section apply.
+If set to None, `master`, `task`, `num_ps_replicas`, `cluster_spec`,
+`job_name`, and `is_chief` are set based on the TF_CONFIG environment
+variable, if the pertinent information is present; otherwise, the defaults
+listed in the Args section apply.
 
 The TF_CONFIG environment variable is a JSON object with two relevant
 attributes: `task` and `cluster_spec`. `cluster_spec` is a JSON serialized
@@ -3596,9 +3596,7 @@ following properties are set on this class:
     cluster_spec.
   * `num_ps_replicas` is set by counting the number of nodes listed
     in the `ps` job of `cluster_spec`.
-
-Note that any of these values can be overridden by explicitly passing
-their value to the constructor.
+  * `is_chief`: true when `job_name` == "master" and `task` == 0.
 
 Example:
 ```
@@ -3613,6 +3611,7 @@ Example:
   assert config.num_ps_replicas == 2
   assert config.cluster_spec == server_lib.ClusterSpec(cluster)
   assert config.job_name == 'worker'
+  assert not config.is_chief
 ```
 
 ##### Args:
@@ -3625,9 +3624,9 @@ Example:
 *  <b>`log_device_placement`</b>: Log the op placement to devices (default: False).
 *  <b>`gpu_memory_fraction`</b>: Fraction of GPU memory used by the process on
     each GPU uniformly on the same machine.
-*  <b>`cluster_spec`</b>: a tf.train.ClusterSpec object that describes the cluster in
-    the case of distributed computation. If missing, reasonable assumptions
-    are made for the addresses of jobs.
+*  <b>`cluster_spec`</b>: a `tf.train.ClusterSpec` object that describes the cluster
+    in the case of distributed computation. If missing, reasonable
+    assumptions are made for the addresses of jobs.
 *  <b>`tf_random_seed`</b>: Random seed for TensorFlow initializers.
     Setting this value allows consistency between reruns.
 *  <b>`save_summary_steps`</b>: Save summaries every this many steps.
@@ -3639,8 +3638,10 @@ Example:
 *  <b>`keep_checkpoint_every_n_hours`</b>: Number of hours between each checkpoint
     to be saved. The default value of 10,000 hours effectively disables
     the feature.
-*  <b>`job_name`</b>: the type of task, e.g., 'ps', 'worker', etc. Must exist in
-    `cluster_spec.jobs`.
+*  <b>`job_name`</b>: the type of task, e.g., 'ps', 'worker', etc. The `job_name`
+    must exist in the `cluster_spec.jobs`.
+*  <b>`is_chief`</b>: whether or not this task (as identified by the other parameters)
+    should be the chief task.
 
 ##### Raises:
 
@@ -3649,6 +3650,13 @@ Example:
     may fome from the TF_CONFIG environment variable).
 
 
+- - -
+
+#### `tf.contrib.learn.RunConfig.is_chief` {#RunConfig.is_chief}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.RunConfig.job_name` {#RunConfig.job_name}
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.RunConfig.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.RunConfig.md
index 4f02f8c723..409cb66b09 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.RunConfig.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.learn.RunConfig.md
@@ -5,14 +5,14 @@ If you're a Google-internal user using command line flags with learn_runner.py
 probably want to use learn_runner.EstimatorConfig instead.
 - - -
 
-#### `tf.contrib.learn.RunConfig.__init__(master=None, task=None, num_ps_replicas=None, num_cores=4, log_device_placement=False, gpu_memory_fraction=1, cluster_spec=None, tf_random_seed=None, save_summary_steps=100, save_checkpoints_secs=60, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000, job_name=None)` {#RunConfig.__init__}
+#### `tf.contrib.learn.RunConfig.__init__(master=None, task=None, num_ps_replicas=None, num_cores=4, log_device_placement=False, gpu_memory_fraction=1, cluster_spec=None, tf_random_seed=None, save_summary_steps=100, save_checkpoints_secs=60, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000, job_name=None, is_chief=None)` {#RunConfig.__init__}
 
 Constructor.
 
-If set to None, `master`, `task`, `num_ps_replicas`, `cluster_spec`, and
-`job_name` are set based on the TF_CONFIG environment variable, if the
-pertinent information is present; otherwise, the defaults listed in the
-Args section apply.
+If set to None, `master`, `task`, `num_ps_replicas`, `cluster_spec`,
+`job_name`, and `is_chief` are set based on the TF_CONFIG environment
+variable, if the pertinent information is present; otherwise, the defaults
+listed in the Args section apply.
 
 The TF_CONFIG environment variable is a JSON object with two relevant
 attributes: `task` and `cluster_spec`. `cluster_spec` is a JSON serialized
@@ -28,9 +28,7 @@ following properties are set on this class:
     cluster_spec.
   * `num_ps_replicas` is set by counting the number of nodes listed
     in the `ps` job of `cluster_spec`.
-
-Note that any of these values can be overridden by explicitly passing
-their value to the constructor.
+  * `is_chief`: true when `job_name` == "master" and `task` == 0.
 
 Example:
 ```
@@ -45,6 +43,7 @@ Example:
   assert config.num_ps_replicas == 2
   assert config.cluster_spec == server_lib.ClusterSpec(cluster)
   assert config.job_name == 'worker'
+  assert not config.is_chief
 ```
 
 ##### Args:
@@ -57,9 +56,9 @@ Example:
 *  <b>`log_device_placement`</b>: Log the op placement to devices (default: False).
 *  <b>`gpu_memory_fraction`</b>: Fraction of GPU memory used by the process on
     each GPU uniformly on the same machine.
-*  <b>`cluster_spec`</b>: a tf.train.ClusterSpec object that describes the cluster in
-    the case of distributed computation. If missing, reasonable assumptions
-    are made for the addresses of jobs.
+*  <b>`cluster_spec`</b>: a `tf.train.ClusterSpec` object that describes the cluster
+    in the case of distributed computation. If missing, reasonable
+    assumptions are made for the addresses of jobs.
 *  <b>`tf_random_seed`</b>: Random seed for TensorFlow initializers.
     Setting this value allows consistency between reruns.
 *  <b>`save_summary_steps`</b>: Save summaries every this many steps.
@@ -71,8 +70,10 @@ Example:
 *  <b>`keep_checkpoint_every_n_hours`</b>: Number of hours between each checkpoint
     to be saved. The default value of 10,000 hours effectively disables
     the feature.
-*  <b>`job_name`</b>: the type of task, e.g., 'ps', 'worker', etc. Must exist in
-    `cluster_spec.jobs`.
+*  <b>`job_name`</b>: the type of task, e.g., 'ps', 'worker', etc. The `job_name`
+    must exist in the `cluster_spec.jobs`.
+*  <b>`is_chief`</b>: whether or not this task (as identified by the other parameters)
+    should be the chief task.
 
 ##### Raises:
 
@@ -81,6 +82,13 @@ Example:
     may fome from the TF_CONFIG environment variable).
 
 
+- - -
+
+#### `tf.contrib.learn.RunConfig.is_chief` {#RunConfig.is_chief}
+
+
+
+
 - - -
 
 #### `tf.contrib.learn.RunConfig.job_name` {#RunConfig.job_name}
-- 
GitLab


From d51cd676896cb04267a10d8ef760267495dd5465 Mon Sep 17 00:00:00 2001
From: David Soergel <soergel@google.com>
Date: Thu, 25 Aug 2016 18:12:56 -0800
Subject: [PATCH 182/223] Distinguish TensorFlowTransform from regular
 Transform; add OrderedDictNumpySource.

The distinction is that a TensorFlowTransform is implemented using TF ops, while a base Transform may be just a composition of other transforms with no direct reference to TF.

OrderedDictNumpySource and _OrderedDictNumpyFeedFn are unrelated (but entangled in a multichange client, oops). They will support df.from_ordereddict() in a future CL.
Change: 131359419
---
 .../learn/python/learn/dataframe/__init__.py  |  7 +-
 .../dataframe/queues/feeding_functions.py     | 55 ++++++++++++
 .../learn/python/learn/dataframe/transform.py | 83 +++++++++++++------
 .../learn/dataframe/transforms/batch.py       |  2 +-
 .../dataframe/transforms/binary_transforms.py |  4 +-
 .../dataframe/transforms/boolean_mask.py      |  2 +-
 .../learn/dataframe/transforms/csv_parser.py  |  2 +-
 .../learn/dataframe/transforms/densify.py     |  2 +-
 .../learn/dataframe/transforms/difference.py  |  2 +-
 .../dataframe/transforms/example_parser.py    |  2 +-
 .../learn/dataframe/transforms/hashes.py      |  4 +-
 .../dataframe/transforms/in_memory_source.py  | 32 ++++++-
 .../dataframe/transforms/reader_source.py     |  6 +-
 .../learn/dataframe/transforms/sparsify.py    |  2 +-
 .../python/learn/dataframe/transforms/sum.py  |  2 +-
 .../dataframe/transforms/unary_transforms.py  |  2 +-
 .../python/learn/tests/dataframe/mocks.py     |  2 +-
 17 files changed, 168 insertions(+), 43 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/dataframe/__init__.py b/tensorflow/contrib/learn/python/learn/dataframe/__init__.py
index 8fba9b6513..d65f246528 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/__init__.py
@@ -25,6 +25,7 @@ from tensorflow.contrib.learn.python.learn.dataframe.series import Series
 from tensorflow.contrib.learn.python.learn.dataframe.series import TransformedSeries
 from tensorflow.contrib.learn.python.learn.dataframe.tensorflow_dataframe import TensorFlowDataFrame
 from tensorflow.contrib.learn.python.learn.dataframe.transform import parameter
+from tensorflow.contrib.learn.python.learn.dataframe.transform import TensorFlowTransform
 from tensorflow.contrib.learn.python.learn.dataframe.transform import Transform
 
 # Transforms
@@ -34,6 +35,9 @@ from tensorflow.contrib.learn.python.learn.dataframe.transforms.hashes import Ha
 from tensorflow.contrib.learn.python.learn.dataframe.transforms.in_memory_source import NumpySource
 from tensorflow.contrib.learn.python.learn.dataframe.transforms.in_memory_source import PandasSource
 from tensorflow.contrib.learn.python.learn.dataframe.transforms.reader_source import ReaderSource
+# Coming soon; multichange client hassle due to no DIFFBASE in Cider
+# from tensorflow.contrib.learn.python.learn.dataframe \
+#     .transforms.split_mask import SplitMask
 from tensorflow.contrib.learn.python.learn.dataframe.transforms.sum import Sum
 
 
@@ -50,4 +54,5 @@ for bt_def in _bt.BINARY_TRANSFORMS:
   _bt.register_binary_op(*bt_def)
 
 __all__ = ['DataFrame', 'Series', 'PredefinedSeries', 'TransformedSeries',
-           'TensorFlowDataFrame', 'parameter', 'Transform']
+           'TensorFlowDataFrame', 'TensorFlowTransform', 'parameter',
+           'Transform']
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/queues/feeding_functions.py b/tensorflow/contrib/learn/python/learn/dataframe/queues/feeding_functions.py
index f1fcba0b04..d4f3c59634 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/queues/feeding_functions.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/queues/feeding_functions.py
@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import collections
 import random
 import numpy as np
 
@@ -82,6 +83,55 @@ class _ArrayFeedFn(object):
             self._placeholders[1]: self._array[integer_indexes]}
 
 
+class _OrderedDictNumpyFeedFn(object):
+  """Creates feed dictionaries from `OrderedDict`s of numpy arrays."""
+
+  def __init__(self,
+               placeholders,
+               ordered_dict_of_arrays,
+               batch_size,
+               random_start=False,
+               seed=None,
+               num_epochs=None):
+    if len(placeholders) != len(ordered_dict_of_arrays) + 1:
+      raise ValueError("Expected {} placeholders; got {}.".format(
+          len(ordered_dict_of_arrays), len(placeholders)))
+    self._index_placeholder = placeholders[0]
+    self._col_placeholders = placeholders[1:]
+    self._ordered_dict_of_arrays = ordered_dict_of_arrays
+    self._max = len(ordered_dict_of_arrays.values()[0])
+    for _, v in ordered_dict_of_arrays.items():
+      if len(v) != self._max:
+        raise ValueError("Array lengths must match.")
+    self._batch_size = batch_size
+    self._num_epochs = num_epochs
+    self._epoch = 0
+    random.seed(seed)
+    self._trav = random.randrange(self._max) if random_start else 0
+    self._epoch_end = (self._trav - 1) % self._max
+
+  def __call__(self):
+    if self._num_epochs and self._epoch >= self._num_epochs:
+      raise errors.OutOfRangeError(None, None,
+                                   "Already emitted %s epochs." % self._epoch)
+
+    integer_indexes = [j % self._max
+                       for j in range(self._trav, self._trav + self._batch_size)
+                      ]
+
+    if self._epoch_end in integer_indexes:
+      # after this batch we will have processed self._epoch epochs, possibly
+      # overshooting a bit to fill out a batch.
+      self._epoch += 1
+
+    self._trav = (integer_indexes[-1] + 1) % self._max
+    feed_dict = {self._index_placeholder: integer_indexes}
+    cols = [column[integer_indexes]
+            for column in self._ordered_dict_of_arrays.values()]
+    feed_dict.update(dict(zip(self._col_placeholders, cols)))
+    return feed_dict
+
+
 class _PandasFeedFn(object):
   """Creates feed dictionaries from pandas `DataFrames`."""
 
@@ -173,6 +223,11 @@ def enqueue_data(data,
       types = [dtypes.int64, dtypes.as_dtype(data.dtype)]
       queue_shapes = [(), data.shape[1:]]
       get_feed_fn = _ArrayFeedFn
+    elif isinstance(data, collections.OrderedDict):
+      types = [dtypes.int64] + [dtypes.as_dtype(col.dtype)
+                                for col in data.values()]
+      queue_shapes = [()] + [col.shape[1:] for col in data.values()]
+      get_feed_fn = _OrderedDictNumpyFeedFn
     elif HAS_PANDAS and isinstance(data, pd.DataFrame):
       types = [dtypes.as_dtype(dt)
                for dt in [data.index.dtype] + list(data.dtypes)]
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transform.py b/tensorflow/contrib/learn/python/learn/dataframe/transform.py
index bbb97d2f29..c28da59ac7 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transform.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transform.py
@@ -185,20 +185,15 @@ class Transform(object):
                                                  self.output_names)
     return self._return_type
 
-  def _check_output_tensors(self, output_tensors):
-    """Helper for `build(...)`; verifies the output of `_build_transform`.
+  def __str__(self):
+    return self.name
 
-    Args:
-      output_tensors: value returned by a call to `_build_transform`.
+  def __repr__(self):
+    parameters_sorted = ["%s: %s" % (repr(k), repr(v))
+                         for k, v in sorted(self.parameters().items())]
+    parameters_joined = ", ".join(parameters_sorted)
 
-    Raises:
-      TypeError: `transform_output` is not a list.
-      ValueError: `transform_output` does not match `output_names`.
-    """
-    if not isinstance(output_tensors, self.return_type):
-      raise TypeError(
-          "Expected a NamedTuple of Tensors with elements %s; got %s." %
-          (self.output_names, type(output_tensors).__name__))
+    return "%s({%s})" % (self.name, parameters_joined)
 
   def __call__(self, input_series=None):
     """Apply this `Transform` to the provided `Series`, producing 'Series'.
@@ -217,12 +212,62 @@ class Transform(object):
     if len(input_series) != self.input_valency:
       raise ValueError("Expected %s input Series but received %s." %
                        (self.input_valency, len(input_series)))
-    output_series = [TransformedSeries(input_series, self, output_name)
-                     for output_name in self.output_names]
+    output_series = self._produce_output_series(input_series)
 
     # pylint: disable=not-callable
     return self.return_type(*output_series)
 
+  @abstractmethod
+  def _produce_output_series(self, input_series):
+    """Applies the transformation to the `transform_input`.
+
+    Args:
+      input_series: a list of Series representing the input to
+        the Transform.
+
+    Returns:
+        A list of Series representing the transformed output, in order
+        corresponding to `_output_names`.
+    """
+    raise NotImplementedError()
+
+
+class TensorFlowTransform(Transform):
+  """A function from a list of `Series` to a namedtuple of `Series`.
+
+  Transforms map zero or more Series of a DataFrame to new Series.
+  """
+
+  __metaclass__ = ABCMeta
+
+  def _check_output_tensors(self, output_tensors):
+    """Helper for `build(...)`; verifies the output of `_build_transform`.
+
+    Args:
+      output_tensors: value returned by a call to `_build_transform`.
+
+    Raises:
+      TypeError: `transform_output` is not a list.
+      ValueError: `transform_output` does not match `output_names`.
+    """
+    if not isinstance(output_tensors, self.return_type):
+      raise TypeError(
+          "Expected a NamedTuple of Tensors with elements %s; got %s." %
+          (self.output_names, type(output_tensors).__name__))
+
+  def _produce_output_series(self, input_series=None):
+    """Apply this `Transform` to the provided `Series`, producing `Series`.
+
+    Args:
+      input_series: None, a `Series`, or a list of input `Series`, acting as
+         positional arguments.
+
+    Returns:
+      A namedtuple of the output `Series`.
+    """
+    return [TransformedSeries(input_series, self, output_name)
+            for output_name in self.output_names]
+
   def build_transitive(self, input_series, cache=None, **kwargs):
     """Apply this `Transform` to the provided `Series`, producing 'Tensor's.
 
@@ -277,13 +322,3 @@ class Transform(object):
         A namedtuple of Tensors representing the transformed output.
     """
     raise NotImplementedError()
-
-  def __str__(self):
-    return self.name
-
-  def __repr__(self):
-    parameters_sorted = ["%s: %s" % (repr(k), repr(v))
-                         for k, v in sorted(self.parameters().items())]
-    parameters_joined = ", ".join(parameters_sorted)
-
-    return "%s({%s})" % (self.name, parameters_joined)
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/batch.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/batch.py
index cf1585634c..7793e296e2 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/batch.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/batch.py
@@ -23,7 +23,7 @@ from tensorflow.contrib.learn.python.learn.dataframe import transform
 from tensorflow.python.training import input as input_ops
 
 
-class AbstractBatchTransform(transform.Transform):
+class AbstractBatchTransform(transform.TensorFlowTransform):
   """Abstract parent class for batching Transforms."""
 
   def __init__(self,
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/binary_transforms.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/binary_transforms.py
index 78a21250c9..b3e5225403 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/binary_transforms.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/binary_transforms.py
@@ -42,7 +42,7 @@ _DOC_FORMAT_STRING = ("A `Transform` that wraps `{0}`. "
                       "Documentation for `{0}`: \n\n {1}")
 
 
-class SeriesBinaryTransform(transform.Transform):
+class SeriesBinaryTransform(transform.TensorFlowTransform):
   """Parent class for `Transform`s that operate on two `Series`."""
 
   @property
@@ -64,7 +64,7 @@ class SeriesBinaryTransform(transform.Transform):
     return self.return_type(self._apply_op(input_tensors[0], input_tensors[1]))
 
 
-class ScalarBinaryTransform(transform.Transform):
+class ScalarBinaryTransform(transform.TensorFlowTransform):
   """Parent class for `Transform`s that combine `Series` to a scalar."""
 
   def __init__(self, scalar):
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py
index cdb244744a..880217f558 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py
@@ -62,7 +62,7 @@ def sparse_boolean_mask(sparse_tensor, mask, name="sparse_boolean_mask"):
 
 
 @series.Series.register_binary_op("select_rows")
-class BooleanMask(transform.Transform):
+class BooleanMask(transform.TensorFlowTransform):
   """Apply a boolean mask to a `Series`."""
 
   @property
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/csv_parser.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/csv_parser.py
index d78b5652d6..08620ae62e 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/csv_parser.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/csv_parser.py
@@ -23,7 +23,7 @@ from tensorflow.python.framework import constant_op
 from tensorflow.python.ops import parsing_ops
 
 
-class CSVParser(transform.Transform):
+class CSVParser(transform.TensorFlowTransform):
   """A Transform that parses lines from a CSV file."""
 
   def __init__(self, column_names, default_values):
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/densify.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/densify.py
index 0f0c1a0891..d5b55ee359 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/densify.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/densify.py
@@ -23,7 +23,7 @@ from tensorflow.contrib.learn.python.learn.dataframe import transform
 from tensorflow.python.ops import sparse_ops
 
 
-class Densify(transform.Transform):
+class Densify(transform.TensorFlowTransform):
   """Transforms Sparse to Dense Tensor."""
 
   def __init__(self,
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/difference.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/difference.py
index b585fceeb6..f9cb0c9485 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/difference.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/difference.py
@@ -32,7 +32,7 @@ def _negate_sparse(sparse_tensor):
 
 
 @series.Series.register_binary_op("__sub__")
-class Difference(transform.Transform):
+class Difference(transform.TensorFlowTransform):
   """Subtracts one 'Series` from another."""
 
   def __init__(self):
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/example_parser.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/example_parser.py
index c2c5e0cbed..f0176600c3 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/example_parser.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/example_parser.py
@@ -24,7 +24,7 @@ from tensorflow.contrib.learn.python.learn.dataframe import transform
 from tensorflow.python.ops import parsing_ops
 
 
-class ExampleParser(transform.Transform):
+class ExampleParser(transform.TensorFlowTransform):
   """A Transform that parses serialized `tensorflow.Example` protos."""
 
   def __init__(self, features):
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/hashes.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/hashes.py
index 325e7827ce..6462a6ff51 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/hashes.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/hashes.py
@@ -23,11 +23,11 @@ from tensorflow.contrib.learn.python.learn.dataframe import transform
 from tensorflow.python.ops import string_ops
 
 
-class HashFast(transform.Transform):
+class HashFast(transform.TensorFlowTransform):
   """Perform a fast hash of a `Series`."""
 
   def __init__(self, num_buckets):
-    """Initialize `CSVParser`.
+    """Initialize `HashFast`.
 
     Args:
       num_buckets: The number of hash buckets to use.
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py
index 53e276adf1..f7b58601b4 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py
@@ -22,7 +22,7 @@ from tensorflow.contrib.learn.python.learn.dataframe import transform
 from tensorflow.contrib.learn.python.learn.dataframe.queues import feeding_functions
 
 
-class BaseInMemorySource(transform.Transform):
+class BaseInMemorySource(transform.TensorFlowTransform):
   """Abstract parent class for NumpySource and PandasSource."""
 
   def __init__(self,
@@ -123,6 +123,36 @@ class NumpySource(BaseInMemorySource):
     return ("index", "value")
 
 
+class OrderedDictNumpySource(BaseInMemorySource):
+  """A zero-input Transform that produces Series from a dict of numpy arrays."""
+
+  def __init__(self,
+               ordered_dict_of_arrays,
+               num_threads=None,
+               enqueue_size=None,
+               batch_size=None,
+               queue_capacity=None,
+               shuffle=False,
+               min_after_dequeue=None,
+               seed=None,
+               data_name="pandas_data"):
+    if "index" in ordered_dict_of_arrays.keys():
+      raise ValueError("Column name `index` is reserved.")
+    super(OrderedDictNumpySource, self).__init__(ordered_dict_of_arrays,
+                                                 num_threads, enqueue_size,
+                                                 batch_size, queue_capacity,
+                                                 shuffle, min_after_dequeue,
+                                                 seed, data_name)
+
+  @property
+  def name(self):
+    return "OrderedDictNumpySource"
+
+  @property
+  def _output_names(self):
+    return tuple(["index"] + self._data.keys())
+
+
 class PandasSource(BaseInMemorySource):
   """A zero-input Transform that produces Series from a DataFrame."""
 
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/reader_source.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/reader_source.py
index ddb2d321d1..e8fa402bd6 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/reader_source.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/reader_source.py
@@ -23,7 +23,7 @@ from tensorflow.python.ops import io_ops
 from tensorflow.python.training import input as input_ops
 
 
-class ReaderSource(transform.Transform):
+class ReaderSource(transform.TensorFlowTransform):
   """Produces `Tensor`s of keys and values using a `tf.Reader`."""
 
   def __init__(self,
@@ -40,8 +40,8 @@ class ReaderSource(transform.Transform):
     """Initializes a ReaderSource.
 
     Args:
-      reader_cls: A subclass of `tesorflow.ReaderBase` that will be used to read
-        from `work_units`.
+      reader_cls: A subclass of `tensorflow.ReaderBase` that will be used to
+        read from `work_units`.
       work_units: A list that describes the source(s) of data to read.
         Typically, this is a list of filenames.
       reader_kwargs: A dictionary of kwargs to be passed to `reader_cls` when it
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/sparsify.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/sparsify.py
index f3447c5d94..05b66a683c 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/sparsify.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/sparsify.py
@@ -29,7 +29,7 @@ from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 
 
-class Sparsify(transform.Transform):
+class Sparsify(transform.TensorFlowTransform):
   """Transforms Dense to Sparse Tensor."""
 
   def __init__(self, strip_value):
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/sum.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/sum.py
index 878b08f4b0..212ac178f2 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/sum.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/sum.py
@@ -26,7 +26,7 @@ from tensorflow.python.ops import sparse_ops
 
 
 @series.Series.register_binary_op("__add__")
-class Sum(transform.Transform):
+class Sum(transform.TensorFlowTransform):
   """Adds two `Series`."""
 
   def __init__(self):
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/unary_transforms.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/unary_transforms.py
index 7f9eb7ce1d..eab476eb99 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/unary_transforms.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/unary_transforms.py
@@ -93,7 +93,7 @@ def register_unary_op(registered_name, operation, ignore_dtype=None):
     return self.return_type(result)
 
   cls = type(operation.__name__,
-             (transform.Transform,),
+             (transform.TensorFlowTransform,),
              {"name": name,
               "__doc__": doc,
               "input_valency": input_valency,
diff --git a/tensorflow/contrib/learn/python/learn/tests/dataframe/mocks.py b/tensorflow/contrib/learn/python/learn/tests/dataframe/mocks.py
index 6e5f2b5ab8..0a64babb95 100644
--- a/tensorflow/contrib/learn/python/learn/tests/dataframe/mocks.py
+++ b/tensorflow/contrib/learn/python/learn/tests/dataframe/mocks.py
@@ -110,7 +110,7 @@ class MockSeries(learn.Series):
     return self._cachekey
 
 
-class MockTransform(learn.Transform):
+class MockTransform(learn.TensorFlowTransform):
   """A mock transform for use in testing."""
 
   __metaclass__ = ABCMeta
-- 
GitLab


From af28f9678c60e3098e6aa0e56034e537bc516bde Mon Sep 17 00:00:00 2001
From: Eugene Brevdo <ebrevdo@google.com>
Date: Thu, 25 Aug 2016 19:22:50 -0800
Subject: [PATCH 183/223] Add betainc (regularized incomplete beta integral) TF
 op. Change: 131362736

---
 tensorflow/core/kernels/BUILD                 |   1 +
 tensorflow/core/kernels/betainc_op.cc         | 169 ++++++++++++++++++
 tensorflow/core/kernels/betainc_op.h          |  51 ++++++
 tensorflow/core/kernels/betainc_op_gpu.cu.cc  |  44 +++++
 tensorflow/core/ops/math_ops.cc               |  24 +++
 tensorflow/python/kernel_tests/BUILD          |   1 +
 .../python/kernel_tests/betainc_op_test.py    | 103 +++++++++++
 tensorflow/python/ops/math_ops.py             |  16 ++
 8 files changed, 409 insertions(+)
 create mode 100644 tensorflow/core/kernels/betainc_op.cc
 create mode 100644 tensorflow/core/kernels/betainc_op.h
 create mode 100644 tensorflow/core/kernels/betainc_op_gpu.cu.cc
 create mode 100644 tensorflow/python/kernel_tests/betainc_op_test.py

diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD
index e9f8f8cb48..aeda266a70 100644
--- a/tensorflow/core/kernels/BUILD
+++ b/tensorflow/core/kernels/BUILD
@@ -1103,6 +1103,7 @@ tf_kernel_libraries(
         "aggregate_ops",
         "argmax_op",
         "batch_matmul_op",
+        "betainc_op",
         "cast_op",
         "check_numerics_op",
         "cross_op",
diff --git a/tensorflow/core/kernels/betainc_op.cc b/tensorflow/core/kernels/betainc_op.cc
new file mode 100644
index 0000000000..f625520374
--- /dev/null
+++ b/tensorflow/core/kernels/betainc_op.cc
@@ -0,0 +1,169 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// See docs in ../ops/nn_ops.cc.
+
+#define EIGEN_USE_THREADS
+// TODO(b/31098934): Figure out why this is necessary here but not in
+// any other place, e.g., the cwise lgamma ops.
+#define EIGEN_HAS_C99_MATH 1
+
+#include "tensorflow/core/kernels/betainc_op.h"
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/numeric_op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/util/bcast.h"
+
+namespace tensorflow {
+
+typedef Eigen::ThreadPoolDevice CPUDevice;
+typedef Eigen::GpuDevice GPUDevice;
+
+template <typename Device, typename T>
+class BetaincOp : public OpKernel {
+ public:
+  explicit BetaincOp(OpKernelConstruction* ctx) : OpKernel(ctx) {}
+
+  void Compute(OpKernelContext* ctx) {
+    const Tensor& a = ctx->input(0);
+    const Tensor& b = ctx->input(1);
+    const Tensor& x = ctx->input(2);
+
+    const TensorShape& a_shape = a.shape();
+    const TensorShape& b_shape = b.shape();
+    const TensorShape& x_shape = x.shape();
+    if (a_shape.dims() > 0 && b_shape.dims() > 0) {
+      OP_REQUIRES(ctx, a_shape == b_shape,
+                  errors::InvalidArgument(
+                      "Shapes of a and b are inconsistent: ",
+                      a_shape.DebugString(), " vs. ", b_shape.DebugString()));
+    }
+    if (a_shape.dims() > 0 && x_shape.dims() > 0) {
+      OP_REQUIRES(ctx, a_shape == x_shape,
+                  errors::InvalidArgument(
+                      "Shapes of a and x are inconsistent: ",
+                      a_shape.DebugString(), " vs. ", x_shape.DebugString()));
+    }
+    if (b_shape.dims() > 0 && x_shape.dims() > 0) {
+      OP_REQUIRES(ctx, b_shape == x_shape,
+                  errors::InvalidArgument(
+                      "Shapes of b and x are inconsistent: ",
+                      b_shape.DebugString(), " vs. ", x_shape.DebugString()));
+    }
+
+    TensorShape merged_shape(a_shape);
+    if (b_shape.dims() > 0) merged_shape = b_shape;
+    if (x_shape.dims() > 0) merged_shape = x_shape;
+
+    Tensor* output = nullptr;
+    OP_REQUIRES_OK(ctx, ctx->allocate_output(0, merged_shape, &output));
+
+    if (a_shape == b_shape && a_shape == x_shape) {
+      functor::Betainc<Device, T, 1> functor;
+      functor(ctx->eigen_device<Device>(), a.flat<T>(), b.flat<T>(),
+              x.flat<T>(), output->flat<T>());
+      return;
+    }
+
+    auto merged_shape_vec = BCast::FromShape(merged_shape);
+    BCast a_shaper(BCast::FromShape(a_shape), merged_shape_vec);
+    BCast b_shaper(BCast::FromShape(b_shape), merged_shape_vec);
+    BCast x_shaper(BCast::FromShape(x_shape), merged_shape_vec);
+
+    int ndims = static_cast<int>(a_shaper.x_reshape().size());
+
+    switch (ndims) {
+#define CASE(NDIM)                                                        \
+  case NDIM: {                                                            \
+    functor::Betainc<Device, T, NDIM> functor;                            \
+    auto a_value = a.shaped<T, NDIM>(a_shaper.x_reshape());               \
+    auto b_value = b.shaped<T, NDIM>(b_shaper.x_reshape());               \
+    auto x_value = x.shaped<T, NDIM>(x_shaper.x_reshape());               \
+    functor.BCast(ctx->eigen_device<Device>(), a_value,                   \
+                  BCast::ToIndexArray<NDIM>(a_shaper.x_bcast()), b_value, \
+                  BCast::ToIndexArray<NDIM>(b_shaper.x_bcast()), x_value, \
+                  BCast::ToIndexArray<NDIM>(x_shaper.x_bcast()),          \
+                  output->shaped<T, NDIM>(a_shaper.y_reshape()));         \
+    return;                                                               \
+  }
+
+      CASE(1);
+      CASE(2);
+      default: {
+        ctx->SetStatus(errors::InvalidArgument(
+            "Broadcasting rank not supported: ", ndims));
+        return;
+      }
+    }
+  }
+};
+
+#define REGISTER_KERNELS(type)                                      \
+  REGISTER_KERNEL_BUILDER(                                          \
+      Name("Betainc").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
+      BetaincOp<CPUDevice, type>);
+
+REGISTER_KERNELS(float);
+REGISTER_KERNELS(double);
+#undef REGISTER_KERNELS
+
+#if GOOGLE_CUDA
+// Forward declarations of the functor specializations for GPU.
+namespace functor {
+#define DECLARE_GPU_SPEC_NDIM(T, NDIM)                               \
+  template <>                                                        \
+  void Betainc<GPUDevice, T, NDIM>::operator()(                      \
+      const GPUDevice& d, typename TTypes<T, NDIM>::ConstTensor a,   \
+      typename TTypes<T, NDIM>::ConstTensor b,                       \
+      typename TTypes<T, NDIM>::ConstTensor x,                       \
+      typename TTypes<T, NDIM>::Tensor output);                      \
+  template <>                                                        \
+  void Betainc<GPUDevice, T, NDIM>::BCast(                           \
+      const GPUDevice& d, typename TTypes<T, NDIM>::ConstTensor a,   \
+      const typename Eigen::array<Eigen::DenseIndex, NDIM>& bcast_a, \
+      typename TTypes<T, NDIM>::ConstTensor b,                       \
+      const typename Eigen::array<Eigen::DenseIndex, NDIM>& bcast_b, \
+      typename TTypes<T, NDIM>::ConstTensor x,                       \
+      const typename Eigen::array<Eigen::DenseIndex, NDIM>& bcast_x, \
+      typename TTypes<T, NDIM>::Tensor output);                      \
+  extern template struct Betainc<GPUDevice, T, NDIM>;
+
+#define DECLARE_GPU_SPEC(T)   \
+  DECLARE_GPU_SPEC_NDIM(T, 1) \
+  DECLARE_GPU_SPEC_NDIM(T, 2)
+
+DECLARE_GPU_SPEC(float);
+DECLARE_GPU_SPEC(double);
+
+#undef DECLARE_GPU_SPEC
+#undef DECLARE_GPU_SPEC_NDIM
+}  // namespace functor
+
+// Registration of the GPU implementations.
+#define REGISTER_GPU_KERNELS(type)                                  \
+  REGISTER_KERNEL_BUILDER(                                          \
+      Name("Betainc").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
+      BetaincOp<GPUDevice, type>);
+
+REGISTER_GPU_KERNELS(float);
+REGISTER_GPU_KERNELS(double);
+#undef REGISTER_GPU_KERNELS
+
+#endif  // GOOGLE_CUDA
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/betainc_op.h b/tensorflow/core/kernels/betainc_op.h
new file mode 100644
index 0000000000..c4aa9543ab
--- /dev/null
+++ b/tensorflow/core/kernels/betainc_op.h
@@ -0,0 +1,51 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef TENSORFLOW_KERNELS_BETAINC_OP_H_
+#define TENSORFLOW_KERNELS_BETAINC_OP_H_
+// Functor definition for BetaincOp, must be compilable by nvcc.
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "tensorflow/core/framework/tensor_types.h"
+
+namespace tensorflow {
+namespace functor {
+
+// Functor used by BetaincOp to do the computations.
+template <typename Device, typename T, int NDIM>
+struct Betainc {
+  void operator()(const Device& d, typename TTypes<T, NDIM>::ConstTensor a,
+                  typename TTypes<T, NDIM>::ConstTensor b,
+                  typename TTypes<T, NDIM>::ConstTensor x,
+                  typename TTypes<T, NDIM>::Tensor output) {
+    output.device(d) = Eigen::betainc(a, b, x);
+  }
+
+  void BCast(const Device& d, typename TTypes<T, NDIM>::ConstTensor a,
+             const typename Eigen::array<Eigen::DenseIndex, NDIM>& bcast_a,
+             typename TTypes<T, NDIM>::ConstTensor b,
+             const typename Eigen::array<Eigen::DenseIndex, NDIM>& bcast_b,
+             typename TTypes<T, NDIM>::ConstTensor x,
+             const typename Eigen::array<Eigen::DenseIndex, NDIM>& bcast_x,
+             typename TTypes<T, NDIM>::Tensor output) {
+    output.device(d) = Eigen::betainc(
+        a.broadcast(bcast_a), b.broadcast(bcast_b), x.broadcast(bcast_x));
+  }
+};
+
+}  // namespace functor
+}  // namespace tensorflow
+
+#endif  // TENSORFLOW_KERNELS_BETAINC_OP_H_
diff --git a/tensorflow/core/kernels/betainc_op_gpu.cu.cc b/tensorflow/core/kernels/betainc_op_gpu.cu.cc
new file mode 100644
index 0000000000..3b99ef323d
--- /dev/null
+++ b/tensorflow/core/kernels/betainc_op_gpu.cu.cc
@@ -0,0 +1,44 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#if GOOGLE_CUDA
+
+#define EIGEN_USE_GPU
+
+#include <stdio.h>
+
+#include "tensorflow/core/kernels/betainc_op.h"
+
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/tensor_types.h"
+
+namespace tensorflow {
+
+typedef Eigen::GpuDevice GPUDevice;
+
+// Definition of the GPU implementations declared in betainc_op.cc.
+#define DEFINE_GPU_KERNELS_NDIM(T, NDIM) \
+  template struct functor::Betainc<GPUDevice, T, NDIM>;
+
+#define DEFINE_GPU_KERNELS(T)   \
+  DEFINE_GPU_KERNELS_NDIM(T, 1) \
+  DEFINE_GPU_KERNELS_NDIM(T, 2)
+
+DEFINE_GPU_KERNELS(float);
+DEFINE_GPU_KERNELS(double);
+
+}  // end namespace tensorflow
+
+#endif  // GOOGLE_CUDA
diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc
index 02cf34182d..f2087aa3ca 100644
--- a/tensorflow/core/ops/math_ops.cc
+++ b/tensorflow/core/ops/math_ops.cc
@@ -687,6 +687,30 @@ The polygamma function is defined as:
 where \\(\psi(x)\\) is the digamma function.
 )doc");
 
+REGISTER_OP("Betainc")
+    .Input("a: T")
+    .Input("b: T")
+    .Input("x: T")
+    .Output("z: T")
+    .Attr("T: {float, double}")
+    .Doc(R"doc(
+Compute the regularized incomplete beta integral \\(I_x(a, b)\\).
+
+The regularized incomplete beta integral is defined as:
+
+```
+I_x(a, b) = \frac{B(x; a, b)}{B(a, b)}
+```
+where
+
+```
+B(x; a, b) = \int_0^x t^{a-1} (1 - t)^{b-1} dt
+```
+
+is the incomplete beta function and \\(B(a, b)\\) is the *complete*
+beta function.
+)doc");
+
 // --------------------------------------------------------------------------
 
 // Declares cwise binary comparison operations signature: 't, 't -> bool,
diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 80d11107fe..4e85907bf9 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -111,6 +111,7 @@ cuda_py_tests(
         "array_ops_test.py",
         "batch_matmul_op_test.py",
         "batchtospace_op_test.py",
+        "betainc_op_test.py",
         "bias_op_test.py",
         "bitcast_op_test.py",
         "check_ops_test.py",
diff --git a/tensorflow/python/kernel_tests/betainc_op_test.py b/tensorflow/python/kernel_tests/betainc_op_test.py
new file mode 100644
index 0000000000..a78a1b934f
--- /dev/null
+++ b/tensorflow/python/kernel_tests/betainc_op_test.py
@@ -0,0 +1,103 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Functional tests for 3d convolutional operations."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import itertools
+
+import numpy as np
+import tensorflow as tf
+
+
+class BetaincTest(tf.test.TestCase):
+  use_gpu = False
+
+  def _testBetaInc(self, dtype):
+    try:
+      from scipy import special  # pylint: disable=g-import-not-at-top
+      np_dt = dtype.as_numpy_dtype
+
+      # Test random values
+      a_s = np.abs(np.random.randn(10, 10) * 30).astype(np_dt)  # in (0, infty)
+      b_s = np.abs(np.random.randn(10, 10) * 30).astype(np_dt)  # in (0, infty)
+      x_s = np.random.rand(10, 10).astype(np_dt)  # in (0, 1)
+      with self.test_session(use_gpu=self.use_gpu):
+        tf_a_s = tf.constant(a_s, dtype=dtype)
+        tf_b_s = tf.constant(b_s, dtype=dtype)
+        tf_x_s = tf.constant(x_s, dtype=dtype)
+        tf_out = tf.betainc(tf_a_s, tf_b_s, tf_x_s).eval()
+      scipy_out = special.betainc(a_s, b_s, x_s).astype(np_dt)
+
+      # the scipy version of betainc uses a double-only implementation.
+      # TODO(ebrevdo): identify reasons for (sometime) precision loss
+      # with doubles
+      tol = 1e-4 if dtype == tf.float32 else 5e-5
+      self.assertAllCloseAccordingToType(scipy_out, tf_out, rtol=tol, atol=tol)
+
+      # Test out-of-range values (most should return nan output)
+      combinations = list(itertools.product([-1, 0, 0.5, 1.0, 1.5], repeat=3))
+      a_comb, b_comb, x_comb = np.asarray(
+          list(zip(*combinations)), dtype=np_dt)
+      with self.test_session(use_gpu=self.use_gpu):
+        tf_comb = tf.betainc(a_comb, b_comb, x_comb).eval()
+      scipy_comb = special.betainc(a_comb, b_comb, x_comb).astype(np_dt)
+      self.assertAllCloseAccordingToType(scipy_comb, tf_comb)
+
+      # Test broadcasting between scalars and other shapes
+      with self.test_session(use_gpu=self.use_gpu):
+        self.assertAllCloseAccordingToType(
+            special.betainc(0.1, b_s, x_s).astype(np_dt),
+            tf.betainc(0.1, b_s, x_s).eval(), rtol=tol, atol=tol)
+        self.assertAllCloseAccordingToType(
+            special.betainc(a_s, 0.1, x_s).astype(np_dt),
+            tf.betainc(a_s, 0.1, x_s).eval(), rtol=tol, atol=tol)
+        self.assertAllCloseAccordingToType(
+            special.betainc(a_s, b_s, 0.1).astype(np_dt),
+            tf.betainc(a_s, b_s, 0.1).eval(), rtol=tol, atol=tol)
+        self.assertAllCloseAccordingToType(
+            special.betainc(0.1, b_s, 0.1).astype(np_dt),
+            tf.betainc(0.1, b_s, 0.1).eval(), rtol=tol, atol=tol)
+        self.assertAllCloseAccordingToType(
+            special.betainc(0.1, 0.1, 0.1).astype(np_dt),
+            tf.betainc(0.1, 0.1, 0.1).eval(), rtol=tol, atol=tol)
+
+      with self.assertRaisesRegexp(ValueError, "Shapes .* are not compatible"):
+        tf.betainc(0.5, [0.5], [[0.5]])
+
+      with self.test_session(use_gpu=self.use_gpu):
+        with self.assertRaisesOpError("Shapes of .* are inconsistent"):
+          a_p = tf.placeholder(dtype)
+          b_p = tf.placeholder(dtype)
+          x_p = tf.placeholder(dtype)
+          tf.betainc(a_p, b_p, x_p).eval(
+              feed_dict={a_p: 0.5, b_p: [0.5], x_p: [[0.5]]})
+
+    except ImportError as e:
+      tf.logging.warn("Cannot test special functions: %s" % str(e))
+
+  def testBetaIncFloat(self):
+    self._testBetaInc(tf.float32)
+
+  def testBetaIncDouble(self):
+    self._testBetaInc(tf.float64)
+
+
+class BetaincTestGPU(BetaincTest):
+  use_gpu = True
+
+if __name__ == "__main__":
+  tf.test.main()
diff --git a/tensorflow/python/ops/math_ops.py b/tensorflow/python/ops/math_ops.py
index 4e1bf7898a..78371c5c9e 100644
--- a/tensorflow/python/ops/math_ops.py
+++ b/tensorflow/python/ops/math_ops.py
@@ -67,6 +67,7 @@ mathematical functions to your graph.
 @@igammac
 @@zeta
 @@polygamma
+@@betainc
 
 ## Matrix Math Functions
 
@@ -1818,6 +1819,21 @@ def _BroadcastShape(op):
       op.inputs[1].get_shape())]
 
 
+@ops.RegisterShape("Betainc")
+def _BetaincOpShape(op):  # pylint: disable=invalid-name
+  """Shape function for BetaincOp."""
+  a_shape = op.inputs[0].get_shape()
+  b_shape = op.inputs[1].get_shape()
+  x_shape = op.inputs[2].get_shape()
+  merged_shape = tensor_shape.TensorShape(None)
+  for shape in (a_shape, b_shape, x_shape):
+    if shape.ndims != 0:
+      merged_shape = merged_shape.merge_with(shape)
+  # Scalars get broadcasted; non-scalar shapes must all match.
+  # Output will be the merged non-scalar shape, if any.
+  return [merged_shape if merged_shape.ndims is not None else a_shape]
+
+
 @ops.RegisterShape("SparseDenseCwiseMul")
 @ops.RegisterShape("SparseDenseCwiseDiv")
 @ops.RegisterShape("SparseDenseCwiseAdd")
-- 
GitLab


From 33b2b19a282e4c338da69dd98c9daa1631b15571 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 19:32:37 -0800
Subject: [PATCH 184/223] Update ops-related pbtxt files. Change: 131363189

---
 .../core/ops/compat/ops_history.v0.pbtxt      | 29 +++++++++++++++++
 tensorflow/core/ops/ops.pbtxt                 | 31 +++++++++++++++++++
 2 files changed, 60 insertions(+)

diff --git a/tensorflow/core/ops/compat/ops_history.v0.pbtxt b/tensorflow/core/ops/compat/ops_history.v0.pbtxt
index 88a6d3cbdb..41a8ad3b66 100644
--- a/tensorflow/core/ops/compat/ops_history.v0.pbtxt
+++ b/tensorflow/core/ops/compat/ops_history.v0.pbtxt
@@ -5485,6 +5485,35 @@ op {
     minimum: 2
   }
 }
+op {
+  name: "Betainc"
+  input_arg {
+    name: "a"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "b"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "z"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+      }
+    }
+  }
+}
 op {
   name: "BiasAdd"
   input_arg {
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index 2583128d8d..fab9001ff5 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -2724,6 +2724,37 @@ op {
   summary: "BatchToSpace for 4-D tensors of type T."
   description: "Rearranges (permutes) data from batch into blocks of spatial data, followed by\ncropping. This is the reverse transformation of SpaceToBatch. More specifically,\nthis op outputs a copy of the input tensor where values from the `batch`\ndimension are moved in spatial blocks to the `height` and `width` dimensions,\nfollowed by cropping along the `height` and `width` dimensions."
 }
+op {
+  name: "Betainc"
+  input_arg {
+    name: "a"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "b"
+    type_attr: "T"
+  }
+  input_arg {
+    name: "x"
+    type_attr: "T"
+  }
+  output_arg {
+    name: "z"
+    type_attr: "T"
+  }
+  attr {
+    name: "T"
+    type: "type"
+    allowed_values {
+      list {
+        type: DT_FLOAT
+        type: DT_DOUBLE
+      }
+    }
+  }
+  summary: "Compute the regularized incomplete beta integral \\\\(I_x(a, b)\\\\)."
+  description: "The regularized incomplete beta integral is defined as:\n\n```\nI_x(a, b) = \\frac{B(x; a, b)}{B(a, b)}\n```\nwhere\n\n```\nB(x; a, b) = \\int_0^x t^{a-1} (1 - t)^{b-1} dt\n```\n\nis the incomplete beta function and \\\\(B(a, b)\\\\) is the *complete*\nbeta function."
+}
 op {
   name: "BiasAdd"
   input_arg {
-- 
GitLab


From 7520047c71d465826e3aca313ef48bf71d217c90 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 19:34:55 -0800
Subject: [PATCH 185/223] Update generated Python Op docs. Change: 131363279

---
 .../shard2/tf.betainc.md                      | 30 +++++++++++++++++
 tensorflow/g3doc/api_docs/python/index.md     |  1 +
 tensorflow/g3doc/api_docs/python/math_ops.md  | 33 +++++++++++++++++++
 3 files changed, 64 insertions(+)
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.betainc.md

diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.betainc.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.betainc.md
new file mode 100644
index 0000000000..9da04a3642
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.betainc.md
@@ -0,0 +1,30 @@
+### `tf.betainc(a, b, x, name=None)` {#betainc}
+
+Compute the regularized incomplete beta integral \\(I_x(a, b)\\).
+
+The regularized incomplete beta integral is defined as:
+
+```
+I_x(a, b) = \frac{B(x; a, b)}{B(a, b)}
+```
+where
+
+```
+B(x; a, b) = \int_0^x t^{a-1} (1 - t)^{b-1} dt
+```
+
+is the incomplete beta function and \\(B(a, b)\\) is the *complete*
+beta function.
+
+##### Args:
+
+
+*  <b>`a`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+*  <b>`b`</b>: A `Tensor`. Must have the same type as `a`.
+*  <b>`x`</b>: A `Tensor`. Must have the same type as `a`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `a`.
+
diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md
index 7a32d5e55e..4968114fc0 100644
--- a/tensorflow/g3doc/api_docs/python/index.md
+++ b/tensorflow/g3doc/api_docs/python/index.md
@@ -189,6 +189,7 @@
   * [`batch_self_adjoint_eig`](../../api_docs/python/math_ops.md#batch_self_adjoint_eig)
   * [`batch_self_adjoint_eigvals`](../../api_docs/python/math_ops.md#batch_self_adjoint_eigvals)
   * [`batch_svd`](../../api_docs/python/math_ops.md#batch_svd)
+  * [`betainc`](../../api_docs/python/math_ops.md#betainc)
   * [`ceil`](../../api_docs/python/math_ops.md#ceil)
   * [`cholesky`](../../api_docs/python/math_ops.md#cholesky)
   * [`cholesky_solve`](../../api_docs/python/math_ops.md#cholesky_solve)
diff --git a/tensorflow/g3doc/api_docs/python/math_ops.md b/tensorflow/g3doc/api_docs/python/math_ops.md
index 5131493f75..84a00b4e61 100644
--- a/tensorflow/g3doc/api_docs/python/math_ops.md
+++ b/tensorflow/g3doc/api_docs/python/math_ops.md
@@ -896,6 +896,39 @@ where \\(\psi(x)\\) is the digamma function.
   A `Tensor`. Has the same type as `a`.
 
 
+- - -
+
+### `tf.betainc(a, b, x, name=None)` {#betainc}
+
+Compute the regularized incomplete beta integral \\(I_x(a, b)\\).
+
+The regularized incomplete beta integral is defined as:
+
+```
+I_x(a, b) = \frac{B(x; a, b)}{B(a, b)}
+```
+where
+
+```
+B(x; a, b) = \int_0^x t^{a-1} (1 - t)^{b-1} dt
+```
+
+is the incomplete beta function and \\(B(a, b)\\) is the *complete*
+beta function.
+
+##### Args:
+
+
+*  <b>`a`</b>: A `Tensor`. Must be one of the following types: `float32`, `float64`.
+*  <b>`b`</b>: A `Tensor`. Must have the same type as `a`.
+*  <b>`x`</b>: A `Tensor`. Must have the same type as `a`.
+*  <b>`name`</b>: A name for the operation (optional).
+
+##### Returns:
+
+  A `Tensor`. Has the same type as `a`.
+
+
 
 ## Matrix Math Functions
 
-- 
GitLab


From 2b90022e65f52d7803865f691dd13c742c58dc14 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Thu, 25 Aug 2016 19:52:12 -0800
Subject: [PATCH 186/223] Temporarily disable flaky
 extract_image_patches_grad_test internally Change: 131364069

---
 tensorflow/python/kernel_tests/BUILD | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD
index 4e85907bf9..effe925df0 100644
--- a/tensorflow/python/kernel_tests/BUILD
+++ b/tensorflow/python/kernel_tests/BUILD
@@ -190,7 +190,6 @@ cuda_py_tests(
         "conv_ops_test.py",
         "depthwise_conv_op_test.py",  # http://b/30603882
         "division_future_test.py",
-        "extract_image_patches_grad_test.py",
         "fft_ops_test.py",
         "pooling_ops_3d_test.py",  # http://b/30600785
         "pooling_ops_test.py",
@@ -205,6 +204,15 @@ cuda_py_tests(
     ],
 )
 
+# TODO(gpapan): Revisit the gradient of extract_image_patches_op to resolve
+# http://b/31080670.
+cuda_py_test(
+    name = "extract_image_patches_grad_test",
+    size = "medium",
+    srcs = ["extract_image_patches_grad_test.py"],
+    tags = ["notap"],  # http://b/31080670
+)
+
 cuda_py_test(
     name = "concat_op_test",
     size = "medium",
-- 
GitLab


From 9f7f6f1af557ba632582289efb2b35276843092a Mon Sep 17 00:00:00 2001
From: David Soergel <soergel@google.com>
Date: Thu, 25 Aug 2016 20:02:48 -0800
Subject: [PATCH 187/223] Add materializing DataFrame to memory; refactor
 dataframe.split().

* rename run_once to run_one_batch
* add run_one_epoch and materialize_to_memory
* refactor split and add split_fast
Change: 131364572
---
 .../learn/dataframe/tensorflow_dataframe.py   | 155 ++++++++++++++++--
 .../learn/dataframe/transforms/split_mask.py  |  81 +++++++++
 .../dataframe/arithmetic_transform_test.py    |   4 +-
 .../dataframe/tensorflow_dataframe_test.py    |   4 +-
 4 files changed, 230 insertions(+), 14 deletions(-)
 create mode 100644 tensorflow/contrib/learn/python/learn/dataframe/transforms/split_mask.py

diff --git a/tensorflow/contrib/learn/python/learn/dataframe/tensorflow_dataframe.py b/tensorflow/contrib/learn/python/learn/dataframe/tensorflow_dataframe.py
index ddd2b8bfb6..70c3953133 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/tensorflow_dataframe.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/tensorflow_dataframe.py
@@ -28,10 +28,10 @@ from tensorflow.contrib.learn.python.learn.dataframe import dataframe as df
 from tensorflow.contrib.learn.python.learn.dataframe.transforms import batch
 from tensorflow.contrib.learn.python.learn.dataframe.transforms import csv_parser
 from tensorflow.contrib.learn.python.learn.dataframe.transforms import example_parser
-from tensorflow.contrib.learn.python.learn.dataframe.transforms import hashes
 from tensorflow.contrib.learn.python.learn.dataframe.transforms import in_memory_source
 from tensorflow.contrib.learn.python.learn.dataframe.transforms import reader_source
 from tensorflow.contrib.learn.python.learn.dataframe.transforms import sparsify
+from tensorflow.contrib.learn.python.learn.dataframe.transforms import split_mask
 from tensorflow.python.client import session as sess
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import errors
@@ -189,16 +189,12 @@ class TensorFlowDataFrame(df.DataFrame):
     Returns:
       Two `DataFrame`s containing the partitioned rows.
     """
-    # TODO(soergel): allow seed?
     if isinstance(index_series, str):
       index_series = self[index_series]
-    num_buckets = 1000000  # close enough for simple splits
-    hashed_input, = hashes.HashFast(num_buckets)(index_series)
-    threshold = int(num_buckets * proportion)
-    left = hashed_input < threshold
-    right = ~left
-    left_rows = self.select_rows(left)
-    right_rows = self.select_rows(right)
+    left_mask, = split_mask.SplitMask(proportion)(index_series)
+    right_mask = ~left_mask
+    left_rows = self.select_rows(left_mask)
+    right_rows = self.select_rows(right_mask)
 
     if batch_size:
       left_rows = left_rows.batch(batch_size=batch_size, shuffle=False)
@@ -206,7 +202,58 @@ class TensorFlowDataFrame(df.DataFrame):
 
     return left_rows, right_rows
 
-  def run_once(self):
+  def split_fast(self, index_series, proportion, batch_size,
+                 base_batch_size=1000):
+    """Deterministically split a `DataFrame` into two `DataFrame`s.
+
+    Note this split is only as deterministic as the underlying hash function;
+    see `tf.string_to_hash_bucket_fast`.  The hash function is deterministic
+    for a given binary, but may change occasionally.  The only way to achieve
+    an absolute guarantee that the split `DataFrame`s do not change across runs
+    is to materialize them.
+
+    Note too that the allocation of a row to one partition or the
+    other is evaluated independently for each row, so the exact number of rows
+    in each partition is binomially distributed.
+
+    Args:
+      index_series: a `Series` of unique strings, whose hash will determine the
+        partitioning; or the name in this `DataFrame` of such a `Series`.
+        (This `Series` must contain strings because TensorFlow provides hash
+        ops only for strings, and there are no number-to-string converter ops.)
+      proportion: The proportion of the rows to select for the 'left'
+        partition; the remaining (1 - proportion) rows form the 'right'
+        partition.
+      batch_size: the batch size to use when rebatching the left and right
+        `DataFrame`s.  If None (default), the `DataFrame`s are not rebatched;
+        thus their batches will have variable sizes, according to which rows
+        are selected from each batch of the original `DataFrame`.
+      base_batch_size: the batch size to use for materialized data, prior to the
+        split.
+
+    Returns:
+      Two `DataFrame`s containing the partitioned rows.
+    """
+    if isinstance(index_series, str):
+      index_series = self[index_series]
+    left_mask, = split_mask.SplitMask(proportion)(index_series)
+    right_mask = ~left_mask
+    self["left_mask__"] = left_mask
+    self["right_mask__"] = right_mask
+    # TODO(soergel): instead of base_batch_size can we just do one big batch?
+    # avoid computing the hashes twice
+    m = self.materialize_to_memory(batch_size=base_batch_size)
+    left_rows_df = m.select_rows(m["left_mask__"])
+    right_rows_df = m.select_rows(m["right_mask__"])
+    del left_rows_df[["left_mask__", "right_mask__"]]
+    del right_rows_df[["left_mask__", "right_mask__"]]
+
+    # avoid recomputing the split repeatedly
+    left_rows_df = left_rows_df.materialize_to_memory(batch_size=batch_size)
+    right_rows_df = right_rows_df.materialize_to_memory(batch_size=batch_size)
+    return left_rows_df, right_rows_df
+
+  def run_one_batch(self):
     """Creates a new 'Graph` and `Session` and runs a single batch.
 
     Returns:
@@ -215,6 +262,46 @@ class TensorFlowDataFrame(df.DataFrame):
     """
     return list(self.run(num_batches=1))[0]
 
+  def run_one_epoch(self):
+    """Creates a new 'Graph` and `Session` and runs a single epoch.
+
+    Naturally this makes sense only for DataFrames that fit in memory.
+
+    Returns:
+      A dictionary mapping column names to numpy arrays that contain a single
+      epoch of the `DataFrame`.
+    """
+    # batches is a list of dicts of numpy arrays
+    batches = [b for b in self.run(num_epochs=1)]
+
+    # first invert that to make a dict of lists of numpy arrays
+    pivoted_batches = {}
+    for k in batches[0].keys():
+      pivoted_batches[k] = []
+    for b in batches:
+      for k, v in b.items():
+        pivoted_batches[k].append(v)
+
+    # then concat the arrays in each column
+    result = {k: np.concatenate(column_batches)
+              for k, column_batches in pivoted_batches.items()}
+    return result
+
+  def materialize_to_memory(self, batch_size):
+    unordered_dict_of_arrays = self.run_one_epoch()
+
+    # there may already be an 'index' column, in which case from_ordereddict)
+    # below will complain because it wants to generate a new one.
+    # for now, just remove it.
+    # TODO(soergel): preserve index history, potentially many levels deep
+    del unordered_dict_of_arrays["index"]
+
+    # the order of the columns in this dict is arbitrary; we just need it to
+    # remain consistent.
+    ordered_dict_of_arrays = collections.OrderedDict(unordered_dict_of_arrays)
+    return TensorFlowDataFrame.from_ordereddict(ordered_dict_of_arrays,
+                                                batch_size=batch_size)
+
   def batch(self,
             batch_size,
             shuffle=False,
@@ -591,3 +678,51 @@ class TensorFlowDataFrame(df.DataFrame):
     dataframe = cls()
     dataframe.assign(**(numpy_source()._asdict()))
     return dataframe
+
+  @classmethod
+  def from_ordereddict(cls,
+                       ordered_dict_of_arrays,
+                       num_threads=None,
+                       enqueue_size=None,
+                       batch_size=None,
+                       queue_capacity=None,
+                       min_after_dequeue=None,
+                       shuffle=True,
+                       seed=None,
+                       data_name="numpy_data"):
+    """Creates a `tf.learn.DataFrame` from a `numpy.ndarray`.
+
+    The returned `DataFrame` contains two columns: 'index' and 'value'. The
+    'value' column contains a row from the array. The 'index' column contains
+    the corresponding row number.
+
+    Args:
+      ordered_dict_of_arrays: `OrderedDict` of `numpy.ndarray` that serves as a
+          data source.
+      num_threads: the number of threads to use for enqueueing.
+      enqueue_size: the number of rows to enqueue per step.
+      batch_size: desired batch size.
+      queue_capacity: capacity of the queue that will store parsed `Example`s
+      min_after_dequeue: minimum number of elements that can be left by a
+        dequeue operation. Only used if `shuffle` is true.
+      shuffle: whether records should be shuffled. Defaults to true.
+      seed: passed to random shuffle operations. Only used if `shuffle` is true.
+      data_name: a scope name identifying the data.
+
+    Returns:
+      A `tf.learn.DataFrame` that contains batches drawn from the given
+      array.
+    """
+    numpy_source = in_memory_source.OrderedDictNumpySource(
+        ordered_dict_of_arrays,
+        num_threads=num_threads,
+        enqueue_size=enqueue_size,
+        batch_size=batch_size,
+        queue_capacity=queue_capacity,
+        shuffle=shuffle,
+        min_after_dequeue=min_after_dequeue,
+        seed=seed,
+        data_name=data_name)
+    dataframe = cls()
+    dataframe.assign(**(numpy_source()._asdict()))
+    return dataframe
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/split_mask.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/split_mask.py
new file mode 100644
index 0000000000..953e74ba84
--- /dev/null
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/split_mask.py
@@ -0,0 +1,81 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Masks one `Series` based on the content of another `Series`."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.contrib.learn.python.learn.dataframe import transform
+from tensorflow.contrib.learn.python.learn.dataframe.transforms import hashes
+
+
+class SplitMask(transform.Transform):
+  """Provide a boolean mask based on a hash of a `Series`."""
+
+  def __init__(self, proportion):
+    """Initialize `SplitMask`.
+
+    Args:
+      proportion: The proportion of the rows to select for the '1'
+        partition; the remaining (1 - proportion) rows form the '0'
+        partition.
+    """
+    # TODO(soergel): allow seed?
+    super(SplitMask, self).__init__()
+    self._proportion = proportion
+
+  @property
+  def name(self):
+    return "SplitMask"
+
+  @property
+  def input_valency(self):
+    return 1
+
+  @property
+  def _output_names(self):
+    return "output",
+
+  def _produce_output_series(self, input_series=None):
+    """Deterministically generate a boolean Series for partitioning rows.
+
+    Note this split is only as deterministic as the underlying hash function;
+    see `tf.string_to_hash_bucket_fast`.  The hash function is deterministic
+    for a given binary, but may change occasionally.  The only way to achieve
+    an absolute guarantee that the split `DataFrame`s do not change across runs
+    is to materialize them.
+
+    Note too that the allocation of a row to one partition or the
+    other is evaluated independently for each row, so the exact number of rows
+    in each partition is binomially distributed.
+
+    Args:
+      input_series: a `Series` of unique strings, whose hash will determine the
+        partitioning.
+        (This `Series` must contain strings because TensorFlow provides hash
+        ops only for strings, and there are no number-to-string converter ops.)
+
+    Returns:
+      Two `DataFrame`s containing the partitioned rows.
+    """
+    # TODO(soergel): allow seed?
+    num_buckets = 1000000  # close enough for simple splits
+    hashed_input, = hashes.HashFast(num_buckets)(input_series[0])
+    threshold = int(num_buckets * self._proportion)
+    left_mask = hashed_input < threshold
+    return [left_mask]
+
diff --git a/tensorflow/contrib/learn/python/learn/tests/dataframe/arithmetic_transform_test.py b/tensorflow/contrib/learn/python/learn/tests/dataframe/arithmetic_transform_test.py
index 045f84580f..3682a6517d 100644
--- a/tensorflow/contrib/learn/python/learn/tests/dataframe/arithmetic_transform_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/dataframe/arithmetic_transform_test.py
@@ -49,7 +49,7 @@ class SumTestCase(tf.test.TestCase):
     frame["a+b"] = frame["a"] + frame["b"]
 
     expected_sum = pandas_df["a"] + pandas_df["b"]
-    actual_sum = frame.run_once()["a+b"]
+    actual_sum = frame.run_one_batch()["a+b"]
     np.testing.assert_array_equal(expected_sum, actual_sum)
 
 
@@ -70,7 +70,7 @@ class DifferenceTestCase(tf.test.TestCase):
     frame["a-b"] = frame["a"] - frame["b"]
 
     expected_diff = pandas_df["a"] - pandas_df["b"]
-    actual_diff = frame.run_once()["a-b"]
+    actual_diff = frame.run_one_batch()["a-b"]
     np.testing.assert_array_equal(expected_diff, actual_diff)
 
 
diff --git a/tensorflow/contrib/learn/python/learn/tests/dataframe/tensorflow_dataframe_test.py b/tensorflow/contrib/learn/python/learn/tests/dataframe/tensorflow_dataframe_test.py
index d7e2fe684b..98e7cf4a07 100644
--- a/tensorflow/contrib/learn/python/learn/tests/dataframe/tensorflow_dataframe_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/dataframe/tensorflow_dataframe_test.py
@@ -131,7 +131,7 @@ class TensorFlowDataFrameTestCase(tf.test.TestCase):
                                                        batch_size=10,
                                                        shuffle=False)
 
-    batch = tensorflow_df.run_once()
+    batch = tensorflow_df.run_one_batch()
 
     np.testing.assert_array_equal(pandas_df.index.values, batch["index"],
                                   "Expected index {}; got {}".format(
@@ -239,7 +239,7 @@ class TensorFlowDataFrameTestCase(tf.test.TestCase):
     s = pandas_df["string"]
     for i in range(0, len(s)):
       if isinstance(s[i], float) and math.isnan(s[i]):
-        s[i] = ""
+        pandas_df.set_value(i, "string", "")
     tensorflow_df = df.TensorFlowDataFrame.from_csv_with_feature_spec(
         [data_path],
         batch_size=batch_size,
-- 
GitLab


From 7810b648a2a086bc4680170ba1052d4aedc839ab Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 20:36:16 -0800
Subject: [PATCH 188/223] Allow getting default schedule from the experiment's
 config task_type when not explicitly passed to learn_runner.run. Change:
 131366238

---
 .../learn/python/learn/learn_runner.py        |  43 ++++++-
 .../python/learn/tests/learn_runner_test.py   | 105 +++++++++++++++---
 2 files changed, 129 insertions(+), 19 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/learn_runner.py b/tensorflow/contrib/learn/python/learn/learn_runner.py
index 331ca8df93..f9fc9838fa 100644
--- a/tensorflow/contrib/learn/python/learn/learn_runner.py
+++ b/tensorflow/contrib/learn/python/learn/learn_runner.py
@@ -22,12 +22,22 @@ from tensorflow.contrib.learn.python.learn.experiment import Experiment
 from tensorflow.python.platform import tf_logging as logging
 
 
-def run(experiment_fn, output_dir, schedule):
+def run(experiment_fn, output_dir, schedule=None):
   """Make and run an experiment.
 
   It creates an Experiment by calling `experiment_fn`. Then it calls the
   function named as `schedule` of the Experiment.
 
+  If schedule is not provided, then the default schedule for the current task
+  type is used. The defaults are as follows:
+
+   * 'ps' maps to 'serve'
+   * 'worker' maps to 'train'
+   * 'master' maps to 'local_run'
+
+  If the experiment's config does not include a task type, then an exception
+  is raised.
+
   Example:
   ```
     def _create_my_experiment(output_dir):
@@ -53,14 +63,13 @@ def run(experiment_fn, output_dir, schedule):
     The return value of function `schedule`.
 
   Raises:
-    ValueError: If output_dir or schedule is empty, or if `schedule` doesn't
-      references a member of `Experiment`.
+    ValueError: If `output_dir` is empty, `schedule` is None but no task
+      type is set in the built experiment's config, the task type has no
+      default, or `schedule` doesn't reference a member of `Experiment`.
     TypeError: `schedule` references non-callable member.
   """
   if not output_dir:
     raise ValueError('Must specify an output directory')
-  if not schedule:
-    raise ValueError('Must specify a schedule')
   if not callable(experiment_fn):
     raise TypeError('Experiment builder "%s" is not callable.' %
                     experiment_fn)
@@ -71,6 +80,12 @@ def run(experiment_fn, output_dir, schedule):
     raise TypeError('Experiment builder did not return an Experiment '
                     'instance, got %s instead.' % type(experiment))
 
+  # Get the schedule
+  config = experiment.estimator.config
+  schedule = schedule or _get_default_schedule(config)
+  if not schedule:
+    raise ValueError('Must specify a schedule')
+
   # Execute the schedule
   if not hasattr(experiment, schedule):
     logging.error('Schedule references non-existent task %s', schedule)
@@ -90,3 +105,21 @@ def run(experiment_fn, output_dir, schedule):
     raise TypeError('Schedule references non-callable member %s', schedule)
 
   return task()
+
+
+def _get_default_schedule(config):
+  """Returns the default schedule for the provided RunConfig."""
+  if not config or not config.job_name:
+    return None
+
+  if not config.job_name or config.job_name == 'master':
+    # TODO(rhaertel): handle the case there are more
+    # than one masters or explicitly disallow.
+    return 'local_run'
+  elif config.job_name == 'ps':
+    return 'serve'
+  elif config.job_name == 'worker':
+    return 'train'
+
+  return ValueError('No default schedule for task type: %s' %
+                    (config.job_name,))
diff --git a/tensorflow/contrib/learn/python/learn/tests/learn_runner_test.py b/tensorflow/contrib/learn/python/learn/tests/learn_runner_test.py
index d01d968505..a916fe54ac 100644
--- a/tensorflow/contrib/learn/python/learn/tests/learn_runner_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/learn_runner_test.py
@@ -18,14 +18,34 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import json
+import os
+
 import tensorflow as tf
 from tensorflow.contrib.learn.python.learn import learn_runner
+from tensorflow.contrib.learn.python.learn import run_config
 
 
 class TestExperiment(tf.contrib.learn.Experiment):
 
-  def __init__(self, default=None):
+  def __init__(self, default=None, config=None):
     self.default = default
+    self.config = config
+
+  @property
+  def estimator(self):
+    class Estimator(object):
+      config = self.config
+    return Estimator()
+
+  def local_run(self):
+    return "train_and_evaluate"
+
+  def train(self):
+    return "train"
+
+  def serve(self):
+    return "serve"
 
   def simple_task(self):
     return "simple_task, default=%s." % self.default
@@ -44,20 +64,66 @@ def build_non_experiment(output_dir):
 
 class MainTest(tf.test.TestCase):
 
-  def test_run(self):
+  def setUp(self):
+    # Ensure the TF_CONFIG environment variable is unset for all tests.
+    os.environ.pop("TF_CONFIG", None)
+
+  def test_run_with_explicit_args(self):
     self.assertEqual(
         "simple_task, default=None.",
-        learn_runner.run(
-            build_experiment, output_dir="/tmp", schedule="simple_task"))
+        learn_runner.run(build_experiment,
+                         output_dir="/tmp",
+                         schedule="simple_task"))
+
+  def test_schedule_from_tf_config(self):
+    os.environ["TF_CONFIG"] = json.dumps({"task": {"type": "worker"}})
+    # RunConfig constructuor will set job_name from TF_CONFIG.
+    config = run_config.RunConfig()
+    self.assertEqual(
+        "train",
+        learn_runner.run(lambda output_dir: TestExperiment(config=config),
+                         output_dir="/tmp"))
+
+  def test_schedule_from_manually_specified_job_name(self):
+    config = run_config.RunConfig(job_name="worker")
+    self.assertEqual(
+        "train",
+        learn_runner.run(lambda output_dir: TestExperiment(config=config),
+                         output_dir="/tmp"))
+
+  def test_schedule_from_config_runs_train_and_evaluate_on_master(self):
+    config = run_config.RunConfig(job_name="master")
+    self.assertEqual(
+        "train_and_evaluate",
+        learn_runner.run(lambda output_dir: TestExperiment(config=config),
+                         output_dir="/tmp"))
+
+  def test_schedule_from_config_runs_serve_on_ps(self):
+    config = run_config.RunConfig(job_name="ps")
+    self.assertEqual(
+        "serve",
+        learn_runner.run(lambda output_dir: TestExperiment(config=config),
+                         output_dir="/tmp"))
+
+  def test_schedule_from_config_runs_train_on_worker(self):
+    config = run_config.RunConfig(job_name="worker")
+    self.assertEqual(
+        "train",
+        learn_runner.run(lambda output_dir: TestExperiment(config=config),
+                         output_dir="/tmp"))
 
   def test_fail_no_output_dir(self):
     self.assertRaisesRegexp(ValueError, "Must specify an output directory",
                             learn_runner.run, build_experiment, "",
                             "simple_task")
 
-  def test_fail_no_schedule(self):
+  def test_fail_no_schedule_and_no_config(self):
+    self.assertRaisesRegexp(ValueError, "Must specify a schedule",
+                            learn_runner.run, build_experiment, "/tmp")
+
+  def test_fail_job_name_with_no_default_schedule(self):
     self.assertRaisesRegexp(ValueError, "Must specify a schedule",
-                            learn_runner.run, build_experiment, "/tmp", "")
+                            learn_runner.run, build_experiment, "/tmp")
 
   def test_fail_non_callable(self):
     self.assertRaisesRegexp(TypeError, "Experiment builder .* is not callable",
@@ -65,19 +131,30 @@ class MainTest(tf.test.TestCase):
                             "simple_test")
 
   def test_fail_not_experiment(self):
-    self.assertRaisesRegexp(
-        TypeError, "Experiment builder did not return an Experiment",
-        learn_runner.run, build_non_experiment, "/tmp", "simple_test")
+    self.assertRaisesRegexp(TypeError,
+                            "Experiment builder did not return an Experiment",
+                            learn_runner.run, build_non_experiment, "/tmp",
+                            "simple_test")
 
   def test_fail_non_existent_task(self):
-    self.assertRaisesRegexp(
-        ValueError, "Schedule references non-existent task",
-        learn_runner.run, build_experiment, "/tmp", "mirage")
+    self.assertRaisesRegexp(ValueError, "Schedule references non-existent task",
+                            learn_runner.run, build_experiment, "/tmp",
+                            "mirage")
 
   def test_fail_non_callable_task(self):
+    self.assertRaisesRegexp(TypeError,
+                            "Schedule references non-callable member",
+                            learn_runner.run, build_experiment, "/tmp",
+                            "default")
+
+  def test_fail_schedule_from_config_with_no_job_name(self):
+    config = run_config.RunConfig(job_name=None)
     self.assertRaisesRegexp(
-        TypeError, "Schedule references non-callable member",
-        learn_runner.run, build_experiment, "/tmp", "default")
+        ValueError,
+        "Must specify a schedule",
+        learn_runner.run,
+        lambda output_dir: TestExperiment(config=config),
+        output_dir="/tmp")
 
 
 if __name__ == "__main__":
-- 
GitLab


From d7cc0cf1d0e616e1454872d3b45c365dc4537fb2 Mon Sep 17 00:00:00 2001
From: Zongheng Yang <zongheng@google.com>
Date: Thu, 25 Aug 2016 20:45:33 -0800
Subject: [PATCH 189/223] Add TensorSlice::IsFull(). Change: 131366634

---
 tensorflow/core/framework/tensor_slice.cc      |  7 +++++++
 tensorflow/core/framework/tensor_slice.h       |  3 +++
 tensorflow/core/framework/tensor_slice_test.cc | 17 +++++++++++++++++
 3 files changed, 27 insertions(+)

diff --git a/tensorflow/core/framework/tensor_slice.cc b/tensorflow/core/framework/tensor_slice.cc
index f5f8c8d3e9..c98ef13535 100644
--- a/tensorflow/core/framework/tensor_slice.cc
+++ b/tensorflow/core/framework/tensor_slice.cc
@@ -82,6 +82,13 @@ void TensorSlice::Clear() {
   lengths_.clear();
 }
 
+bool TensorSlice::IsFull() const {
+  for (int d = 0; d < dims(); ++d) {
+    if (!IsFullAt(d)) return false;
+  }
+  return true;
+}
+
 void TensorSlice::SetFullSlice(int dim) {
   Clear();
   starts_.reserve(dim);
diff --git a/tensorflow/core/framework/tensor_slice.h b/tensorflow/core/framework/tensor_slice.h
index 2ccb4d1083..8c4a2adeb3 100644
--- a/tensorflow/core/framework/tensor_slice.h
+++ b/tensorflow/core/framework/tensor_slice.h
@@ -96,6 +96,9 @@ class TensorSlice {
   // If we have a full slice along dimension "d".
   bool IsFullAt(int d) const { return lengths_[d] < 0; }
 
+  // If this is a full slice, i.e. IsFullAt(d) for every d.
+  bool IsFull() const;
+
   // Set the slice to be a full slice of "dim" dimensions
   void SetFullSlice(int dim);
 
diff --git a/tensorflow/core/framework/tensor_slice_test.cc b/tensorflow/core/framework/tensor_slice_test.cc
index a88a444358..e26c840998 100644
--- a/tensorflow/core/framework/tensor_slice_test.cc
+++ b/tensorflow/core/framework/tensor_slice_test.cc
@@ -29,9 +29,11 @@ TEST(TensorSliceTest, Basic) {
     // Repeatedly setting FullSlice should work.
     TensorSlice s(3);
     EXPECT_EQ("-:-:-", s.DebugString());
+    EXPECT_TRUE(s.IsFull());
 
     s.SetFullSlice(4);
     EXPECT_EQ("-:-:-:-", s.DebugString());
+    EXPECT_TRUE(s.IsFull());
   }
 }
 
@@ -41,6 +43,7 @@ TEST(TensorSliceTest, Serialization) {
   {
     TensorSlice s({{0, -1}, {0, 10}, {14, 1}, {0, -1}});
     EXPECT_EQ("-:0,10:14,1:-", s.DebugString());
+    EXPECT_TRUE(!s.IsFull());
   }
 
   {
@@ -58,6 +61,7 @@ TEST(TensorSliceTest, Serialization) {
     ASSERT_TRUE(protobuf::TextFormat::ParseFromString(ptxt, &proto));
     TensorSlice s(proto);
     EXPECT_EQ("-:0,10:14,1:-", s.DebugString());
+    EXPECT_TRUE(!s.IsFull());
   }
 
   // Parsing
@@ -71,6 +75,7 @@ TEST(TensorSliceTest, Serialization) {
         "extent { start: 1 length: 3 } "
         "extent { start: 4 length: 5 }",
         proto.ShortDebugString());
+    EXPECT_TRUE(!s.IsFull());
   }
 
   // Failed parsing
@@ -102,6 +107,7 @@ TEST(TensorSliceTest, Serialization) {
     EXPECT_EQ(
         "extent { start: 9223372036854775807 length: 9223372036854775807 }",
         proto.ShortDebugString());
+    EXPECT_TRUE(!s.IsFull());
   }
 
   // int64 parsing failure
@@ -282,5 +288,16 @@ TEST(TensorSliceTest, UpdateToCover) {
   EXPECT_EQ("-:-:2,8", s.DebugString());
 }
 
+TEST(TensorSliceTest, IsFull) {
+  TensorSlice slice(3);
+  EXPECT_TRUE(slice.IsFull());
+
+  TensorSlice slice2({{0, -1}});
+  EXPECT_TRUE(slice2.IsFull());
+
+  TensorSlice slice3({{0, -1}, {0, -1}, {14, 1}});
+  EXPECT_TRUE(!slice3.IsFull());
+}
+
 }  // namespace
 }  // namespace tensorflow
-- 
GitLab


From f30403181f0ccbb30bb38777fbccf6c1b6576ca9 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Thu, 25 Aug 2016 21:14:36 -0800
Subject: [PATCH 190/223] Fix a memory leak in TF_SetAttrBoolList. Caught by a
 unittest in another change being worked on. Change: 131368103

---
 tensorflow/c/c_api.cc | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc
index f10e2a3ddd..b081dff36a 100644
--- a/tensorflow/c/c_api.cc
+++ b/tensorflow/c/c_api.cc
@@ -773,11 +773,12 @@ void TF_SetAttrBool(TF_OperationDescription* desc, const char* attr_name,
 
 void TF_SetAttrBoolList(TF_OperationDescription* desc, const char* attr_name,
                         const unsigned char* values, int num_values) {
-  bool* b = new bool[num_values];
+  std::unique_ptr<bool[]> b(new bool[num_values]);
   for (int i = 0; i < num_values; ++i) {
     b[i] = values[i];
   }
-  desc->node_builder.Attr(attr_name, ArraySlice<const bool>(b, num_values));
+  desc->node_builder.Attr(attr_name,
+                          ArraySlice<const bool>(b.get(), num_values));
 }
 
 void TF_SetAttrType(TF_OperationDescription* desc, const char* attr_name,
-- 
GitLab


From 10d0ef7414058544f11cb984b58f3b2b6c107c90 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 26 Aug 2016 09:37:21 -0400
Subject: [PATCH 191/223] Fix typo in README.md

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 3de86a2165..e356ff931c 100644
--- a/README.md
+++ b/README.md
@@ -34,9 +34,9 @@ and discussion.**
 People who are a little more adventurous can also try our nightly binaries:
 
 * Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
-* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/140/artifact/pip_test/whl/tensorflow-0.8.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
+* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/140/artifact/pip_test/whl/tensorflow-0.8.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
 * Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac1-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac1-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac1-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac1-slave/))
-* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
+* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
 * [Android](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-android/TF_BUILD_CONTAINER_TYPE=ANDROID,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=NO_PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=android-slave/lastSuccessfulBuild/artifact/bazel-out/local_linux/bin/tensorflow/examples/android/tensorflow_demo.apk) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-android/TF_BUILD_CONTAINER_TYPE=ANDROID,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=NO_PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=android-slave/))
 
 #### *Try your first TensorFlow program*
-- 
GitLab


From 15a304971850832258c22da3af0e7144d393ae02 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 26 Aug 2016 05:31:41 -0800
Subject: [PATCH 192/223] Added copy_with_input_replacements and graph_replace.
 Change: 131395399

---
 tensorflow/contrib/graph_editor/subgraph.py  |   3 +-
 tensorflow/contrib/graph_editor/transform.py | 231 ++++++++++++++++++-
 tensorflow/contrib/graph_editor/util.py      |  49 ++++
 3 files changed, 271 insertions(+), 12 deletions(-)

diff --git a/tensorflow/contrib/graph_editor/subgraph.py b/tensorflow/contrib/graph_editor/subgraph.py
index 56eeeca102..c5fa9614de 100644
--- a/tensorflow/contrib/graph_editor/subgraph.py
+++ b/tensorflow/contrib/graph_editor/subgraph.py
@@ -440,7 +440,8 @@ class SubGraphView(object):
     def print_list(name, iterable, get_name):
       if iterable:
         print("** {}[{}]:".format(name, len(iterable)), file=res)
-        print("\n".join([get_name(elem) for elem in iterable]), file=res)
+        print("\n".join(["  {}".format(get_name(elem)) for elem in iterable]),
+              file=res)
       else:
         print("** {}: empty".format(name), file=res)
 
diff --git a/tensorflow/contrib/graph_editor/transform.py b/tensorflow/contrib/graph_editor/transform.py
index 908631ebcc..a33a85ad3f 100644
--- a/tensorflow/contrib/graph_editor/transform.py
+++ b/tensorflow/contrib/graph_editor/transform.py
@@ -20,11 +20,14 @@ from __future__ import division
 from __future__ import print_function
 
 from copy import deepcopy
+import types
 
 from six import iteritems
+from six import StringIO
 
 from tensorflow.contrib.graph_editor import edit
 from tensorflow.contrib.graph_editor import reroute
+from tensorflow.contrib.graph_editor import select
 from tensorflow.contrib.graph_editor import subgraph
 from tensorflow.contrib.graph_editor import util
 from tensorflow.python.framework import ops as tf_ops
@@ -39,6 +42,8 @@ __all__ = [
     "transform_op_in_place",
     "Transformer",
     "copy",
+    "copy_with_input_replacements",
+    "graph_replace",
 ]
 
 
@@ -232,9 +237,119 @@ class Transformer(object):
       self.transformed_ops = {}
       self.transformed_ts = {}
 
-    def create_ops_mapping(self):
-      """Return the mapping from original ops to transformed ops."""
-      return {op.name: op_.name for op, op_ in iteritems(self.transformed_ops)}
+  class ResultInfo(object):
+    """"Contains information about the result of a transform operation."""
+
+    def __init__(self, info):
+      """Constructor.
+
+      Args:
+        info: an instance of Transformer._Info containing various internal
+          information about the transform operation.
+      """
+      self._graph = info.graph
+      self._scope = info.scope
+      self._graph_ = info.graph_
+      self._scope_ = info.scope_
+      self._transformed_ops = info.transformed_ops
+      self._transformed_ts = info.transformed_ts
+
+    def _get_transformed_map(self, top):
+      """Return the correct container depending on the type of `top`."""
+      if isinstance(top, tf_ops.Operation):
+        return self._transformed_ops
+      elif isinstance(top, tf_ops.Tensor):
+        return self._transformed_ts
+      else:
+        raise TypeError(
+            "Expected a tf.Tensor or a tf.Operation, got a {}".format(
+                type(top)))
+
+    def _transformed_elem(self, original_top):
+      """Return the transformed op/tensor corresponding to the original one.
+
+      Args:
+        original_top: the original tensor/operation.
+      Returns:
+        the transformed tensor/operation (or None if no match is found).
+      """
+      transformed_map = self._get_transformed_map(original_top)
+      if isinstance(original_top, str):
+        for original, transformed in iteritems(transformed_map):
+          if original.name == original_top:
+            return transformed
+        return None
+      else:
+        if original_top not in transformed_map:
+          return None
+        return transformed_map[original_top]
+
+    def _original_elem(self, transformed_top):
+      """Return the original op/tensor corresponding to the transformed one.
+
+      Args:
+        transformed_top: the transformed tensor/operation.
+      Returns:
+        the original tensor/operation (or None if no match is found).
+      """
+      transformed_map = self._get_transformed_map(transformed_top)
+      if isinstance(transformed_top, types.StringTypes):
+        finder = lambda transformed: transformed.name == transformed_top
+      else:
+        finder = lambda transformed: transformed == transformed_top
+      for original, transformed in iteritems(transformed_map):
+        if finder(transformed):
+          return original
+      return None
+
+    def transformed(self, original):
+      """Return the transformed op/tensor corresponding to the original one.
+
+      Note that the output of this function mimics the hierarchy
+      of its input argument `original`.
+      Given an iterable, it returns a list. Given an operation or a tensor,
+      it will return an operation or a tensor.
+
+      Args:
+        original: the original tensor/operation.
+      Returns:
+        the transformed tensor/operation (or None if no match is found).
+      """
+      return util.transform_tree(original, self._transformed_elem)
+
+    def original(self, transformed):
+      """Return the original op/tensor corresponding to the transformed one.
+
+      Note that the output of this function mimics the hierarchy
+      of its input argument `transformed`.
+      Given an iterable, it returns a list. Given an operation or a tensor,
+      it will return an operation or a tensor.
+
+      Args:
+        transformed: the transformed tensor/operation.
+      Returns:
+        the original tensor/operation (or None if no match is found).
+      """
+      return util.transform_tree(transformed, self._original_elem)
+
+    def __str__(self):
+      res = StringIO()
+      print("Transform result info:", file=res)
+      if self._graph == self._graph_:
+        in_place_str = "" if self._scope_ else " IN-PLACE"
+        print("  Within graph[{}]{}".format(
+            id(self._graph), in_place_str), file=res)
+      else:
+        print("  graph[{}] => graph[{}]".format(
+            id(self._graph), id(self._graph_)), file=res)
+      if self._scope:
+        print("  Relative to source scope: {}".format(self._scope), file=res)
+      if self._scope_:
+        print("  Scope destination: {}".format(self._scope_), file=res)
+      print("Operations mapping:", file=res)
+      for op, op_ in iteritems(self._transformed_ops):
+        print("  {} => {}".format(op.name, op_.name), file=res)
+      return res.getvalue()
 
   def __init__(self):
     """Transformer constructor.
@@ -287,12 +402,13 @@ class Transformer(object):
         relative path of x/y and will be transformed into b/x/y.
       reuse_dst_scope: if True the dst_scope is re-used if it already exists.
         Otherwise, the scope is given a unique name based on the one given
-        by postfixing an underscore followed by a digit (default).
+        by appending an underscore followed by a digit (default).
     Returns:
-      A tuple `(sgv, ops_mapping)` where:
+      A tuple `(sgv, info)` where:
         `sgv` is the transformed subgraph view;
-        `ops_mapping` is a dictionary mapping the name of the original ops
-        to the name of the transformed ops.
+        `info` is an instance of Transformer.ResultInfo containing
+        information about the transform, including mapping between
+        original and transformed tensors and operations.
     Raises:
       ValueError: if the argumens are invalid.
     """
@@ -330,9 +446,9 @@ class Transformer(object):
 
     sgv_ = self._transform_sgv(sgv)
 
-    ops_mapping = self._info.create_ops_mapping()
+    res_info = Transformer.ResultInfo(self._info)
     self._info = None
-    return sgv_, ops_mapping
+    return sgv_, res_info
 
   def _transform_sgv(self, sgv):
     """Transform a subgraph view.
@@ -470,9 +586,13 @@ def copy(sgv, dst_graph=None, dst_scope="", src_scope="",
     src_scope: the source scope.
     reuse_dst_scope: if True the dst_scope is re-used if it already exists.
       Otherwise, the scope is given a unique name based on the one given
-      by postfixing an underscore followed by a digit (default).
+      by appending an underscore followed by a digit (default).
   Returns:
-    The subgraph view of the copied subgraph.
+    A tuple `(sgv, info)` where:
+      `sgv` is the transformed subgraph view;
+      `info` is an instance of Transformer.ResultInfo containing
+      information about the transform, including mapping between
+      original and transformed tensors and operations.
   Raises:
     TypeError: if dst_graph is not a tf.Graph.
     StandardError: if sgv cannot be converted to a SubGraphView using
@@ -487,3 +607,92 @@ def copy(sgv, dst_graph=None, dst_scope="", src_scope="",
   copier = Transformer()
   return copier(
       sgv, dst_graph, dst_scope, src_scope, reuse_dst_scope=reuse_dst_scope)
+
+
+def copy_with_input_replacements(sgv, replacement_ts,
+                                 dst_graph=None, dst_scope="", src_scope="",
+                                 reuse_dst_scope=False):
+  """Copy a subgraph, replacing some of its inputs.
+
+  Note a replacement only happens if the tensor to be replaced
+  is an input of the given subgraph. The inputs of a subgraph can
+  be queried using sgv.inputs.
+
+  Args:
+    sgv: the source subgraph-view. This argument is converted to a subgraph
+      using the same rules as the function subgraph.make_view.
+    replacement_ts: dictionary mapping from original tensors to the
+      replaced one.
+    dst_graph: the destination graph.
+    dst_scope: the destination scope.
+    src_scope: the source scope.
+    reuse_dst_scope: if True the dst_scope is re-used if it already exists.
+      Otherwise, the scope is given a unique name based on the one given
+      by appending an underscore followed by a digit (default).
+  Returns:
+    A tuple `(sgv, info)` where:
+      `sgv` is the transformed subgraph view;
+      `info` is an instance of Transformer.ResultInfo containing
+      information about the transform, including mapping between
+      original and transformed tensors and operations.
+  Raises:
+    TypeError: if dst_graph is not a tf.Graph.
+    StandardError: if sgv cannot be converted to a SubGraphView using
+      the same rules as the function subgraph.make_view.
+  """
+  sgv = subgraph.make_view(sgv)
+  if dst_graph is None:
+    dst_graph = sgv.graph
+  if not isinstance(dst_graph, tf_ops.Graph):
+    raise TypeError("Expected a tf.Graph, got: {}".format(type(dst_graph)))
+
+  copier = Transformer()
+  # Replace tensor if possible.
+  def replace_t_with_replacement_handler(info, t):
+    if t in replacement_ts:
+      return replacement_ts[t]
+    else:
+      return keep_t_if_possible_handler(info, t)
+  copier.transform_external_input_handler = replace_t_with_replacement_handler
+  return copier(
+      sgv, dst_graph, dst_scope, src_scope, reuse_dst_scope=reuse_dst_scope)
+
+
+def graph_replace(target_ts, replacement_ts, dst_scope="",
+                  src_scope="", reuse_dst_scope=False):
+  """Create a new graph which compute the targets from the replaced Tensors.
+
+  Args:
+    target_ts: a single tf.Tensor or an iterabble of tf.Tensor.
+    replacement_ts: dictionary mapping from original tensors to replaced tensors
+    dst_scope: the destination scope.
+    src_scope: the source scope.
+    reuse_dst_scope: if True the dst_scope is re-used if it already exists.
+      Otherwise, the scope is given a unique name based on the one given
+      by appending an underscore followed by a digit (default).
+  Returns:
+    A single tf.Tensor or a list of target tf.Tensor, depending on
+    the type of the input argument `target_ts`.
+    The returned tensors are recomputed using the tensors from replacement_ts.
+  Raises:
+    ValueError: if the targets are not connected to replacement_ts.
+  """
+  # Identify operations in the graph that will change.
+  # Start forward walk at Tensors that will be replaced, and
+  # backward walk at the target output Tensors.
+  flatten_target_ts = util.flatten_tree(target_ts)
+  # Construct the forward control dependencies edges so that
+  # the get_walks_intersection_ops can also traverse the
+  # control dependencies.
+  graph = util.get_unique_graph(flatten_target_ts, check_types=(tf_ops.Tensor))
+  control_ios = util.ControlOutputs(graph)
+  ops = select.get_walks_intersection_ops(replacement_ts.keys(),
+                                          flatten_target_ts,
+                                          control_ios=control_ios)
+  if not ops:
+    raise ValueError("Targets and replacements are not connected!")
+  # Create a copy of the relevant subgraph
+  _, info = copy_with_input_replacements(
+      ops, replacement_ts, None, dst_scope, src_scope, reuse_dst_scope)
+  # Return the transformed targets
+  return info.transformed(target_ts)
diff --git a/tensorflow/contrib/graph_editor/util.py b/tensorflow/contrib/graph_editor/util.py
index 5067265805..fd6c64d70b 100644
--- a/tensorflow/contrib/graph_editor/util.py
+++ b/tensorflow/contrib/graph_editor/util.py
@@ -19,6 +19,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from six import iteritems
 from tensorflow.python.framework import ops as tf_ops
 from tensorflow.python.ops import array_ops as tf_array_ops
 
@@ -86,6 +87,54 @@ def is_iterable(obj):
   return True
 
 
+def flatten_tree(tree, leaves=None):
+  """Flatten a tree into a list.
+
+  Args:
+    tree: iterable or not. If iterable, its elements (child) can also be
+      iterable or not.
+    leaves: list to which the tree leaves are appended (None by default).
+  Returns:
+    A list of all the leaves in the tree.
+  """
+  if leaves is None:
+    leaves = []
+  if is_iterable(tree):
+    for child in tree:
+      flatten_tree(child, leaves)
+  else:
+    leaves.append(tree)
+  return leaves
+
+
+def transform_tree(tree, fn, iterable_type=tuple):
+  """Transform all the nodes of a tree.
+
+  Args:
+    tree: iterable or not. If iterable, its elements (child) can also be
+      iterable or not.
+    fn: function to apply to each leaves.
+    iterable_type: type use to construct the resulting tree for unknwon
+      iterable, typically `list` or `tuple`.
+  Returns:
+    A tree whose leaves has been transformed by `fn`.
+    The hierarchy of the output tree mimics the one of the input tree.
+  """
+  if is_iterable(tree):
+    if isinstance(tree, list):
+      return [transform_tree(child, fn) for child in tree]
+    elif isinstance(tree, tuple):
+      # this works for named tupled as well:
+      return tree.__new__(type(tree),
+                          (transform_tree(child, fn) for child in tree))
+    elif isinstance(tree, dict):
+      return {k: transform_tree(child, fn) for k, child in iteritems(tree)}
+    else:
+      return iterable_type(transform_tree(child, fn) for child in tree)
+  else:
+    return fn(tree)
+
+
 def check_graphs(*args):
   """Check that all the element in args belong to the same graph.
 
-- 
GitLab


From eda21c986a95912fb4a97e195f41a57d9007bb20 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 26 Aug 2016 06:03:25 -0800
Subject: [PATCH 193/223] Update generated Python Op docs. Change: 131397509

---
 .../api_docs/python/contrib.graph_editor.md   | 79 ++++++++++++++++++-
 .../shard2/tf.contrib.graph_editor.copy.md    |  8 +-
 .../tf.contrib.graph_editor.graph_replace.md  | 26 ++++++
 ...aph_editor.copy_with_input_replacements.md | 37 +++++++++
 tensorflow/g3doc/api_docs/python/index.md     |  2 +
 5 files changed, 148 insertions(+), 4 deletions(-)
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.graph_editor.graph_replace.md
 create mode 100644 tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.graph_editor.copy_with_input_replacements.md

diff --git a/tensorflow/g3doc/api_docs/python/contrib.graph_editor.md b/tensorflow/g3doc/api_docs/python/contrib.graph_editor.md
index cd1935685c..68e9619fc7 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.graph_editor.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.graph_editor.md
@@ -1857,11 +1857,15 @@ Copy a subgraph.
 *  <b>`src_scope`</b>: the source scope.
 *  <b>`reuse_dst_scope`</b>: if True the dst_scope is re-used if it already exists.
     Otherwise, the scope is given a unique name based on the one given
-    by postfixing an underscore followed by a digit (default).
+    by appending an underscore followed by a digit (default).
 
 ##### Returns:
 
-  The subgraph view of the copied subgraph.
+  A tuple `(sgv, info)` where:
+    `sgv` is the transformed subgraph view;
+    `info` is an instance of Transformer.ResultInfo containing
+    information about the transform, including mapping between
+    original and transformed tensors and operations.
 
 ##### Raises:
 
@@ -2098,3 +2102,74 @@ Add output matches.
 
 
 
+
+## Other Functions and Classes
+- - -
+
+### `tf.contrib.graph_editor.copy_with_input_replacements(sgv, replacement_ts, dst_graph=None, dst_scope='', src_scope='', reuse_dst_scope=False)` {#copy_with_input_replacements}
+
+Copy a subgraph, replacing some of its inputs.
+
+Note a replacement only happens if the tensor to be replaced
+is an input of the given subgraph. The inputs of a subgraph can
+be queried using sgv.inputs.
+
+##### Args:
+
+
+*  <b>`sgv`</b>: the source subgraph-view. This argument is converted to a subgraph
+    using the same rules as the function subgraph.make_view.
+*  <b>`replacement_ts`</b>: dictionary mapping from original tensors to the
+    replaced one.
+*  <b>`dst_graph`</b>: the destination graph.
+*  <b>`dst_scope`</b>: the destination scope.
+*  <b>`src_scope`</b>: the source scope.
+*  <b>`reuse_dst_scope`</b>: if True the dst_scope is re-used if it already exists.
+    Otherwise, the scope is given a unique name based on the one given
+    by appending an underscore followed by a digit (default).
+
+##### Returns:
+
+  A tuple `(sgv, info)` where:
+    `sgv` is the transformed subgraph view;
+    `info` is an instance of Transformer.ResultInfo containing
+    information about the transform, including mapping between
+    original and transformed tensors and operations.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if dst_graph is not a tf.Graph.
+*  <b>`StandardError`</b>: if sgv cannot be converted to a SubGraphView using
+    the same rules as the function subgraph.make_view.
+
+
+- - -
+
+### `tf.contrib.graph_editor.graph_replace(target_ts, replacement_ts, dst_scope='', src_scope='', reuse_dst_scope=False)` {#graph_replace}
+
+Create a new graph which compute the targets from the replaced Tensors.
+
+##### Args:
+
+
+*  <b>`target_ts`</b>: a single tf.Tensor or an iterabble of tf.Tensor.
+*  <b>`replacement_ts`</b>: dictionary mapping from original tensors to replaced tensors
+*  <b>`dst_scope`</b>: the destination scope.
+*  <b>`src_scope`</b>: the source scope.
+*  <b>`reuse_dst_scope`</b>: if True the dst_scope is re-used if it already exists.
+    Otherwise, the scope is given a unique name based on the one given
+    by appending an underscore followed by a digit (default).
+
+##### Returns:
+
+  A single tf.Tensor or a list of target tf.Tensor, depending on
+  the type of the input argument `target_ts`.
+  The returned tensors are recomputed using the tensors from replacement_ts.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if the targets are not connected to replacement_ts.
+
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.graph_editor.copy.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.graph_editor.copy.md
index d892a6cc4b..60e3acd9bd 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.graph_editor.copy.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.graph_editor.copy.md
@@ -12,11 +12,15 @@ Copy a subgraph.
 *  <b>`src_scope`</b>: the source scope.
 *  <b>`reuse_dst_scope`</b>: if True the dst_scope is re-used if it already exists.
     Otherwise, the scope is given a unique name based on the one given
-    by postfixing an underscore followed by a digit (default).
+    by appending an underscore followed by a digit (default).
 
 ##### Returns:
 
-  The subgraph view of the copied subgraph.
+  A tuple `(sgv, info)` where:
+    `sgv` is the transformed subgraph view;
+    `info` is an instance of Transformer.ResultInfo containing
+    information about the transform, including mapping between
+    original and transformed tensors and operations.
 
 ##### Raises:
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.graph_editor.graph_replace.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.graph_editor.graph_replace.md
new file mode 100644
index 0000000000..e28b21c50e
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.graph_editor.graph_replace.md
@@ -0,0 +1,26 @@
+### `tf.contrib.graph_editor.graph_replace(target_ts, replacement_ts, dst_scope='', src_scope='', reuse_dst_scope=False)` {#graph_replace}
+
+Create a new graph which compute the targets from the replaced Tensors.
+
+##### Args:
+
+
+*  <b>`target_ts`</b>: a single tf.Tensor or an iterabble of tf.Tensor.
+*  <b>`replacement_ts`</b>: dictionary mapping from original tensors to replaced tensors
+*  <b>`dst_scope`</b>: the destination scope.
+*  <b>`src_scope`</b>: the source scope.
+*  <b>`reuse_dst_scope`</b>: if True the dst_scope is re-used if it already exists.
+    Otherwise, the scope is given a unique name based on the one given
+    by appending an underscore followed by a digit (default).
+
+##### Returns:
+
+  A single tf.Tensor or a list of target tf.Tensor, depending on
+  the type of the input argument `target_ts`.
+  The returned tensors are recomputed using the tensors from replacement_ts.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if the targets are not connected to replacement_ts.
+
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.graph_editor.copy_with_input_replacements.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.graph_editor.copy_with_input_replacements.md
new file mode 100644
index 0000000000..980efd52d1
--- /dev/null
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.graph_editor.copy_with_input_replacements.md
@@ -0,0 +1,37 @@
+### `tf.contrib.graph_editor.copy_with_input_replacements(sgv, replacement_ts, dst_graph=None, dst_scope='', src_scope='', reuse_dst_scope=False)` {#copy_with_input_replacements}
+
+Copy a subgraph, replacing some of its inputs.
+
+Note a replacement only happens if the tensor to be replaced
+is an input of the given subgraph. The inputs of a subgraph can
+be queried using sgv.inputs.
+
+##### Args:
+
+
+*  <b>`sgv`</b>: the source subgraph-view. This argument is converted to a subgraph
+    using the same rules as the function subgraph.make_view.
+*  <b>`replacement_ts`</b>: dictionary mapping from original tensors to the
+    replaced one.
+*  <b>`dst_graph`</b>: the destination graph.
+*  <b>`dst_scope`</b>: the destination scope.
+*  <b>`src_scope`</b>: the source scope.
+*  <b>`reuse_dst_scope`</b>: if True the dst_scope is re-used if it already exists.
+    Otherwise, the scope is given a unique name based on the one given
+    by appending an underscore followed by a digit (default).
+
+##### Returns:
+
+  A tuple `(sgv, info)` where:
+    `sgv` is the transformed subgraph view;
+    `info` is an instance of Transformer.ResultInfo containing
+    information about the transform, including mapping between
+    original and transformed tensors and operations.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if dst_graph is not a tf.Graph.
+*  <b>`StandardError`</b>: if sgv cannot be converted to a SubGraphView using
+    the same rules as the function subgraph.make_view.
+
diff --git a/tensorflow/g3doc/api_docs/python/index.md b/tensorflow/g3doc/api_docs/python/index.md
index 4968114fc0..1b176934a5 100644
--- a/tensorflow/g3doc/api_docs/python/index.md
+++ b/tensorflow/g3doc/api_docs/python/index.md
@@ -712,6 +712,7 @@
   * [`ControlOutputs`](../../api_docs/python/contrib.graph_editor.md#ControlOutputs)
   * [`copy`](../../api_docs/python/contrib.graph_editor.md#copy)
   * [`copy_op_handler`](../../api_docs/python/contrib.graph_editor.md#copy_op_handler)
+  * [`copy_with_input_replacements`](../../api_docs/python/contrib.graph_editor.md#copy_with_input_replacements)
   * [`detach`](../../api_docs/python/contrib.graph_editor.md#detach)
   * [`detach_control_inputs`](../../api_docs/python/contrib.graph_editor.md#detach_control_inputs)
   * [`detach_control_outputs`](../../api_docs/python/contrib.graph_editor.md#detach_control_outputs)
@@ -731,6 +732,7 @@
   * [`get_walks_intersection_ops`](../../api_docs/python/contrib.graph_editor.md#get_walks_intersection_ops)
   * [`get_walks_union_ops`](../../api_docs/python/contrib.graph_editor.md#get_walks_union_ops)
   * [`get_within_boundary_ops`](../../api_docs/python/contrib.graph_editor.md#get_within_boundary_ops)
+  * [`graph_replace`](../../api_docs/python/contrib.graph_editor.md#graph_replace)
   * [`keep_t_if_possible_handler`](../../api_docs/python/contrib.graph_editor.md#keep_t_if_possible_handler)
   * [`make_list_of_op`](../../api_docs/python/contrib.graph_editor.md#make_list_of_op)
   * [`make_list_of_t`](../../api_docs/python/contrib.graph_editor.md#make_list_of_t)
-- 
GitLab


From a5629419f11cd552935d678557e74201dbe992ed Mon Sep 17 00:00:00 2001
From: Benoit Steiner <bsteiner@google.com>
Date: Fri, 26 Aug 2016 07:13:24 -0800
Subject: [PATCH 194/223] Optimize the handling of shapes inside loops now that
 we can rely on static   shape inference inside loops Change: 131402520

---
 tensorflow/python/ops/array_ops.py | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py
index ebc1687dc5..77487b65e4 100644
--- a/tensorflow/python/ops/array_ops.py
+++ b/tensorflow/python/ops/array_ops.py
@@ -143,9 +143,6 @@ def shape_internal(input, name=None, optimize=True):
     else:
       input_tensor = ops.convert_to_tensor(input)
       input_shape = input_tensor.get_shape()
-      # Static shape inference can be incorrect when loops are involved: disable
-      # shape optimization in this case to avoid generating invalid constants.
-      optimize &= input_tensor.graph._get_control_flow_context() is None
       if optimize and input_shape.is_fully_defined():
         return constant(input_shape.as_list(), dtypes.int32, name=name)
       return gen_array_ops.shape(input, name=name)
@@ -192,9 +189,6 @@ def size_internal(input, name=None, optimize=True):
     else:
       input_tensor = ops.convert_to_tensor(input)
       input_shape = input_tensor.get_shape()
-      # Static shape inference can be incorrect when loops are involved: disable
-      # shape optimization in this case to avoid generating invalid constants.
-      optimize &= input_tensor.graph._get_control_flow_context() is None
       if optimize and input_shape.is_fully_defined():
         return constant(input_shape.num_elements(), dtypes.int32, name=name)
       return gen_array_ops.size(input, name=name)
@@ -244,9 +238,6 @@ def rank_internal(input, name=None, optimize=True):
     else:
       input_tensor = ops.convert_to_tensor(input)
       input_shape = input_tensor.get_shape()
-      # Static shape inference can be incorrect when loops are involved: disable
-      # shape optimization in this case to avoid generating invalid constants.
-      optimize &= input_tensor.graph._get_control_flow_context() is None
       if optimize and input_shape.ndims is not None:
         return constant(input_shape.ndims, dtypes.int32, name=name)
       return gen_array_ops.rank(input, name=name)
-- 
GitLab


From 426e36d6f351dd556ccb1c8defa1ddd88015942d Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 26 Aug 2016 07:33:33 -0800
Subject: [PATCH 195/223] Reduce subclass boilerplate by embracing duck-typing
 and adopting a "public-calls-private" design pattern. This pattern reduces
 lines of Distribution sub-class code by ~33%.

Specifically, this change:

1. Makes BaseDistribution purely virtual.
2. The new idiom is that all public methods are implemented in the base class, and all possible argument checking is done there. Subclasses implement _methods called by the base class; if they fail to implement it will fire a NotImplementedError during tf graph construction.
3. Fix style inconsistencies, eg, function order, remove unnecessary indirection, use tf.ones instead of tf.constant, etc.
4. Fix efficiency when I noticed a problem. Example Dirichlet._variance.
Change: 131404360
---
 .../bayesflow/python/ops/stochastic_graph.py  |  13 +-
 .../python/kernel_tests/bernoulli_test.py     |  14 +
 .../python/kernel_tests/categorical_test.py   |   6 +-
 .../distributions/python/ops/bernoulli.py     | 218 ++----
 .../contrib/distributions/python/ops/beta.py  | 366 ++++------
 .../distributions/python/ops/binomial.py      | 227 ++-----
 .../distributions/python/ops/categorical.py   | 216 +++---
 .../contrib/distributions/python/ops/chi2.py  |   4 +-
 .../distributions/python/ops/dirichlet.py     | 378 ++++-------
 .../python/ops/dirichlet_multinomial.py       | 305 +++------
 .../distributions/python/ops/distribution.py  | 573 ++++++++++------
 .../python/ops/distribution_util.py           |  25 +
 .../distributions/python/ops/exponential.py   |  67 +-
 .../contrib/distributions/python/ops/gamma.py | 362 +++-------
 .../distributions/python/ops/inverse_gamma.py | 394 ++++-------
 .../distributions/python/ops/laplace.py       | 271 ++------
 .../distributions/python/ops/multinomial.py   | 223 ++-----
 .../contrib/distributions/python/ops/mvn.py   | 297 +++------
 .../distributions/python/ops/normal.py        | 265 ++------
 .../distributions/python/ops/poisson.py       | 209 ++----
 .../distributions/python/ops/student_t.py     | 375 ++++-------
 .../python/ops/transformed_distribution.py    | 204 ++----
 .../distributions/python/ops/uniform.py       | 262 +++-----
 .../distributions/python/ops/wishart.py       | 627 +++++++-----------
 24 files changed, 2040 insertions(+), 3861 deletions(-)

diff --git a/tensorflow/contrib/bayesflow/python/ops/stochastic_graph.py b/tensorflow/contrib/bayesflow/python/ops/stochastic_graph.py
index 499c91ea6f..7d29ae23d1 100644
--- a/tensorflow/contrib/bayesflow/python/ops/stochastic_graph.py
+++ b/tensorflow/contrib/bayesflow/python/ops/stochastic_graph.py
@@ -43,6 +43,7 @@ import threading
 
 import six
 
+from tensorflow.contrib import distributions
 from tensorflow.contrib.bayesflow.python.ops import stochastic_gradient_estimators as sge
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
@@ -333,7 +334,7 @@ class DistributionTensor(StochasticTensor):
     `MeanValueType` or if `loss_fn=None`.
 
     Args:
-      dist_cls: a class deriving from `BaseDistribution`.
+      dist_cls: a `Distribution` class.
       name: a name for this `DistributionTensor` and its ops.
       dist_value_type: a `_StochasticValueType`, which will determine what the
           `value` of this `DistributionTensor` will be. If not provided, the
@@ -346,7 +347,13 @@ class DistributionTensor(StochasticTensor):
           module for additional loss functions and baselines.
       **dist_args: keyword arguments to be passed through to `dist_cls` on
           construction.
+
+    Raises:
+      TypeError: if `dist_cls` is not a `Distribution`.
+      TypeError: if `loss_fn` is not `callable`.
     """
+    if not issubclass(dist_cls, distributions.Distribution):
+      raise TypeError("dist_cls must be a subclass of Distribution")
     self._dist_cls = dist_cls
     self._dist_args = dist_args
     if dist_value_type is None:
@@ -395,12 +402,12 @@ class DistributionTensor(StochasticTensor):
     if isinstance(self._value_type, MeanValue):
       value_tensor = self._dist.mean()
     elif isinstance(self._value_type, SampleValue):
-      value_tensor = self._dist.sample_n(self._value_type.n)
+      value_tensor = self._dist.sample(self._value_type.n)
     elif isinstance(self._value_type, SampleAndReshapeValue):
       if self._value_type.n == 1:
         value_tensor = self._dist.sample()
       else:
-        samples = self._dist.sample_n(self._value_type.n)
+        samples = self._dist.sample(self._value_type.n)
         samples_shape = array_ops.shape(samples)
         samples_static_shape = samples.get_shape()
         new_batch_size = samples_shape[0] * samples_shape[1]
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bernoulli_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bernoulli_test.py
index 82f77fbfd1..565cb7f76c 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/bernoulli_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/bernoulli_test.py
@@ -198,6 +198,20 @@ class BernoulliTest(tf.test.TestCase):
       self.assertAllClose(p, np.mean(sample_values, axis=0), atol=1e-2)
       self.assertEqual(set([0, 1]), set(sample_values.flatten()))
 
+  def testSampleActsLikeSampleN(self):
+    with self.test_session() as sess:
+      p = [0.2, 0.6]
+      dist = tf.contrib.distributions.Bernoulli(p=p)
+      n = 1000
+      seed = 42
+      self.assertAllEqual(dist.sample(n, seed).eval(),
+                          dist.sample_n(n, seed).eval())
+      n = tf.placeholder(tf.int32)
+      sample, sample_n = sess.run([dist.sample(n, seed),
+                                   dist.sample_n(n, seed)],
+                                  feed_dict={n: 1000})
+      self.assertAllEqual(sample, sample_n)
+
   def testMean(self):
     with self.test_session():
       p = np.array([[0.2, 0.7], [0.5, 0.4]], dtype=np.float32)
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/categorical_test.py b/tensorflow/contrib/distributions/python/kernel_tests/categorical_test.py
index f91f2c33ac..4fbcc1d812 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/categorical_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/categorical_test.py
@@ -70,8 +70,10 @@ class CategoricalTest(tf.test.TestCase):
     self.assertEqual(dist.dtype, dist.mode().dtype)
     self.assertEqual(dist.logits.dtype, tf.float32)
     self.assertEqual(dist.logits.dtype, dist.entropy().dtype)
-    self.assertEqual(dist.logits.dtype, dist.pmf(0).dtype)
-    self.assertEqual(dist.logits.dtype, dist.log_pmf(0).dtype)
+    self.assertEqual(dist.logits.dtype, dist.pmf(
+        np.array(0, dtype=np.int64)).dtype)
+    self.assertEqual(dist.logits.dtype, dist.log_pmf(
+        np.array(0, dtype=np.int64)).dtype)
 
   def testUnknownShape(self):
     with self.test_session():
diff --git a/tensorflow/contrib/distributions/python/ops/bernoulli.py b/tensorflow/contrib/distributions/python/ops/bernoulli.py
index 148e8b8562..49a4e6f8fc 100644
--- a/tensorflow/contrib/distributions/python/ops/bernoulli.py
+++ b/tensorflow/contrib/distributions/python/ops/bernoulli.py
@@ -24,7 +24,6 @@ from tensorflow.contrib.distributions.python.ops import kullback_leibler
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn
@@ -67,55 +66,18 @@ class Bernoulli(distribution.Distribution):
     Raises:
       ValueError: If p and logits are passed, or if neither are passed.
     """
-    self._allow_nan_stats = allow_nan_stats
-    self._name = name
-    self._dtype = dtype
-    self._validate_args = validate_args
     self._logits, self._p = distribution_util.get_logits_and_prob(
         name=name, logits=logits, p=p, validate_args=validate_args)
     with ops.name_scope(name):
       with ops.name_scope("q"):
         self._q = 1. - self._p
-    self._batch_shape = array_ops.shape(self._logits)
-    self._event_shape = array_ops.constant([], dtype=dtypes.int32)
-
-  @property
-  def allow_nan_stats(self):
-    """Boolean describing behavior when a stat is undefined for batch member."""
-    return self._allow_nan_stats
-
-  @property
-  def validate_args(self):
-    """Boolean describing behavior on invalid input."""
-    return self._validate_args
-
-  @property
-  def name(self):
-    return self._name
-
-  @property
-  def dtype(self):
-    return self._dtype
-
-  @property
-  def is_reparameterized(self):
-    return False
-
-  def batch_shape(self, name="batch_shape"):
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._batch_shape]):
-        return array_ops.identity(self._batch_shape)
-
-  def get_batch_shape(self):
-    return self._logits.get_shape()
-
-  def event_shape(self, name="event_shape"):
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._batch_shape]):
-        return array_ops.constant([], dtype=self._batch_shape.dtype)
-
-  def get_event_shape(self):
-    return tensor_shape.scalar()
+        super(Bernoulli, self).__init__(
+            dtype=dtype,
+            parameters={"p": self._p, "q": self._q, "logits": self._logits},
+            is_continuous=False,
+            validate_args=validate_args,
+            allow_nan_stats=allow_nan_stats,
+            name=name)
 
   @property
   def logits(self):
@@ -130,143 +92,69 @@ class Bernoulli(distribution.Distribution):
     """1-p."""
     return self._q
 
-  def prob(self, event, name="prob"):
-    """Probability mass function.
+  def _batch_shape(self):
+    return array_ops.shape(self._logits)
 
-    Args:
-      event: `int32` or `int64` binary Tensor; must be broadcastable with `p`.
-      name: A name for this operation.
+  def _get_batch_shape(self):
+    return self._logits.get_shape()
 
-    Returns:
-      The probabilities of the events.
-    """
-    return super(Bernoulli, self).prob(event, name)
+  def _event_shape(self):
+    return array_ops.constant([], dtype=dtypes.int32)
 
-  def log_prob(self, event, name="log_prob"):
-    """Log of the probability mass function.
+  def _get_event_shape(self):
+    return tensor_shape.scalar()
 
-    Args:
-      event: `int32` or `int64` binary Tensor.
-      name: A name for this operation (optional).
+  def _sample_n(self, n, seed=None):
+    new_shape = array_ops.concat(0, ([n], self.batch_shape()))
+    uniform = random_ops.random_uniform(
+        new_shape, seed=seed, dtype=dtypes.float32)
+    sample = math_ops.less(uniform, self.p)
+    return math_ops.cast(sample, self.dtype)
 
-    Returns:
-      The log-probabilities of the events.
-    """
+  def _log_prob(self, event):
     # TODO(jaana): The current sigmoid_cross_entropy_with_logits has
     # inconsistent  behavior for logits = inf/-inf.
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.logits, event]):
-        event = ops.convert_to_tensor(event, name="event")
-        event = math_ops.cast(event, self.logits.dtype)
-        logits = self.logits
-        # sigmoid_cross_entropy_with_logits doesn't broadcast shape,
-        # so we do this here.
-        # TODO(b/30637701): Check dynamic shape, and don't broadcast if the
-        # dynamic shapes are the same.
-        if (not event.get_shape().is_fully_defined() or
-            not logits.get_shape().is_fully_defined() or
-            event.get_shape() != logits.get_shape()):
-          logits = array_ops.ones_like(event) * logits
-          event = array_ops.ones_like(logits) * event
-        return -nn.sigmoid_cross_entropy_with_logits(logits, event)
-
-  def sample_n(self, n, seed=None, name="sample_n"):
-    """Generate `n` samples.
+    event = ops.convert_to_tensor(event, name="event")
+    event = math_ops.cast(event, self.logits.dtype)
+    logits = self.logits
+    # sigmoid_cross_entropy_with_logits doesn't broadcast shape,
+    # so we do this here.
+    # TODO(b/30637701): Check dynamic shape, and don't broadcast if the
+    # dynamic shapes are the same.
+    if (not event.get_shape().is_fully_defined() or
+        not logits.get_shape().is_fully_defined() or
+        event.get_shape() != logits.get_shape()):
+      logits = array_ops.ones_like(event) * logits
+      event = array_ops.ones_like(logits) * event
+    return -nn.sigmoid_cross_entropy_with_logits(logits, event)
 
-    Args:
-      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-        observations to sample.
-      seed: Python integer seed for RNG.
-      name: name to give to the op.
-
-    Returns:
-      samples: a `Tensor` of shape `(n,) + self.batch_shape` with values of type
-          `self.dtype`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.p, n]):
-        n = ops.convert_to_tensor(n, name="n")
-        new_shape = array_ops.concat(0, ([n], self.batch_shape()))
-        uniform = random_ops.random_uniform(
-            new_shape, seed=seed, dtype=dtypes.float32)
-        sample = math_ops.less(uniform, self.p)
-        sample.set_shape(tensor_shape.vector(tensor_util.constant_value(n))
-                         .concatenate(self.get_batch_shape()))
-        return math_ops.cast(sample, self.dtype)
+  def _prob(self, event):
+    return math_ops.exp(self._log_prob(event))
 
-  def entropy(self, name="entropy"):
-    """Entropy of the distribution.
+  def _entropy(self):
+    # TODO(b/31086883): use tf.nn.softplus; fix inconsistent behavior between
+    # cpu and gpu at -inf/inf.
+    return (-self.logits * (math_ops.sigmoid(self.logits) - 1) +
+            math_ops.log(1. + math_ops.exp(-self.logits)))
 
-    Args:
-      name: Name for the op.
+  def _mean(self):
+    return array_ops.identity(self.p)
 
-    Returns:
-      entropy: `Tensor` of the same type and shape as `p`.
-    """
-    # TODO(jaana): fix inconsistent behavior between cpu and gpu at -inf/inf.
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.logits]):
-        return (-self.logits * (math_ops.sigmoid(
-            self.logits) - 1) + math_ops.log(
-                math_ops.exp(-self.logits) + 1))
+  def _variance(self):
+    return self.q * self.p
 
-  def mean(self, name="mean"):
-    """Mean of the distribution.
+  def _std(self):
+    return math_ops.sqrt(self._variance())
 
-    Args:
-      name: Name for the op.
+  def _mode(self):
+    return math_ops.cast(self.p > self.q, self.dtype)
 
-    Returns:
-      mean: `Tensor` of the same type and shape as `p`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.p]):
-        return array_ops.identity(self.p)
 
-  def mode(self, name="mode"):
-    """Mode of the distribution.
+distribution_util.append_class_fun_doc(Bernoulli.mode, doc_str="""
 
+  Specific notes:
     1 if p > 1-p. 0 otherwise.
-
-    Args:
-      name: Name for the op.
-
-    Returns:
-      mode: binary `Tensor` of type self.dtype.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.p, self.q]):
-        return math_ops.cast(self.p > self.q, self.dtype)
-
-  def variance(self, name="variance"):
-    """Variance of the distribution.
-
-    Args:
-      name: Name for the op.
-
-    Returns:
-      variance: `Tensor` of the same type and shape as `p`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.p, self.q]):
-        return self.q * self.p
-
-  def std(self, name="std"):
-    """Standard deviation of the distribution.
-
-    Args:
-      name: Name for the op.
-
-    Returns:
-      std: `Tensor` of the same type and shape as `p`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return math_ops.sqrt(self.variance())
-
-  @property
-  def is_continuous(self):
-    return False
+""")
 
 
 @kullback_leibler.RegisterKL(Bernoulli, Bernoulli)
diff --git a/tensorflow/contrib/distributions/python/ops/beta.py b/tensorflow/contrib/distributions/python/ops/beta.py
index cc6beb110a..04f9d9acb9 100644
--- a/tensorflow/contrib/distributions/python/ops/beta.py
+++ b/tensorflow/contrib/distributions/python/ops/beta.py
@@ -18,22 +18,21 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-# pylint: disable=line-too-long
+import numpy as np
 
 from tensorflow.contrib.distributions.python.ops import distribution
+from tensorflow.contrib.distributions.python.ops import distribution_util
+from tensorflow.contrib.framework.python.framework import tensor_util as contrib_tensor_util
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
 
-# pylint: enable=line-too-long
-
 
 class Beta(distribution.Distribution):
   """Beta distribution.
@@ -134,21 +133,19 @@ class Beta(distribution.Distribution):
     with ops.name_scope(name, values=[a, b]):
       with ops.control_dependencies([
           check_ops.assert_positive(a),
-          check_ops.assert_positive(b)] if validate_args else []):
-        a = array_ops.identity(a, name="a")
-        b = array_ops.identity(b, name="b")
-
-      self._a = a
-      self._b = b
-      self._name = name
-
-      # Used for mean/mode/variance/entropy/sampling computations
-      self._a_b_sum = self._a + self._b
-
-      self._get_batch_shape = self._a_b_sum.get_shape()
-      self._get_event_shape = tensor_shape.TensorShape([])
-      self._validate_args = validate_args
-      self._allow_nan_stats = allow_nan_stats
+          check_ops.assert_positive(b),
+      ] if validate_args else []):
+        self._a = array_ops.identity(a, name="a")
+        self._b = array_ops.identity(b, name="b")
+        contrib_tensor_util.assert_same_float_dtype((self._a, self._b))
+        # Used for mean/mode/variance/entropy/sampling computations
+        self._a_b_sum = self._a + self._b
+        super(Beta, self).__init__(
+            dtype=self._a_b_sum.dtype,
+            parameters={"a": self._a, "b": self._b, "a_b_sum": self._a_b_sum},
+            validate_args=validate_args,
+            allow_nan_stats=allow_nan_stats,
+            name=name)
 
   @property
   def a(self):
@@ -161,246 +158,109 @@ class Beta(distribution.Distribution):
     return self._b
 
   @property
-  def name(self):
-    """Name to prepend to all ops."""
-    return self._name
-
-  @property
-  def dtype(self):
-    """dtype of samples from this distribution."""
-    return self._a_b_sum.dtype
-
-  @property
-  def allow_nan_stats(self):
-    """Boolean describing behavior when a stat is undefined for batch member."""
-    return self._allow_nan_stats
-
-  @property
-  def validate_args(self):
-    """Boolean describing behavior on invalid input."""
-    return self._validate_args
-
-  def batch_shape(self, name="batch_shape"):
-    """Batch dimensions of this instance as a 1-D int32 `Tensor`.
-
-    The product of the dimensions of the `batch_shape` is the number of
-    independent distributions of this kind the instance represents.
-
-    Args:
-      name: name to give to the op
-
-    Returns:
-      `Tensor` `batch_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._a_b_sum]):
-        return array_ops.shape(self._a_b_sum)
-
-  def get_batch_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `batch_shape`. May be only partially defined.
-
-    Returns:
-      batch shape
-    """
-    return self._get_batch_shape
-
-  def event_shape(self, name="event_shape"):
-    """Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+  def a_b_sum(self):
+    """Sum of parameters."""
+    return self._a_b_sum
+
+  def _batch_shape(self):
+    return array_ops.shape(self.a_b_sum)
+
+  def _get_batch_shape(self):
+    return self.a_b_sum.get_shape()
+
+  def _event_shape(self):
+    return constant_op.constant([], dtype=dtypes.int32)
+
+  def _get_event_shape(self):
+    return tensor_shape.scalar()
+
+  def _sample_n(self, n, seed=None):
+    a = array_ops.ones_like(self.a_b_sum, dtype=self.dtype) * self.a
+    b = array_ops.ones_like(self.a_b_sum, dtype=self.dtype) * self.b
+    gamma1_sample = random_ops.random_gamma(
+        [n,], a, dtype=self.dtype, seed=seed)
+    gamma2_sample = random_ops.random_gamma(
+        [n,], b, dtype=self.dtype, seed=seed)
+    beta_sample = gamma1_sample / (gamma1_sample + gamma2_sample)
+    return beta_sample
+
+  def _log_prob(self, x):
+    x = self._assert_valid_sample(x)
+    log_unnormalized_prob = ((self.a - 1.) * math_ops.log(x) +
+                             (self.b - 1.) * math_ops.log(1. - x))
+    log_normalization = (math_ops.lgamma(self.a) +
+                         math_ops.lgamma(self.b) -
+                         math_ops.lgamma(self.a_b_sum))
+    return log_unnormalized_prob - log_normalization
+
+  def _prob(self, x):
+    return math_ops.exp(self._log_prob(x))
+
+  def _entropy(self):
+    return (math_ops.lgamma(self.a) -
+            (self.a - 1.) * math_ops.digamma(self.a) +
+            math_ops.lgamma(self.b) -
+            (self.b - 1.) * math_ops.digamma(self.b) -
+            math_ops.lgamma(self.a_b_sum) +
+            (self.a_b_sum - 2.) * math_ops.digamma(self.a_b_sum))
+
+  def _mean(self):
+    return self.a / self.a_b_sum
+
+  def _variance(self):
+    return (self.a * self.b) / (self.a_b_sum**2. * (self.a_b_sum + 1.))
+
+  def _std(self):
+    return math_ops.sqrt(self.variance())
+
+  def _mode(self):
+    mode = (self.a - 1.)/ (self.a_b_sum - 2.)
+    if self.allow_nan_stats:
+      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
+      return math_ops.select(
+          math_ops.logical_and(
+              math_ops.greater(self.a, 1.),
+              math_ops.greater(self.b, 1.)),
+          mode,
+          array_ops.fill(self.batch_shape(), nan, name="nan"))
+    else:
+      return control_flow_ops.with_dependencies([
+          check_ops.assert_less(
+              array_ops.ones((), dtype=self.dtype), self.a,
+              message="Mode not defined for components of a <= 1."),
+          check_ops.assert_less(
+              array_ops.ones((), dtype=self.dtype), self.b,
+              message="Mode not defined for components of b <= 1."),
+      ], mode)
+
+  def _assert_valid_sample(self, x):
+    """Check x for proper shape, values, then return tensor version."""
+    if not self.validate_args: return x
+    return control_flow_ops.with_dependencies([
+        check_ops.assert_positive(
+            x,
+            message="Negative events lie outside Beta distribution support."),
+        check_ops.assert_less(
+            x, array_ops.ones((), self.dtype),
+            message="Event>=1 lies outside Beta distribution support."),
+    ], x)
 
-    Args:
-      name: name to give to the op
 
-    Returns:
-      `Tensor` `event_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return constant_op.constant([], name=name, dtype=dtypes.int32)
+_prob_note = """
 
-  def get_event_shape(self):
-    """`TensorShape` available at graph construction time.
+    Note that the argument `x` must be a non-negative floating point tensor
+    whose shape can be broadcast with `self.a` and `self.b`.  For fixed leading
+    dimensions, the last dimension represents counts for the corresponding Beta
+    distribution in `self.a` and `self.b`. `x` is only legal if `0 < x < 1`.
+"""
 
-    Same meaning as `event_shape`. May be only partially defined.
+distribution_util.append_class_fun_doc(Beta.log_prob, doc_str=_prob_note)
+distribution_util.append_class_fun_doc(Beta.prob, doc_str=_prob_note)
 
-    Returns:
-      event shape
-    """
-    return self._get_event_shape
-
-  def mean(self, name="mean"):
-    """Mean of the distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._a, self._a_b_sum]):
-        return self._a / self._a_b_sum
-
-  def variance(self, name="variance"):
-    """Variance of the distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._a, self._b, self._a_b_sum]):
-        return (self._a * self._b) / (
-            self._a_b_sum **2 * (self._a_b_sum + 1))
-
-  def std(self, name="std"):
-    """Standard deviation of the distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return math_ops.sqrt(self.variance())
-
-  def mode(self, name="mode"):
-    """Mode of the distribution.
+distribution_util.append_class_fun_doc(Beta.mode, doc_str="""
 
     Note that the mode for the Beta distribution is only defined
     when `a > 1`, `b > 1`. This returns the mode when `a > 1` and `b > 1`,
-    and NaN otherwise. If `self.allow_nan_stats` is `False`, an exception
+    and `NaN` otherwise. If `self.allow_nan_stats` is `False`, an exception
     will be raised rather than returning `NaN`.
-
-    Args:
-      name: The name for this op.
-
-    Returns:
-      Mode of the Beta distribution.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._a, self._b, self._a_b_sum]):
-        a = self._a
-        b = self._b
-        a_b_sum = self._a_b_sum
-        one = constant_op.constant(1, self.dtype)
-        mode = (a - 1)/ (a_b_sum - 2)
-
-        if self.allow_nan_stats:
-          return math_ops.select(
-              math_ops.logical_and(
-                  math_ops.greater(a, 1), math_ops.greater(b, 1)),
-              mode,
-              (constant_op.constant(float("NaN"), dtype=self.dtype) *
-               array_ops.ones_like(a_b_sum, dtype=self.dtype)))
-        else:
-          return control_flow_ops.with_dependencies([
-              check_ops.assert_less(
-                  one, a,
-                  message="mode not defined for components of a <= 1"
-              ),
-              check_ops.assert_less(
-                  one, b,
-                  message="mode not defined for components of b <= 1"
-              )], mode)
-
-  def entropy(self, name="entropy"):
-    """Entropy of the distribution in nats."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._a, self._b, self._a_b_sum]):
-        a = self._a
-        b = self._b
-        a_b_sum = self._a_b_sum
-
-        entropy = math_ops.lgamma(a) - (a - 1) * math_ops.digamma(a)
-        entropy += math_ops.lgamma(b) - (b - 1) * math_ops.digamma(b)
-        entropy += -math_ops.lgamma(a_b_sum) + (
-            a_b_sum - 2) * math_ops.digamma(a_b_sum)
-        return entropy
-
-  def cdf(self, x, name="cdf"):
-    """Cumulative distribution function."""
-    # TODO(srvasude): Implement this once betainc op is checked in.
-    raise NotImplementedError("Beta cdf not implemented.")
-
-  def log_cdf(self, x, name="log_cdf"):
-    """Log CDF."""
-    raise NotImplementedError("Beta cdf not implemented.")
-
-  def log_prob(self, x, name="log_prob"):
-    """`Log(P[counts])`, computed for every batch member.
-
-    Args:
-      x:  Non-negative floating point tensor whose shape can
-        be broadcast with `self.a` and `self.b`.  For fixed leading
-        dimensions, the last dimension represents counts for the corresponding
-        Beta distribution in `self.a` and `self.b`. `x` is only legal if
-        0 < x < 1.
-      name:  Name to give this Op, defaults to "log_prob".
-
-    Returns:
-      Log probabilities for each record, shape `[N1,...,Nm]`.
-    """
-    a = self._a
-    b = self._b
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[a, x]):
-        x = self._check_x(x)
-
-        unnorm_pdf = (a - 1) * math_ops.log(x) + (
-            b - 1) * math_ops.log(1 - x)
-        normalization_factor = -(math_ops.lgamma(a) + math_ops.lgamma(b)
-                                 - math_ops.lgamma(a + b))
-        log_prob = unnorm_pdf + normalization_factor
-
-        return log_prob
-
-  def prob(self, x, name="prob"):
-    """`P[x]`, computed for every batch member.
-
-    Args:
-      x:  Non-negative floating point tensor whose shape can
-        be broadcast with `self.a` and `self.b`.  For fixed leading
-        dimensions, the last dimension represents x for the corresponding Beta
-        distribution in `self.a` and `self.b`. `x` is only legal if is
-        between 0 and 1.
-      name:  Name to give this Op, defaults to "pdf".
-
-    Returns:
-      Probabilities for each record, shape `[N1,...,Nm]`.
-    """
-    return super(Beta, self).prob(x, name=name)
-
-  def sample_n(self, n, seed=None, name="sample_n"):
-    """Sample `n` observations from the Beta Distributions.
-
-    Args:
-      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-        observations to sample.
-      seed: Python integer, the random seed.
-      name: The name to give this op.
-
-    Returns:
-      samples: `[n, ...]`, a `Tensor` of `n` samples for each
-        of the distributions determined by broadcasting the hyperparameters.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.a, self.b, n]):
-        a = array_ops.ones_like(self._a_b_sum, dtype=self.dtype) * self.a
-        b = array_ops.ones_like(self._a_b_sum, dtype=self.dtype) * self.b
-        n = ops.convert_to_tensor(n, name="n")
-
-        gamma1_sample = random_ops.random_gamma(
-            [n,], a, dtype=self.dtype, seed=seed)
-        gamma2_sample = random_ops.random_gamma(
-            [n,], b, dtype=self.dtype, seed=seed)
-
-        beta_sample = gamma1_sample / (gamma1_sample + gamma2_sample)
-
-        n_val = tensor_util.constant_value(n)
-        final_shape = tensor_shape.vector(n_val).concatenate(
-            self._a_b_sum.get_shape())
-
-        beta_sample.set_shape(final_shape)
-        return beta_sample
-
-  @property
-  def is_continuous(self):
-    return True
-
-  @property
-  def is_reparameterized(self):
-    return False
-
-  def _check_x(self, x):
-    """Check x for proper shape, values, then return tensor version."""
-    x = ops.convert_to_tensor(x, name="x_before_deps")
-    dependencies = [
-        check_ops.assert_positive(x),
-        check_ops.assert_less(x, constant_op.constant(
-            1, self.dtype))] if self.validate_args else []
-    return control_flow_ops.with_dependencies(dependencies, x)
+""")
diff --git a/tensorflow/contrib/distributions/python/ops/binomial.py b/tensorflow/contrib/distributions/python/ops/binomial.py
index 697fb02495..fc799d30b4 100644
--- a/tensorflow/contrib/distributions/python/ops/binomial.py
+++ b/tensorflow/contrib/distributions/python/ops/binomial.py
@@ -125,93 +125,23 @@ class Binomial(distribution.Distribution):
     ```
 
     """
-
     self._logits, self._p = distribution_util.get_logits_and_prob(
         name=name, logits=logits, p=p, validate_args=validate_args)
-
     with ops.name_scope(name, values=[n]):
       with ops.control_dependencies([
           check_ops.assert_non_negative(
               n, message="n has negative components."),
           distribution_util.assert_integer_form(
-              n, message="n has non-integer components."
-          )] if validate_args else []):
-        self._n = array_ops.identity(n, name="convert_n")
-
-        self._name = name
-        self._validate_args = validate_args
-        self._allow_nan_stats = allow_nan_stats
-
-        self._get_batch_shape = common_shapes.broadcast_shape(
-            self._n.get_shape(), self._p.get_shape())
-        self._get_event_shape = tensor_shape.TensorShape([])
-
-  @property
-  def name(self):
-    """Name to prepend to all ops."""
-    return self._name
-
-  @property
-  def dtype(self):
-    """dtype of samples from this distribution."""
-    return self._p.dtype
-
-  @property
-  def validate_args(self):
-    """Boolean describing behavior on invalid input."""
-    return self._validate_args
-
-  @property
-  def allow_nan_stats(self):
-    """Boolean describing behavior when a stat is undefined for batch member."""
-    return self._allow_nan_stats
-
-  def batch_shape(self, name="batch_shape"):
-    """Batch dimensions of this instance as a 1-D int32 `Tensor`.
-
-    The product of the dimensions of the `batch_shape` is the number of
-    independent distributions of this kind the instance represents.
-
-    Args:
-      name: name to give to the op
-
-    Returns:
-      `Tensor` `batch_shape`
-    """
-    return array_ops.shape(self._n + self._p)
-
-  def get_batch_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `batch_shape`. May be only partially defined.
-
-    Returns:
-      batch shape
-    """
-    return self._get_batch_shape
-
-  def event_shape(self, name="event_shape"):
-    """Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
-
-    Args:
-      name: name to give to the op
-
-    Returns:
-      `Tensor` `event_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return constant_op.constant([], name=name, dtype=dtypes.int32)
-
-  def get_event_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `event_shape`. May be only partially defined.
-
-    Returns:
-      event shape
-    """
-    return self._get_event_shape
+              n, message="n has non-integer components."),
+      ] if validate_args else []):
+        self._n = array_ops.identity(n, name="n")
+        super(Binomial, self).__init__(
+            dtype=self._p.dtype,
+            parameters={"n": self._n, "p": self._p, "logits": self._logits},
+            is_continuous=False,
+            validate_args=validate_args,
+            allow_nan_stats=allow_nan_stats,
+            name=name)
 
   @property
   def n(self):
@@ -228,101 +158,43 @@ class Binomial(distribution.Distribution):
     """Probability of success."""
     return self._p
 
-  def mean(self, name="mean"):
-    """Mean of the distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._n, self._p]):
-        return self._n * self._p
-
-  def variance(self, name="variance"):
-    """Variance of the distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._n, self._p]):
-        return self._n * self._p * (1 - self._p)
-
-  def std(self, name="std"):
-    """Standard deviation of the distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._n, self._p]):
-        return math_ops.sqrt(self.variance())
-
-  def mode(self, name="mode"):
-    """Mode of the distribution.
-
-    Note that when `(n + 1) * p` is an integer, there are actually two modes.
-    Namely, `(n + 1) * p` and `(n + 1) * p - 1` are both modes. Here we return
-    only the larger of the two modes.
-
-    Args:
-      name: The name for this op.
-
-    Returns:
-      The mode of the Binomial distribution.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._n, self._p]):
-        return math_ops.floor((self._n + 1) * self._p)
-
-  def log_prob(self, counts, name="log_prob"):
-    """`Log(P[counts])`, computed for every batch member.
-
-    For each batch member of counts `k`, `P[counts]` is the probability that
-    after sampling `n` draws from this Binomial distribution, the number of
-    successes is `k`.  Note that different sequences of draws can result in the
-    same counts, thus the probability includes a combinatorial coefficient.
+  def _batch_shape(self):
+    return array_ops.shape(self._n + self._p)
 
-    Args:
-      counts:  Non-negative tensor with dtype `dtype` and whose shape can be
-        broadcast with `self.p` and `self.n`. `counts` is only legal if it is
-        less than or equal to `n` and its components are equal to integer
-        values.
-      name:  Name to give this Op, defaults to "log_prob".
-
-    Returns:
-      Log probabilities for each record, shape `[N1,...,Nm]`.
-    """
-    n = self._n
-    p = self._p
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._n, self._p, counts]):
-        counts = self._check_counts(counts)
+  def _get_batch_shape(self):
+    return common_shapes.broadcast_shape(self.n.get_shape(),
+                                         self.p.get_shape())
 
-        prob_prob = counts * math_ops.log(p) + (
-            n - counts) * math_ops.log(1 - p)
+  def _event_shape(self):
+    return constant_op.constant([], dtype=dtypes.int32)
 
-        combinations = math_ops.lgamma(n + 1) - math_ops.lgamma(
-            counts + 1) - math_ops.lgamma(n - counts + 1)
-        log_prob = prob_prob + combinations
-        return log_prob
+  def _get_event_shape(self):
+    return tensor_shape.scalar()
 
-  def prob(self, counts, name="prob"):
-    """`P[counts]`, computed for every batch member.
+  def _log_prob(self, counts):
+    counts = self._check_counts(counts)
+    prob_prob = (counts * math_ops.log(self.p) +
+                 (self.n - counts) * math_ops.log(1. - self.p))
+    combinations = (math_ops.lgamma(self.n + 1) -
+                    math_ops.lgamma(counts + 1) -
+                    math_ops.lgamma(self.n - counts + 1))
+    log_prob = prob_prob + combinations
+    return log_prob
 
+  def _prob(self, counts):
+    return math_ops.exp(self._log_prob(counts))
 
-    For each batch member of counts `k`, `P[counts]` is the probability that
-    after sampling `n` draws from this Binomial distribution, the number of
-    successes is `k`.  Note that different sequences of draws can result in the
-    same counts, thus the probability includes a combinatorial coefficient.
+  def _mean(self):
+    return self._n * self._p
 
-    Args:
-      counts:  Non-negative tensor with dtype `dtype` and whose shape can be
-        broadcast with `self.p` and `self.n`. `counts` is only legal if it is
-        less than or equal to `n` and its components are equal to integer
-        values.
-      name:  Name to give this Op, defaults to "prob".
-
-    Returns:
-      Probabilities for each record, shape `[N1,...,Nm]`.
-    """
-    return super(Binomial, self).prob(counts, name=name)
+  def _variance(self):
+    return self._n * self._p * (1 - self._p)
 
-  @property
-  def is_continuous(self):
-    return False
+  def _std(self):
+    return math_ops.sqrt(self._variance())
 
-  @property
-  def is_reparameterized(self):
-    return False
+  def _mode(self):
+    return math_ops.floor((self._n + 1) * self._p)
 
   def _check_counts(self, counts):
     """Check counts for proper shape, values, then return tensor version."""
@@ -336,3 +208,26 @@ class Binomial(distribution.Distribution):
             counts, self._n, message="counts are not less than or equal to n."),
         distribution_util.assert_integer_form(
             counts, message="counts have non-integer components.")], counts)
+
+
+_prob_note = """
+
+    For each batch member of counts `k`, `P[counts]` is the probability that
+    after sampling `n` draws from this Binomial distribution, the number of
+    successes is `k`.  Note that different sequences of draws can result in the
+    same counts, thus the probability includes a combinatorial coefficient.
+
+    counts:  Non-negative tensor with dtype `dtype` and whose shape can be
+      broadcast with `self.p` and `self.n`. `counts` is only legal if it is
+      less than or equal to `n` and its components are equal to integer
+      values.
+"""
+distribution_util.append_class_fun_doc(Binomial.log_prob, doc_str=_prob_note)
+distribution_util.append_class_fun_doc(Binomial.prob, doc_str=_prob_note)
+
+distribution_util.append_class_fun_doc(Binomial.mode, doc_str="""
+
+    Note that when `(n + 1) * p` is an integer, there are actually two modes.
+    Namely, `(n + 1) * p` and `(n + 1) * p - 1` are both modes. Here we return
+    only the larger of the two modes.
+""")
diff --git a/tensorflow/contrib/distributions/python/ops/categorical.py b/tensorflow/contrib/distributions/python/ops/categorical.py
index 02e74e1d54..a84c31d104 100644
--- a/tensorflow/contrib/distributions/python/ops/categorical.py
+++ b/tensorflow/contrib/distributions/python/ops/categorical.py
@@ -19,10 +19,10 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.distributions.python.ops import distribution
+from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
@@ -58,66 +58,45 @@ class Categorical(distribution.Distribution):
         undefined statistics will return NaN for this statistic.
       name: A name for this distribution (optional).
     """
-    self._allow_nan_stats = allow_nan_stats
-    self._name = name
-    self._dtype = dtype
-    self._validate_args = validate_args
     with ops.name_scope(name, values=[logits]):
       self._logits = ops.convert_to_tensor(logits, name="logits")
-      logits_shape = array_ops.shape(self._logits, name="logits_shape")
-      static_logits_shape = self._logits.get_shape().with_rank_at_least(1)
-      static_logits_rank = static_logits_shape.ndims
-      if static_logits_rank is not None:
+
+      logits_shape_static = self._logits.get_shape().with_rank_at_least(1)
+      if logits_shape_static.ndims is not None:
         self._batch_rank = ops.convert_to_tensor(
-            static_logits_rank - 1, dtype=dtypes.int32,
+            logits_shape_static.ndims - 1,
+            dtype=dtypes.int32,
             name="batch_rank")
       else:
-        self._batch_rank = array_ops.rank(self._logits) - 1
+        with ops.name_scope(name="batch_rank"):
+          self._batch_rank = array_ops.rank(self._logits) - 1
 
-      if static_logits_shape[-1].value is not None:
+      logits_shape = array_ops.shape(self._logits, name="logits_shape")
+      if logits_shape_static[-1].value is not None:
         self._num_classes = ops.convert_to_tensor(
-            static_logits_shape[-1].value,
-            dtype=dtypes.int32, name="num_classes")
+            logits_shape_static[-1].value,
+            dtype=dtypes.int32,
+            name="num_classes")
       else:
-        self._num_classes = array_ops.gather(logits_shape, self._batch_rank)
-
-      self._batch_shape = logits_shape[:-1]
-
-  @property
-  def allow_nan_stats(self):
-    """Boolean describing behavior when a stat is undefined for batch member."""
-    return self._allow_nan_stats
-
-  @property
-  def validate_args(self):
-    """Boolean describing behavior on invalid input."""
-    return self._validate_args
-
-  @property
-  def name(self):
-    return self._name
-
-  @property
-  def dtype(self):
-    return self._dtype
-
-  @property
-  def is_reparameterized(self):
-    return False
-
-  def batch_shape(self, name="batch_shape"):
-    with ops.name_scope(self.name):
-      return array_ops.identity(self._batch_shape, name=name)
-
-  def get_batch_shape(self):
-    return self.logits.get_shape()[:-1]
-
-  def event_shape(self, name="event_shape"):
-    with ops.name_scope(self.name):
-      return array_ops.constant([], dtype=self._batch_shape.dtype, name=name)
-
-  def get_event_shape(self):
-    return tensor_shape.scalar()
+        self._num_classes = array_ops.gather(logits_shape,
+                                             self._batch_rank,
+                                             name="num_classes")
+
+      if logits_shape_static[:-1].is_fully_defined():
+        self._batch_shape_val = constant_op.constant(
+            logits_shape_static[:-1].as_list(),
+            dtype=dtypes.int32,
+            name="batch_shape")
+      else:
+        with ops.name_scope(name="batch_shape"):
+          self._batch_shape_val = logits_shape[:-1]
+      super(Categorical, self).__init__(
+          dtype=dtype,
+          parameters={"logits": self._logits, "num_classes": self._num_classes},
+          is_continuous=False,
+          validate_args=validate_args,
+          allow_nan_stats=allow_nan_stats,
+          name=name)
 
   @property
   def num_classes(self):
@@ -128,90 +107,55 @@ class Categorical(distribution.Distribution):
   def logits(self):
     return self._logits
 
-  def log_prob(self, k, name="log_prob"):
-    """Log-probability of class `k`.
-
-    Args:
-      k: `int32` or `int64` Tensor. Must be broadcastable with a `batch_shape`
-        `Tensor`.
-      name: A name for this operation (optional).
-
-    Returns:
-      The log-probabilities of the classes indexed by `k`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[k, self.logits]):
-        k = ops.convert_to_tensor(k, name="k")
-
-        logits = self.logits * array_ops.ones_like(
-            array_ops.expand_dims(k, -1),
-            dtype=self.logits.dtype)
-        k *= array_ops.ones(
-            array_ops.slice(
-                array_ops.shape(logits), [0], [array_ops.rank(logits) - 1]),
-            dtype=k.dtype)
-        k.set_shape(tensor_shape.TensorShape(logits.get_shape()[:-1]))
-
-        return -nn_ops.sparse_softmax_cross_entropy_with_logits(logits, k)
-
-  def prob(self, k, name="prob"):
-    """Probability of class `k`.
+  def _batch_shape(self):
+    # Use identity to inherit callers "name".
+    return array_ops.identity(self._batch_shape_val)
 
-    Args:
-      k: `int32` or `int64` Tensor. Must be broadcastable with logits.
-      name: A name for this operation (optional).
-
-    Returns:
-      The probabilities of the classes indexed by `k`
-    """
-    return super(Categorical, self).prob(k, name)
+  def _get_batch_shape(self):
+    return self.logits.get_shape()[:-1]
 
-  def sample_n(self, n, seed=None, name="sample_n"):
-    """Sample `n` observations from the Categorical distribution.
+  def _event_shape(self):
+    return constant_op.constant([], dtype=dtypes.int32)
 
-    Args:
-      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-        observations to sample.
-      seed: Random seed (optional).
-      name: A name for this operation (optional).
-
-    Returns:
-      An `int64` `Tensor` with shape `[n, batch_shape, event_shape]`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.logits, n]):
-        n = ops.convert_to_tensor(n, name="n")
-        logits_2d = array_ops.reshape(
-            self.logits, array_ops.pack([-1, self.num_classes]))
-        samples = random_ops.multinomial(logits_2d, n, seed=seed)
-        samples = math_ops.cast(samples, self._dtype)
-        ret = array_ops.reshape(
-            array_ops.transpose(samples),
-            array_ops.concat(0, ([n], self.batch_shape())))
-        ret.set_shape(tensor_shape.vector(tensor_util.constant_value(n))
-                      .concatenate(self.get_batch_shape()))
-        return ret
-
-  def entropy(self, name="sample"):
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        logits_2d = array_ops.reshape(
-            self.logits, array_ops.pack([-1, self.num_classes]))
-        histogram_2d = nn_ops.softmax(logits_2d)
-        ret = array_ops.reshape(
-            nn_ops.softmax_cross_entropy_with_logits(logits_2d, histogram_2d),
-            self.batch_shape())
-        ret.set_shape(self.get_batch_shape())
-        return ret
-
-  def mode(self, name="mode"):
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        ret = math_ops.argmax(self.logits, dimension=self._batch_rank)
-        ret = math_ops.cast(ret, self._dtype)
-        ret.set_shape(self.get_batch_shape())
-        return ret
+  def _get_event_shape(self):
+    return tensor_shape.scalar()
 
-  @property
-  def is_continuous(self):
-    return False
+  def _sample_n(self, n, seed=None):
+    logits_2d = array_ops.reshape(
+        self.logits, array_ops.pack([-1, self.num_classes]))
+    samples = random_ops.multinomial(logits_2d, n, seed=seed)
+    samples = math_ops.cast(samples, self.dtype)
+    ret = array_ops.reshape(
+        array_ops.transpose(samples),
+        array_ops.concat(0, ([n], self.batch_shape())))
+    return ret
+
+  def _log_prob(self, k):
+    k = ops.convert_to_tensor(k, name="k")
+    logits = self.logits * array_ops.ones_like(
+        array_ops.expand_dims(k, -1),
+        dtype=self.logits.dtype)
+    shape = array_ops.slice(array_ops.shape(logits), [0],
+                            [array_ops.rank(logits) - 1])
+    k *= array_ops.ones(shape, dtype=k.dtype)
+    k.set_shape(tensor_shape.TensorShape(logits.get_shape()[:-1]))
+    return -nn_ops.sparse_softmax_cross_entropy_with_logits(logits, k)
+
+  def _prob(self, k):
+    return math_ops.exp(self._log_prob(k))
+
+  def _entropy(self):
+    logits_2d = array_ops.reshape(
+        self.logits, array_ops.pack([-1, self.num_classes]))
+    histogram_2d = nn_ops.softmax(logits_2d)
+    ret = array_ops.reshape(
+        nn_ops.softmax_cross_entropy_with_logits(logits_2d, histogram_2d),
+        self.batch_shape())
+    ret.set_shape(self.get_batch_shape())
+    return ret
+
+  def _mode(self):
+    ret = math_ops.argmax(self.logits, dimension=self._batch_rank)
+    ret = math_ops.cast(ret, self.dtype)
+    ret.set_shape(self.get_batch_shape())
+    return ret
diff --git a/tensorflow/contrib/distributions/python/ops/chi2.py b/tensorflow/contrib/distributions/python/ops/chi2.py
index 8abb906c8d..bb1771c390 100644
--- a/tensorflow/contrib/distributions/python/ops/chi2.py
+++ b/tensorflow/contrib/distributions/python/ops/chi2.py
@@ -58,9 +58,9 @@ class Chi2(gamma.Gamma):
     # allow_nan_stats=False
     # through to the parent class results in unnecessary asserts.
     with ops.name_scope(name, values=[df]):
-      df = ops.convert_to_tensor(df)
+      df = ops.convert_to_tensor(df, name="df")
       self._df = df
-      super(Chi2, self).__init__(alpha=df / 2,
+      super(Chi2, self).__init__(alpha=0.5 * df,
                                  beta=constant_op.constant(0.5, dtype=df.dtype),
                                  validate_args=validate_args,
                                  allow_nan_stats=allow_nan_stats)
diff --git a/tensorflow/contrib/distributions/python/ops/dirichlet.py b/tensorflow/contrib/distributions/python/ops/dirichlet.py
index 2b27954124..899867fe7f 100644
--- a/tensorflow/contrib/distributions/python/ops/dirichlet.py
+++ b/tensorflow/contrib/distributions/python/ops/dirichlet.py
@@ -17,12 +17,11 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
+
 from tensorflow.contrib.distributions.python.ops import distribution
 from tensorflow.contrib.distributions.python.ops import distribution_util
-from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_shape
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
@@ -129,25 +128,21 @@ class Dirichlet(distribution.Distribution):
 
     """
     with ops.name_scope(name, values=[alpha]):
-      alpha = ops.convert_to_tensor(alpha, name="alpha_before_deps")
+      alpha = ops.convert_to_tensor(alpha, name="alpha")
       with ops.control_dependencies([
-          check_ops.assert_positive(alpha), check_ops.assert_rank_at_least(
-              alpha, 1)
+          check_ops.assert_positive(alpha),
+          check_ops.assert_rank_at_least(alpha, 1)
       ] if validate_args else []):
-        alpha = array_ops.identity(alpha, name="alpha")
-
-      self._alpha = alpha
-      self._name = name
-
-      # Used for mean/mode/variance/entropy computations
-      self._alpha_0 = math_ops.reduce_sum(alpha,
-                                          reduction_indices=[-1],
-                                          keep_dims=False)
-
-      self._get_batch_shape = self._alpha_0.get_shape()
-      self._get_event_shape = self._alpha.get_shape().with_rank_at_least(1)[-1:]
-      self._validate_args = validate_args
-      self._allow_nan_stats = allow_nan_stats
+        self._alpha = array_ops.identity(alpha, name="alpha")
+        self._alpha_sum = math_ops.reduce_sum(alpha,
+                                              reduction_indices=[-1],
+                                              keep_dims=False)
+        super(Dirichlet, self).__init__(
+            dtype=self._alpha.dtype,
+            parameters={"alpha": self._alpha, "alpha_sum": self._alpha_sum},
+            validate_args=validate_args,
+            allow_nan_stats=allow_nan_stats,
+            name=name)
 
   @property
   def alpha(self):
@@ -155,241 +150,108 @@ class Dirichlet(distribution.Distribution):
     return self._alpha
 
   @property
-  def name(self):
-    """Name to prepend to all ops."""
-    return self._name
-
-  @property
-  def dtype(self):
-    """dtype of samples from this distribution."""
-    return self._alpha.dtype
-
-  @property
-  def allow_nan_stats(self):
-    """Boolean describing behavior when a stat is undefined for batch member."""
-    return self._allow_nan_stats
-
-  @property
-  def validate_args(self):
-    """Boolean describing behavior on invalid input."""
-    return self._validate_args
-
-  def batch_shape(self, name="batch_shape"):
-    """Batch dimensions of this instance as a 1-D int32 `Tensor`.
-
-    The product of the dimensions of the `batch_shape` is the number of
-    independent distributions of this kind the instance represents.
-
-    Args:
-      name: name to give to the op
-
-    Returns:
-      `Tensor` `batch_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha]):
-        return array_ops.shape(self._alpha_0)
-
-  def get_batch_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `batch_shape`. May be only partially defined.
-
-    Returns:
-      batch shape
-    """
-    return self._get_batch_shape
-
-  def event_shape(self, name="event_shape"):
-    """Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
-
-    Args:
-      name: name to give to the op
-
-    Returns:
-      `Tensor` `event_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha]):
-        return array_ops.gather(array_ops.shape(self._alpha),
-                                [array_ops.rank(self._alpha) - 1])
-
-  def get_event_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `event_shape`. May be only partially defined.
-
-    Returns:
-      event shape
-    """
-    return self._get_event_shape
-
-  def mean(self, name="mean"):
-    """Mean of the distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._alpha_0]):
-        return self._alpha / array_ops.expand_dims(self._alpha_0, -1)
-
-  def variance(self, name="variance"):
-    """Variance of the distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._alpha_0]):
-        alpha = array_ops.expand_dims(self._alpha, -1)
-        alpha_0 = array_ops.expand_dims(self._alpha_0, -1)
-
-        expanded_alpha_0 = array_ops.expand_dims(alpha_0, -1)
-
-        variance = -math_ops.batch_matmul(alpha, alpha, adj_y=True) / (
-            expanded_alpha_0 ** 2 * (expanded_alpha_0 + 1))
-        diagonal = self._alpha / (alpha_0 * (alpha_0 + 1))
-        variance += array_ops.batch_matrix_diag(diagonal)
-        return variance
-
-  def std(self, name="std"):
-    """Standard deviation of the distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return math_ops.sqrt(self.variance())
-
-  def mode(self, name="mode"):
-    """Mode of the distribution.
-
-    Note that the mode for the Beta distribution is only defined
-    when `alpha > 1`. This returns the mode when `alpha > 1`,
-    and NaN otherwise. If `self.allow_nan_stats` is `False`, an exception
-    will be raised rather than returning `NaN`.
-
-    Args:
-      name: The name for this op.
-
-    Returns:
-      Mode of the Dirichlet distribution.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._alpha_0]):
-        one = constant_op.constant(1, self.dtype)
-        mode = (self._alpha - 1)/ (
-            array_ops.expand_dims(self._alpha_0, -1) - math_ops.cast(
-                self.event_shape()[0], self.dtype))
-
-        if self.allow_nan_stats:
-          return math_ops.select(
-              math_ops.greater(self._alpha, 1),
-              mode,
-              (constant_op.constant(float("NaN"), dtype=self.dtype) *
-               array_ops.ones_like(self._alpha, dtype=self.dtype)))
-        else:
-          return control_flow_ops.with_dependencies([
-              check_ops.assert_less(
-                  one, self._alpha,
-                  message="mode not defined for components of alpha <= 1")
-          ], mode)
-
-  def entropy(self, name="entropy"):
-    """Entropy of the distribution in nats."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._alpha_0]):
-        alpha = self._alpha
-        alpha_0 = self._alpha_0
-
-        entropy = special_math_ops.lbeta(alpha)
-        entropy += (alpha_0 - math_ops.cast(
-            self.event_shape()[0], self.dtype)) * math_ops.digamma(
-                alpha_0)
-        entropy += -math_ops.reduce_sum(
-            (alpha - 1) * math_ops.digamma(alpha),
-            reduction_indices=[-1],
-            keep_dims=False)
-        return entropy
-
-  def cdf(self, x, name="cdf"):
-    """Cumulative distribution function."""
-    raise NotImplementedError("Dirichlet does not have a well-defined cdf.")
-
-  def log_cdf(self, x, name="log_cdf"):
-    """Log CDF."""
-    raise NotImplementedError("Dirichlet does not have a well-defined cdf.")
-
-  def log_prob(self, x, name="log_prob"):
-    """`Log(P[counts])`, computed for every batch member.
-
-    Args:
-      x:  Non-negative tensor with dtype `dtype` and whose shape can
-        be broadcast with `self.alpha`.  For fixed leading dimensions, the last
-        dimension represents counts for the corresponding Dirichlet distribution
-        in `self.alpha`. `x` is only legal if it sums up to one.
-      name:  Name to give this Op, defaults to "log_prob".
-
-    Returns:
-      Log probabilities for each record, shape `[N1,...,Nm]`.
-    """
-    alpha = self._alpha
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[alpha, x]):
-        x = self._check_x(x)
-
-        unnorm_prob = (alpha - 1) * math_ops.log(x)
-        log_prob = math_ops.reduce_sum(
-            unnorm_prob, reduction_indices=[-1],
-            keep_dims=False) - special_math_ops.lbeta(alpha)
-
-        return log_prob
-
-  def prob(self, x, name="prob"):
-    """`P[x]`, computed for every batch member.
-
-    Args:
-      x:  Non-negative tensor with dtype `dtype` and whose shape can
-        be broadcast with `self.alpha`.  For fixed leading dimensions, the last
-        dimension represents x for the corresponding Dirichlet distribution in
-        `self.alpha` and `self.beta`. `x` is only legal if it sums up to one.
-      name:  Name to give this Op, defaults to "prob".
-
-    Returns:
-      Probabilities for each record, shape `[N1,...,Nm]`.
-    """
-    return super(Dirichlet, self).prob(x, name=name)
-
-  def sample_n(self, n, seed=None, name="sample_n"):
-    """Sample `n` observations from the distributions.
-
-    Args:
-      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-        observations to sample.
-      seed: Python integer, the random seed.
-      name: The name to give this op.
-
-    Returns:
-      samples: `[n, ...]`, a `Tensor` of `n` samples for each
-        of the distributions determined by broadcasting the hyperparameters.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.alpha, n]):
-        gamma_sample = random_ops.random_gamma(
-            [n,], self.alpha, dtype=self.dtype, seed=seed)
-        n_val = tensor_util.constant_value(n)
-        final_shape = tensor_shape.vector(n_val).concatenate(
-            self.alpha.get_shape())
-
-        gamma_sample.set_shape(final_shape)
-        return gamma_sample / math_ops.reduce_sum(
-            gamma_sample, reduction_indices=[-1], keep_dims=True)
-
-  @property
-  def is_continuous(self):
-    return True
-
-  @property
-  def is_reparameterized(self):
-    return False
-
-  def _check_x(self, x):
-    """Check x for proper shape, values, then return tensor version."""
-    x = ops.convert_to_tensor(x, name="x_before_deps")
-    candidate_one = math_ops.reduce_sum(x, reduction_indices=[-1])
-    one = constant_op.constant(1., self.dtype)
-    dependencies = [check_ops.assert_positive(x), check_ops.assert_less(
-        x, one, message="x has components greater than or equal to 1"),
-                    distribution_util.assert_close(one, candidate_one)
-                   ] if self.validate_args else []
-    return control_flow_ops.with_dependencies(dependencies, x)
+  def alpha_sum(self):
+    """Sum of shape parameter."""
+    return self._alpha_sum
+
+  def _batch_shape(self):
+    return array_ops.shape(self.alpha_sum)
+
+  def _get_batch_shape(self):
+    return self.alpha_sum.get_shape()
+
+  def _event_shape(self):
+    return array_ops.gather(array_ops.shape(self.alpha),
+                            [array_ops.rank(self.alpha) - 1])
+
+  def _get_event_shape(self):
+    return self.alpha.get_shape().with_rank_at_least(1)[-1:]
+
+  def _sample_n(self, n, seed=None):
+    gamma_sample = random_ops.random_gamma(
+        [n,], self.alpha, dtype=self.dtype, seed=seed)
+    return gamma_sample / math_ops.reduce_sum(
+        gamma_sample, reduction_indices=[-1], keep_dims=True)
+
+  def _log_prob(self, x):
+    x = ops.convert_to_tensor(x, name="x")
+    x = self._assert_valid_sample(x)
+    unnorm_prob = (self.alpha - 1.) * math_ops.log(x)
+    log_prob = math_ops.reduce_sum(
+        unnorm_prob, reduction_indices=[-1],
+        keep_dims=False) - special_math_ops.lbeta(self.alpha)
+    return log_prob
+
+  def _prob(self, x):
+    return math_ops.exp(self._log_prob(x))
+
+  def _entropy(self):
+    entropy = special_math_ops.lbeta(self.alpha)
+    entropy += math_ops.digamma(self.alpha_sum) * (
+        self.alpha_sum - math_ops.cast(self.event_shape()[0], self.dtype))
+    entropy += -math_ops.reduce_sum(
+        (self.alpha - 1.) * math_ops.digamma(self.alpha),
+        reduction_indices=[-1],
+        keep_dims=False)
+    return entropy
+
+  def _mean(self):
+    return self.alpha / array_ops.expand_dims(self.alpha_sum, -1)
+
+  def _variance(self):
+    scale = self.alpha_sum * math_ops.sqrt(1. + self.alpha_sum)
+    alpha = self.alpha / scale
+    outer_prod = -math_ops.batch_matmul(
+        array_ops.expand_dims(alpha, dim=-1),  # column
+        array_ops.expand_dims(alpha, dim=-2))  # row
+    return array_ops.batch_matrix_set_diag(
+        outer_prod, alpha * (self.alpha_sum / scale - alpha))
+
+  def _std(self):
+    return math_ops.sqrt(self._variance())
+
+  def _mode(self):
+    mode = ((self.alpha - 1.) /
+            (array_ops.expand_dims(self.alpha_sum, dim=-1) -
+             math_ops.cast(self.event_shape()[0], self.dtype)))
+    if self.allow_nan_stats:
+      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
+      shape = array_ops.concat(0, (self.batch_shape(), self.event_shape()))
+      return math_ops.select(
+          math_ops.greater(self.alpha, 1.),
+          mode,
+          array_ops.fill(shape, nan, name="nan"))
+    else:
+      return control_flow_ops.with_dependencies([
+          check_ops.assert_less(
+              array_ops.ones((), dtype=self.dtype), self.alpha,
+              message="mode not defined for components of alpha <= 1")
+      ], mode)
+
+  def _assert_valid_sample(self, x):
+    if not self.validate_args: return x
+    return control_flow_ops.with_dependencies([
+        check_ops.assert_positive(x),
+        distribution_util.assert_close(
+            array_ops.ones((), dtype=self.dtype),
+            math_ops.reduce_sum(x, reduction_indices=[-1])),
+    ], x)
+
+
+_prob_note = """
+
+  Note that the input must be a non-negative tensor with dtype `dtype` and whose
+  shape can be broadcast with `self.alpha`.  For fixed leading dimensions, the
+  last dimension represents counts for the corresponding Dirichlet distribution
+  in `self.alpha`. `x` is only legal if it sums up to one.
+"""
+distribution_util.append_class_fun_doc(Dirichlet.log_prob, doc_str=_prob_note)
+distribution_util.append_class_fun_doc(Dirichlet.prob, doc_str=_prob_note)
+
+distribution_util.append_class_fun_doc(Dirichlet.mode, doc_str="""
+
+  Note that the mode for the Dirichlet distribution is only defined
+  when `alpha > 1`. This returns the mode when `alpha > 1`,
+  and NaN otherwise. If `self.allow_nan_stats` is `False`, an exception
+  will be raised rather than returning `NaN`.
+""")
diff --git a/tensorflow/contrib/distributions/python/ops/dirichlet_multinomial.py b/tensorflow/contrib/distributions/python/ops/dirichlet_multinomial.py
index d6fc8522d0..45e31e5580 100644
--- a/tensorflow/contrib/distributions/python/ops/dirichlet_multinomial.py
+++ b/tensorflow/contrib/distributions/python/ops/dirichlet_multinomial.py
@@ -139,9 +139,6 @@ class DirichletMultinomial(distribution.Distribution):
     ```
 
     """
-    self._allow_nan_stats = allow_nan_stats
-    self._validate_args = validate_args
-    self._name = name
     with ops.name_scope(name, values=[n, alpha]):
       # Broadcasting works because:
       # * The broadcasting convention is to prepend dimensions of size [1], and
@@ -152,16 +149,19 @@ class DirichletMultinomial(distribution.Distribution):
       #   explicitivity.
       #   * All calls involving `counts` eventually require a broadcast between
       #   `counts` and alpha.
-      self._alpha = self._check_alpha(alpha)
-      self._n = self._check_n(n)
-
+      self._alpha = self._assert_valid_alpha(alpha, validate_args)
+      self._n = self._assert_valid_n(n, validate_args)
       self._alpha_sum = math_ops.reduce_sum(
           self._alpha, reduction_indices=[-1], keep_dims=False)
-
-      self._get_batch_shape = self._alpha_sum.get_shape()
-
-      # event shape depends only on alpha, not "n".
-      self._get_event_shape = self._alpha.get_shape().with_rank_at_least(1)[-1:]
+      super(DirichletMultinomial, self).__init__(
+          dtype=self._alpha.dtype,
+          parameters={"alpha": self._alpha,
+                      "alpha_sum": self._alpha_sum,
+                      "n": self._n},
+          is_continuous=False,
+          validate_args=validate_args,
+          allow_nan_stats=allow_nan_stats,
+          name=name)
 
   @property
   def n(self):
@@ -174,195 +174,57 @@ class DirichletMultinomial(distribution.Distribution):
     return self._alpha
 
   @property
-  def allow_nan_stats(self):
-    """Boolean describing behavior when a stat is undefined for batch member."""
-    return self._allow_nan_stats
-
-  @property
-  def validate_args(self):
-    """Boolean describing behavior on invalid input."""
-    return self._validate_args
-
-  @property
-  def name(self):
-    """Name to prepend to all ops."""
-    return self._name
-
-  @property
-  def dtype(self):
-    """dtype of samples from this distribution."""
-    return self._alpha.dtype
-
-  def mean(self, name="mean"):
-    """Class means for every batch member."""
-    alpha = self._alpha
-    alpha_sum = self._alpha_sum
-    n = self._n
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[alpha, alpha_sum, n]):
-        mean_no_n = alpha / array_ops.expand_dims(alpha_sum, -1)
-        return array_ops.expand_dims(n, -1) * mean_no_n
-
-  def variance(self, name="mean"):
-    """Class variances for every batch member.
-
-    The variance for each batch member is defined as the following:
-
-    ```
-    Var(X_j) = n * alpha_j / alpha_0 * (1 - alpha_j / alpha_0) *
-      (n + alpha_0) / (1 + alpha_0)
-    ```
-
-    where `alpha_0 = sum_j alpha_j`.
-
-    The covariance between elements in a batch is defined as:
-
-    ```
-    Cov(X_i, X_j) = -n * alpha_i * alpha_j / alpha_0 ** 2 *
-      (n + alpha_0) / (1 + alpha_0)
-    ```
-
-    Args:
-      name: The name for this op.
-
-    Returns:
-      A `Tensor` representing the variances for each batch member.
-    """
-    alpha = self._alpha
-    alpha_sum = self._alpha_sum
-    n = self._n
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[alpha, alpha_sum, n]):
-        expanded_alpha_sum = array_ops.expand_dims(alpha_sum, -1)
-        shared_factor = n * (expanded_alpha_sum + n) / (
-            expanded_alpha_sum + 1) * array_ops.ones_like(alpha)
-
-        mean_no_n = alpha / expanded_alpha_sum
-        expanded_mean_no_n = array_ops.expand_dims(mean_no_n, -1)
-        variance = -math_ops.batch_matmul(
-            expanded_mean_no_n, expanded_mean_no_n, adj_y=True)
-        variance += array_ops.batch_matrix_diag(mean_no_n)
-        variance *= array_ops.expand_dims(shared_factor, -1)
-        return variance
-
-  def batch_shape(self, name="batch_shape"):
-    """Batch dimensions of this instance as a 1-D int32 `Tensor`.
-
-    The product of the dimensions of the `batch_shape` is the number of
-    independent distributions of this kind the instance represents.
-
-    Args:
-      name: name to give to the op
-
-    Returns:
-      `Tensor` `batch_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha_sum]):
-        return array_ops.shape(self._alpha_sum)
-
-  def get_batch_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `batch_shape`. May be only partially defined.
-
-    Returns:
-      batch shape
-    """
-    return self._get_batch_shape
-
-  def event_shape(self, name="event_shape"):
-    """Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
-
-    Args:
-      name: name to give to the op
-
-    Returns:
-      `Tensor` `event_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha]):
-        return array_ops.reverse(array_ops.shape(self._alpha), [True])[0]
-
-  def get_event_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `event_shape`. May be only partially defined.
-
-    Returns:
-      event shape
-    """
-    return self._get_event_shape
-
-  def cdf(self, x, name="cdf"):
-    raise NotImplementedError(
-        "DirichletMultinomial does not have a well-defined cdf.")
-
-  def log_cdf(self, x, name="log_cdf"):
-    raise NotImplementedError(
-        "DirichletMultinomial does not have a well-defined cdf.")
-
-  def log_prob(self, counts, name="log_prob"):
-    """`Log(P[counts])`, computed for every batch member.
-
-    For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
-    that after sampling `n` draws from this Dirichlet Multinomial
-    distribution, the number of draws falling in class `j` is `n_j`.  Note that
-    different sequences of draws can result in the same counts, thus the
-    probability includes a combinatorial coefficient.
-
-    Args:
-      counts:  Non-negative tensor with dtype `dtype` and whose shape can be
-        broadcast with `self.alpha`.  For fixed leading dimensions, the last
-        dimension represents counts for the corresponding Dirichlet Multinomial
-        distribution in `self.alpha`. `counts` is only legal if it sums up to
-        `n` and its components are equal to integer values.
-      name:  Name to give this Op, defaults to "log_prob".
-
-    Returns:
-      Log probabilities for each record, shape `[N1,...,Nn]`.
-    """
-    n = self._n
-    alpha = self._alpha
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[n, alpha, counts]):
-        counts = self._check_counts(counts)
-
-        ordered_prob = (special_math_ops.lbeta(alpha + counts) -
-                        special_math_ops.lbeta(alpha))
-        log_prob = ordered_prob + distribution_util.log_combinations(
-            n, counts)
-        return log_prob
-
-  def prob(self, counts, name="prob"):
-    """`P[counts]`, computed for every batch member.
-
-    For each batch of counts `[c_1,...,c_k]`, `P[counts]` is the probability
-    that after sampling `sum_j c_j` draws from this Dirichlet Multinomial
-    distribution, the number of draws falling in class `j` is `c_j`.  Note that
-    different sequences of draws can result in the same counts, thus the
-    probability includes a combinatorial coefficient.
-
-    Args:
-      counts:  Non-negative tensor with dtype `dtype` and whose shape can be
-        broadcast with `self.alpha`.  For fixed leading dimensions, the last
-        dimension represents counts for the corresponding Dirichlet Multinomial
-        distribution in `self.alpha`. `counts` is only legal if it sums up to
-        `n` and its components are equal to integer values.
-      name:  Name to give this Op, defaults to "prob".
-
-    Returns:
-      Probabilities for each record, shape `[N1,...,Nn]`.
-    """
-    return super(DirichletMultinomial, self).prob(counts, name=name)
-
-  def _check_counts(self, counts):
+  def alpha_sum(self):
+    """Summation of alpha parameter."""
+    return self._alpha_sum
+
+  def _batch_shape(self):
+    return array_ops.shape(self.alpha_sum)
+
+  def _get_batch_shape(self):
+    return self.alpha_sum.get_shape()
+
+  def _event_shape(self):
+    return array_ops.reverse(array_ops.shape(self.alpha), [True])[0]
+
+  def _get_event_shape(self):
+    # Event shape depends only on alpha, not "n".
+    return self.alpha.get_shape().with_rank_at_least(1)[-1:]
+
+  def _log_prob(self, counts):
+    counts = self._assert_valid_counts(counts)
+    ordered_prob = (special_math_ops.lbeta(self.alpha + counts) -
+                    special_math_ops.lbeta(self.alpha))
+    log_prob = ordered_prob + distribution_util.log_combinations(
+        self.n, counts)
+    return log_prob
+
+  def _prob(self, counts):
+    return math_ops.exp(self._log_prob(counts))
+
+  def _mean(self):
+    normalized_alpha = self.alpha / array_ops.expand_dims(self.alpha_sum, -1)
+    return array_ops.expand_dims(self.n, -1) * normalized_alpha
+
+  def _variance(self):
+    alpha_sum = array_ops.expand_dims(self.alpha_sum, -1)
+    normalized_alpha = self.alpha / alpha_sum
+    variance = -math_ops.batch_matmul(
+        array_ops.expand_dims(normalized_alpha, -1),
+        array_ops.expand_dims(normalized_alpha, -2))
+    variance = array_ops.batch_matrix_set_diag(
+        variance, normalized_alpha * (1. - normalized_alpha))
+    shared_factor = (self.n * (alpha_sum + self.n) /
+                     (alpha_sum + 1) * array_ops.ones_like(self.alpha))
+    variance *= array_ops.expand_dims(shared_factor, -1)
+    return variance
+
+  def _assert_valid_counts(self, counts):
     """Check counts for proper shape, values, then return tensor version."""
     counts = ops.convert_to_tensor(counts, name="counts")
     if not self.validate_args:
       return counts
     candidate_n = math_ops.reduce_sum(counts, reduction_indices=[-1])
-
     return control_flow_ops.with_dependencies([
         check_ops.assert_non_negative(counts),
         check_ops.assert_equal(
@@ -370,26 +232,59 @@ class DirichletMultinomial(distribution.Distribution):
             message="counts do not sum to n"),
         distribution_util.assert_integer_form(counts)], counts)
 
-  def _check_alpha(self, alpha):
+  def _assert_valid_alpha(self, alpha, validate_args):
     alpha = ops.convert_to_tensor(alpha, name="alpha")
-    if not self.validate_args:
+    if not validate_args:
       return alpha
     return control_flow_ops.with_dependencies(
         [check_ops.assert_rank_at_least(alpha, 1),
          check_ops.assert_positive(alpha)], alpha)
 
-  def _check_n(self, n):
+  def _assert_valid_n(self, n, validate_args):
     n = ops.convert_to_tensor(n, name="n")
-    if not self.validate_args:
+    if not validate_args:
       return n
     return control_flow_ops.with_dependencies(
         [check_ops.assert_non_negative(n),
          distribution_util.assert_integer_form(n)], n)
 
-  @property
-  def is_continuous(self):
-    return False
 
-  @property
-  def is_reparameterized(self):
-    return False
+_prob_note = """
+
+  For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
+  that after sampling `n` draws from this Dirichlet Multinomial
+  distribution, the number of draws falling in class `j` is `n_j`.  Note that
+  different sequences of draws can result in the same counts, thus the
+  probability includes a combinatorial coefficient.
+
+  Note that input, "counts", must be a non-negative tensor with dtype `dtype`
+  and whose shape can be broadcast with `self.alpha`.  For fixed leading
+  dimensions, the last dimension represents counts for the corresponding
+  Dirichlet Multinomial distribution in `self.alpha`. `counts` is only legal if
+  it sums up to `n` and its components are equal to integer values.
+"""
+distribution_util.append_class_fun_doc(DirichletMultinomial.log_prob,
+                                       doc_str=_prob_note)
+distribution_util.append_class_fun_doc(DirichletMultinomial.prob,
+                                       doc_str=_prob_note)
+
+distribution_util.append_class_fun_doc(DirichletMultinomial.variance,
+                                       doc_str="""
+
+  The variance for each batch member is defined as the following:
+
+  ```
+  Var(X_j) = n * alpha_j / alpha_0 * (1 - alpha_j / alpha_0) *
+    (n + alpha_0) / (1 + alpha_0)
+  ```
+
+  where `alpha_0 = sum_j alpha_j`.
+
+  The covariance between elements in a batch is defined as:
+
+  ```
+  Cov(X_i, X_j) = -n * alpha_i * alpha_j / alpha_0 ** 2 *
+    (n + alpha_0) / (1 + alpha_0)
+  ```
+
+""")
diff --git a/tensorflow/contrib/distributions/python/ops/distribution.py b/tensorflow/contrib/distributions/python/ops/distribution.py
index 1115d30a4c..30c79bc75e 100644
--- a/tensorflow/contrib/distributions/python/ops/distribution.py
+++ b/tensorflow/contrib/distributions/python/ops/distribution.py
@@ -19,8 +19,11 @@ from __future__ import division
 from __future__ import print_function
 
 import abc
+import contextlib
+import numpy as np
 import six
 
+from tensorflow.contrib.distributions.python.ops import distribution_util
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
@@ -38,89 +41,37 @@ class BaseDistribution(object):
   that want to fulfill a simpler distribution contract.
   """
 
-  @abc.abstractproperty
-  def name(self):
-    """Name to prepend to all ops."""
-    # return self._name.
-    pass
-
   @abc.abstractmethod
-  def prob(self, value, name="prob"):
-    """Probability density/mass function."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[value]):
-        value = ops.convert_to_tensor(value)
-        return math_ops.exp(self.log_prob(value))
+  def sample_n(self, n, seed=None, name="sample"):
+    # See `Distribution.sample_n` for docstring.
+    pass
 
   @abc.abstractmethod
   def log_prob(self, value, name="log_prob"):
-    """Log of the probability density/mass function."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[value]):
-        value = ops.convert_to_tensor(value)
-        return math_ops.log(self.prob(value))
-
-  def sample_n(self, n, seed=None, name="sample_n"):
-    """Generate `n` samples.
-
-    Args:
-      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-        observations to sample.
-      seed: Python integer seed for RNG
-      name: name to give to the op.
-
-    Returns:
-      samples: a `Tensor` with a prepended dimension (n,).
-    """
-    raise NotImplementedError("sample_n not implemented")
-
-  def sample(self, sample_shape=(), seed=None, name="sample"):
-    """Generate samples of the specified shape.
-
-    Note that a call to `sample()` without arguments will generate a single
-    sample.
-
-    Args:
-      sample_shape: Rank 1 `int32` `Tensor`. Shape of the generated samples.
-      seed: Python integer seed for RNG
-      name: name to give to the op.
-
-    Returns:
-      samples: a `Tensor` with prepended dimensions `sample_shape`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[sample_shape]):
-        sample_shape = ops.convert_to_tensor(sample_shape,
-                                             dtype=dtypes.int32,
-                                             name="sample_shape")
-        total = math_ops.reduce_prod(sample_shape)
-        samples = self.sample_n(total, seed)
-        output_shape = array_ops.concat(0, [sample_shape, array_ops.slice(
-            array_ops.shape(samples), [1], [-1])])
-        output = array_ops.reshape(samples, output_shape, name=name)
-        output.set_shape(tensor_util.constant_value_as_shape(
-            sample_shape).concatenate(samples.get_shape()[1:]))
-    return output
+    # See `Distribution.log_prob` for docstring.
+    pass
 
 
-@six.add_metaclass(abc.ABCMeta)
 class Distribution(BaseDistribution):
-  """Fully-featured abstract base class for probability distributions.
+  """A generic probability distribution base class.
 
-  This class defines the API for probability distributions. Users will only ever
-  instantiate subclasses of `Distribution`.
+  `Distribution` is a base class for constructing and organizing properties
+  (e.g., mean, variance) of random variables (e.g, Bernoulli, Gaussian).
 
-  ### API
+  ### Subclassing
 
-  The key methods for probability distributions are defined here.
+  Subclasess are expected to implement a leading-underscore version of the
+  same-named function.  The argument signature should be identical except for
+  the omission of `name="..."`.  For example, to enable `log_prob(value,
+  name="log_prob")` a subclass should implement `_log_prob(value)`.
 
-  To keep ops generated by the distribution tied together by name, subclasses
-  should override `name` and use it to prepend names of ops in other methods
-  (see `cdf` for an example).
+  Subclasses can rewrite/append to public-level docstrings. For example,
+
+  ```python
+  Subclass.prob.__func__.__doc__ += "Some other details."
+  ```
 
-  Subclasses that wish to support `cdf` and `log_cdf` can override `log_cdf`
-  and use the base class's implementation for `cdf`, or vice versa. The same
-  goes for `log_prob` and `prob`.
+  would add the string "Some other details." to the `prob` function docstring.
 
   ### Broadcasting, batching, and shapes
 
@@ -210,6 +161,42 @@ class Distribution(BaseDistribution):
 
   """
 
+  def __init__(self,
+               dtype=None,
+               parameters=None,
+               is_continuous=True,
+               is_reparameterized=False,
+               validate_args=True,
+               allow_nan_stats=False,
+               name=None):
+    """Constructs the `Distribution`.
+
+    Args:
+      dtype: The type of the event samples. `None` implies no type-enforcement.
+      parameters: Python dictionary of parameters used by this `Distribution`.
+      is_continuous: Python boolean, default `True`. If `True` this
+        `Distribution` is continuous over its supported domain.
+      is_reparameterized: Python boolean, default `False`. If `True` this
+        `Distribution` can be reparameterized in terms of some standard
+        distribution with a function whose Jacobian is constant for the support
+        of the standard distribution.
+      validate_args: Whether to validate input with asserts. If `validate_args`
+        is `False`, and the inputs are invalid, correct behavior is not
+        guaranteed.
+      allow_nan_stats: Python boolean, default `False`. If `False`, raise an
+        exception if a statistic (e.g., mean, mode) is undefined for any batch
+        member. If True, batch members with valid parameters leading to
+        undefined statistics will return `NaN` for this statistic.
+      name: A name for this distribution (optional).
+    """
+    self._name = name or type(self).__name__
+    self._dtype = dtype
+    self._parameters = parameters or {}
+    self._is_continuous = is_continuous
+    self._is_reparameterized = is_reparameterized
+    self._allow_nan_stats = allow_nan_stats
+    self._validate_args = validate_args
+
   @classmethod
   def param_shapes(cls, sample_shape, name="DistributionParamShapes"):
     """Shapes of parameters given the desired shape of a call to `sample()`.
@@ -277,8 +264,8 @@ class Distribution(BaseDistribution):
 
     # shapes has a Tensor shape for mu and sigma
     # shapes == {
-    #   'mu': tf.constant([batch_size, 10]),
-    #   'sigma': tf.constant([batch_size, 10]),
+    #   "mu": tf.constant([batch_size, 10]),
+    #   "sigma": tf.constant([batch_size, 10]),
     # }
 
     # Here we parameterize mu and sigma with the output of a linear
@@ -349,70 +336,54 @@ class Distribution(BaseDistribution):
     """
     raise NotImplementedError("_safe_transforms is not implemented")
 
-  @abc.abstractproperty
-  def allow_nan_stats(self):
-    """Boolean describing behavior when a stat is undefined for batch member."""
-    # return self._allow_nan_stats
-    # Notes:
-    #
-    # When it makes sense, return +- infinity for statistics.  E.g. the variance
-    # of a Cauchy distribution would be +infinity.  However, sometimes the
-    # statistic is undefined (e.g. if a distribution's pdf does not achieve a
-    # maximum within the support of the distribution, mode is undefined).
-    # If the mean is undefined, then by definition the variance is undefined.
-    # E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
-    # it is either + or - infinity), so the variance = E[(X - mean)^2] is also
-    # undefined.
-    #
-    # Distributions should be initialized with a kwarg "allow_nan_stats" with
-    # the following docstring (refer to above docstring note on undefined
-    # statistics for more detail).
-    # allow_nan_stats:  Boolean, default False.  If False, raise an exception if
-    #   a statistic (e.g. mean/mode/etc...) is undefined for any batch member.
-    #   If True, batch members with valid parameters leading to undefined
-    #   statistics will return NaN for this statistic.
-    pass
-
-  @abc.abstractproperty
-  def validate_args(self):
-    """Boolean describing behavior on invalid input."""
-    # return self._validate_args.
-    pass
+  @property
+  def name(self):
+    """Name prepended to all ops created by this `Distribution`."""
+    return self._name
 
-  @abc.abstractproperty
+  @property
   def dtype(self):
-    """dtype of samples from this distribution."""
-    # return self._dtype
-    pass
+    """The `DType` of `Tensor`s handled by this `Distribution`."""
+    return self._dtype
 
-  @abc.abstractmethod
-  def event_shape(self, name="event_shape"):
-    """Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+  @property
+  def parameters(self):
+    """Dictionary of parameters used by this `Distribution`."""
+    return self._parameters
 
-    Args:
-      name: name to give to the op
+  @property
+  def is_continuous(self):
+    return self._is_continuous
 
-    Returns:
-      `Tensor` `event_shape`
-    """
-    # For scalar distributions, constant([], int32)
-    # with ops.name_scope(self.name):
-    #   with ops.name_scope(name, values=[tensor_arguments]):
-    #     Your code here
-    pass
+  @property
+  def is_reparameterized(self):
+    return self._is_reparameterized
 
-  @abc.abstractmethod
-  def get_event_shape(self):
-    """`TensorShape` available at graph construction time.
+  @property
+  def allow_nan_stats(self):
+    """Python boolean describing behavior when a stat is undefined.
 
-    Same meaning as `event_shape`. May be only partially defined.
+    Stats return +/- infinity when it makes sense.  E.g., the variance
+    of a Cauchy distribution is infinity.  However, sometimes the
+    statistic is undefined, e.g., if a distribution's pdf does not achieve a
+    maximum within the support of the distribution, the mode is undefined.
+    If the mean is undefined, then by definition the variance is undefined.
+    E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+    it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+    undefined.
+
+    Returns:
+      allow_nan_stats: Python boolean.
     """
-    # return self._event_shape
-    pass
+    return self._allow_nan_stats
+
+  @property
+  def validate_args(self):
+    """Python boolean indicated possibly expensive checks are enabled."""
+    return self._validate_args
 
-  @abc.abstractmethod
   def batch_shape(self, name="batch_shape"):
-    """Batch dimensions of this instance as a 1-D int32 `Tensor`.
+    """Shape of a single sample from a single event index as a 1-D `Tensor`.
 
     The product of the dimensions of the `batch_shape` is the number of
     independent distributions of this kind the instance represents.
@@ -421,119 +392,327 @@ class Distribution(BaseDistribution):
       name: name to give to the op
 
     Returns:
-      `Tensor` `batch_shape`
+      batch_shape: `Tensor`.
     """
-    # with ops.name_scope(self.name):
-    #   with ops.name_scope(name, values=[tensor_arguments]):
-    #     Your code here
-    pass
+    self._check_hasattr(self._batch_shape)
+    with self._name_scope(name):
+      return self._batch_shape()
 
-  @abc.abstractmethod
   def get_batch_shape(self):
-    """`TensorShape` available at graph construction time.
+    """Shape of a single sample from a single event index as a `TensorShape`.
 
     Same meaning as `batch_shape`. May be only partially defined.
+
+    Returns:
+      batch_shape: `TensorShape`, possibly unknown.
     """
-    pass
+    self._check_hasattr(self._get_batch_shape)
+    return self._get_batch_shape()
 
-  def sample_n(self, n, seed=None, name="sample_n"):
-    """Generate `n` samples.
+  def event_shape(self, name="event_shape"):
+    """Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
     Args:
-      n: scalar. Number of samples to draw from each distribution.
-      seed: Python integer seed for RNG
-      name: name to give to the op.
+      name: name to give to the op
 
     Returns:
-      samples: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-          with values of type `self.dtype`.
+      event_shape: `Tensor`.
     """
-    return super(Distribution, self).sample_n(n, seed, name)
+    self._check_hasattr(self._event_shape)
+    with self._name_scope(name):
+      return self._event_shape()
+
+  def get_event_shape(self):
+    """Shape of a single sample from a single batch as a `TensorShape`.
+
+    Same meaning as `event_shape`. May be only partially defined.
+
+    Returns:
+      event_shape: `TensorShape`, possibly unknown.
+    """
+    self._check_hasattr(self._get_event_shape)
+    return self._get_event_shape()
 
   def sample(self, sample_shape=(), seed=None, name="sample"):
-    """Generate samples of the specified shape for each batched distribution.
+    """Generate samples of the specified shape.
 
     Note that a call to `sample()` without arguments will generate a single
-    sample per batched distribution.
+    sample.
 
     Args:
-      sample_shape: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+      sample_shape: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
       seed: Python integer seed for RNG
       name: name to give to the op.
 
     Returns:
-      samples: a `Tensor` of dtype `self.dtype` and shape
-          `sample_shape + self.batch_shape + self.event_shape`.
+      samples: a `Tensor` with prepended dimensions `sample_shape`.
     """
-    return super(Distribution, self).sample(sample_shape, seed, name)
+    with self._name_scope(name, values=[sample_shape]):
+      sample_shape = ops.convert_to_tensor(
+          sample_shape, dtype=dtypes.int32, name="sample_shape")
+      if sample_shape.get_shape().ndims == 0:
+        return self.sample_n(sample_shape, seed)
+      sample_shape, total = self._expand_sample_shape(sample_shape)
+      samples = self.sample_n(total, seed)
+      output_shape = array_ops.concat(0, [sample_shape, array_ops.slice(
+          array_ops.shape(samples), [1], [-1])])
+      output = array_ops.reshape(samples, output_shape)
+      output.set_shape(tensor_util.constant_value_as_shape(
+          sample_shape).concatenate(samples.get_shape()[1:]))
+      return output
 
-  def cdf(self, value, name="cdf"):
-    """Cumulative distribution function."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[value]):
-        value = ops.convert_to_tensor(value)
-        return math_ops.exp(self.log_cdf(value))
+  def sample_n(self, n, seed=None, name="sample_n"):
+    """Generate `n` samples.
+
+    Args:
+      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+        observations to sample.
+      seed: Python integer seed for RNG
+      name: name to give to the op.
+
+    Returns:
+      samples: a `Tensor` with a prepended dimension (n,).
+
+    Raises:
+      TypeError: if `n` is not an integer type.
+    """
+    self._check_hasattr(self._sample_n)
+    with self._name_scope(name, values=[n]):
+      n = ops.convert_to_tensor(n, name="n")
+      if not n.dtype.is_integer:
+        raise TypeError("n.dtype=%s is not an integer type" % n.dtype)
+      x = self._sample_n(n, seed)
+
+      # Set shape hints.
+      sample_shape = tensor_shape.TensorShape(
+          tensor_util.constant_value(n))
+      batch_ndims = self.get_batch_shape().ndims
+      event_ndims = self.get_event_shape().ndims
+      if batch_ndims is not None and event_ndims is not None:
+        inferred_shape = sample_shape.concatenate(
+            self.get_batch_shape().concatenate(
+                self.get_event_shape()))
+        x.set_shape(inferred_shape)
+      elif x.get_shape().ndims is not None and x.get_shape().ndims > 0:
+        x.get_shape()[0].merge_with(sample_shape)
+        if batch_ndims is not None and batch_ndims > 0:
+          x.get_shape()[1:1+batch_ndims].merge_with(self.get_batch_shape())
+        if event_ndims is not None and event_ndims > 0:
+          x.get_shape()[-event_ndims:].merge_with(self.get_event_shape())
+
+      return x
+
+  def log_prob(self, value, name="log_prob"):
+    """Log probability density/mass function (depending on `is_continuous`).
+
+    Args:
+      value: `float` or `double` `Tensor`.
+      name: The name to give this op.
+
+    Returns:
+      log_prob: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+        values of type `self.dtype`.
+    """
+    self._check_hasattr(self._log_prob)
+    with self._name_scope(name, values=[value]):
+      value = ops.convert_to_tensor(value, name="value")
+      return self._log_prob(value)
+
+  def prob(self, value, name="prob"):
+    """Probability density/mass function (depending on `is_continuous`).
+
+    Args:
+      value: `float` or `double` `Tensor`.
+      name: The name to give this op.
+
+    Returns:
+      prob: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+        values of type `self.dtype`.
+    """
+    self._check_hasattr(self._prob)
+    with self._name_scope(name, values=[value]):
+      value = ops.convert_to_tensor(value, name="value")
+      return self._prob(value)
 
   def log_cdf(self, value, name="log_cdf"):
-    """Log CDF."""
-    raise NotImplementedError("log_cdf is not implemented")
+    """Log cumulative distribution function.
+
+    Args:
+      value: `float` or `double` `Tensor`.
+      name: The name to give this op.
+
+    Returns:
+      logcdf: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+        values of type `self.dtype`.
+    """
+    self._check_hasattr(self._log_cdf)
+    with self._name_scope(name, values=[value]):
+      value = ops.convert_to_tensor(value, name="value")
+      return self._log_cdf(value)
+
+  def cdf(self, value, name="cdf"):
+    """Cumulative distribution function.
+
+    Args:
+      value: `float` or `double` `Tensor`.
+      name: The name to give this op.
+
+    Returns:
+      cdf: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+        values of type `self.dtype`.
+    """
+    self._check_hasattr(self._cdf)
+    with self._name_scope(name, values=[value]):
+      value = ops.convert_to_tensor(value, name="value")
+      return self._cdf(value)
 
   def entropy(self, name="entropy"):
-    """Entropy of the distribution in nats."""
-    raise NotImplementedError("entropy not implemented")
+    """Shanon entropy in nats."""
+    self._check_hasattr(self._entropy)
+    with self._name_scope(name):
+      return self._entropy()
 
   def mean(self, name="mean"):
-    """Mean of the distribution."""
-    raise NotImplementedError("mean not implemented")
+    """Mean."""
+    self._check_hasattr(self._mean)
+    with self._name_scope(name):
+      return self._mean()
 
-  def mode(self, name="mode"):
-    """Mode of the distribution."""
-    raise NotImplementedError("mode not implemented")
+  def variance(self, name="variance"):
+    """Variance."""
+    self._check_hasattr(self._variance)
+    with self._name_scope(name):
+      return self._variance()
 
   def std(self, name="std"):
-    """Standard deviation of the distribution."""
-    raise NotImplementedError("std not implemented")
+    """Standard deviation."""
+    self._check_hasattr(self._std)
+    with self._name_scope(name):
+      return self._std()
 
-  def variance(self, name="variance"):
-    """Variance of the distribution."""
-    raise NotImplementedError("variance not implemented")
+  def mode(self, name="mode"):
+    """Mode."""
+    self._check_hasattr(self._mode)
+    with self._name_scope(name):
+      return self._mode()
 
-  @abc.abstractproperty
-  def is_continuous(self):
-    pass
+  def log_pdf(self, value, name="log_pdf"):
+    """Log probability density function.
 
-  @abc.abstractproperty
-  def is_reparameterized(self):
-    pass
+    Args:
+      value: `float` or `double` `Tensor`.
+      name: The name to give this op.
 
-  def log_pdf(self, value, name="log_pdf"):
-    """Log of the probability density function."""
-    if self.is_continuous:
-      return self.log_prob(value, name=name)
-    else:
-      raise NotImplementedError(
-          "log_pdf is not implemented for non-continuous distributions")
+    Returns:
+      log_prob: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+        values of type `self.dtype`.
+
+    Raises:
+      AttributeError: if not `is_continuous`.
+    """
+    if not self.is_continuous:
+      raise AttributeError(
+          "log_pdf is undefined for non-continuous distributions.")
+    return self.log_prob(value, name=name)
 
   def pdf(self, value, name="pdf"):
-    """The probability density function."""
-    if self.is_continuous:
-      return self.prob(value, name=name)
-    else:
-      raise NotImplementedError(
-          "pdf is not implemented for non-continuous distributions")
+    """Probability density function.
+
+    Args:
+      value: `float` or `double` `Tensor`.
+      name: The name to give this op.
+
+    Returns:
+      prob: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+        values of type `self.dtype`.
+
+    Raises:
+      AttributeError: if not `is_continuous`.
+    """
+    if not self.is_continuous:
+      raise AttributeError("pdf is undefined for non-continuous distributions.")
+    return self.prob(value, name)
 
   def log_pmf(self, value, name="log_pmf"):
-    """Log of the probability mass function."""
+    """Log probability mass function.
+
+    Args:
+      value: `float` or `double` `Tensor`.
+      name: The name to give this op.
+
+    Returns:
+      log_pmf: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+        values of type `self.dtype`.
+
+    Raises:
+      AttributeError: if `is_continuous`.
+    """
     if self.is_continuous:
-      raise NotImplementedError(
-          "log_pmf is not implemented for continuous distributions")
-    else:
-      return self.log_prob(value, name=name)
+      raise AttributeError("log_pmf is undefined for continuous distributions.")
+    return self.log_prob(value, name=name)
 
   def pmf(self, value, name="pmf"):
-    """The probability mass function."""
+    """Probability mass function.
+
+    Args:
+      value: `float` or `double` `Tensor`.
+      name: The name to give this op.
+
+    Returns:
+      pmf: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+        values of type `self.dtype`.
+
+    Raises:
+      AttributeError: if `is_continuous`.
+    """
     if self.is_continuous:
-      raise NotImplementedError(
-          "pmf is not implemented for continuous distributions")
+      raise AttributeError("pmf is undefined for continuous distributions.")
+    return self.prob(value, name=name)
+
+  @contextlib.contextmanager
+  def _name_scope(self, name=None, values=None):
+    """Helper function to standardize op scope."""
+    with ops.name_scope(self.name):
+      with ops.name_scope(name, values=(
+          (values or []) + list(self.parameters.values()))) as scope:
+        yield scope
+
+  def _check_hasattr(self, func):
+    if hasattr(self, func.__func__.__name__) and callable(func): return
+    raise NotImplementedError(
+        "Subclass %s does not implement %s" %
+        (type(self).__name__, func.__func__.__name__))
+
+  def _expand_sample_shape(self, sample_shape):
+    """Helper to `sample` which ensures sample_shape is 1D."""
+    sample_shape_static_val = tensor_util.constant_value(sample_shape)
+    ndims = sample_shape.get_shape().ndims
+    if sample_shape_static_val is None:
+      if ndims is None or not sample_shape.get_shape().is_fully_defined():
+        ndims = array_ops.rank(sample_shape)
+      expanded_shape = distribution_util.pick_vector(
+          math_ops.equal(ndims, 0),
+          np.array((1,), dtype=dtypes.int32.as_numpy_dtype()),
+          array_ops.shape(sample_shape))
+      sample_shape = array_ops.reshape(sample_shape, expanded_shape)
+      total = math_ops.reduce_prod(sample_shape)  # reduce_prod([]) == 1
     else:
-      return self.prob(value, name=name)
+      if ndims is None:
+        raise ValueError(
+            "Shouldn't be here; ndims cannot be none when we have a "
+            "tf.constant shape.")
+      if ndims == 0:
+        sample_shape_static_val = np.reshape(sample_shape_static_val, [1])
+        sample_shape = ops.convert_to_tensor(
+            sample_shape_static_val,
+            dtype=dtypes.int32,
+            name="sample_shape")
+      total = np.prod(sample_shape_static_val,
+                      dtype=dtypes.int32.as_numpy_dtype())
+    return sample_shape, total
+
+
+distribution_util.append_class_fun_doc(BaseDistribution.sample_n,
+                                       doc_str=Distribution.sample_n.__doc__)
+distribution_util.append_class_fun_doc(BaseDistribution.log_prob,
+                                       doc_str=Distribution.log_prob.__doc__)
diff --git a/tensorflow/contrib/distributions/python/ops/distribution_util.py b/tensorflow/contrib/distributions/python/ops/distribution_util.py
index c6386b905b..a1ab35f1f0 100644
--- a/tensorflow/contrib/distributions/python/ops/distribution_util.py
+++ b/tensorflow/contrib/distributions/python/ops/distribution_util.py
@@ -18,6 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import sys
 import numpy as np
 
 from tensorflow.python.framework import constant_op
@@ -374,3 +375,27 @@ def pick_vector(cond,
     return array_ops.slice(array_ops.concat(0, (true_vector, false_vector)),
                            [math_ops.select(cond, 0, n)],
                            [math_ops.select(cond, n, -1)])
+
+
+def append_class_fun_doc(fn, doc_str):
+  """Appends the `doc_str` argument to `fn.__doc__`.
+
+  This function is primarily needed because Python 3 changes how docstrings are
+  programmatically set.
+
+  Args:
+    fn: Class function.
+    doc_str: String
+  """
+  # TODO(b/31100586): Figure out why appending accumulates rather than resets
+  # for each subclass.
+  if sys.version_info.major < 3:
+    if fn.__func__.__doc__ is None:
+      fn.__func__.__doc__ = doc_str
+    # else:
+    #   fn.__func__.__doc__ += doc_str
+  else:
+    if fn.__doc__ is None:
+      fn.__doc__ = doc_str
+    # else:
+    #   fn.__doc__ += doc_str
diff --git a/tensorflow/contrib/distributions/python/ops/exponential.py b/tensorflow/contrib/distributions/python/ops/exponential.py
index 95aedc88a9..59ac239060 100644
--- a/tensorflow/contrib/distributions/python/ops/exponential.py
+++ b/tensorflow/contrib/distributions/python/ops/exponential.py
@@ -21,10 +21,7 @@ from __future__ import print_function
 import numpy as np
 
 from tensorflow.contrib.distributions.python.ops import gamma
-from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_shape
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import random_ops
@@ -41,8 +38,11 @@ class Exponential(gamma.Gamma):
   distribution, with Exponential(lam) = Gamma(1, lam).
   """
 
-  def __init__(
-      self, lam, validate_args=True, allow_nan_stats=False, name="Exponential"):
+  def __init__(self,
+               lam,
+               validate_args=True,
+               allow_nan_stats=False,
+               name="Exponential"):
     """Construct Exponential distribution with parameter `lam`.
 
     Args:
@@ -62,51 +62,28 @@ class Exponential(gamma.Gamma):
     # allow_nan_stats=False
     # through to the parent class results in unnecessary asserts.
     with ops.name_scope(name, values=[lam]):
-      lam = ops.convert_to_tensor(lam)
-      self._lam = lam
+      self._lam = ops.convert_to_tensor(lam, name="lam")
       super(Exponential, self).__init__(
-          alpha=constant_op.constant(1.0, dtype=lam.dtype),
-          beta=lam,
+          alpha=array_ops.ones((), dtype=self._lam.dtype),
+          beta=self._lam,
           allow_nan_stats=allow_nan_stats,
           validate_args=validate_args)
+      # While the Gamma distribution is not reparameterizeable, the
+      # exponential distribution is.
+      self._is_reparameterized = True
 
   @property
   def lam(self):
     return self._lam
 
-  @property
-  def is_reparameterized(self):
-    # While the Gamma distribution is not reparameterizeable, the
-    # exponential distribution is.
-    return True
-
-  def sample_n(self, n, seed=None, name="sample_n"):
-    """Sample `n` observations from the Exponential Distributions.
-
-    Args:
-      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-        observations to sample.
-      seed: Python integer, the random seed.
-      name: The name to give this op.
-
-    Returns:
-      samples: `[n, ...]`, a `Tensor` of `n` samples for each
-        of the distributions determined by the hyperparameters.
-    """
-    broadcast_shape = self._lam.get_shape()
-    with ops.name_scope(name, "ExponentialSample", [self.lam, n]):
-      n = ops.convert_to_tensor(n, name="n")
-      shape = array_ops.concat(0, ([n], array_ops.shape(self._lam)))
-      # Sample uniformly-at-random from the open-interval (0, 1).
-      sampled = random_ops.random_uniform(
-          shape, minval=np.nextafter(
-              self.dtype.as_numpy_dtype(0.), self.dtype.as_numpy_dtype(1.)),
-          maxval=constant_op.constant(1.0, dtype=self.dtype),
-          seed=seed,
-          dtype=self.dtype)
-
-      n_val = tensor_util.constant_value(n)
-      final_shape = tensor_shape.vector(n_val).concatenate(broadcast_shape)
-      sampled.set_shape(final_shape)
-
-      return -math_ops.log(sampled) / self._lam
+  def _sample_n(self, n, seed=None):
+    shape = array_ops.concat(0, ([n], array_ops.shape(self._lam)))
+    # Sample uniformly-at-random from the open-interval (0, 1).
+    sampled = random_ops.random_uniform(
+        shape,
+        minval=np.nextafter(self.dtype.as_numpy_dtype(0.),
+                            self.dtype.as_numpy_dtype(1.)),
+        maxval=array_ops.ones((), dtype=self.dtype),
+        seed=seed,
+        dtype=self.dtype)
+    return -math_ops.log(sampled) / self._lam
diff --git a/tensorflow/contrib/distributions/python/ops/gamma.py b/tensorflow/contrib/distributions/python/ops/gamma.py
index 4363a1b7c5..a02b21ce10 100644
--- a/tensorflow/contrib/distributions/python/ops/gamma.py
+++ b/tensorflow/contrib/distributions/python/ops/gamma.py
@@ -20,8 +20,9 @@ from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.contrib.distributions.python.ops import distribution  # pylint: disable=line-too-long
-from tensorflow.contrib.framework.python.framework import tensor_util as contrib_tensor_util  # pylint: disable=line-too-long
+from tensorflow.contrib.distributions.python.ops import distribution
+from tensorflow.contrib.distributions.python.ops import distribution_util
+from tensorflow.contrib.framework.python.framework import tensor_util as contrib_tensor_util
 from tensorflow.python.framework import common_shapes
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -88,41 +89,20 @@ class Gamma(distribution.Distribution):
     Raises:
       TypeError: if `alpha` and `beta` are different dtypes.
     """
-    self._allow_nan_stats = allow_nan_stats
-    self._validate_args = validate_args
-    with ops.name_scope(name, values=[alpha, beta]) as scope:
-      self._name = scope
-      with ops.control_dependencies([check_ops.assert_positive(
-          alpha), check_ops.assert_positive(beta)] if validate_args else []):
-        alpha = array_ops.identity(alpha, name="alpha")
-        beta = array_ops.identity(beta, name="beta")
-
-    self._get_batch_shape = common_shapes.broadcast_shape(
-        alpha.get_shape(), beta.get_shape())
-    self._get_event_shape = tensor_shape.TensorShape([])
-
-    self._alpha = alpha
-    self._beta = beta
-
-  @property
-  def allow_nan_stats(self):
-    """Boolean describing behavior when a stat is undefined for batch member."""
-    return self._allow_nan_stats
-
-  @property
-  def validate_args(self):
-    """Boolean describing behavior on invalid input."""
-    return self._validate_args
-
-  @property
-  def name(self):
-    """Name to prepend to all ops."""
-    return self._name
-
-  @property
-  def dtype(self):
-    """dtype of samples from this distribution."""
-    return self._alpha.dtype
+    with ops.name_scope(name, values=[alpha, beta]):
+      with ops.control_dependencies([
+          check_ops.assert_positive(alpha),
+          check_ops.assert_positive(beta),
+      ] if validate_args else []):
+        self._alpha = array_ops.identity(alpha, name="alpha")
+        self._beta = array_ops.identity(beta, name="beta")
+        contrib_tensor_util.assert_same_float_dtype((self._alpha, self._beta))
+        super(Gamma, self).__init__(
+            dtype=self._alpha.dtype,
+            parameters={"alpha": self._alpha, "beta": self._beta},
+            validate_args=validate_args,
+            allow_nan_stats=allow_nan_stats,
+            name=name)
 
   @property
   def alpha(self):
@@ -134,181 +114,88 @@ class Gamma(distribution.Distribution):
     """Inverse scale parameter."""
     return self._beta
 
-  def batch_shape(self, name="batch_shape"):
-    """Batch dimensions of this instance as a 1-D int32 `Tensor`.
-
-    The product of the dimensions of the `batch_shape` is the number of
-    independent distributions of this kind the instance represents.
-
-    Args:
-      name: name to give to the op
-
-    Returns:
-      `Tensor` `batch_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._beta]):
-        return array_ops.shape(self._alpha + self._beta)
-
-  def get_batch_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `batch_shape`. May be only partially defined.
-
-    Returns:
-      `TensorShape` object.
-    """
-    return self._get_batch_shape
-
-  def event_shape(self, name="event_shape"):
-    """Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
-
-    Args:
-      name: name to give to the op
-
-    Returns:
-      `Tensor` `event_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return constant_op.constant([], dtype=dtypes.int32)
-
-  def get_event_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `event_shape`. May be only partially defined.
-
-    Returns:
-      `TensorShape` object.
-    """
-    return self._get_event_shape
-
-  def mean(self, name="mean"):
-    """Mean of each batch member."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._beta]):
-        return self._alpha / self._beta
-
-  def mode(self, name="mode"):
-    """Mode of each batch member.
-
-    The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`,
-    and `NaN` otherwise.  If `self.allow_nan_stats` is `False`, an exception
-    will be raised rather than returning `NaN`.
-
-    Args:
-      name:  A name to give this op.
-
-    Returns:
-      The mode for every batch member, a `Tensor` with same `dtype` as self.
-    """
-    alpha = self._alpha
-    beta = self._beta
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[alpha, beta]):
-        mode_if_defined = (alpha - 1.0) / beta
-        if self.allow_nan_stats:
-          alpha_ge_1 = alpha >= 1.0
-          nan = np.nan * self._ones()
-          return math_ops.select(alpha_ge_1, mode_if_defined, nan)
-        else:
-          one = constant_op.constant(1.0, dtype=self.dtype)
-          return control_flow_ops.with_dependencies(
-              [check_ops.assert_less(
-                  one, alpha,
-                  message="mode not defined for components of alpha <= 1"
-              )], mode_if_defined)
-
-  def variance(self, name="variance"):
-    """Variance of each batch member."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._beta]):
-        return self._alpha / math_ops.square(self._beta)
-
-  def std(self, name="std"):
-    """Standard deviation of this distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._beta]):
-        return math_ops.sqrt(self._alpha) / self._beta
-
-  def log_prob(self, x, name="log_prob"):
-    """Log prob of observations in `x` under these Gamma distribution(s).
-
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-      name: The name to give this op.
-
-    Returns:
-      log_prob: tensor of dtype `dtype`, the log-PDFs of `x`.
-
-    Raises:
-      TypeError: if `x` and `alpha` are different dtypes.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._beta, x]):
-        alpha = self._alpha
-        beta = self._beta
-        x = ops.convert_to_tensor(x)
-        x = control_flow_ops.with_dependencies([check_ops.assert_positive(x)] if
-                                               self.validate_args else [], x)
-        contrib_tensor_util.assert_same_float_dtype(tensors=[x,],
-                                                    dtype=self.dtype)
-
-        return (alpha * math_ops.log(beta) + (alpha - 1) * math_ops.log(x) -
-                beta * x - math_ops.lgamma(self._alpha))
-
-  def prob(self, x, name="prob"):
-    """Pdf of observations in `x` under these Gamma distribution(s).
-
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-      name: The name to give this op.
-
-    Returns:
-      prob: tensor of dtype `dtype`, the PDFs of `x`
-
-    Raises:
-      TypeError: if `x` and `alpha` are different dtypes.
-    """
-    return super(Gamma, self).prob(x, name)
-
-  def log_cdf(self, x, name="log_cdf"):
-    """Log CDF of observations `x` under these Gamma distribution(s).
-
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-      name: The name to give this op.
-
-    Returns:
-      log_cdf: tensor of dtype `dtype`, the log-CDFs of `x`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._beta, x]):
-        x = ops.convert_to_tensor(x)
-        x = control_flow_ops.with_dependencies([check_ops.assert_positive(x)] if
-                                               self.validate_args else [], x)
-        contrib_tensor_util.assert_same_float_dtype(tensors=[x,],
-                                                    dtype=self.dtype)
-        # Note that igamma returns the regularized incomplete gamma function,
-        # which is what we want for the CDF.
-        return math_ops.log(math_ops.igamma(self._alpha, self._beta * x))
-
-  def cdf(self, x, name="cdf"):
-    """CDF of observations `x` under these Gamma distribution(s).
-
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-      name: The name to give this op.
-
-    Returns:
-      cdf: tensor of dtype `dtype`, the CDFs of `x`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._beta, x]):
-        return math_ops.igamma(self._alpha, self._beta * x)
-
-  def entropy(self, name="entropy"):
-    """The entropy of Gamma distribution(s).
+  def _batch_shape(self):
+    return array_ops.shape(self.alpha + self.beta)
+
+  def _get_batch_shape(self):
+    return common_shapes.broadcast_shape(self.alpha.get_shape(),
+                                         self.beta.get_shape())
+
+  def _event_shape(self):
+    return constant_op.constant([], dtype=dtypes.int32)
+
+  def _get_event_shape(self):
+    return tensor_shape.scalar()
+
+  def _sample_n(self, n, seed=None):
+    return random_ops.random_gamma([n],
+                                   self.alpha,
+                                   beta=self.beta,
+                                   dtype=self.dtype,
+                                   seed=seed)
+
+  def _log_prob(self, x):
+    x = control_flow_ops.with_dependencies([check_ops.assert_positive(x)] if
+                                           self.validate_args else [], x)
+    contrib_tensor_util.assert_same_float_dtype(tensors=[x],
+                                                dtype=self.dtype)
+    return (self.alpha * math_ops.log(self.beta) +
+            (self.alpha - 1.) * math_ops.log(x) -
+            self.beta * x -
+            math_ops.lgamma(self.alpha))
+
+  def _prob(self, x):
+    return math_ops.exp(self._log_prob(x))
+
+  def _log_cdf(self, x):
+    x = control_flow_ops.with_dependencies([check_ops.assert_positive(x)] if
+                                           self.validate_args else [], x)
+    contrib_tensor_util.assert_same_float_dtype(tensors=[x], dtype=self.dtype)
+    # Note that igamma returns the regularized incomplete gamma function,
+    # which is what we want for the CDF.
+    return math_ops.log(math_ops.igamma(self.alpha, self.beta * x))
+
+  def _cdf(self, x):
+    return math_ops.igamma(self.alpha, self.beta * x)
+
+  def _entropy(self):
+    return (self.alpha -
+            math_ops.log(self.beta) +
+            math_ops.lgamma(self.alpha) +
+            (1. - self.alpha) * math_ops.digamma(self.alpha))
+
+  def _mean(self):
+    return self.alpha / self.beta
+
+  def _variance(self):
+    return self.alpha / math_ops.square(self.beta)
+
+  def _std(self):
+    return math_ops.sqrt(self.alpha) / self.beta
+
+  def _mode(self):
+    mode = (self.alpha - 1.) / self.beta
+    if self.allow_nan_stats:
+      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
+      return math_ops.select(
+          self.alpha >= 1.,
+          mode,
+          array_ops.fill(self.batch_shape(), nan, name="nan"))
+    else:
+      return control_flow_ops.with_dependencies([
+          check_ops.assert_less(
+              array_ops.ones((), self.dtype),
+              self.alpha,
+              message="mode not defined for components of alpha <= 1"),
+          ], mode)
+
+
+distribution_util.append_class_fun_doc(Gamma.sample_n, doc_str="""
+
+    See the documentation for tf.random_gamma for more details.
+""")
+
+distribution_util.append_class_fun_doc(Gamma.entropy, doc_str="""
 
     This is defined to be
 
@@ -318,50 +205,11 @@ class Gamma(distribution.Distribution):
     ```
 
     where digamma(alpha) is the digamma function.
+""")
 
-    Args:
-      name: The name to give this op.
-
-    Returns:
-      entropy: tensor of dtype `dtype`, the entropy.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.alpha, self._beta]):
-        alpha = self._alpha
-        beta = self._beta
-        return (alpha - math_ops.log(beta) + math_ops.lgamma(alpha) +
-                (1 - alpha) * math_ops.digamma(alpha))
-
-  def sample_n(self, n, seed=None, name="sample_n"):
-    """Draws `n` samples from the Gamma distribution(s).
-
-    See the doc for tf.random_gamma for further detail.
-
-    Args:
-      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-        observations to sample.
-      seed: Python integer, the random seed for this operation.
-      name: Optional name for the operation.
-
-    Returns:
-      samples: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-          with values of type `self.dtype`.
-    """
-    with ops.name_scope(self.name, values=[n, self.alpha, self._beta]):
-      return random_ops.random_gamma([n],
-                                     self.alpha,
-                                     beta=self._beta,
-                                     dtype=self.dtype,
-                                     seed=seed,
-                                     name=name)
-
-  @property
-  def is_reparameterized(self):
-    return False
-
-  def _ones(self):
-    return array_ops.ones_like(self._alpha + self._beta, dtype=self.dtype)
+distribution_util.append_class_fun_doc(Gamma.mode, doc_str="""
 
-  @property
-  def is_continuous(self):
-    return True
+    The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`,
+    and `NaN` otherwise.  If `self.allow_nan_stats` is `False`, an exception
+    will be raised rather than returning `NaN`.
+""")
diff --git a/tensorflow/contrib/distributions/python/ops/inverse_gamma.py b/tensorflow/contrib/distributions/python/ops/inverse_gamma.py
index 869b61bd3e..58c92a9aca 100644
--- a/tensorflow/contrib/distributions/python/ops/inverse_gamma.py
+++ b/tensorflow/contrib/distributions/python/ops/inverse_gamma.py
@@ -20,8 +20,8 @@ from __future__ import print_function
 
 import numpy as np
 
-from tensorflow.contrib.distributions.python.ops import distribution  # pylint: disable=line-too-long
-from tensorflow.contrib.framework.python.framework import tensor_util as contrib_tensor_util  # pylint: disable=line-too-long
+from tensorflow.contrib.distributions.python.ops import distribution
+from tensorflow.contrib.distributions.python.ops import distribution_util
 from tensorflow.python.framework import common_shapes
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
@@ -87,41 +87,19 @@ class InverseGamma(distribution.Distribution):
     Raises:
       TypeError: if `alpha` and `beta` are different dtypes.
     """
-    self._allow_nan_stats = allow_nan_stats
-    self._validate_args = validate_args
-    with ops.name_scope(name, values=[alpha, beta]) as scope:
-      self._name = scope
-      with ops.control_dependencies([check_ops.assert_positive(
-          alpha), check_ops.assert_positive(beta)] if validate_args else []):
-        alpha = array_ops.identity(alpha, name="alpha")
-        beta = array_ops.identity(beta, name="beta")
-
-    self._get_batch_shape = common_shapes.broadcast_shape(
-        alpha.get_shape(), beta.get_shape())
-    self._get_event_shape = tensor_shape.TensorShape([])
-
-    self._alpha = alpha
-    self._beta = beta
-
-  @property
-  def allow_nan_stats(self):
-    """Boolean describing behavior when a stat is undefined for batch member."""
-    return self._allow_nan_stats
-
-  @property
-  def validate_args(self):
-    """Boolean describing behavior on invalid input."""
-    return self._validate_args
-
-  @property
-  def name(self):
-    """Name to prepend to all ops."""
-    return self._name
-
-  @property
-  def dtype(self):
-    """dtype of samples from this distribution."""
-    return self._alpha.dtype
+    with ops.name_scope(name, values=[alpha, beta]):
+      with ops.control_dependencies([
+          check_ops.assert_positive(alpha),
+          check_ops.assert_positive(beta),
+      ] if validate_args else []):
+        self._alpha = array_ops.identity(alpha, name="alpha")
+        self._beta = array_ops.identity(beta, name="beta")
+        super(InverseGamma, self).__init__(
+            dtype=self._alpha.dtype,
+            parameters={"alpha": self._alpha, "beta": self._beta},
+            validate_args=validate_args,
+            allow_nan_stats=allow_nan_stats,
+            name=name)
 
   @property
   def alpha(self):
@@ -133,210 +111,88 @@ class InverseGamma(distribution.Distribution):
     """Scale parameter."""
     return self._beta
 
-  def batch_shape(self, name="batch_shape"):
-    """Batch dimensions of this instance as a 1-D int32 `Tensor`.
-
-    The product of the dimensions of the `batch_shape` is the number of
-    independent distributions of this kind the instance represents.
-
-    Args:
-      name: name to give to the op
-
-    Returns:
-      `Tensor` `batch_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._beta]):
-        return array_ops.shape(self._alpha + self._beta)
-
-  def get_batch_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `batch_shape`. May be only partially defined.
-
-    Returns:
-      `TensorShape` object.
-    """
-    return self._get_batch_shape
-
-  def event_shape(self, name="event_shape"):
-    """Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
-
-    Args:
-      name: name to give to the op
-
-    Returns:
-      `Tensor` `event_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return constant_op.constant([], dtype=dtypes.int32)
-
-  def get_event_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `event_shape`. May be only partially defined.
-
-    Returns:
-      `TensorShape` object.
-    """
-    return self._get_event_shape
-
-  def mean(self, name="mean"):
-    """Mean of each batch member.
-
-    The mean of an inverse gamma distribution is `beta / (alpha - 1)`,
-    when `alpha > 1`, and `NaN` otherwise.  If `self.allow_nan_stats` is
-    `False`, an exception will be raised rather than returning `NaN`
-
-    Args:
-      name: A name to give this op.
-
-    Returns:
-      The mean for every batch member, a `Tensor` with same `dtype` as self.
-    """
-    alpha = self._alpha
-    beta = self._beta
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[alpha, beta]):
-        mean_if_defined = beta / (alpha - 1.0)
-        if self.allow_nan_stats:
-          alpha_gt_1 = alpha > 1.0
-          nan = np.nan * self._ones()
-          return math_ops.select(alpha_gt_1, mean_if_defined, nan)
-        else:
-          one = constant_op.constant(1.0, dtype=self.dtype)
-          return control_flow_ops.with_dependencies(
-              [check_ops.assert_less(
-                  one, alpha,
-                  message="mean not defined for components of alpha <= 1")],
-              mean_if_defined)
-
-  def mode(self, name="mode"):
-    """Mode of each batch member.
-
-    The mode of an inverse gamma distribution is `beta / (alpha + 1)`.
-
-    Args:
-      name: A name to give this op.
-
-    Returns:
-      The mode for every batch member, a `Tensor` with same `dtype` as self.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._beta]):
-        return self._beta / (self._alpha + 1.0)
-
-  def variance(self, name="variance"):
-    """Variance of each batch member.
-
-    Variance for inverse gamma is defined only for `alpha > 2`. If
-    `self.allow_nan_stats` is `False`, an exception will be raised rather
-    than returning `NaN`.
-
-    Args:
-      name: A name to give this op.
-
-    Returns:
-      The variance for every batch member, a `Tensor` with same `dtype` as self.
-    """
-    alpha = self._alpha
-    beta = self._beta
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[alpha, beta]):
-        var_if_defined = (math_ops.square(self._beta) /
-                          (math_ops.square(self._alpha - 1.0) *
-                           (self._alpha - 2.0)))
-        if self.allow_nan_stats:
-          alpha_gt_2 = alpha > 2.0
-          nan = np.nan * self._ones()
-          return math_ops.select(alpha_gt_2, var_if_defined, nan)
-        else:
-          two = constant_op.constant(2.0, dtype=self.dtype)
-          return control_flow_ops.with_dependencies(
-              [check_ops.assert_less(
-                  two, alpha,
-                  message="variance not defined for components of alpha <= 2")],
-              var_if_defined)
-
-  def log_prob(self, x, name="log_prob"):
-    """Log prob of observations in `x` under these InverseGamma distribution(s).
-
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-      name: The name to give this op.
-
-    Returns:
-      log_prob: tensor of dtype `dtype`, the log-PDFs of `x`.
-
-    Raises:
-      TypeError: if `x` and `alpha` are different dtypes.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._beta, x]):
-        alpha = self._alpha
-        beta = self._beta
-        x = ops.convert_to_tensor(x)
-        x = control_flow_ops.with_dependencies([check_ops.assert_positive(x)] if
-                                               self.validate_args else [], x)
-        contrib_tensor_util.assert_same_float_dtype(tensors=[x,],
-                                                    dtype=self.dtype)
-
-        return (alpha * math_ops.log(beta) - math_ops.lgamma(self._alpha) -
-                (alpha + 1) * math_ops.log(x) - beta / x)
-
-  def prob(self, x, name="prob"):
-    """Pdf of observations in `x` under these Gamma distribution(s).
-
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-      name: The name to give this op.
-
-    Returns:
-      prob: tensor of dtype `dtype`, the PDFs of `x`
-
-    Raises:
-      TypeError: if `x` and `alpha` are different dtypes.
-    """
-    return super(InverseGamma, self).prob(x, name)
-
-  def log_cdf(self, x, name="log_cdf"):
-    """Log CDF of observations `x` under these InverseGamma distribution(s).
-
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-      name: The name to give this op.
-
-    Returns:
-      log_cdf: tensor of dtype `dtype`, the log-CDFs of `x`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._beta, x]):
-        x = ops.convert_to_tensor(x)
-        x = control_flow_ops.with_dependencies([check_ops.assert_positive(x)] if
-                                               self.validate_args else [], x)
-        contrib_tensor_util.assert_same_float_dtype(tensors=[x,],
-                                                    dtype=self.dtype)
-        # Note that igammac returns the upper regularized incomplete gamma
-        # function Q(a, x), which is what we want for the CDF.
-        return math_ops.log(math_ops.igammac(self._alpha, self._beta / x))
-
-  def cdf(self, x, name="cdf"):
-    """CDF of observations `x` under these InverseGamma distribution(s).
-
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
-      name: The name to give this op.
-
-    Returns:
-      cdf: tensor of dtype `dtype`, the CDFs of `x`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._beta, x]):
-        return math_ops.igammac(self._alpha, self._beta / x)
-
-  def entropy(self, name="entropy"):
-    """The entropy of these InverseGamma distribution(s).
+  def _batch_shape(self):
+    return array_ops.shape(self.alpha + self.beta)
+
+  def _get_batch_shape(self):
+    return common_shapes.broadcast_shape(self.alpha.get_shape(),
+                                         self.beta.get_shape())
+
+  def _event_shape(self):
+    return constant_op.constant([], dtype=dtypes.int32)
+
+  def _get_event_shape(self):
+    return tensor_shape.scalar()
+
+  def _sample_n(self, n, seed=None):
+    return 1. / random_ops.random_gamma([n], self.alpha, beta=self.beta,
+                                        dtype=self.dtype, seed=seed)
+
+  def _log_prob(self, x):
+    x = control_flow_ops.with_dependencies([check_ops.assert_positive(x)] if
+                                           self.validate_args else [], x)
+    return (self.alpha * math_ops.log(self.beta) -
+            math_ops.lgamma(self.alpha) -
+            (self.alpha + 1.) * math_ops.log(x) - self.beta / x)
+
+  def _prob(self, x):
+    return math_ops.exp(self._log_prob(x))
+
+  def _log_cdf(self, x):
+    return math_ops.log(self._cdf(x))
+
+  def _cdf(self, x):
+    x = control_flow_ops.with_dependencies([check_ops.assert_positive(x)] if
+                                           self.validate_args else [], x)
+    # Note that igammac returns the upper regularized incomplete gamma
+    # function Q(a, x), which is what we want for the CDF.
+    return math_ops.igammac(self.alpha, self.beta / x)
+
+  def _entropy(self):
+    return (self.alpha +
+            math_ops.log(self.beta) +
+            math_ops.lgamma(self.alpha) -
+            (1. + self.alpha) * math_ops.digamma(self.alpha))
+
+  def _mean(self):
+    mean = self.beta / (self.alpha - 1.)
+    if self.allow_nan_stats:
+      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
+      return math_ops.select(
+          self.alpha > 1., mean,
+          array_ops.fill(self.batch_shape(), nan, name="nan"))
+    else:
+      return control_flow_ops.with_dependencies([
+          check_ops.assert_less(
+              array_ops.ones((), self.dtype), self.alpha,
+              message="mean not defined for components of self.alpha <= 1"),
+      ], mean)
+
+  def _variance(self):
+    var = (math_ops.square(self.beta) /
+           (math_ops.square(self.alpha - 1.) * (self.alpha - 2.)))
+    if self.allow_nan_stats:
+      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
+      return math_ops.select(
+          self.alpha > 2., var,
+          array_ops.fill(self.batch_shape(), nan, name="nan"))
+    else:
+      return control_flow_ops.with_dependencies([
+          check_ops.assert_less(
+              constant_op.constant(2., dtype=self.dtype), self.alpha,
+              message="variance not defined for components of alpha <= 2"),
+      ], var)
+
+  def _mode(self):
+    return self.beta / (self.alpha + 1.)
+
+
+distribution_util.append_class_fun_doc(InverseGamma.sample_n, doc_str="""
+
+    See the documentation for tf.random_gamma for more details.
+""")
+
+distribution_util.append_class_fun_doc(InverseGamma.entropy, doc_str="""
 
     This is defined to be
 
@@ -346,51 +202,23 @@ class InverseGamma(distribution.Distribution):
     ```
 
     where digamma(alpha) is the digamma function.
+""")
 
-    Args:
-      name: The name to give this op.
-
-    Returns:
-      entropy: tensor of dtype `dtype`, the entropy.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._alpha, self._beta]):
-        alpha = self._alpha
-        beta = self._beta
-        return (alpha + math_ops.log(beta) + math_ops.lgamma(alpha) -
-                (1 + alpha) * math_ops.digamma(alpha))
+distribution_util.append_class_fun_doc(InverseGamma.mean, doc_str="""
 
-  def sample_n(self, n, seed=None, name="sample_n"):
-    """Draws `n` samples from these InverseGamma distribution(s).
-
-    See the doc for tf.random_gamma for further details on sampling strategy.
+    The mean of an inverse gamma distribution is `beta / (alpha - 1)`,
+    when `alpha > 1`, and `NaN` otherwise.  If `self.allow_nan_stats` is
+    `False`, an exception will be raised rather than returning `NaN`
+""")
 
-    Args:
-      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-        observations to sample.
-      seed: Python integer, the random seed for this operation.
-      name: Optional name for the operation.
-
-    Returns:
-      samples: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-          with values of type `self.dtype`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[n, self._alpha, self._beta]):
-        one = constant_op.constant(1.0, dtype=self.dtype)
-        return one / random_ops.random_gamma([n],
-                                             self._alpha,
-                                             beta=self._beta,
-                                             dtype=self.dtype,
-                                             seed=seed)
+distribution_util.append_class_fun_doc(InverseGamma.variance, doc_str="""
 
-  @property
-  def is_reparameterized(self):
-    return False
+    Variance for inverse gamma is defined only for `alpha > 2`. If
+    `self.allow_nan_stats` is `False`, an exception will be raised rather
+    than returning `NaN`.
+""")
 
-  def _ones(self):
-    return array_ops.ones_like(self._alpha + self._beta, dtype=self.dtype)
+distribution_util.append_class_fun_doc(InverseGamma.mode, doc_str="""
 
-  @property
-  def is_continuous(self):
-    return True
+    The mode of an inverse gamma distribution is `beta / (alpha + 1)`.
+""")
diff --git a/tensorflow/contrib/distributions/python/ops/laplace.py b/tensorflow/contrib/distributions/python/ops/laplace.py
index bc8e4fc38c..59e96739df 100644
--- a/tensorflow/contrib/distributions/python/ops/laplace.py
+++ b/tensorflow/contrib/distributions/python/ops/laplace.py
@@ -29,7 +29,6 @@ from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import math_ops
@@ -77,88 +76,19 @@ class Laplace(distribution.Distribution):
     Raises:
       TypeError: if `loc` and `scale` are of different dtype.
     """
-    self._allow_nan_stats = allow_nan_stats
-    self._validate_args = validate_args
     with ops.name_scope(name, values=[loc, scale]):
-      loc = ops.convert_to_tensor(loc)
-      scale = ops.convert_to_tensor(scale)
       with ops.control_dependencies([check_ops.assert_positive(scale)] if
                                     validate_args else []):
-        self._name = name
         self._loc = array_ops.identity(loc, name="loc")
         self._scale = array_ops.identity(scale, name="scale")
-        self._batch_shape = common_shapes.broadcast_shape(
-            self._loc.get_shape(), self._scale.get_shape())
-        self._event_shape = tensor_shape.TensorShape([])
-
-    contrib_tensor_util.assert_same_float_dtype((loc, scale))
-
-  @property
-  def allow_nan_stats(self):
-    """Boolean describing behavior when a stat is undefined for batch member."""
-    return self._allow_nan_stats
-
-  @property
-  def validate_args(self):
-    """Boolean describing behavior on invalid input."""
-    return self._validate_args
-
-  @property
-  def name(self):
-    return self._name
-
-  @property
-  def dtype(self):
-    return self._loc.dtype
-
-  def batch_shape(self, name="batch_shape"):
-    """Batch dimensions of this instance as a 1-D int32 `Tensor`.
-
-    The product of the dimensions of the `batch_shape` is the number of
-    independent distributions of this kind the instance represents.
-
-    Args:
-      name: name to give to the op.
-
-    Returns:
-      `Tensor` `batch_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return array_ops.shape(self._loc + self._scale)
-
-  def get_batch_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `batch_shape`. May be only partially defined.
-
-    Returns:
-      batch shape
-    """
-    return self._batch_shape
-
-  def event_shape(self, name="event_shape"):
-    """Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
-
-    Args:
-      name: name to give to the op.
-
-    Returns:
-      `Tensor` `event_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return constant_op.constant([], dtype=dtypes.int32)
-
-  def get_event_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `event_shape`. May be only partially defined.
-
-    Returns:
-      event shape
-    """
-    return self._event_shape
+        contrib_tensor_util.assert_same_float_dtype((self._loc, self._scale))
+        super(Laplace, self).__init__(
+            dtype=self._loc.dtype,
+            parameters={"loc": self._loc, "scale": self._scale},
+            is_reparameterized=True,
+            validate_args=validate_args,
+            allow_nan_stats=allow_nan_stats,
+            name=name)
 
   @property
   def loc(self):
@@ -170,155 +100,64 @@ class Laplace(distribution.Distribution):
     """Distribution parameter for scale."""
     return self._scale
 
-  def mean(self, name="mean"):
-    """Mean of this distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._scale, self._loc]):
-        return self._loc + array_ops.zeros_like(self._scale)
-
-  def median(self, name="median"):
-    """Median of this distribution."""
-    return self.mean(name="median")
-
-  def mode(self, name="mode"):
-    """Mode of this distribution."""
-    return self.mean(name="mode")
-
-  def std(self, name="std"):
-    """Standard deviation of this distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._scale, self._loc]):
-        sqrt_2 = constant_op.constant(math.sqrt(2.), dtype=self.dtype)
-        return sqrt_2 * self._scale + array_ops.zeros_like(self._loc)
+  def _batch_shape(self):
+    return array_ops.shape(self.loc + self.scale)
 
-  def variance(self, name="variance"):
-    """Variance of this distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return math_ops.square(self.std())
+  def _get_batch_shape(self):
+    return common_shapes.broadcast_shape(self.loc.get_shape(),
+                                         self.scale.get_shape())
 
-  def prob(self, x, name="pdf"):
-    """The prob of observations in `x` under the Laplace distribution(s).
+  def _event_shape(self):
+    return constant_op.constant([], dtype=dtypes.int32)
 
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
-      name: The name to give this op.
-
-    Returns:
-      pdf: tensor of dtype `dtype`, the pdf values of `x`.
-    """
-    return 0.5 / self._scale * math_ops.exp(
-        -math_ops.abs(x - self._loc) / self._scale)
+  def _get_event_shape(self):
+    return tensor_shape.scalar()
 
-  def log_prob(self, x, name="log_prob"):
-    """Log prob of observations in `x` under these Laplace distribution(s).
+  def _sample_n(self, n, seed=None):
+    shape = array_ops.concat(0, ([n], self.batch_shape()))
+    # Sample uniformly-at-random from the open-interval (-1, 1).
+    uniform_samples = random_ops.random_uniform(
+        shape=shape,
+        minval=np.nextafter(self.dtype.as_numpy_dtype(-1.),
+                            self.dtype.as_numpy_dtype(0.)),
+        maxval=1.,
+        dtype=self.dtype,
+        seed=seed)
+    return (self.loc - self.scale * math_ops.sign(uniform_samples) *
+            math_ops.log(1. - math_ops.abs(uniform_samples)))
 
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
-      name: The name to give this op.
+  def _log_prob(self, x):
+    return (-math.log(2.) - math_ops.log(self.scale) -
+            math_ops.abs(x - self.loc) / self.scale)
 
-    Returns:
-      log_prob: tensor of dtype `dtype`, the log-probability of `x`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._loc, self._scale, x]):
-        x = ops.convert_to_tensor(x)
-        if x.dtype != self.dtype:
-          raise TypeError("Input x dtype does not match dtype: %s vs. %s"
-                          % (x.dtype, self.dtype))
-        log_2 = constant_op.constant(math.log(2.), dtype=self.dtype)
-        return (-log_2 - math_ops.log(self._scale) -
-                math_ops.abs(x - self._loc) / self._scale)
+  def _prob(self, x):
+    return 0.5 / self.scale * math_ops.exp(
+        -math_ops.abs(x - self.loc) / self.scale)
 
-  def cdf(self, x, name="cdf"):
-    """CDF of observations in `x` under the Laplace distribution(s).
+  def _log_cdf(self, x):
+    return math_ops.log(self.cdf(x))
 
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
-      name: The name to give this op.
+  def _cdf(self, x):
+    y = x - self.loc
+    return (0.5 + 0.5 * math_ops.sign(y) *
+            (1. - math_ops.exp(-math_ops.abs(y) / self.scale)))
 
-    Returns:
-      cdf: tensor of dtype `dtype`, the CDFs of `x`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._loc, self._scale, x]):
-        x = ops.convert_to_tensor(x)
-        if x.dtype != self.dtype:
-          raise TypeError("Input x dtype does not match dtype: %s vs. %s"
-                          % (x.dtype, self.dtype))
-        y = x - self._loc
-        return 0.5 + 0.5 * math_ops.sign(y) * (
-            1. - math_ops.exp(-math_ops.abs(y) / self._scale))
+  def _entropy(self):
+    # Use broadcasting rules to calculate the full broadcast scale.
+    scale = self.scale + array_ops.zeros_like(self.loc)
+    return math.log(2.) + 1. + math_ops.log(scale)
 
-  def log_cdf(self, x, name="log_cdf"):
-    """Log CDF of observations `x` under the Laplace distribution(s).
+  def _mean(self):
+    return self.loc + array_ops.zeros_like(self.scale)
 
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
-      name: The name to give this op.
+  def _variance(self):
+    return math_ops.square(self._std())
 
-    Returns:
-      log_cdf: tensor of dtype `dtype`, the log-CDFs of `x`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._loc, self._scale, x]):
-        return math_ops.log(self.cdf(x))
+  def _std(self):
+    return math.sqrt(2.) * self.scale + array_ops.zeros_like(self.loc)
 
-  def entropy(self, name="entropy"):
-    """The entropy of Laplace distribution(s).
+  def _median(self):
+    return self._mean()
 
-    Args:
-      name: The name to give this op.
-
-    Returns:
-      entropy: tensor of dtype `dtype`, the entropy.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._loc, self._scale]):
-        log_2_e = constant_op.constant(math.log(2.) + 1., dtype=self.dtype)
-        # Use broadcasting rules to calculate the full broadcast scale.
-        scale = self._scale + array_ops.zeros_like(self._loc)
-        return log_2_e + math_ops.log(scale)
-
-  def sample_n(self, n, seed=None, name="sample_n"):
-    """Sample `n` observations from the Laplace Distributions.
-
-    Args:
-      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-        observations to sample.
-      seed: Python integer, the random seed.
-      name: The name to give this op.
-
-    Returns:
-      samples: `[n, ...]`, a `Tensor` of `n` samples for each
-        of the distributions determined by broadcasting the parameters.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._loc, self._scale, n]):
-        n = ops.convert_to_tensor(n, name="n")
-        n_val = tensor_util.constant_value(n)
-        shape = array_ops.concat(0, ([n], self.batch_shape()))
-        # Sample uniformly-at-random from the open-interval (-1, 1).
-        uniform_samples = random_ops.random_uniform(
-            shape=shape,
-            minval=np.nextafter(self.dtype.as_numpy_dtype(-1.),
-                                self.dtype.as_numpy_dtype(0.)),
-            maxval=self.dtype.as_numpy_dtype(1.),
-            dtype=self.dtype,
-            seed=seed)
-
-        # Provide some hints to shape inference
-        inferred_shape = tensor_shape.vector(n_val).concatenate(
-            self.get_batch_shape())
-        uniform_samples.set_shape(inferred_shape)
-
-        return (self._loc - self._scale * math_ops.sign(uniform_samples) *
-                math_ops.log(1. - math_ops.abs(uniform_samples)))
-
-  @property
-  def is_reparameterized(self):
-    return True
-
-  @property
-  def is_continuous(self):
-    return True
+  def _mode(self):
+    return self._mean()
diff --git a/tensorflow/contrib/distributions/python/ops/multinomial.py b/tensorflow/contrib/distributions/python/ops/multinomial.py
index 931daecc86..bc79510e12 100644
--- a/tensorflow/contrib/distributions/python/ops/multinomial.py
+++ b/tensorflow/contrib/distributions/python/ops/multinomial.py
@@ -144,20 +144,20 @@ class Multinomial(distribution.Distribution):
               n, message="n has non-integer components.")
       ] if validate_args else []):
         self._n = array_ops.identity(n, name="convert_n")
-        self._name = name
-
-        self._validate_args = validate_args
-        self._allow_nan_stats = allow_nan_stats
-
-        self._mean = array_ops.expand_dims(n, -1) * self._p
-        # Only used for inferring shape.
-        self._broadcast_shape = math_ops.reduce_sum(self._mean,
-                                                    reduction_indices=[-1],
-                                                    keep_dims=False)
-
-        self._get_batch_shape = self._broadcast_shape.get_shape()
-        self._get_event_shape = (
-            self._mean.get_shape().with_rank_at_least(1)[-1:])
+        self._mean_val = array_ops.expand_dims(n, -1) * self._p
+        self._broadcast_shape = math_ops.reduce_sum(
+            self._mean_val, reduction_indices=[-1], keep_dims=False)
+        super(Multinomial, self).__init__(
+            dtype=self._p.dtype,
+            parameters={"p": self._p,
+                        "n": self._n,
+                        "mean": self._mean,
+                        "logits": self._logits,
+                        "broadcast_shape": self._broadcast_shape},
+            is_continuous=False,
+            validate_args=validate_args,
+            allow_nan_stats=allow_nan_stats,
+            name=name)
 
   @property
   def n(self):
@@ -174,127 +174,55 @@ class Multinomial(distribution.Distribution):
     """Log-odds."""
     return self._logits
 
-  @property
-  def name(self):
-    """Name to prepend to all ops."""
-    return self._name
-
-  @property
-  def dtype(self):
-    """dtype of samples from this distribution."""
-    return self._p.dtype
-
-  @property
-  def validate_args(self):
-    """Boolean describing behavior on invalid input."""
-    return self._validate_args
-
-  @property
-  def allow_nan_stats(self):
-    """Boolean describing behavior when a stat is undefined for batch member."""
-    return self._allow_nan_stats
-
-  def batch_shape(self, name="batch_shape"):
-    """Batch dimensions of this instance as a 1-D int32 `Tensor`.
-
-    The product of the dimensions of the `batch_shape` is the number of
-    independent distributions of this kind the instance represents.
-
-    Args:
-      name: name to give to the op
-
-    Returns:
-      `Tensor` `batch_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._broadcast_shape]):
-        return array_ops.shape(self._broadcast_shape)
+  def _batch_shape(self):
+    return array_ops.shape(self._broadcast_shape)
 
-  def get_batch_shape(self):
-    """`TensorShape` available at graph construction time.
+  def _get_batch_shape(self):
+    return self._broadcast_shape.get_shape()
 
-    Same meaning as `batch_shape`. May be only partially defined.
+  def _event_shape(self):
+    return array_ops.gather(array_ops.shape(self._mean_val),
+                            [array_ops.rank(self._mean_val) - 1])
 
-    Returns:
-      batch shape
-    """
-    return self._get_batch_shape
+  def _get_event_shape(self):
+    return self._mean_val.get_shape().with_rank_at_least(1)[-1:]
 
-  def event_shape(self, name="event_shape"):
-    """Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+  def _log_prob(self, counts):
+    counts = self._assert_valid_sample(counts)
+    log_unnormalized_prob = math_ops.reduce_sum(
+        counts * math_ops.log(self.p),
+        reduction_indices=[-1])
+    log_normalizer = -distribution_util.log_combinations(self.n, counts)
+    return log_unnormalized_prob - log_normalizer
 
-    Args:
-      name: name to give to the op
-
-    Returns:
-      `Tensor` `event_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._mean]):
-        return array_ops.gather(array_ops.shape(self._mean),
-                                [array_ops.rank(self._mean) - 1])
+  def _prob(self, counts):
+    return math_ops.exp(self._log_prob(counts))
 
-  def get_event_shape(self):
-    """`TensorShape` available at graph construction time.
+  def _mean(self):
+    return array_ops.identity(self._mean_val)
 
-    Same meaning as `event_shape`. May be only partially defined.
+  def _variance(self):
+    p = self.p * array_ops.expand_dims(array_ops.ones_like(self.n), -1)
+    outer_prod = math_ops.batch_matmul(
+        array_ops.expand_dims(self._mean_val, -1),
+        array_ops.expand_dims(p, -2))
+    return array_ops.batch_matrix_set_diag(
+        -outer_prod, self._mean_val - self._mean_val * p)
 
-    Returns:
-      event shape
-    """
-    return self._get_event_shape
-
-  def mean(self, name="mean"):
-    """Mean of the distribution."""
-    with ops.name_scope(self.name):
-      return array_ops.identity(self._mean, name=name)
-
-  def variance(self, name="variance"):
-    """Variance of the distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._n, self._p, self._mean]):
-        p = array_ops.expand_dims(
-            self._p * array_ops.expand_dims(
-                array_ops.ones_like(self._n), -1), -1)
-        variance = -math_ops.batch_matmul(
-            array_ops.expand_dims(self._mean, -1), p, adj_y=True)
-        variance += array_ops.batch_matrix_diag(self._mean)
-        return variance
-
-  def log_prob(self, counts, name="log_prob"):
-    """`Log(P[counts])`, computed for every batch member.
-
-    For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
-    that after sampling `n` draws from this Multinomial distribution, the
-    number of draws falling in class `j` is `n_j`.  Note that different
-    sequences of draws can result in the same counts, thus the probability
-    includes a combinatorial coefficient.
-
-    Args:
-      counts:  Non-negative tensor with dtype `dtype` and whose shape can
-        be broadcast with `self.p` and `self.n`.  For fixed leading dimensions,
-        the last dimension represents counts for the corresponding Multinomial
-        distribution in `self.p`. `counts` is only legal if it sums up to `n`
-        and its components are equal to integer values.
-      name:  Name to give this Op, defaults to "log_prob".
-
-    Returns:
-      Log probabilities for each record, shape `[N1,...,Nm]`.
-    """
-    n = self._n
-    p = self._p
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[n, p, counts]):
-        counts = self._check_counts(counts)
-
-        prob_prob = math_ops.reduce_sum(counts * math_ops.log(self._p),
-                                        reduction_indices=[-1])
-        log_prob = prob_prob + distribution_util.log_combinations(
-            n, counts)
-        return log_prob
+  def _assert_valid_sample(self, counts):
+    """Check counts for proper shape, values, then return tensor version."""
+    if not self.validate_args: return counts
+    return control_flow_ops.with_dependencies([
+        check_ops.assert_non_negative(
+            counts, message="counts has negative components."),
+        check_ops.assert_equal(
+            self.n, math_ops.reduce_sum(counts, reduction_indices=[-1]),
+            message="counts do not sum to n."),
+        distribution_util.assert_integer_form(
+            counts, message="counts have non-integer components.")
+    ], counts)
 
-  def prob(self, counts, name="prob"):
-    """`P[counts]`, computed for every batch member.
+_prob_note = """
 
     For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
     that after sampling `n` draws from this Multinomial distribution, the
@@ -302,38 +230,11 @@ class Multinomial(distribution.Distribution):
     sequences of draws can result in the same counts, thus the probability
     includes a combinatorial coefficient.
 
-    Args:
-      counts:  Non-negative tensor with dtype `dtype` and whose shape can
-        be broadcast with `self.p` and `self.n`.  For fixed leading dimensions,
-        the last dimension represents counts for the corresponding Multinomial
-        distribution in `self.p`. `counts` is only legal if it sums up to `n`
-        and its components are equal to integer values.
-      name:  Name to give this Op, defaults to "prob".
-
-    Returns:
-      Probabilities for each record, shape `[N1,...,Nm]`.
-    """
-    return super(Multinomial, self).prob(counts, name=name)
-
-  @property
-  def is_continuous(self):
-    return False
-
-  @property
-  def is_reparameterized(self):
-    return False
-
-  def _check_counts(self, counts):
-    """Check counts for proper shape, values, then return tensor version."""
-    counts = ops.convert_to_tensor(counts, name="counts_before_deps")
-    candidate_n = math_ops.reduce_sum(counts, reduction_indices=[-1])
-    if not self.validate_args:
-      return counts
-
-    return control_flow_ops.with_dependencies([
-        check_ops.assert_non_negative(
-            counts, message="counts has negative components."),
-        check_ops.assert_equal(
-            self._n, candidate_n, message="counts do not sum to n."),
-        distribution_util.assert_integer_form(
-            counts, message="counts have non-integer components.")], counts)
+    Note that input "counts" must be a non-negative tensor with dtype `dtype`
+    and whose shape can be broadcast with `self.p` and `self.n`.  For fixed
+    leading dimensions, the last dimension represents counts for the
+    corresponding Multinomial distribution in `self.p`. `counts` is only legal
+    if it sums up to `n` and its components are equal to integer values.
+"""
+distribution_util.append_class_fun_doc(Multinomial.log_prob, doc_str=_prob_note)
+distribution_util.append_class_fun_doc(Multinomial.prob, doc_str=_prob_note)
diff --git a/tensorflow/contrib/distributions/python/ops/mvn.py b/tensorflow/contrib/distributions/python/ops/mvn.py
index 0546733cb9..0f003c22e0 100644
--- a/tensorflow/contrib/distributions/python/ops/mvn.py
+++ b/tensorflow/contrib/distributions/python/ops/mvn.py
@@ -21,6 +21,7 @@ from __future__ import print_function
 import math
 
 from tensorflow.contrib.distributions.python.ops import distribution
+from tensorflow.contrib.distributions.python.ops import distribution_util
 from tensorflow.contrib.distributions.python.ops import kullback_leibler
 from tensorflow.contrib.distributions.python.ops import operator_pd_cholesky
 from tensorflow.contrib.distributions.python.ops import operator_pd_diag
@@ -29,8 +30,6 @@ from tensorflow.contrib.distributions.python.ops import operator_pd_vdvt_update
 from tensorflow.contrib.framework.python.framework import tensor_util as contrib_tensor_util
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_shape
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
@@ -120,24 +119,27 @@ class _MultivariateNormalOperatorPD(distribution.Distribution):
     Raises:
       TypeError: If `mu` and `cov` are different dtypes.
     """
-    self._allow_nan_stats = allow_nan_stats
-    self._validate_args = validate_args
     with ops.name_scope(name):
       with ops.name_scope("init", values=[mu] + cov.inputs):
+        self._mu = array_ops.identity(mu, name="mu")
         self._cov = cov
-        self._mu = self._check_mu(mu)
-        self._name = name
-
-  def _check_mu(self, mu):
+        self._validate_args = validate_args  # Needed by _assert_valid_mu.
+        self._mu = self._assert_valid_mu(self._mu)
+        super(_MultivariateNormalOperatorPD, self).__init__(
+            dtype=self._mu.dtype,
+            parameters={"mu": self._mu, "cov": self._cov},
+            is_reparameterized=True,
+            validate_args=validate_args,
+            allow_nan_stats=allow_nan_stats,
+            name=name)
+
+  def _assert_valid_mu(self, mu):
     """Return `mu` after validity checks and possibly with assertations."""
-    mu = ops.convert_to_tensor(mu)
     cov = self._cov
-
     if mu.dtype != cov.dtype:
       raise TypeError(
           "mu and cov must have the same dtype.  Found mu.dtype = %s, "
-          "cov.dtype = %s"
-          % (mu.dtype, cov.dtype))
+          "cov.dtype = %s" % (mu.dtype, cov.dtype))
 
     # Try to validate with static checks.
     mu_shape = mu.get_shape()
@@ -170,44 +172,6 @@ class _MultivariateNormalOperatorPD(distribution.Distribution):
         )
         return control_flow_ops.with_dependencies([assert_same_shape], mu)
 
-  @property
-  def validate_args(self):
-    """`Boolean` describing behavior on invalid input."""
-    return self._validate_args
-
-  @property
-  def allow_nan_stats(self):
-    """`Boolean` describing behavior when stats are undefined."""
-    return self._allow_nan_stats
-
-  @property
-  def dtype(self):
-    return self._mu.dtype
-
-  def get_event_shape(self):
-    """`TensorShape` available at graph construction time."""
-    # Recall _check_mu ensures mu and self._cov have same batch shape.
-    return self._cov.get_shape()[-1:]
-
-  def event_shape(self, name="event_shape"):
-    """Shape of a sample from a single distribution as a 1-D int32 `Tensor`."""
-    # Recall _check_mu ensures mu and self._cov have same batch shape.
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=self._cov.inputs):
-        return array_ops.pack([self._cov.vector_space_dimension()])
-
-  def batch_shape(self, name="batch_shape"):
-    """Batch dimensions of this instance as a 1-D int32 `Tensor`."""
-    # Recall _check_mu ensures mu and self._cov have same batch shape.
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=self._cov.inputs):
-        return self._cov.batch_shape()
-
-  def get_batch_shape(self):
-    """`TensorShape` available at graph construction time."""
-    # Recall _check_mu ensures mu and self._cov have same batch shape.
-    return self._cov.get_batch_shape()
-
   @property
   def mu(self):
     return self._mu
@@ -218,23 +182,6 @@ class _MultivariateNormalOperatorPD(distribution.Distribution):
     with ops.name_scope(self.name):
       return self._cov.to_dense()
 
-  def mean(self, name="mean"):
-    """Mean of each batch member."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._mu]):
-        return array_ops.identity(self._mu)
-
-  def mode(self, name="mode"):
-    """Mode of each batch member."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._mu]):
-        return array_ops.identity(self._mu)
-
-  def variance(self, name="variance"):
-    """Variance of each batch member."""
-    with ops.name_scope(self.name):
-      return self.sigma
-
   def log_sigma_det(self, name="log_sigma_det"):
     """Log of determinant of covariance matrix."""
     with ops.name_scope(self.name):
@@ -247,161 +194,111 @@ class _MultivariateNormalOperatorPD(distribution.Distribution):
       with ops.name_scope(name, values=self._cov.inputs):
         return math_ops.exp(self._cov.log_det())
 
-  def log_prob(self, x, name="log_prob"):
-    """Log prob of observations `x` given these Multivariate Normals.
+  def _batch_shape(self):
+    return self._cov.batch_shape()
 
-    `x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-    shape can be broadcast up to either:
+  def _get_batch_shape(self):
+    return self._cov.get_batch_shape()
 
-    ````
-    self.batch_shape + self.event_shape
-    OR
-    [M1,...,Mm] + self.batch_shape + self.event_shape
-    ```
+  def _event_shape(self):
+    return array_ops.pack([self._cov.vector_space_dimension()])
 
-    Args:
-      x: Compatible batch vector with same `dtype` as this distribution.
-      name: The name to give this op.
+  def _get_event_shape(self):
+    return self._cov.get_shape()[-1:]
 
-    Returns:
-      log_prob: tensor of dtype `dtype`, the log-PDFs of `x`.
-    """
+  def _sample_n(self, n, seed=None):
+    # Recall _assert_valid_mu ensures mu and self._cov have same batch shape.
+    shape = array_ops.concat(0, [self._cov.vector_shape(), [n]])
+    white_samples = random_ops.random_normal(shape=shape,
+                                             mean=0,
+                                             stddev=1,
+                                             dtype=self.dtype,
+                                             seed=seed)
+
+    correlated_samples = self._cov.sqrt_matmul(white_samples)
+
+    # Move the last dimension to the front
+    perm = array_ops.concat(0, (
+        array_ops.pack([array_ops.rank(correlated_samples) - 1]),
+        math_ops.range(0, array_ops.rank(correlated_samples) - 1)))
+
+    # TODO(ebrevdo): Once we get a proper tensor contraction op,
+    # perform the inner product using that instead of batch_matmul
+    # and this slow transpose can go away!
+    correlated_samples = array_ops.transpose(correlated_samples, perm)
+    samples = correlated_samples + self.mu
+    return samples
+
+  def _log_prob(self, x):
     # Q:  Why are shape requirements as stated above?
     # A:  The compatible shapes are precisely the ones that will broadcast to
     #     a shape compatible with self._cov.
     # See Operator base class for notes about shapes compatible with self._cov.
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._mu, x] + self._cov.inputs):
-        x = ops.convert_to_tensor(x)
-        contrib_tensor_util.assert_same_float_dtype((self._mu, x))
-
-        # _check_mu asserts that self.mu has same batch shape as self.cov.
-        # so batch shape of self.mu = that of self._cov and self, and the
-        # batch shape of x_centered is a broadcast version of these.  If this
-        # broadcast results in a shape like
-        # [M1,...,Mm] + self.batch_shape + self.event_shape
-        # OR
-        # self.batch_shape + self.event_shape
-        # then subsequent operator calls are guaranteed to work.
-        x_centered = x - self.mu
-
-        # Compute the term x^{-1} sigma^{-1} x which appears in the exponent of
-        # the pdf.
-        x_whitened_norm = self._cov.inv_quadratic_form_on_vectors(x_centered)
-
-        log_sigma_det = self.log_sigma_det()
-
-        log_two_pi = constant_op.constant(
-            math.log(2 * math.pi), dtype=self.dtype)
-        k = math_ops.cast(self._cov.vector_space_dimension(), self.dtype)
-        log_prob_value = -(log_sigma_det + k * log_two_pi + x_whitened_norm) / 2
-
-        output_static_shape = x_centered.get_shape()[:-1]
-        log_prob_value.set_shape(output_static_shape)
-        return log_prob_value
-
-  def prob(self, x, name="prob"):
-    """The PDF of observations `x` under these Multivariate Normals.
-
-    `x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-    shape can be broadcast up to either:
-
-    ````
-    self.batch_shape + self.event_shape
-    OR
-    [M1,...,Mm] + self.batch_shape + self.event_shape
-    ```
-
-    Args:
-      x: Compatible batch vector with same `dtype` as this distribution.
-      name: The name to give this op.
-
-    Returns:
-      prob: tensor of dtype `dtype`, the prob values of `x`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._mu, x] + self._cov.inputs):
-        return math_ops.exp(self.log_prob(x))
-
-  def entropy(self, name="entropy"):
-    """The entropies of these Multivariate Normals.
+    x = ops.convert_to_tensor(x)
+    contrib_tensor_util.assert_same_float_dtype((self._mu, x))
 
-    Args:
-      name: The name to give this op.
+    # _assert_valid_mu asserts that self.mu has same batch shape as self.cov.
+    # so batch shape of self.mu = that of self._cov and self, and the
+    # batch shape of x_centered is a broadcast version of these.  If this
+    # broadcast results in a shape like
+    # [M1,...,Mm] + self.batch_shape + self.event_shape
+    # OR
+    # self.batch_shape + self.event_shape
+    # then subsequent operator calls are guaranteed to work.
+    x_centered = x - self.mu
 
-    Returns:
-      entropy: tensor of dtype `dtype`, the entropies.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._mu] + self._cov.inputs):
-        log_sigma_det = self.log_sigma_det()
-        one_plus_log_two_pi = constant_op.constant(1 + math.log(2 * math.pi),
-                                                   dtype=self.dtype)
+    # Compute the term x^{-1} sigma^{-1} x which appears in the exponent of
+    # the pdf.
+    x_whitened_norm = self._cov.inv_quadratic_form_on_vectors(x_centered)
 
-        # Use broadcasting rules to calculate the full broadcast sigma.
-        k = math_ops.cast(self._cov.vector_space_dimension(), dtype=self.dtype)
-        entropy_value = (k * one_plus_log_two_pi + log_sigma_det) / 2
-        entropy_value.set_shape(log_sigma_det.get_shape())
-        return entropy_value
+    k = math_ops.cast(self._cov.vector_space_dimension(), self.dtype)
+    log_prob_value = -0.5 * (self.log_sigma_det() +
+                             k * math.log(2. * math.pi) +
+                             x_whitened_norm)
 
-  def sample_n(self, n, seed=None, name="sample_n"):
-    """Sample `n` observations from the Multivariate Normal Distributions.
+    output_static_shape = x_centered.get_shape()[:-1]
+    log_prob_value.set_shape(output_static_shape)
+    return log_prob_value
 
-    Args:
-      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-        observations to sample.
-      seed: Python integer, the random seed.
-      name: The name to give this op.
-
-    Returns:
-      samples: `[n, ...]`, a `Tensor` of `n` samples for each
-        of the distributions determined by broadcasting the hyperparameters.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._mu, n] + self._cov.inputs):
-        # Recall _check_mu ensures mu and self._cov have same batch shape.
-        broadcast_shape = self.mu.get_shape()
-        n = ops.convert_to_tensor(n, name="n")
-
-        shape = array_ops.concat(0, [self._cov.vector_shape(), [n]])
-        white_samples = random_ops.random_normal(shape=shape,
-                                                 mean=0,
-                                                 stddev=1,
-                                                 dtype=self.dtype,
-                                                 seed=seed)
+  def _prob(self, x):
+    return math_ops.exp(self.log_prob(x))
 
-        correlated_samples = self._cov.sqrt_matmul(white_samples)
+  def _entropy(self):
+    log_sigma_det = self.log_sigma_det()
+    one_plus_log_two_pi = constant_op.constant(1 + math.log(2 * math.pi),
+                                               dtype=self.dtype)
 
-        # Move the last dimension to the front
-        perm = array_ops.concat(0, (
-            array_ops.pack([array_ops.rank(correlated_samples) - 1]),
-            math_ops.range(0, array_ops.rank(correlated_samples) - 1)))
+    # Use broadcasting rules to calculate the full broadcast sigma.
+    k = math_ops.cast(self._cov.vector_space_dimension(), dtype=self.dtype)
+    entropy_value = (k * one_plus_log_two_pi + log_sigma_det) / 2
+    entropy_value.set_shape(log_sigma_det.get_shape())
+    return entropy_value
 
-        # TODO(ebrevdo): Once we get a proper tensor contraction op,
-        # perform the inner product using that instead of batch_matmul
-        # and this slow transpose can go away!
-        correlated_samples = array_ops.transpose(correlated_samples, perm)
+  def _mean(self):
+    return array_ops.identity(self._mu)
 
-        samples = correlated_samples + self.mu
+  def _variance(self):
+    return self.sigma
 
-        # Provide some hints to shape inference
-        n_val = tensor_util.constant_value(n)
-        final_shape = tensor_shape.vector(n_val).concatenate(broadcast_shape)
-        samples.set_shape(final_shape)
+  def _mode(self):
+    return array_ops.identity(self._mu)
 
-        return samples
 
-  @property
-  def is_reparameterized(self):
-    return True
+_prob_note = """
+    `x` is a batch vector with compatible shape if `x` is a `Tensor` whose
+    shape can be broadcast up to either:
 
-  @property
-  def name(self):
-    return self._name
+    ````
+    self.batch_shape + self.event_shape
+    OR
+    [M1,...,Mm] + self.batch_shape + self.event_shape
+    ```
 
-  @property
-  def is_continuous(self):
-    return True
+"""
+distribution_util.append_class_fun_doc(_MultivariateNormalOperatorPD.log_prob,
+                                       doc_str=_prob_note)
+distribution_util.append_class_fun_doc(_MultivariateNormalOperatorPD.prob,
+                                       doc_str=_prob_note)
 
 
 class MultivariateNormalDiag(_MultivariateNormalOperatorPD):
diff --git a/tensorflow/contrib/distributions/python/ops/normal.py b/tensorflow/contrib/distributions/python/ops/normal.py
index d5444991b1..2660f2970b 100644
--- a/tensorflow/contrib/distributions/python/ops/normal.py
+++ b/tensorflow/contrib/distributions/python/ops/normal.py
@@ -20,15 +20,14 @@ from __future__ import print_function
 
 import math
 
-from tensorflow.contrib.distributions.python.ops import distribution  # pylint: disable=line-too-long
-from tensorflow.contrib.distributions.python.ops import kullback_leibler  # pylint: disable=line-too-long
-from tensorflow.contrib.framework.python.framework import tensor_util as contrib_tensor_util  # pylint: disable=line-too-long
+from tensorflow.contrib.distributions.python.ops import distribution
+from tensorflow.contrib.distributions.python.ops import kullback_leibler
+from tensorflow.contrib.framework.python.framework import tensor_util as contrib_tensor_util
 from tensorflow.python.framework import common_shapes
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import math_ops
@@ -108,21 +107,19 @@ class Normal(distribution.Distribution):
     Raises:
       TypeError: if mu and sigma are different dtypes.
     """
-    self._allow_nan_stats = allow_nan_stats
-    self._validate_args = validate_args
     with ops.name_scope(name, values=[mu, sigma]):
-      mu = ops.convert_to_tensor(mu)
-      sigma = ops.convert_to_tensor(sigma)
       with ops.control_dependencies([check_ops.assert_positive(sigma)] if
                                     validate_args else []):
-        self._name = name
         self._mu = array_ops.identity(mu, name="mu")
         self._sigma = array_ops.identity(sigma, name="sigma")
-        self._batch_shape = common_shapes.broadcast_shape(
-            self._mu.get_shape(), self._sigma.get_shape())
-        self._event_shape = tensor_shape.TensorShape([])
-
-    contrib_tensor_util.assert_same_float_dtype((mu, sigma))
+        contrib_tensor_util.assert_same_float_dtype((self._mu, self._sigma))
+        super(Normal, self).__init__(
+            dtype=self._sigma.dtype,
+            parameters={"mu": self._mu, "sigma": self._sigma},
+            is_reparameterized=True,
+            validate_args=validate_args,
+            allow_nan_stats=allow_nan_stats,
+            name=name)
 
   @staticmethod
   def _param_shapes(sample_shape):
@@ -141,73 +138,6 @@ class Normal(distribution.Distribution):
     """
     return {"sigma": nn.softplus}
 
-  @property
-  def allow_nan_stats(self):
-    """Boolean describing behavior when a stat is undefined for batch member."""
-    return self._allow_nan_stats
-
-  @property
-  def validate_args(self):
-    """Boolean describing behavior on invalid input."""
-    return self._validate_args
-
-  @property
-  def name(self):
-    return self._name
-
-  @property
-  def dtype(self):
-    return self._mu.dtype
-
-  def batch_shape(self, name="batch_shape"):
-    """Batch dimensions of this instance as a 1-D int32 `Tensor`.
-
-    The product of the dimensions of the `batch_shape` is the number of
-    independent distributions of this kind the instance represents.
-
-    Args:
-      name: name to give to the op.
-
-    Returns:
-      `Tensor` `batch_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._mu, self._sigma]):
-        return array_ops.shape(self._mu + self._sigma)
-
-  def get_batch_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `batch_shape`. May be only partially defined.
-
-    Returns:
-      batch shape
-    """
-    return self._batch_shape
-
-  def event_shape(self, name="event_shape"):
-    """Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
-
-    Args:
-      name: name to give to the op.
-
-    Returns:
-      `Tensor` `event_shape`
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return constant_op.constant([], dtype=dtypes.int32)
-
-  def get_event_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `event_shape`. May be only partially defined.
-
-    Returns:
-      event shape
-    """
-    return self._event_shape
-
   @property
   def mu(self):
     """Distribution parameter for the mean."""
@@ -218,149 +148,58 @@ class Normal(distribution.Distribution):
     """Distribution parameter for standard deviation."""
     return self._sigma
 
-  def mean(self, name="mean"):
-    """Mean of this distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._sigma, self._mu]):
-        return self._mu * array_ops.ones_like(self._sigma)
-
-  def mode(self, name="mode"):
-    """Mode of this distribution."""
-    return self.mean(name="mode")
-
-  def std(self, name="std"):
-    """Standard deviation of this distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._sigma, self._mu]):
-        return self._sigma * array_ops.ones_like(self._mu)
-
-  def variance(self, name="variance"):
-    """Variance of this distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return math_ops.square(self.std())
-
-  def log_prob(self, x, name="log_prob"):
-    """Log prob of observations in `x` under these Normal distribution(s).
-
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
-      name: The name to give this op.
-
-    Returns:
-      log_prob: tensor of dtype `dtype`, the log-PDFs of `x`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._mu, self._sigma, x]):
-        x = ops.convert_to_tensor(x)
-        if x.dtype != self.dtype:
-          raise TypeError("Input x dtype does not match dtype: %s vs. %s"
-                          % (x.dtype, self.dtype))
-        log_2_pi = constant_op.constant(math.log(2 * math.pi), dtype=self.dtype)
-        return (-0.5*log_2_pi - math_ops.log(self._sigma)
-                -0.5*math_ops.square((x - self._mu) / self._sigma))
-
-  def cdf(self, x, name="cdf"):
-    """CDF of observations in `x` under these Normal distribution(s).
-
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
-      name: The name to give this op.
-
-    Returns:
-      cdf: tensor of dtype `dtype`, the CDFs of `x`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._mu, self._sigma, x]):
-        x = ops.convert_to_tensor(x)
-        if x.dtype != self.dtype:
-          raise TypeError("Input x dtype does not match dtype: %s vs. %s"
-                          % (x.dtype, self.dtype))
-        # TODO(ebrevdo): wrap this in a Defun with a custom Defun
-        # gradient because the analytic gradient may be faster than
-        # automatic differentiation.
-        return (0.5 + 0.5*math_ops.erf(
-            1.0/(math.sqrt(2.0) * self._sigma)*(x - self._mu)))
-
-  def log_cdf(self, x, name="log_cdf"):
-    """Log CDF of observations `x` under these Normal distribution(s).
+  def _batch_shape(self):
+    return array_ops.shape(self.mu + self.sigma)
 
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
-      name: The name to give this op.
+  def _get_batch_shape(self):
+    return common_shapes.broadcast_shape(
+        self._mu.get_shape(), self.sigma.get_shape())
 
-    Returns:
-      log_cdf: tensor of dtype `dtype`, the log-CDFs of `x`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._mu, self._sigma, x]):
-        return math_ops.log(self.cdf(x))
+  def _event_shape(self):
+    return constant_op.constant([], dtype=dtypes.int32)
 
-  def prob(self, x, name="prob"):
-    """The PDF of observations in `x` under these Normal distribution(s).
+  def _get_event_shape(self):
+    return tensor_shape.scalar()
 
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
-      name: The name to give this op.
+  def _sample_n(self, n, seed=None):
+    shape = array_ops.concat(0, ([n], array_ops.shape(self.mean())))
+    sampled = random_ops.random_normal(
+        shape=shape, mean=0, stddev=1, dtype=self.mu.dtype, seed=seed)
+    return sampled * self.sigma + self.mu
 
-    Returns:
-      prob: tensor of dtype `dtype`, the prob values of `x`.
-    """
-    return super(Normal, self).prob(x, name=name)
+  def _log_prob(self, x):
+    return (-0.5 * math.log(2. * math.pi) - math_ops.log(self.sigma)
+            -0.5 * math_ops.square((x - self.mu) / self.sigma))
 
-  def entropy(self, name="entropy"):
-    """The entropy of Normal distribution(s).
+  def _prob(self, x):
+    return math_ops.exp(self._log_prob(x))
 
-    Args:
-      name: The name to give this op.
+  def _log_cdf(self, x):
+    return math_ops.log(self._cdf(x))
 
-    Returns:
-      entropy: tensor of dtype `dtype`, the entropy.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._mu, self._sigma]):
-        two_pi_e1 = constant_op.constant(
-            2 * math.pi * math.exp(1), dtype=self.dtype)
-        # Use broadcasting rules to calculate the full broadcast sigma.
-        sigma = self._sigma * array_ops.ones_like(self._mu)
-        return 0.5 * math_ops.log(two_pi_e1 * math_ops.square(sigma))
+  def _cdf(self, x):
+    # TODO(ebrevdo): wrap this in a Defun with a custom Defun
+    # gradient because the analytic gradient may be faster than
+    # automatic differentiation.
+    return (0.5 + 0.5*math_ops.erf(
+        1. / (math.sqrt(2.) * self.sigma) * (x - self.mu)))
 
-  def sample_n(self, n, seed=None, name="sample_n"):
-    """Sample `n` observations from the Normal Distributions.
+  def _entropy(self):
+    # Use broadcasting rules to calculate the full broadcast sigma.
+    sigma = self.sigma * array_ops.ones_like(self.mu)
+    return 0.5 * math.log(2. * math.pi * math.e) + math_ops.log(sigma)
 
-    Args:
-      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-        observations to sample.
-      seed: Python integer, the random seed.
-      name: The name to give this op.
+  def _mean(self):
+    return self.mu * array_ops.ones_like(self.sigma)
 
-    Returns:
-      samples: `[n, ...]`, a `Tensor` of `n` samples for each
-        of the distributions determined by broadcasting the hyperparameters.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._mu, self._sigma, n]):
-        broadcast_shape = common_shapes.broadcast_shape(
-            self._mu.get_shape(), self._sigma.get_shape())
-        n = ops.convert_to_tensor(n, name="n")
-        shape = array_ops.concat(0, ([n], array_ops.shape(self.mean())))
-        sampled = random_ops.random_normal(
-            shape=shape, mean=0, stddev=1, dtype=self._mu.dtype, seed=seed)
-
-        # Provide some hints to shape inference
-        n_val = tensor_util.constant_value(n)
-        final_shape = tensor_shape.vector(n_val).concatenate(broadcast_shape)
-        sampled.set_shape(final_shape)
-
-        return sampled * self._sigma + self._mu
+  def _variance(self):
+    return math_ops.square(self.std())
 
-  @property
-  def is_reparameterized(self):
-    return True
+  def _std(self):
+    return self.sigma * array_ops.ones_like(self.mu)
 
-  @property
-  def is_continuous(self):
-    return True
+  def _mode(self):
+    return self._mean()
 
 
 @kullback_leibler.RegisterKL(Normal, Normal)
@@ -383,5 +222,5 @@ def _kl_normal_normal(n_a, n_b, name=None):
     s_a_squared = math_ops.square(n_a.sigma)
     s_b_squared = math_ops.square(n_b.sigma)
     ratio = s_a_squared / s_b_squared
-    return (math_ops.square(n_a.mu - n_b.mu) / (two * s_b_squared)
-            + half * (ratio - one - math_ops.log(ratio)))
+    return (math_ops.square(n_a.mu - n_b.mu) / (two * s_b_squared) +
+            half * (ratio - one - math_ops.log(ratio)))
diff --git a/tensorflow/contrib/distributions/python/ops/poisson.py b/tensorflow/contrib/distributions/python/ops/poisson.py
index 524dd13442..778b6af33d 100644
--- a/tensorflow/contrib/distributions/python/ops/poisson.py
+++ b/tensorflow/contrib/distributions/python/ops/poisson.py
@@ -29,10 +29,6 @@ from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
 from tensorflow.python.ops import math_ops
 
-__all__ = [
-  'Poisson',
-]
-
 
 class Poisson(distribution.Distribution):
   """Poisson distribution.
@@ -68,186 +64,83 @@ class Poisson(distribution.Distribution):
         undefined statistics will return NaN for this statistic.
       name: A name for this distribution.
     """
-    with ops.name_scope(name, values=[lam]) as scope:
-      self._name = scope
-      with ops.control_dependencies(
-          [check_ops.assert_positive(lam)] if validate_args else []):
+    with ops.name_scope(name, values=[lam]):
+      with ops.control_dependencies([check_ops.assert_positive(lam)] if
+                                    validate_args else []):
         self._lam = array_ops.identity(lam, name="lam")
-        self._validate_args = validate_args
-        self._allow_nan_stats = allow_nan_stats
-
-  @property
-  def name(self):
-    return self._name
-
-  @property
-  def dtype(self):
-    return self._lam.dtype
+        super(Poisson, self).__init__(
+            dtype=self._lam.dtype,
+            parameters={"lam": self._lam},
+            is_continuous=False,
+            validate_args=validate_args,
+            allow_nan_stats=allow_nan_stats,
+            name=name)
 
   @property
   def lam(self):
     """Rate parameter."""
     return self._lam
 
-  @property
-  def validate_args(self):
-    """Boolean describing behavior on invalid input."""
-    return self._validate_args
-
-  @property
-  def allow_nan_stats(self):
-    """Boolean describing behavior when a stat is undefined for batch member."""
-    return self._allow_nan_stats
-
-  def batch_shape(self, name="batch_shape"):
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.lam]):
-        return array_ops.shape(self.lam)
+  def _batch_shape(self):
+    return array_ops.shape(self.lam)
 
-  def get_batch_shape(self):
+  def _get_batch_shape(self):
     return self.lam.get_shape()
 
-  def event_shape(self, name="event_shape"):
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return constant_op.constant([], dtype=dtypes.int32)
+  def _event_shape(self):
+    return constant_op.constant([], dtype=dtypes.int32)
 
-  def get_event_shape(self):
+  def _get_event_shape(self):
     return tensor_shape.scalar()
 
-  def log_cdf(self, x, name="log_cdf"):
-    """Log cumulative density function.
+  def _log_prob(self, x):
+    x = self._assert_valid_sample(x, check_integer=True)
+    return x * math_ops.log(self.lam) - self.lam - math_ops.lgamma(x + 1)
 
-    Args:
-      x: Non-negative floating point tensor with dtype `dtype` and whose shape
-        can be broadcast with `self.lam`.
-      name: A name for this operation.
+  def _prob(self, x):
+    return math_ops.exp(self._log_prob(x))
 
-    Returns:
-      The Log CDF of the events.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[x]):
-        return math_ops.log(self.cdf(x))
+  def _log_cdf(self, x):
+    return math_ops.log(self.cdf(x))
 
-  def cdf(self, x, name="cdf"):
-    """Cumulative density function.
+  def _cdf(self, x):
+    x = self._assert_valid_sample(x, check_integer=False)
+    return math_ops.igammac(math_ops.floor(x + 1), self.lam)
 
-    Args:
-      x: Non-negative floating point tensor with dtype `dtype` and whose shape
-        can be broadcast with `self.lam`.
-      name: A name for this operation.
-
-    Returns:
-      The CDF of the events.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.lam, x]):
-        x = self._check_x(x, check_integer=False)
-        return math_ops.igammac(math_ops.floor(x + 1), self.lam)
+  def _mean(self):
+    return array_ops.identity(self.lam)
 
-  def prob(self, x, name="prob"):
-    """Probability mass function.
+  def _variance(self):
+    return array_ops.identity(self.lam)
 
-    Args:
-      x: Non-negative floating point tensor with dtype `dtype` and whose shape
-        can be broadcast with `self.lam`. `x` is only legal if it is
-        non-negative and its components are equal to integer values.
-      name: A name for this operation.
-
-    Returns:
-      The probabilities of the events.
-    """
-    return super(Poisson, self).prob(x, name)
-
-  def log_prob(self, x, name="log_prob"):
-    """Log probability mass function.
-
-    Args:
-      x: Non-negative floating point tensor with dtype `dtype` and whose shape
-        can be broadcast with `self.lam`. `x` is only legal if it is
-        non-negative and its components are equal to integer values.
-      name: A name for this operation (optional).
-
-    Returns:
-      The log-probabilities of the events.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.lam, x]):
-        x = self._check_x(x, check_integer=True)
-        return x * math_ops.log(self.lam) - self.lam - math_ops.lgamma(x + 1)
-
-  def mean(self, name="mean"):
-    """Mean of the distribution.
-
-    Args:
-      name: Name for the op.
-
-    Returns:
-      mean: `Tensor` of the same type and shape as `lam`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.lam]):
-        return array_ops.identity(self.lam)
+  def _std(self):
+    return math_ops.sqrt(self.variance())
 
-  def variance(self, name="variance"):
-    """Variance of the distribution.
+  def _mode(self):
+    return math_ops.floor(self.lam)
 
-    Args:
-      name: Name for the op.
-
-    Returns:
-      variance: `Tensor` of the same type and shape as `lam`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.lam]):
-        return array_ops.identity(self.lam)
+  def _assert_valid_sample(self, x, check_integer=True):
+    if not self.validate_args: return x
+    with ops.name_scope('check_x', values=[x]):
+      dependencies = [check_ops.assert_non_negative(x)]
+      if check_integer:
+        dependencies += [distribution_util.assert_integer_form(
+            x, message="x has non-integer components.")]
+      return control_flow_ops.with_dependencies(dependencies, x)
 
-  def std(self, name="std"):
-    """Standard deviation of the distribution.
 
-    Args:
-      name: Name for the op.
+_prob_note = """
 
-    Returns:
-      std: `Tensor` of the same type and shape as `lam`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.lam]):
-        return math_ops.sqrt(self.variance())
+    Note thet the input value must be a non-negative floating point tensor with
+    dtype `dtype` and whose shape can be broadcast with `self.lam`. `x` is only
+    legal if it is non-negative and its components are equal to integer values.
+"""
+distribution_util.append_class_fun_doc(Poisson.log_prob, doc_str=_prob_note)
+distribution_util.append_class_fun_doc(Poisson.prob, doc_str=_prob_note)
 
-  def mode(self, name="mode"):
-    """Mode of the distribution.
+distribution_util.append_class_fun_doc(Poisson.mode, doc_str="""
 
     Note that when `lam` is an integer, there are actually two modes.
     Namely, `lam` and `lam - 1` are both modes. Here we return
     only the larger of the two modes.
-
-    Args:
-      name: Name for the op.
-
-    Returns:
-      mode: `Tensor` of the same type and shape as `lam`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.lam]):
-        return math_ops.floor(self.lam)
-
-  @property
-  def is_continuous(self):
-    return False
-
-  @property
-  def is_reparameterized(self):
-    return False
-
-  def _check_x(self, x, check_integer=True):
-    with ops.name_scope('check_x', values=[x]):
-      x = ops.convert_to_tensor(x, name="x")
-      if not self.validate_args:
-        return x
-      dependencies = [check_ops.assert_non_negative(x)]
-      if check_integer:
-        dependencies += [distribution_util.assert_integer_form(
-            x, message="x has non-integer components.")]
-      return control_flow_ops.with_dependencies(dependencies, x)
+""")
diff --git a/tensorflow/contrib/distributions/python/ops/student_t.py b/tensorflow/contrib/distributions/python/ops/student_t.py
index dfcc2f2c3f..7ed661e0cb 100644
--- a/tensorflow/contrib/distributions/python/ops/student_t.py
+++ b/tensorflow/contrib/distributions/python/ops/student_t.py
@@ -19,16 +19,15 @@ from __future__ import division
 from __future__ import print_function
 
 import math
-
 import numpy as np
 
-from tensorflow.contrib.distributions.python.ops import distribution  # pylint: disable=line-too-long
-from tensorflow.contrib.framework.python.framework import tensor_util as contrib_tensor_util  # pylint: disable=line-too-long
+from tensorflow.contrib.distributions.python.ops import distribution
+from tensorflow.contrib.distributions.python.ops import distribution_util
+from tensorflow.contrib.framework.python.framework import tensor_util as contrib_tensor_util
 from tensorflow.python.framework import common_shapes
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import control_flow_ops
@@ -119,39 +118,23 @@ class StudentT(distribution.Distribution):
     Raises:
       TypeError: if mu and sigma are different dtypes.
     """
-    self._allow_nan_stats = allow_nan_stats
-    self._validate_args = validate_args
-    with ops.name_scope(name, values=[df, mu, sigma]) as scope:
-      with ops.control_dependencies([check_ops.assert_positive(
-          df), check_ops.assert_positive(sigma)] if validate_args else []):
-        self._df = ops.convert_to_tensor(df, name="df")
-        self._mu = ops.convert_to_tensor(mu, name="mu")
-        self._sigma = ops.convert_to_tensor(sigma, name="sigma")
+    with ops.name_scope(name, values=[df, mu, sigma]):
+      with ops.control_dependencies([
+          check_ops.assert_positive(df),
+          check_ops.assert_positive(sigma),
+      ] if validate_args else []):
+        self._df = array_ops.identity(df, name="df")
+        self._mu = array_ops.identity(mu, name="mu")
+        self._sigma = array_ops.identity(sigma, name="sigma")
         contrib_tensor_util.assert_same_float_dtype(
             (self._df, self._mu, self._sigma))
-      self._name = scope
-      self._get_batch_shape = common_shapes.broadcast_shape(
-          self._sigma.get_shape(), common_shapes.broadcast_shape(
-              self._df.get_shape(), self._mu.get_shape()))
-      self._get_event_shape = tensor_shape.TensorShape([])
-
-  @property
-  def allow_nan_stats(self):
-    """Boolean describing behavior when a stat is undefined for batch member."""
-    return self._allow_nan_stats
-
-  @property
-  def validate_args(self):
-    """Boolean describing behavior on invalid input."""
-    return self._validate_args
-
-  @property
-  def name(self):
-    return self._name
-
-  @property
-  def dtype(self):
-    return self._df.dtype
+        super(StudentT, self).__init__(
+            dtype=self._sigma.dtype,
+            parameters={"df": self._df, "mu": self._mu, "sigma": self._sigma},
+            is_reparameterized=True,
+            validate_args=validate_args,
+            allow_nan_stats=allow_nan_stats,
+            name=name)
 
   @property
   def df(self):
@@ -168,41 +151,125 @@ class StudentT(distribution.Distribution):
     """Scaling factors of these Student's t distribution(s)."""
     return self._sigma
 
-  def mean(self, name="mean"):
-    """Mean of the distribution.
+  def _batch_shape(self):
+    return array_ops.shape(self.df + self.mu + self.sigma)
+
+  def _get_batch_shape(self):
+    return common_shapes.broadcast_shape(
+        self.sigma.get_shape(),
+        common_shapes.broadcast_shape(
+            self.df.get_shape(),
+            self.mu.get_shape()))
+
+  def _event_shape(self):
+    return constant_op.constant([], dtype=math_ops.int32)
+
+  def _get_event_shape(self):
+    return tensor_shape.scalar()
+
+  def _sample_n(self, n, seed=None):
+    # We use 2 uniform random floats to generate polar random variates.
+    # http://dl.acm.org/citation.cfm?id=179631
+    # Theorem 2. Let G, H be iid variates, uniformly distributed on [0,1].
+    # Let theta = 2*pi*H, let R = sqrt(df*(G^(-2/df) - 1)) for df > 0.
+    # Let X = R*cos(theta), and let Y = R*sin(theta).
+    # Then X ~ t_df and Y ~ t_df.
+    # The variates X and Y are not independent.
+    shape = array_ops.concat(0, ([2, n], self.batch_shape()))
+    uniform = random_ops.random_uniform(shape=shape,
+                                        dtype=self.dtype,
+                                        seed=seed)
+    samples_g, samples_h = array_ops.unpack(uniform, num=2)
+    theta = (2. * math.pi) * samples_h
+    r = math_ops.sqrt(self.df *
+                      (math_ops.pow(samples_g, -2 / self.df) - 1))
+    samples = r * math_ops.cos(theta)
+    return samples * self.sigma + self.mu
+
+  def _log_prob(self, x):
+    y = (x - self.mu) / self.sigma
+    half_df = 0.5 * self.df
+    return (math_ops.lgamma(0.5 + half_df) -
+            math_ops.lgamma(half_df) -
+            0.5 * math_ops.log(self.df) -
+            0.5 * math.log(math.pi) -
+            math_ops.log(self.sigma) -
+            (0.5 + half_df) * math_ops.log(1. + math_ops.square(y) / self.df))
+
+  def _prob(self, x):
+    y = (x - self.mu) / self.sigma
+    half_df = 0.5 * self.df
+    return (math_ops.exp(math_ops.lgamma(0.5 + half_df) -
+                         math_ops.lgamma(half_df)) /
+            (math_ops.sqrt(self.df) * math.sqrt(math.pi) * self.sigma) *
+            math_ops.pow(1. + math_ops.square(y) / self.df, -(0.5 + half_df)))
+
+  def _entropy(self):
+    u = array_ops.expand_dims(self.df * self._ones(), -1)
+    v = array_ops.expand_dims(self._ones(), -1)
+    beta_arg = array_ops.concat(len(u.get_shape()) - 1, [u, v]) / 2
+    half_df = 0.5 * self.df
+    return ((0.5 + half_df) * (math_ops.digamma(0.5 + half_df) -
+                               math_ops.digamma(half_df)) +
+            0.5 * math_ops.log(self.df) +
+            special_math_ops.lbeta(beta_arg) +
+            math_ops.log(self.sigma))
+
+  def _mean(self):
+    mean = self.mu * self._ones()
+    if self.allow_nan_stats:
+      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
+      return math_ops.select(
+          math_ops.greater(self.df, self._ones()), mean,
+          array_ops.fill(self.batch_shape(), nan, name="nan"))
+    else:
+      return control_flow_ops.with_dependencies([
+          check_ops.assert_less(
+              array_ops.ones((), dtype=self.dtype), self.df,
+              message="mean not defined for components of df <= 1"),
+      ], mean)
+
+  def _variance(self):
+    var = (self._ones() *
+           math_ops.square(self.sigma) * self.df / (self.df - 2))
+    # When 1 < df <= 2, variance is infinite.
+    inf = np.array(np.inf, dtype=self.dtype.as_numpy_dtype())
+    result_where_defined = math_ops.select(
+        math_ops.greater(self.df, array_ops.fill(self.batch_shape(), 2.)),
+        var,
+        array_ops.fill(self.batch_shape(), inf, name="inf"))
+
+    if self.allow_nan_stats:
+      nan = np.array(np.nan, dtype=self.dtype.as_numpy_dtype())
+      return math_ops.select(
+          math_ops.greater(self.df, self._ones()),
+          result_where_defined,
+          array_ops.fill(self.batch_shape(), nan, name="nan"))
+    else:
+      return control_flow_ops.with_dependencies([
+          check_ops.assert_less(
+              array_ops.ones((), dtype=self.dtype), self.df,
+              message="variance not defined for components of df <= 1"),
+      ], result_where_defined)
+
+  def _std(self):
+    return math_ops.sqrt(self.variance())
+
+  def _mode(self):
+    return array_ops.identity(self.mu)
+
+  def _ones(self):
+    return array_ops.ones(self.batch_shape(), dtype=self.dtype)
+
+
+distribution_util.append_class_fun_doc(StudentT.mean, doc_str="""
 
     The mean of Student's T equals `mu` if `df > 1`, otherwise it is `NaN`.  If
     `self.allow_nan_stats=False`, then an exception will be raised rather than
     returning `NaN`.
+""")
 
-    Args:
-      name:  A name to give this op.
-
-    Returns:
-      The mean for every batch member, a `Tensor` with same `dtype` as self.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._mu]):
-        result_if_defined = self._mu * self._ones()
-        if self.allow_nan_stats:
-          df_gt_1 = self._df > self._ones()
-          nan = np.nan + self._zeros()
-          return math_ops.select(df_gt_1, result_if_defined, nan)
-        else:
-          one = constant_op.constant(1.0, dtype=self.dtype)
-          return control_flow_ops.with_dependencies(
-              [check_ops.assert_less(
-                  one, self._df,
-                  message="mean not defined for components of df <= 1"
-              )], result_if_defined)
-
-  def mode(self, name="mode"):
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._mu]):
-        return array_ops.identity(self._mu)
-
-  def variance(self, name="variance"):
-    """Variance of the distribution.
+distribution_util.append_class_fun_doc(StudentT.variance, doc_str="""
 
     Variance for Student's T equals
 
@@ -212,180 +279,4 @@ class StudentT(distribution.Distribution):
     NaN, when df <= 1
     ```
 
-    The NaN state occurs because mean is undefined for `df <= 1`, and if
-    `self.allow_nan_stats` is `False`, an exception will be raised if any batch
-    members fall into this state.
-
-    Args:
-      name:  A name for this op.
-
-    Returns:
-      The variance for every batch member, a `Tensor` with same `dtype` as self.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._df, self._sigma]):
-        result_where_finite = (
-            self._zeros()
-            + math_ops.square(self._sigma) * self._df / (self._df - 2))
-        # When 1 < df <= 2, variance is infinite.
-        result_where_defined = math_ops.select(
-            self._zeros() + self._df > 2,
-            result_where_finite,
-            self._zeros() + np.inf)
-
-        if self.allow_nan_stats:
-          return math_ops.select(
-              (self._zeros() + self._df > 1),
-              result_where_defined,
-              self._zeros() + np.nan)
-        else:
-          one = constant_op.constant(1.0, dtype=self.dtype)
-          return control_flow_ops.with_dependencies(
-              [check_ops.assert_less(
-                  one, self._df,
-                  message="variance not defined for components of df <= 1"
-              )], result_where_defined)
-
-  def std(self, name="std"):
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return math_ops.sqrt(self.variance())
-
-  def batch_shape(self, name="batch_shape"):
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return array_ops.shape(self._ones())
-
-  def get_batch_shape(self):
-    return self._get_batch_shape
-
-  def event_shape(self, name="event_shape"):
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return constant_op.constant([], dtype=math_ops.int32)
-
-  def get_event_shape(self):
-    return self._event_shape
-
-  def log_prob(self, x, name="log_prob"):
-    """Log prob of observations in `x` under these Student's t-distribution(s).
-
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `mu` and `df`.
-      name: The name to give this op.
-
-    Returns:
-      log_prob: tensor of dtype `dtype`, the log-PDFs of `x`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._df, self._mu, self._sigma, x]):
-        x = ops.convert_to_tensor(x)
-        if x.dtype != self.dtype:
-          raise TypeError("Input x dtype does not match dtype: %s vs. %s" %
-                          (x.dtype, self.dtype))
-        df_2 = self._df / 2
-        log_beta = (math_ops.lgamma(0.5) + math_ops.lgamma(df_2) -
-                    math_ops.lgamma(0.5 + df_2))
-        return (-math_ops.log(self._df) / 2 - log_beta - (self._df + 1) / 2 *
-                math_ops.log(1 + math_ops.square((x - self._mu) / self._sigma) /
-                             self._df) - math_ops.log(self._sigma))
-
-  def prob(self, x, name="prob"):
-    """The PDF of observations in `x` under these Student's t distribution(s).
-
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `df`, `mu`, and
-        `sigma`.
-      name: The name to give this op.
-
-    Returns:
-      prob: tensor of dtype `dtype`, the prob values of `x`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._df, self._mu, self._sigma, x]):
-        x = ops.convert_to_tensor(x)
-        if x.dtype != self.dtype:
-          raise TypeError("Input x dtype does not match dtype: %s vs. %s" %
-                          (x.dtype, self.dtype))
-        reloc_scaled = (x - self._mu) / self._sigma
-        return (math_ops.exp(math_ops.lgamma((self._df + 1) / 2) -
-                             math_ops.lgamma(self._df / 2)) /
-                math_ops.sqrt(self._df) / math.sqrt(np.pi) *
-                math_ops.pow(1 + math_ops.square(reloc_scaled) / self._df,
-                             -(self._df + 1) / 2) / self.sigma)
-
-  def entropy(self, name="entropy"):
-    """The entropy of Student t distribution(s).
-
-    Args:
-      name: The name to give this op.
-
-    Returns:
-      entropy: tensor of dtype `dtype`, the entropy.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._df, self._sigma]):
-        u = array_ops.expand_dims(self._df + self._zeros(), -1)
-        v = array_ops.expand_dims(self._ones(), -1)
-        beta_arg = array_ops.concat(len(u.get_shape()) - 1, [u, v]) / 2
-        return ((self._df + 1) / 2 * (math_ops.digamma((self._df + 1) / 2) -
-                                      math_ops.digamma(self._df / 2)) +
-                math_ops.log(self._df) / 2 +
-                special_math_ops.lbeta(beta_arg) +
-                math_ops.log(self._sigma))
-
-  def sample_n(self, n, seed=None, name="sample_n"):
-    """Sample `n` observations from the Student t Distributions.
-
-    Args:
-      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-        observations to sample.
-      seed: Python integer, the random seed.
-      name: The name to give this op.
-
-    Returns:
-      samples: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-          with values of type `self.dtype`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._df, self._mu, self._sigma, n]):
-        n = ops.convert_to_tensor(n, name="n")
-        n_val = tensor_util.constant_value(n)
-
-        # We use 2 uniform random floats to generate polar random variates.
-        # http://dl.acm.org/citation.cfm?id=179631
-        # Theorem 2. Let G, H be iid variates, uniformly distributed on [0,1].
-        # Let theta = 2*pi*H, let R = sqrt(df*(G^(-2/df) - 1)) for df > 0.
-        # Let X = R*cos(theta), and let Y = R*sin(theta).
-        # Then X ~ t_df and Y ~ t_df.
-        # The variates X and Y are not independent.
-        shape = array_ops.concat(0, ([2, n], self.batch_shape()))
-        uniform = random_ops.random_uniform(shape=shape,
-                                            dtype=self.dtype,
-                                            seed=seed)
-        samples_g, samples_h = array_ops.unpack(uniform, num=2)
-        theta = (2 * np.pi) * samples_h
-        r = math_ops.sqrt(self._df *
-                          (math_ops.pow(samples_g, -2 / self._df) - 1))
-        samples = r * math_ops.cos(theta)
-
-        # Provide some hints to shape inference
-        inferred_shape = tensor_shape.vector(n_val).concatenate(
-            self.get_batch_shape())
-        samples.set_shape(inferred_shape)
-
-        return samples * self._sigma + self._mu
-
-  @property
-  def is_reparameterized(self):
-    return True
-
-  def _ones(self):
-    return array_ops.ones_like(self._df + self._mu + self._sigma)
-
-  def _zeros(self):
-    return array_ops.zeros_like(self._df + self._mu + self._sigma)
-
-  @property
-  def is_continuous(self):
-    return True
+""")
diff --git a/tensorflow/contrib/distributions/python/ops/transformed_distribution.py b/tensorflow/contrib/distributions/python/ops/transformed_distribution.py
index 657700e9ed..68a19cd9eb 100644
--- a/tensorflow/contrib/distributions/python/ops/transformed_distribution.py
+++ b/tensorflow/contrib/distributions/python/ops/transformed_distribution.py
@@ -17,8 +17,10 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.distributions.python.ops import distribution  # pylint: disable=line-too-long
+from tensorflow.contrib.distributions.python.ops import distribution
+from tensorflow.contrib.distributions.python.ops import distribution_util
 from tensorflow.python.framework import ops
+from tensorflow.python.ops import math_ops
 
 
 class TransformedDistribution(distribution.Distribution):
@@ -85,70 +87,18 @@ class TransformedDistribution(distribution.Distribution):
       TypeError: if `base_dist_cls` is not a subclass of
           `Distribution`.
     """
-    if not issubclass(base_dist_cls, distribution.Distribution):
-      raise TypeError("base_dist_cls must be a subclass of Distribution.")
-    with ops.name_scope(name, values=base_dist_args.values()) as scope:
-      self._name = scope
+    with ops.name_scope(name, values=base_dist_args.values()):
       self._base_dist = base_dist_cls(**base_dist_args)
-    self._transform = transform
-    self._inverse = inverse
-    self._log_det_jacobian = log_det_jacobian
-    self._inverse_cache = {}
-
-  @property
-  def name(self):
-    return self._name
-
-  @property
-  def dtype(self):
-    return self._base_dist.dtype
-
-  def batch_shape(self, name="batch_shape"):
-    """Batch dimensions of this instance as a 1-D int32 `Tensor`.
-
-    The product of the dimensions of the `batch_shape` is the number of
-    independent distributions of this kind the instance represents.
-
-    Args:
-      name: name to give to the op.
-
-    Returns:
-      `Tensor` `batch_shape`
-    """
-    with ops.name_scope(self.name):
-      return self._base_dist.batch_shape(name)
-
-  def get_batch_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `batch_shape`. May be only partially defined.
-
-    Returns:
-      batch shape
-    """
-    return self._base_dist.get_batch_shape()
-
-  def event_shape(self, name="event_shape"):
-    """Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
-
-    Args:
-      name: name to give to the op.
-
-    Returns:
-      `Tensor` `event_shape`
-    """
-    with ops.name_scope(self.name):
-      return self._base_dist.event_shape(name)
-
-  def get_event_shape(self):
-    """`TensorShape` available at graph construction time.
-
-    Same meaning as `event_shape`. May be only partially defined.
-
-    Returns:
-      event shape
-    """
-    return self._base_dist.get_event_shape()
+      self._transform = transform
+      self._inverse = inverse
+      self._log_det_jacobian = log_det_jacobian
+      self._inverse_cache = {}
+      super(TransformedDistribution, self).__init__(
+          dtype=self._base_dist.dtype,
+          is_reparameterized=self._base_dist.is_reparameterized,
+          validate_args=self._base_dist.validate_args,
+          allow_nan_stats=self._base_dist.allow_nan_stats,
+          name=name)
 
   @property
   def base_distribution(self):
@@ -170,88 +120,74 @@ class TransformedDistribution(distribution.Distribution):
     """Function computing the log determinant of the Jacobian of transform."""
     return self._log_det_jacobian
 
-  def log_prob(self, y, name="log_prob"):
-    """Log prob of observations in `y`.
+  def _batch_shape(self):
+    return self.base_distribution.batch_shape()
 
-    `log ( p(g(y)) / det|J(g(y))| )`, where `g` is the inverse of `transform`.
+  def _get_batch_shape(self):
+    return self.base_distribution.get_batch_shape()
 
-    Args:
-      y: tensor of dtype `dtype`.
-      name: The name to give this op.
+  def _event_shape(self):
+    return self.base_distribution.event_shape()
 
-    Returns:
-      log_pdf: tensor of dtype `dtype`, the log-PDFs of `y`.
+  def _get_event_shape(self):
+    return self.base_distribution.get_event_shape()
 
-    Raises:
-      ValueError: if `inverse` was not provided to the distribution and `y` was
-          not returned from `sample`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[y]):
-        y = ops.convert_to_tensor(y)
-        if y.dtype != self.dtype:
-          raise TypeError("Input x dtype does not match dtype: %s vs. %s" %
-                          (y.dtype, self.dtype))
-        with ops.name_scope("inverse"):
-          if y in self._inverse_cache:
-            x = self._inverse_cache[y]
-          elif self._inverse:
-            x = self._inverse(y)
-          else:
-            raise ValueError("No inverse function exists and input `y` was not "
-                             "returned from `sample`.")
-        with ops.name_scope("log_det_jacobian"):
-          log_det_jacobian = self._log_det_jacobian(x)
-        return self._base_dist.log_prob(x) - log_det_jacobian
-
-  def prob(self, y, name="prob"):
-    """The prob of observations in `y`.
-
-    `p(g(y)) / det|J(g(y))|`, where `g` is the inverse of `transform`.
+  def _sample_n(self, n, seed=None):
+    samples = self.base_distribution.sample_n(n=n, seed=seed)
+    with ops.name_scope("transform"):
+      transformed = self.transform(samples)
+      self._inverse_cache[transformed] = samples
+      return transformed
 
-    Args:
-      y: `Tensor` of dtype `dtype`.
-      name: The name to give this op.
+  def _log_prob(self, y):
+    y = ops.convert_to_tensor(y, name="y")
+    with ops.name_scope("inverse"):
+      if y in self._inverse_cache:
+        x = self._inverse_cache[y]
+      elif self.inverse:
+        x = self.inverse(y)
+      else:
+        raise ValueError("No inverse function exists and input `y` was not "
+                         "returned from `sample`.")
+    with ops.name_scope("log_det_jacobian"):
+      log_det_jacobian = self.log_det_jacobian(x)
+    return self.base_distribution.log_prob(x) - log_det_jacobian
 
-    Returns:
-      pdf: `Tensor` of dtype `dtype`, the pdf values of `y`.
-    """
-    return super(TransformedDistribution, self).prob(y, name=name)
+  def _prob(self, y):
+    return math_ops.exp(self._log_prob(y))
 
-  def sample_n(self, n, seed=None, name="sample_n"):
-    """Sample `n` observations.
+
+distribution_util.append_class_fun_doc(TransformedDistribution.batch_shape,
+                                       doc_str="""
+
+    The product of the dimensions of the `batch_shape` is the number of
+    independent distributions of this kind the instance represents.
+
+""")
+
+distribution_util.append_class_fun_doc(TransformedDistribution.sample_n,
+                                       doc_str="""
 
     Samples from the base distribution and then passes through the transform.
+""")
 
-    Args:
-      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-        observations to sample.
-      seed: Python integer, the random seed.
-      name: The name to give this op.
+distribution_util.append_class_fun_doc(TransformedDistribution.log_prob,
+                                       doc_str="""
 
-    Returns:
-      samples: `[n, ...]`, a `Tensor` of `n` samples.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        samples = self._base_dist.sample_n(n=n, seed=seed)
-        with ops.name_scope("transform"):
-          transformed = self._transform(samples)
-          self._inverse_cache[transformed] = samples
-          return transformed
+  `(log o p o g)(y) - (log o det o J o g)(y)`,
+  where `g` is the inverse of `transform`.
 
-  @property
-  def is_reparameterized(self):
-    return self._base_dist.is_reparameterized
+  Raises:
+    ValueError: if `inverse` was not provided to the distribution and `y` was
+        not returned from `sample`.
+""")
 
-  @property
-  def allow_nan_stats(self):
-    return self._base_dist.allow_nan_stats
+distribution_util.append_class_fun_doc(TransformedDistribution.prob,
+                                       doc_str="""
 
-  @property
-  def validate_args(self):
-    return self._base_dist.validate_args
+  `p(g(y)) / det|J(g(y))|`, where `g` is the inverse of `transform`.
 
-  @property
-  def is_continuous(self):
-    return True
+  Raises:
+    ValueError: if `inverse` was not provided to the distribution and `y` was
+        not returned from `sample`.
+""")
diff --git a/tensorflow/contrib/distributions/python/ops/uniform.py b/tensorflow/contrib/distributions/python/ops/uniform.py
index f5d4e3e48f..0df823315f 100644
--- a/tensorflow/contrib/distributions/python/ops/uniform.py
+++ b/tensorflow/contrib/distributions/python/ops/uniform.py
@@ -18,14 +18,15 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib.distributions.python.ops import distribution  # pylint: disable=line-too-long
-from tensorflow.contrib.framework.python.framework import tensor_util as contrib_tensor_util  # pylint: disable=line-too-long
+import math
+
+from tensorflow.contrib.distributions.python.ops import distribution
+from tensorflow.contrib.framework.python.framework import tensor_util as contrib_tensor_util
 from tensorflow.python.framework import common_shapes
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import tensor_shape
-from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
 from tensorflow.python.ops import math_ops
@@ -39,8 +40,8 @@ class Uniform(distribution.Distribution):
   """
 
   def __init__(self,
-               a=0.0,
-               b=1.0,
+               a=0.,
+               b=1.,
                validate_args=True,
                allow_nan_stats=False,
                name="Uniform"):
@@ -81,57 +82,19 @@ class Uniform(distribution.Distribution):
     Raises:
       InvalidArgumentError: if `a >= b` and `validate_args=True`.
     """
-    self._allow_nan_stats = allow_nan_stats
-    self._validate_args = validate_args
-    with ops.name_scope(name, values=[a, b]):
-      with ops.control_dependencies([check_ops.assert_less(
-          a, b, message="uniform not defined when a > b.")] if validate_args
-                                    else []):
-        a = array_ops.identity(a, name="a")
-        b = array_ops.identity(b, name="b")
-
-    self._a = a
-    self._b = b
-    self._name = name
-    self._batch_shape = common_shapes.broadcast_shape(
-        self._a.get_shape(), self._b.get_shape())
-    self._event_shape = tensor_shape.TensorShape([])
-
-    contrib_tensor_util.assert_same_float_dtype((a, b))
-
-  @property
-  def allow_nan_stats(self):
-    """Boolean describing behavior when a stat is undefined for batch member."""
-    return self._allow_nan_stats
-
-  @property
-  def validate_args(self):
-    """Boolean describing behavior on invalid input."""
-    return self._validate_args
-
-  @property
-  def name(self):
-    return self._name
-
-  @property
-  def dtype(self):
-    return self.a.dtype
-
-  def batch_shape(self, name="batch_shape"):
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._a, self._b]):
-        return array_ops.shape(self._a + self._b)
-
-  def get_batch_shape(self):
-    return self._batch_shape
-
-  def event_shape(self, name="event_shape"):
-    with ops.name_scope(self.name):
-      with ops.name_scope(name):
-        return constant_op.constant([], dtype=dtypes.int32)
-
-  def get_event_shape(self):
-    return self._event_shape
+    with ops.control_dependencies([
+        check_ops.assert_less(a, b, message="uniform not defined when a > b.")
+    ] if validate_args else []):
+      self._a = array_ops.identity(a, name="a")
+      self._b = array_ops.identity(b, name="b")
+      contrib_tensor_util.assert_same_float_dtype((self._a, self._b))
+      super(Uniform, self).__init__(
+          dtype=self._a.dtype,
+          parameters={"a": self._a, "b": self._b},
+          is_reparameterized=True,
+          validate_args=validate_args,
+          allow_nan_stats=allow_nan_stats,
+          name=name)
 
   @property
   def a(self):
@@ -141,138 +104,65 @@ class Uniform(distribution.Distribution):
   def b(self):
     return self._b
 
-  def prob(self, x, name="prob"):
-    """The PDF of observations in `x` under these Uniform distribution(s).
-
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `a` and `b`.
-      name: The name to give this op.
-
-    Returns:
-      prob: tensor of dtype `dtype`, the prob values of `x`. If `x` is `nan`,
-          will return `nan`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.a, self.b, x]):
-        x = ops.convert_to_tensor(x, name="x")
-        if x.dtype != self.dtype:
-          raise TypeError("Input x dtype does not match dtype: %s vs. %s" %
-                          (x.dtype, self.dtype))
-
-        broadcasted_x = x * self._ones()
-        return math_ops.select(
-            math_ops.is_nan(broadcasted_x), broadcasted_x, math_ops.select(
-                math_ops.logical_or(broadcasted_x < self.a,
-                                    broadcasted_x > self.b),
-                array_ops.zeros_like(broadcasted_x),
-                (1.0 / self.range()) * array_ops.ones_like(broadcasted_x)))
-
-  def log_prob(self, x, name="log_prob"):
-    return super(Uniform, self).log_prob(x, name)
-
-  def cdf(self, x, name="cdf"):
-    """CDF of observations in `x` under these Uniform distribution(s).
-
-    Args:
-      x: tensor of dtype `dtype`, must be broadcastable with `a` and `b`.
-      name: The name to give this op.
-
-    Returns:
-      cdf: tensor of dtype `dtype`, the CDFs of `x`. If `x` is `nan`, will
-          return `nan`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.a, self.b, x]):
-        x = ops.convert_to_tensor(x, name="x")
-        if x.dtype != self.dtype:
-          raise TypeError("Input x dtype does not match dtype: %s vs. %s" %
-                          (x.dtype, self.dtype))
-
-        broadcasted_x = x * self._ones()
-        zeros = array_ops.zeros_like(x + self.a + self.b, dtype=self.dtype)
-        ones = array_ops.ones_like(x + self.a + self.b, dtype=self.dtype)
-        result_if_not_big = math_ops.select(
-            x < self.a, zeros, (broadcasted_x - self.a) / self.range())
-        return math_ops.select(x >= self.b, ones, result_if_not_big)
-
-  def log_cdf(self, x, name="log_cdf"):
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.a, self.b, x]):
-        x = ops.convert_to_tensor(x, name="x")
-        return math_ops.log(self.cdf(x))
-
-  def entropy(self, name="entropy"):
-    """The entropy of Uniform distribution(s).
-
-    Args:
-      name: The name to give this op.
-
-    Returns:
-      entropy: tensor of dtype `dtype`, the entropy.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.a, self.b, self.range()]):
-        return math_ops.log(self.range())
-
-  def sample_n(self, n, seed=None, name="sample_n"):
-    """Sample `n` observations from the Uniform Distributions.
-
-    Args:
-      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-        observations to sample.
-      seed: Python integer, the random seed.
-      name: The name to give this op.
-
-    Returns:
-      samples: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-          with values of type `self.dtype`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.a, self.b, n]):
-        n = ops.convert_to_tensor(n, name="n")
-        n_val = tensor_util.constant_value(n)
-
-        shape = array_ops.concat(0, ([n], self.batch_shape()))
-        samples = random_ops.random_uniform(shape=shape,
-                                            dtype=self.dtype,
-                                            seed=seed)
-
-        # Provide some hints to shape inference
-        inferred_shape = tensor_shape.vector(n_val).concatenate(
-            self.get_batch_shape())
-        samples.set_shape(inferred_shape)
-
-        return (array_ops.expand_dims(self.a, 0) + array_ops.expand_dims(
-            self.range(), 0) * samples)
-
-  def mean(self, name="mean"):
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self._a, self._b]):
-        return (self.a + self.b) / 2
-
-  def variance(self, name="variance"):
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.range()]):
-        return math_ops.square(self.range()) / 12.
-
-  def std(self, name="std"):
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.range()]):
-        return self.range() / math_ops.sqrt(12.)
-
   def range(self, name="range"):
     """`b - a`."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[self.a, self.b]):
-        return self.b - self.a
+    with self._name_scope(name):
+      return self.b - self.a
 
-  @property
-  def is_reparameterized(self):
-    return True
+  def _batch_shape(self):
+    return array_ops.shape(self._a + self._b)
 
-  def _ones(self):
-    return array_ops.ones_like(self.a + self.b)
+  def _get_batch_shape(self):
+    return common_shapes.broadcast_shape(
+        self._a.get_shape(), self._b.get_shape())
 
-  @property
-  def is_continuous(self):
-    return True
+  def _event_shape(self):
+    return constant_op.constant([], dtype=dtypes.int32)
+
+  def _get_event_shape(self):
+    return tensor_shape.scalar()
+
+  def _sample_n(self, n, seed=None):
+    shape = array_ops.concat(0, ([n], self.batch_shape()))
+    samples = random_ops.random_uniform(shape=shape,
+                                        dtype=self.dtype,
+                                        seed=seed)
+    return (array_ops.expand_dims(self.a, 0) +
+            array_ops.expand_dims(self.range(), 0) * samples)
+
+  def _log_prob(self, x):
+    return math_ops.log(self._prob(x))
+
+  def _prob(self, x):
+    broadcasted_x = x * array_ops.ones(self.batch_shape())
+    return math_ops.select(
+        math_ops.is_nan(broadcasted_x),
+        broadcasted_x,
+        math_ops.select(
+            math_ops.logical_or(broadcasted_x < self.a,
+                                broadcasted_x > self.b),
+            array_ops.zeros_like(broadcasted_x),
+            (1. / self.range()) * array_ops.ones_like(broadcasted_x)))
+
+  def _log_cdf(self, x):
+    return math_ops.log(self.cdf(x))
+
+  def _cdf(self, x):
+    broadcasted_x = x * array_ops.ones(self.batch_shape())
+    zeros = array_ops.zeros_like(x + self.a + self.b, dtype=self.dtype)
+    ones = array_ops.ones_like(x + self.a + self.b, dtype=self.dtype)
+    result_if_not_big = math_ops.select(
+        x < self.a, zeros, (broadcasted_x - self.a) / self.range())
+    return math_ops.select(x >= self.b, ones, result_if_not_big)
+
+  def _entropy(self):
+    return math_ops.log(self.range())
+
+  def _mean(self):
+    return (self.a + self.b) / 2.
+
+  def _variance(self):
+    return math_ops.square(self.range()) / 12.
+
+  def _std(self):
+    return self.range() / math.sqrt(12.)
diff --git a/tensorflow/contrib/distributions/python/ops/wishart.py b/tensorflow/contrib/distributions/python/ops/wishart.py
index 0c82720283..3ffcf531e0 100644
--- a/tensorflow/contrib/distributions/python/ops/wishart.py
+++ b/tensorflow/contrib/distributions/python/ops/wishart.py
@@ -28,7 +28,6 @@ from tensorflow.contrib.framework.python.framework import tensor_util as contrib
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import check_ops
@@ -72,9 +71,9 @@ class _WishartOperatorPD(distribution.Distribution):
                df,
                scale_operator_pd,
                cholesky_input_output_matrices=False,
-               allow_nan_stats=False,
                validate_args=True,
-               name='Wishart'):
+               allow_nan_stats=False,
+               name=None):
     """Construct Wishart distributions.
 
     Args:
@@ -86,13 +85,13 @@ class _WishartOperatorPD(distribution.Distribution):
         Cholesky factored matrix. Example`log_pdf` input takes a Cholesky and
         `sample_n` returns a Cholesky when
         `cholesky_input_output_matrices=True`.
+      validate_args: Whether to validate input with asserts. If `validate_args`
+        is `False`, and the inputs are invalid, correct behavior is not
+        guaranteed.
       allow_nan_stats:  `Boolean`, default `False`. If `False`, raise an
         exception if a statistic (e.g., mean, mode) is undefined for any batch
         member. If True, batch members with valid parameters leading to
         undefined statistics will return `NaN` for this statistic.
-      validate_args: Whether to validate input with asserts. If `validate_args`
-        is `False`, and the inputs are invalid, correct behavior is not
-        guaranteed.
       name: The name to give Ops created by the initializer.
 
     Raises:
@@ -100,31 +99,29 @@ class _WishartOperatorPD(distribution.Distribution):
       TypeError: if scale.dtype != df.dtype
       ValueError: if df < k, where scale operator event shape is `(k, k)`
     """
-    self._scale_operator_pd = scale_operator_pd
     self._cholesky_input_output_matrices = cholesky_input_output_matrices
-    self._allow_nan_stats = allow_nan_stats
-    self._validate_args = validate_args
-    self._name = name
     with ops.name_scope(name):
       with ops.name_scope('init', values=[df, scale_operator_pd]):
-        if not self.dtype.is_floating:
+        if not scale_operator_pd.dtype.is_floating:
           raise TypeError(
               'scale_operator_pd.dtype=%s is not a floating-point type' %
-              self.dtype)
-        self._df = ops.convert_to_tensor(df, dtype=self.dtype, name='df')
+              scale_operator_pd.dtype)
+        self._scale_operator_pd = scale_operator_pd
+        self._df = ops.convert_to_tensor(
+            df, dtype=scale_operator_pd.dtype, name='df')
         contrib_tensor_util.assert_same_float_dtype(
-            (self._df, self.scale_operator_pd))
-        if (self.scale_operator_pd.get_shape().ndims is None or
-            self.scale_operator_pd.get_shape()[-1].value is None):
+            (self._df, self._scale_operator_pd))
+        if (self._scale_operator_pd.get_shape().ndims is None or
+            self._scale_operator_pd.get_shape()[-1].value is None):
           self._dimension = math_ops.cast(
-              self.scale_operator_pd.vector_space_dimension(),
-              dtype=self.dtype, name='dimension')
+              self._scale_operator_pd.vector_space_dimension(),
+              dtype=self._scale_operator_pd.dtype, name='dimension')
         else:
           self._dimension = ops.convert_to_tensor(
-              self.scale_operator_pd.get_shape()[-1].value,
-              dtype=self.dtype, name='dimension')
-        df_val = tensor_util.constant_value(self.df)
-        dim_val = tensor_util.constant_value(self.dimension)
+              self._scale_operator_pd.get_shape()[-1].value,
+              dtype=self._scale_operator_pd.dtype, name='dimension')
+        df_val = tensor_util.constant_value(self._df)
+        dim_val = tensor_util.constant_value(self._dimension)
         if df_val is not None and dim_val is not None:
           df_val = np.asarray(df_val)
           if not df_val.shape: df_val = (df_val,)
@@ -133,38 +130,21 @@ class _WishartOperatorPD(distribution.Distribution):
                 'Degrees of freedom (df = %s) cannot be less than dimension of '
                 'scale matrix (scale.dimension = %s)'
                 % (df_val, dim_val))
-        elif self.validate_args:
+        elif validate_args:
           assertions = check_ops.assert_less_equal(
-              self.dimension, self.df,
+              self._dimension, self._df,
               message=('Degrees of freedom (df = %s) cannot be less than '
                        'dimension of scale matrix (scale.dimension = %s)' %
-                       (self.dimension, self.df)))
+                       (self._dimension, self._df)))
           self._df = control_flow_ops.with_dependencies([assertions], self._df)
-
-  @property
-  def inputs(self):
-    """Dictionary of inputs provided at initialization."""
-    return {'scale_operator_pd': self.scale_operator_pd, 'df': self._df}
-
-  @property
-  def allow_nan_stats(self):
-    """Boolean describing behavior when a stat is undefined for batch member."""
-    return self._allow_nan_stats
-
-  @property
-  def validate_args(self):
-    """Boolean describing behavior on invalid input."""
-    return self._validate_args
-
-  @property
-  def name(self):
-    """Name prepended to all ops."""
-    return self._name
-
-  @property
-  def dtype(self):
-    """dtype of samples from this distribution."""
-    return self.scale_operator_pd.dtype
+        super(_WishartOperatorPD, self).__init__(
+            dtype=self._scale_operator_pd.dtype,
+            parameters={'df': self._df,
+                        'scale_operator_pd': self._scale_operator_pd,
+                        'dimension': self._dimension},
+            validate_args=validate_args,
+            allow_nan_stats=allow_nan_stats,
+            name=name)
 
   @property
   def df(self):
@@ -193,240 +173,165 @@ class _WishartOperatorPD(distribution.Distribution):
     """Dimension of underlying vector space. The `p` in `R^(p*p)`."""
     return self._dimension
 
-  def is_continuous(self):
-    return True
-
-  def is_reparameterized(self):
-    return True
+  def _event_shape(self):
+    s = self.scale_operator_pd.shape()
+    return array_ops.slice(s, array_ops.shape(s) - 2, [2])
 
-  def event_shape(self, name='event_shape'):
-    """Shape of a sample from a single distribution as a 1-D int32 `Tensor`."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=list(self.inputs.values())):
-        s = self.scale_operator_pd.shape()
-        return array_ops.slice(s, array_ops.shape(s) - 2, [2])
-
-  def get_event_shape(self):
-    """`TensorShape` available at graph construction time."""
+  def _get_event_shape(self):
     return self.scale_operator_pd.get_shape()[-2:]
 
-  def batch_shape(self, name='batch_shape'):
-    """Batch dimensions of this instance as a 1-D int32 `Tensor`."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=list(self.inputs.values())):
-        return self.scale_operator_pd.batch_shape()
+  def _batch_shape(self):
+    return self.scale_operator_pd.batch_shape()
 
-  def get_batch_shape(self):
-    """`TensorShape` available at graph construction time."""
+  def _get_batch_shape(self):
     return self.scale_operator_pd.get_batch_shape()
 
-  def prob(self, value, name='prob'):
-    """Probability density/mass function."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[value]):
-        return math_ops.exp(self.log_prob(value))
-
-  def log_prob(self, x, name='log_prob'):
-    """Log of the probability density/mass function.
-
-    Args:
-      x: `float` or `double` `Tensor`.
-      name: The name to give this op.
-
-    Returns:
-      log_prob: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
-        values of type `self.dtype`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[x] + list(self.inputs.values())):
-        x = ops.convert_to_tensor(x, name='x')
-        contrib_tensor_util.assert_same_float_dtype(
-            (self.scale_operator_pd, x))
-        if self.cholesky_input_output_matrices:
-          x_sqrt = x
-        else:
-          # Complexity: O(nbk^3)
-          x_sqrt = linalg_ops.batch_cholesky(x)
-
-        batch_shape = self.batch_shape()
-        event_shape = self.event_shape()
-        ndims = array_ops.rank(x_sqrt)
-        # sample_ndims = ndims - batch_ndims - event_ndims
-        sample_ndims = ndims - array_ops.shape(batch_shape)[0] - 2
-        sample_shape = array_ops.slice(
-            array_ops.shape(x_sqrt), [0], [sample_ndims])
-
-        # We need to be able to pre-multiply each matrix by its corresponding
-        # batch scale matrix.  Since a Distribution Tensor supports multiple
-        # samples per batch, this means we need to reshape the input matrix `x`
-        # so that the first b dimensions are batch dimensions and the last two
-        # are of shape [dimension, dimensions*number_of_samples]. Doing these
-        # gymnastics allows us to do a batch_solve.
-        #
-        # After we're done with sqrt_solve (the batch operation) we need to undo
-        # this reshaping so what we're left with is a Tensor partitionable by
-        # sample, batch, event dimensions.
-
-        # Complexity: O(nbk^2) since transpose must access every element.
-        scale_sqrt_inv_x_sqrt = x_sqrt
-        perm = array_ops.concat(0, (math_ops.range(sample_ndims, ndims),
-                                    math_ops.range(0, sample_ndims)))
-        scale_sqrt_inv_x_sqrt = array_ops.transpose(scale_sqrt_inv_x_sqrt, perm)
-        shape = array_ops.concat(
-            0, (batch_shape,
-                (math_ops.cast(self.dimension, dtype=dtypes.int32), -1)))
-        scale_sqrt_inv_x_sqrt = array_ops.reshape(scale_sqrt_inv_x_sqrt, shape)
-
-        # Complexity: O(nbM*k) where M is the complexity of the operator solving
-        # a vector system.  E.g., for OperatorPDDiag, each solve is O(k), so
-        # this complexity is O(nbk^2). For OperatorPDCholesky, each solve is
-        # O(k^2) so this step has complexity O(nbk^3).
-        scale_sqrt_inv_x_sqrt = self.scale_operator_pd.sqrt_solve(
-            scale_sqrt_inv_x_sqrt)
-
-        # Undo make batch-op ready.
-        # Complexity: O(nbk^2)
-        shape = array_ops.concat(0, (batch_shape, event_shape, sample_shape))
-        scale_sqrt_inv_x_sqrt = array_ops.reshape(scale_sqrt_inv_x_sqrt, shape)
-        perm = array_ops.concat(0, (math_ops.range(ndims - sample_ndims, ndims),
-                                    math_ops.range(0, ndims - sample_ndims)))
-        scale_sqrt_inv_x_sqrt = array_ops.transpose(scale_sqrt_inv_x_sqrt, perm)
-
-        # Write V = SS', X = LL'. Then:
-        # tr[inv(V) X] = tr[inv(S)' inv(S) L L']
-        #              = tr[inv(S) L L' inv(S)']
-        #              = tr[(inv(S) L) (inv(S) L)']
-        #              = sum_{ik} (inv(S) L)_{ik}^2
-        # The second equality follows from the cyclic permutation property.
-        # Complexity: O(nbk^2)
-        trace_scale_inv_x = math_ops.reduce_sum(
-            math_ops.square(scale_sqrt_inv_x_sqrt),
-            reduction_indices=[-2, -1])
-
-        # Complexity: O(nbk)
-        half_log_det_x = math_ops.reduce_sum(
-            math_ops.log(array_ops.batch_matrix_diag_part(x_sqrt)),
-            reduction_indices=[-1])
-
-        # Complexity: O(nbk^2)
-        log_prob = ((self.df - self.dimension - 1.) * half_log_det_x -
-                    0.5 * trace_scale_inv_x -
-                    self.log_normalizing_constant())
-
-        # Set shape hints.
-        # Try to merge what we know from the input then what we know from the
-        # parameters of this distribution.
-        if x.get_shape().ndims is not None:
-          log_prob.set_shape(x.get_shape()[:-2])
-        if (log_prob.get_shape().ndims is not None and
-            self.get_batch_shape().ndims is not None and
-            self.get_batch_shape().ndims > 0):
-          log_prob.get_shape()[-self.get_batch_shape().ndims:].merge_with(
-              self.get_batch_shape())
-
-        return log_prob
-
-  def sample_n(self, n, seed=None, name='sample'):
-    # pylint: disable=line-too-long
-    """Generate `n` samples.
-
-    Complexity: O(nbk^3)
-
-    The sampling procedure is based on the [Bartlett decomposition](
-    https://en.wikipedia.org/wiki/Wishart_distribution#Bartlett_decomposition)
-    and [using a Gamma distribution to generate Chi2 random variates](
-    https://en.wikipedia.org/wiki/Chi-squared_distribution#Gamma.2C_exponential.2C_and_related_distributions).
-
-    Args:
-      n: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-        observations to sample.
-      seed: Python integer; random number generator seed.
-      name: The name of this op.
-
-    Returns:
-      samples: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-          with values of type `self.dtype`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[n] + list(self.inputs.values())):
-        n = ops.convert_to_tensor(n, name='n')
-        if n.dtype != dtypes.int32:
-          raise TypeError('n.dtype=%s which is not int32' % n.dtype)
-        batch_shape = self.batch_shape()
-        event_shape = self.event_shape()
-        batch_ndims = array_ops.shape(batch_shape)[0]
-
-        ndims = batch_ndims + 3  # sample_ndims=1, event_ndims=2
-        shape = array_ops.concat(0, ((n,), batch_shape, event_shape))
-
-        # Complexity: O(nbk^2)
-        x = random_ops.random_normal(shape=shape,
-                                     mean=0.,
-                                     stddev=1.,
-                                     dtype=self.dtype,
-                                     seed=seed)
-
-        # Complexity: O(nbk)
-        # This parametrization is equivalent to Chi2, i.e.,
-        # ChiSquared(k) == Gamma(alpha=k/2, beta=1/2)
-        g = random_ops.random_gamma(shape=(n,),
-                                    alpha=self._multi_gamma_sequence(
-                                        0.5 * self.df, self.dimension),
-                                    beta=0.5,
-                                    dtype=self.dtype,
-                                    seed=seed)
-
-        # Complexity: O(nbk^2)
-        x = array_ops.batch_matrix_band_part(x, -1, 0)  # Tri-lower.
-
-        # Complexity: O(nbk)
-        x = array_ops.batch_matrix_set_diag(x, math_ops.sqrt(g))
-
-        # Make batch-op ready.
-        # Complexity: O(nbk^2)
-        perm = array_ops.concat(0, (math_ops.range(1, ndims), (0,)))
-        x = array_ops.transpose(x, perm)
-        shape = array_ops.concat(0, (batch_shape, (event_shape[0], -1)))
-        x = array_ops.reshape(x, shape)
-
-        # Complexity: O(nbM) where M is the complexity of the operator solving a
-        # vector system.  E.g., for OperatorPDDiag, each matmul is O(k^2), so
-        # this complexity is O(nbk^2). For OperatorPDCholesky, each matmul is
-        # O(k^3) so this step has complexity O(nbk^3).
-        x = self.scale_operator_pd.sqrt_matmul(x)
-
-        # Undo make batch-op ready.
-        # Complexity: O(nbk^2)
-        shape = array_ops.concat(0, (batch_shape, event_shape, (n,)))
-        x = array_ops.reshape(x, shape)
-        perm = array_ops.concat(0, ((ndims-1,), math_ops.range(0, ndims-1)))
-        x = array_ops.transpose(x, perm)
-
-        if not self.cholesky_input_output_matrices:
-          # Complexity: O(nbk^3)
-          x = math_ops.batch_matmul(x, x, adj_y=True)
-
-        # Set shape hints.
-        if self.scale_operator_pd.get_shape().ndims is not None:
-          x.set_shape(tensor_shape.TensorShape(
-              [tensor_util.constant_value(n)] +
-              self.scale_operator_pd.get_shape().as_list()))
-        elif x.get_shape().ndims is not None:
-          x.get_shape()[0].merge_with(
-              tensor_shape.TensorDimension(tensor_util.constant_value(n)))
-
-        return x
-
-  def cdf(self, value, name='cdf'):
-    """Cumulative distribution function."""
-    raise NotImplementedError('cdf is not implemented')
-
-  def log_cdf(self, value, name='log_cdf'):
-    """Log CDF."""
-    raise NotImplementedError('log_cdf is not implemented')
-
-  def entropy(self, name='entropy'):
-    """Entropy of the distribution in nats."""
+  def _sample_n(self, n, seed):
+    batch_shape = self.batch_shape()
+    event_shape = self.event_shape()
+    batch_ndims = array_ops.shape(batch_shape)[0]
+
+    ndims = batch_ndims + 3  # sample_ndims=1, event_ndims=2
+    shape = array_ops.concat(0, ((n,), batch_shape, event_shape))
+
+    # Complexity: O(nbk^2)
+    x = random_ops.random_normal(shape=shape,
+                                 mean=0.,
+                                 stddev=1.,
+                                 dtype=self.dtype,
+                                 seed=seed)
+
+    # Complexity: O(nbk)
+    # This parametrization is equivalent to Chi2, i.e.,
+    # ChiSquared(k) == Gamma(alpha=k/2, beta=1/2)
+    g = random_ops.random_gamma(shape=(n,),
+                                alpha=self._multi_gamma_sequence(
+                                    0.5 * self.df, self.dimension),
+                                beta=0.5,
+                                dtype=self.dtype,
+                                seed=seed)
+
+    # Complexity: O(nbk^2)
+    x = array_ops.batch_matrix_band_part(x, -1, 0)  # Tri-lower.
+
+    # Complexity: O(nbk)
+    x = array_ops.batch_matrix_set_diag(x, math_ops.sqrt(g))
+
+    # Make batch-op ready.
+    # Complexity: O(nbk^2)
+    perm = array_ops.concat(0, (math_ops.range(1, ndims), (0,)))
+    x = array_ops.transpose(x, perm)
+    shape = array_ops.concat(0, (batch_shape, (event_shape[0], -1)))
+    x = array_ops.reshape(x, shape)
+
+    # Complexity: O(nbM) where M is the complexity of the operator solving a
+    # vector system.  E.g., for OperatorPDDiag, each matmul is O(k^2), so
+    # this complexity is O(nbk^2). For OperatorPDCholesky, each matmul is
+    # O(k^3) so this step has complexity O(nbk^3).
+    x = self.scale_operator_pd.sqrt_matmul(x)
+
+    # Undo make batch-op ready.
+    # Complexity: O(nbk^2)
+    shape = array_ops.concat(0, (batch_shape, event_shape, (n,)))
+    x = array_ops.reshape(x, shape)
+    perm = array_ops.concat(0, ((ndims-1,), math_ops.range(0, ndims-1)))
+    x = array_ops.transpose(x, perm)
+
+    if not self.cholesky_input_output_matrices:
+      # Complexity: O(nbk^3)
+      x = math_ops.batch_matmul(x, x, adj_y=True)
+
+    return x
+
+  def _log_prob(self, x):
+    if self.cholesky_input_output_matrices:
+      x_sqrt = x
+    else:
+      # Complexity: O(nbk^3)
+      x_sqrt = linalg_ops.batch_cholesky(x)
+
+    batch_shape = self.batch_shape()
+    event_shape = self.event_shape()
+    ndims = array_ops.rank(x_sqrt)
+    # sample_ndims = ndims - batch_ndims - event_ndims
+    sample_ndims = ndims - array_ops.shape(batch_shape)[0] - 2
+    sample_shape = array_ops.slice(
+        array_ops.shape(x_sqrt), [0], [sample_ndims])
+
+    # We need to be able to pre-multiply each matrix by its corresponding
+    # batch scale matrix.  Since a Distribution Tensor supports multiple
+    # samples per batch, this means we need to reshape the input matrix `x`
+    # so that the first b dimensions are batch dimensions and the last two
+    # are of shape [dimension, dimensions*number_of_samples]. Doing these
+    # gymnastics allows us to do a batch_solve.
+    #
+    # After we're done with sqrt_solve (the batch operation) we need to undo
+    # this reshaping so what we're left with is a Tensor partitionable by
+    # sample, batch, event dimensions.
+
+    # Complexity: O(nbk^2) since transpose must access every element.
+    scale_sqrt_inv_x_sqrt = x_sqrt
+    perm = array_ops.concat(0, (math_ops.range(sample_ndims, ndims),
+                                math_ops.range(0, sample_ndims)))
+    scale_sqrt_inv_x_sqrt = array_ops.transpose(scale_sqrt_inv_x_sqrt, perm)
+    shape = array_ops.concat(
+        0, (batch_shape,
+            (math_ops.cast(self.dimension, dtype=dtypes.int32), -1)))
+    scale_sqrt_inv_x_sqrt = array_ops.reshape(scale_sqrt_inv_x_sqrt, shape)
+
+    # Complexity: O(nbM*k) where M is the complexity of the operator solving
+    # a vector system.  E.g., for OperatorPDDiag, each solve is O(k), so
+    # this complexity is O(nbk^2). For OperatorPDCholesky, each solve is
+    # O(k^2) so this step has complexity O(nbk^3).
+    scale_sqrt_inv_x_sqrt = self.scale_operator_pd.sqrt_solve(
+        scale_sqrt_inv_x_sqrt)
+
+    # Undo make batch-op ready.
+    # Complexity: O(nbk^2)
+    shape = array_ops.concat(0, (batch_shape, event_shape, sample_shape))
+    scale_sqrt_inv_x_sqrt = array_ops.reshape(scale_sqrt_inv_x_sqrt, shape)
+    perm = array_ops.concat(0, (math_ops.range(ndims - sample_ndims, ndims),
+                                math_ops.range(0, ndims - sample_ndims)))
+    scale_sqrt_inv_x_sqrt = array_ops.transpose(scale_sqrt_inv_x_sqrt, perm)
+
+    # Write V = SS', X = LL'. Then:
+    # tr[inv(V) X] = tr[inv(S)' inv(S) L L']
+    #              = tr[inv(S) L L' inv(S)']
+    #              = tr[(inv(S) L) (inv(S) L)']
+    #              = sum_{ik} (inv(S) L)_{ik}^2
+    # The second equality follows from the cyclic permutation property.
+    # Complexity: O(nbk^2)
+    trace_scale_inv_x = math_ops.reduce_sum(
+        math_ops.square(scale_sqrt_inv_x_sqrt),
+        reduction_indices=[-2, -1])
+
+    # Complexity: O(nbk)
+    half_log_det_x = math_ops.reduce_sum(
+        math_ops.log(array_ops.batch_matrix_diag_part(x_sqrt)),
+        reduction_indices=[-1])
+
+    # Complexity: O(nbk^2)
+    log_prob = ((self.df - self.dimension - 1.) * half_log_det_x -
+                0.5 * trace_scale_inv_x -
+                self.log_normalizing_constant())
+
+    # Set shape hints.
+    # Try to merge what we know from the input then what we know from the
+    # parameters of this distribution.
+    if x.get_shape().ndims is not None:
+      log_prob.set_shape(x.get_shape()[:-2])
+    if (log_prob.get_shape().ndims is not None and
+        self.get_batch_shape().ndims is not None and
+        self.get_batch_shape().ndims > 0):
+      log_prob.get_shape()[-self.get_batch_shape().ndims:].merge_with(
+          self.get_batch_shape())
+
+    return log_prob
+
+  def _prob(self, x):
+    return math_ops.exp(self._log_prob(x))
+
+  def _entropy(self):
     half_dp1 = 0.5 * self.dimension + 0.5
     half_df = 0.5 * self.df
     return (self.dimension * (half_df + half_dp1 * math.log(2.)) +
@@ -434,110 +339,74 @@ class _WishartOperatorPD(distribution.Distribution):
             self._multi_lgamma(half_df, self.dimension) +
             (half_dp1 - half_df) * self._multi_digamma(half_df, self.dimension))
 
-  def mean(self, name='mean'):
-    """Mean of the distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=list(self.inputs.values())):
-        if self.cholesky_input_output_matrices:
-          return math_ops.sqrt(self.df) * self.scale_operator_pd.sqrt_to_dense()
-        else:
-          return self.df * self.scale_operator_pd.to_dense()
-
-  def mode(self, name='mode'):
-    """Mode of the distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=list(self.inputs.values())):
-        s = self.df - self.dimension - 1.
-        s = math_ops.select(
-            math_ops.less(s, 0.),
-            constant_op.constant(float('NaN'), dtype=self.dtype, name='nan'),
-            s)
-        if self.cholesky_input_output_matrices:
-          return math_ops.sqrt(s) * self.scale_operator_pd.sqrt_to_dense()
-        else:
-          return s * self.scale_operator_pd.to_dense()
-
-  def std(self, name='std'):
-    """Standard deviation of the Wishart distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=list(self.inputs.values())):
-        if self.cholesky_input_output_matrices:
-          raise ValueError(
-              'Computing std. dev. when is cholesky_input_output_matrices=True '
-              'does not make sense.')
-        return linalg_ops.batch_cholesky(self.variance())
-
-  def variance(self, name='variance'):
-    """Variance of the Wishart distribution.
-
-    This function should not be confused with the covariance of the Wishart. The
-    covariance matrix would have shape `q x q` where,
-    `q = dimension * (dimension+1) / 2`
-    and having elements corresponding to some mapping from a lower-triangular
-    matrix to a vector-space.
-
-    This function returns the diagonal of the Covariance matrix but shaped
-    as a `dimension x dimension` matrix.
-
-    Args:
-      name: The name of this op.
-
-    Returns:
-      variance: `Tensor` of dtype `self.dtype`.
-    """
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=list(self.inputs.values())):
-        x = math_ops.sqrt(self.df) * self.scale_operator_pd.to_dense()
-        d = array_ops.expand_dims(array_ops.batch_matrix_diag_part(x), -1)
-        v = math_ops.square(x) + math_ops.batch_matmul(d, d, adj_y=True)
-        if self.cholesky_input_output_matrices:
-          return linalg_ops.batch_cholesky(v)
-        else:
-          return v
+  def _mean(self):
+    if self.cholesky_input_output_matrices:
+      return math_ops.sqrt(self.df) * self.scale_operator_pd.sqrt_to_dense()
+    return self.df * self.scale_operator_pd.to_dense()
+
+  def _variance(self):
+    x = math_ops.sqrt(self.df) * self.scale_operator_pd.to_dense()
+    d = array_ops.expand_dims(array_ops.batch_matrix_diag_part(x), -1)
+    v = math_ops.square(x) + math_ops.batch_matmul(d, d, adj_y=True)
+    if self.cholesky_input_output_matrices:
+      return linalg_ops.batch_cholesky(v)
+    return v
+
+  def _std(self):
+    if self.cholesky_input_output_matrices:
+      raise ValueError(
+          'Computing std. dev. when is cholesky_input_output_matrices=True '
+          'does not make sense.')
+    return linalg_ops.batch_cholesky(self.variance())
+
+  def _mode(self):
+    s = self.df - self.dimension - 1.
+    s = math_ops.select(
+        math_ops.less(s, 0.),
+        constant_op.constant(float('NaN'), dtype=self.dtype, name='nan'),
+        s)
+    if self.cholesky_input_output_matrices:
+      return math_ops.sqrt(s) * self.scale_operator_pd.sqrt_to_dense()
+    return s * self.scale_operator_pd.to_dense()
 
   def mean_log_det(self, name='mean_log_det'):
     """Computes E[log(det(X))] under this Wishart distribution."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=list(self.inputs.values())):
-        return (self._multi_digamma(0.5 * self.df, self.dimension) +
-                self.dimension * math.log(2.) +
-                self.scale_operator_pd.log_det())
+    with self._name_scope(name):
+      return (self._multi_digamma(0.5 * self.df, self.dimension) +
+              self.dimension * math.log(2.) +
+              self.scale_operator_pd.log_det())
 
   def log_normalizing_constant(self, name='log_normalizing_constant'):
     """Computes the log normalizing constant, log(Z)."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=list(self.inputs.values())):
-        return (self.df * self.scale_operator_pd.sqrt_log_det() +
-                0.5 * self.df * self.dimension * math.log(2.) +
-                self._multi_lgamma(0.5 * self.df, self.dimension))
+    with self._name_scope(name):
+      return (self.df * self.scale_operator_pd.sqrt_log_det() +
+              0.5 * self.df * self.dimension * math.log(2.) +
+              self._multi_lgamma(0.5 * self.df, self.dimension))
 
   def _multi_gamma_sequence(self, a, p, name='multi_gamma_sequence'):
     """Creates sequence used in multivariate (di)gamma; shape = shape(a)+[p]."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[a, p]):
-        # Linspace only takes scalars, so we'll add in the offset afterwards.
-        seq = math_ops.linspace(
-            constant_op.constant(0., dtype=self.dtype),
-            0.5 - 0.5 * p,
-            math_ops.cast(p, dtypes.int32))
-        return seq + array_ops.expand_dims(a, [-1])
+    with self._name_scope(name, values=[a, p]):
+      # Linspace only takes scalars, so we'll add in the offset afterwards.
+      seq = math_ops.linspace(
+          constant_op.constant(0., dtype=self.dtype),
+          0.5 - 0.5 * p,
+          math_ops.cast(p, dtypes.int32))
+      return seq + array_ops.expand_dims(a, [-1])
 
   def _multi_lgamma(self, a, p, name='multi_lgamma'):
     """Computes the log multivariate gamma function; log(Gamma_p(a))."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[a, p]):
-        seq = self._multi_gamma_sequence(a, p)
-        return (0.25 * p * (p - 1.) * math.log(math.pi) +
-                math_ops.reduce_sum(math_ops.lgamma(seq),
-                                    reduction_indices=(-1,)))
+    with self._name_scope(name, values=[a, p]):
+      seq = self._multi_gamma_sequence(a, p)
+      return (0.25 * p * (p - 1.) * math.log(math.pi) +
+              math_ops.reduce_sum(math_ops.lgamma(seq),
+                                  reduction_indices=(-1,)))
 
   def _multi_digamma(self, a, p, name='multi_digamma'):
     """Computes the multivariate digamma function; Psi_p(a)."""
-    with ops.name_scope(self.name):
-      with ops.name_scope(name, values=[a, p]):
-        seq = self._multi_gamma_sequence(a, p)
-        return math_ops.reduce_sum(math_ops.digamma(seq),
-                                   reduction_indices=(-1,))
+    with self._name_scope(name, values=[a, p]):
+      seq = self._multi_gamma_sequence(a, p)
+      return math_ops.reduce_sum(math_ops.digamma(seq),
+                                 reduction_indices=(-1,))
 
 
 class WishartCholesky(_WishartOperatorPD):
@@ -607,9 +476,9 @@ class WishartCholesky(_WishartOperatorPD):
                df,
                scale,
                cholesky_input_output_matrices=False,
-               allow_nan_stats=False,
                validate_args=True,
-               name='Wishart'):
+               allow_nan_stats=False,
+               name='WishartCholesky'):
     """Construct Wishart distributions.
 
     Args:
@@ -622,13 +491,13 @@ class WishartCholesky(_WishartOperatorPD):
         Cholesky factored matrix. Example`log_pdf` input takes a Cholesky and
         `sample_n` returns a Cholesky when
         `cholesky_input_output_matrices=True`.
+      validate_args: Whether to validate input with asserts. If `validate_args`
+        is `False`, and the inputs are invalid, correct behavior is not
+        guaranteed.
       allow_nan_stats:  `Boolean`, default `False`. If `False`, raise an
         exception if a statistic (e.g., mean, mode) is undefined for any batch
         member. If True, batch members with valid parameters leading to
         undefined statistics will return `NaN` for this statistic.
-      validate_args: Whether to validate input with asserts. If `validate_args`
-        is `False`, and the inputs are invalid, correct behavior is not
-        guaranteed.
       name: The name scope to give class member ops.
     """
     super(WishartCholesky, self).__init__(
@@ -636,8 +505,8 @@ class WishartCholesky(_WishartOperatorPD):
         scale_operator_pd=operator_pd_cholesky.OperatorPDCholesky(
             scale, verify_pd=validate_args),
         cholesky_input_output_matrices=cholesky_input_output_matrices,
-        allow_nan_stats=allow_nan_stats,
         validate_args=validate_args,
+        allow_nan_stats=allow_nan_stats,
         name=name)
 
 
@@ -704,9 +573,9 @@ class WishartFull(_WishartOperatorPD):
                df,
                scale,
                cholesky_input_output_matrices=False,
-               allow_nan_stats=False,
                validate_args=True,
-               name='Wishart'):
+               allow_nan_stats=False,
+               name='WishartFull'):
     """Construct Wishart distributions.
 
     Args:
@@ -719,13 +588,13 @@ class WishartFull(_WishartOperatorPD):
         Cholesky factored matrix. Example`log_pdf` input takes a Cholesky and
         `sample_n` returns a Cholesky when
         `cholesky_input_output_matrices=True`.
+      validate_args: Whether to validate input with asserts. If `validate_args`
+        is `False`, and the inputs are invalid, correct behavior is not
+        guaranteed.
       allow_nan_stats:  `Boolean`, default `False`. If `False`, raise an
         exception if a statistic (e.g., mean, mode) is undefined for any batch
         member. If True, batch members with valid parameters leading to
         undefined statistics will return `NaN` for this statistic.
-      validate_args: Whether to validate input with asserts. If `validate_args`
-        is `False`, and the inputs are invalid, correct behavior is not
-        guaranteed.
       name: The name scope to give class member ops.
     """
     super(WishartFull, self).__init__(
@@ -733,6 +602,6 @@ class WishartFull(_WishartOperatorPD):
         scale_operator_pd=operator_pd_full.OperatorPDFull(
             scale, verify_pd=validate_args),
         cholesky_input_output_matrices=cholesky_input_output_matrices,
-        allow_nan_stats=allow_nan_stats,
         validate_args=validate_args,
+        allow_nan_stats=allow_nan_stats,
         name=name)
-- 
GitLab


From 79a0bc82a46b85e6bc4cc38a953949091e57d2fc Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 26 Aug 2016 07:36:23 -0800
Subject: [PATCH 196/223] Add support for input weights to SampleInputs op.
 Change: 131404637

---
 .../core/ops/sample_inputs_op.cc              | 96 +++++++++++++++----
 .../core/ops/training_ops_test.cc             | 10 +-
 .../kernel_tests/sample_inputs_op_test.py     | 69 +++++++++----
 .../tensor_forest/python/ops/training_ops.py  |  2 +-
 .../tensor_forest/python/tensor_forest.py     |  9 +-
 5 files changed, 137 insertions(+), 49 deletions(-)

diff --git a/tensorflow/contrib/tensor_forest/core/ops/sample_inputs_op.cc b/tensorflow/contrib/tensor_forest/core/ops/sample_inputs_op.cc
index 06ddb795ce..565353bcf3 100644
--- a/tensorflow/contrib/tensor_forest/core/ops/sample_inputs_op.cc
+++ b/tensorflow/contrib/tensor_forest/core/ops/sample_inputs_op.cc
@@ -23,6 +23,7 @@
 #include "tensorflow/core/framework/op_kernel.h"
 #include "tensorflow/core/framework/shape_inference.h"
 #include "tensorflow/core/kernels/bounds_check.h"
+#include "tensorflow/core/lib/random/distribution_sampler.h"
 #include "tensorflow/core/lib/random/philox_random.h"
 #include "tensorflow/core/lib/random/simple_philox.h"
 #include "tensorflow/core/platform/logging.h"
@@ -43,6 +44,7 @@ REGISTER_OP("SampleInputs")
     .Input("sparse_input_indices: int64")
     .Input("sparse_input_values: float")
     .Input("sparse_input_shape: int64")
+    .Input("input_weights: float")
     .Input("node_to_accumulator: int32")
     .Input("leaves: int32")
     .Input("candidate_split_features: int32")
@@ -53,7 +55,7 @@ REGISTER_OP("SampleInputs")
     .SetShapeFn([](InferenceContext* c) {
       ShapeHandle candidate_split_features;
       TF_RETURN_IF_ERROR(
-          c->WithRank(c->input(6), 2, &candidate_split_features));
+          c->WithRank(c->input(7), 2, &candidate_split_features));
       DimensionHandle split_dim = c->Dim(candidate_split_features, 1);
       c->set_output(0, c->Vector(InferenceContext::kUnknownDim));
       c->set_output(1, c->Matrix(InferenceContext::kUnknownDim, split_dim));
@@ -81,6 +83,12 @@ input_data: The features for the current batch of training data.
 sparse_input_indices: The indices tensor from the SparseTensor input.
 sparse_input_values: The values tensor from the SparseTensor input.
 sparse_input_shape: The shape tensor from the SparseTensor input.
+input_weights: For a dense input, input_weights[i] is the weight associated
+  with input_data[i].  For sparse input, input_weights[i] is the weight
+  associated with sparse_input_values[i].  Or in either case, if all the
+  weights are 1, input_weights can be empty.  SampleInputs will reject inputs
+  with weight less than Uniform([0,1)), so weights outside of that range may
+  not be what you want.
 node_to_accumulator: For a fertile node i, node_to_accumulator[i] is the
   associated accumulator slot.  For non-fertile nodes, it is -1.
 leaves: `leaves[i]` is the leaf that the i-th input landed in, as
@@ -166,18 +174,33 @@ class SampleInputs : public OpKernel {
     return false;
   }
 
+  // increment_input implements a "++" operation for the situation when
+  // you want to do something n times on an underlying iterator.
+  // In an ideal world, this would be a built-in iterator adaptor.
+  template <typename T>
+  static void increment_input(const int n, T* it, int* count) {
+    *count += 1;
+    if (*count == n) {
+      *count = 0;
+      (*it)++;
+    }
+  }
+
   void Compute(OpKernelContext* context) override {
     const Tensor& input_data = context->input(0);
     const Tensor& sparse_input_indices = context->input(1);
     const Tensor& sparse_input_values = context->input(2);
     const Tensor& sparse_input_shape = context->input(3);
-    const Tensor& node_to_accumulator = context->input(4);
-    const Tensor& leaves = context->input(5);
-    const Tensor& split_features = context->input(6);
-    const Tensor& split_thresholds = context->input(7);
+    const Tensor& input_weights = context->input(4);
+    const Tensor& node_to_accumulator = context->input(5);
+    const Tensor& leaves = context->input(6);
+    const Tensor& split_features = context->input(7);
+    const Tensor& split_thresholds = context->input(8);
 
     bool sparse_input = (sparse_input_indices.shape().dims() == 2);
 
+    bool have_weights = (input_weights.shape().dim_size(0) > 0);
+
     if (sparse_input) {
       OP_REQUIRES(context, sparse_input_shape.shape().dims() == 1,
                   errors::InvalidArgument(
@@ -198,10 +221,24 @@ class SampleInputs : public OpKernel {
                   errors::InvalidArgument(
                       "sparse_input_indices and sparse_input_values should "
                       "agree on the number of non-zero values"));
+      if (have_weights) {
+        OP_REQUIRES(context, sparse_input_values.shape().dim_size(0) ==
+                                 input_weights.shape().dim_size(0),
+                    errors::InvalidArgument(
+                        "sparse_input_values and input_weights should agree "
+                        "on the number of inputs"));
+      }
     } else {
       OP_REQUIRES(context, input_data.shape().dims() == 2,
                   errors::InvalidArgument(
                   "input_data should be two-dimensional"));
+      if (have_weights) {
+        OP_REQUIRES(context, input_data.shape().dim_size(0) ==
+                                 input_weights.shape().dim_size(0),
+                    errors::InvalidArgument(
+                        "input_data and input_weights should agree on the "
+                        "number of inputs"));
+      }
     }
 
     OP_REQUIRES(context, node_to_accumulator.shape().dims() == 1,
@@ -228,6 +265,7 @@ class SampleInputs : public OpKernel {
     if (!CheckTensorBounds(context, sparse_input_indices)) return;
     if (!CheckTensorBounds(context, sparse_input_values)) return;
     if (!CheckTensorBounds(context, sparse_input_shape)) return;
+    if (!CheckTensorBounds(context, input_weights)) return;
     if (!CheckTensorBounds(context, node_to_accumulator)) return;
     if (!CheckTensorBounds(context, leaves)) return;
     if (!CheckTensorBounds(context, split_features)) return;
@@ -260,6 +298,7 @@ class SampleInputs : public OpKernel {
     const auto node_map = node_to_accumulator.unaligned_flat<int32>();
     const auto features = split_features.tensor<int32, 2>();
     const auto thresholds = split_thresholds.tensor<float, 2>();
+    const auto weights = input_weights.tensor<float, 1>();
 
     const int32 num_data = static_cast<int32>(leaves.shape().dim_size(0));
     const int32 num_splits = static_cast<int32>(
@@ -338,24 +377,41 @@ class SampleInputs : public OpKernel {
             thresholds(accumulator, split);
       }
 
-      for (const int32 i : inputs_for_accumulator) {
-        VLOG(2) << "Looking at data # " << i;
-
-        int32 num_inits = split_initializations_per_input_;
-        for (int split = 0; split < num_splits && num_inits > 0; split++) {
-          if (new_split_feature_rows_flat(output_slot, split) < 0) {
-            VLOG(1) << "Over-writing @ " << output_slot << "," << split;
-            int32 index;
-            float val;
-            const bool success = get_random_feature(i, &index, &val);
-            if (success) {
-              new_split_feature_rows_flat(output_slot, split) = index;
-              new_split_threshold_rows_flat(output_slot, split) = val;
-              --num_inits;
-            } else {
+      auto it = inputs_for_accumulator.begin();
+      int input_used_count = 0;
+      for (int split = 0;
+           split < num_splits && it != inputs_for_accumulator.end(); split++) {
+        if (new_split_feature_rows_flat(output_slot, split) < 0) {
+          if (have_weights) {
+            // If we have weights, we probabilistically reject inputs with
+            // low weight.  Which means we might have to look at a bunch of
+            // inputs -- maybe even all of them -- to fill this slot.
+            while (it != inputs_for_accumulator.end()) {
+              float w = weights(*it);
+              if (rng_->RandFloat() <= w) {
+                break;
+              }
+              increment_input(split_initializations_per_input_, &it,
+                              &input_used_count);
+            }
+            if (it == inputs_for_accumulator.end()) {
               break;
             }
           }
+          int32 index;
+          float val;
+          const bool success = get_random_feature(*it, &index, &val);
+          increment_input(split_initializations_per_input_, &it,
+                          &input_used_count);
+          if (success) {
+            VLOG(1) << "Over-writing @ " << output_slot << "," << split;
+            new_split_feature_rows_flat(output_slot, split) = index;
+            new_split_threshold_rows_flat(output_slot, split) = val;
+          } else {
+            VLOG(1) << "get_random_feature failed, bailing on output for "
+                    << "accumulator " << accumulator;
+            break;
+          }
         }
       }
       ++output_slot;
diff --git a/tensorflow/contrib/tensor_forest/core/ops/training_ops_test.cc b/tensorflow/contrib/tensor_forest/core/ops/training_ops_test.cc
index ba9a3a18c2..cbb899a3e9 100644
--- a/tensorflow/contrib/tensor_forest/core/ops/training_ops_test.cc
+++ b/tensorflow/contrib/tensor_forest/core/ops/training_ops_test.cc
@@ -55,12 +55,12 @@ TEST(TrainingOpsTest, BestSplits_ShapeFn) {
 TEST(TrainingOpsTest, SampleInputs_ShapeFn) {
   ShapeInferenceTestOp op("SampleInputs");
 
-  // input[6].dim(1) determines dims in the output.
-  INFER_OK(op, "?;?;?;?;?;?;?;?", "[?];[?,?];[?,?]");
-  INFER_OK(op, "?;?;?;?;?;?;[?,?];?", "[?];[?,d6_1];[?,d6_1]");
-  INFER_OK(op, "?;?;?;?;?;?;[1,2];?", "[?];[?,d6_1];[?,d6_1]");
+  // input[7].dim(1) determines dims in the output.
+  INFER_OK(op, "?;?;?;?;?;?;?;?;?", "[?];[?,?];[?,?]");
+  INFER_OK(op, "?;?;?;?;?;?;?;[?,?];?", "[?];[?,d7_1];[?,d7_1]");
+  INFER_OK(op, "?;?;?;?;?;?;?;[1,2];?", "[?];[?,d7_1];[?,d7_1]");
   INFER_ERROR("Shape must be rank 2 but is rank 3", op,
-              "?;?;?;?;?;?;[1,2,3];?");
+              "?;?;?;?;?;?;?;[1,2,3];?");
 }
 
 TEST(TrainingOpsTest, CountExtremelyRandomStats_ShapeFn) {
diff --git a/tensorflow/contrib/tensor_forest/python/kernel_tests/sample_inputs_op_test.py b/tensorflow/contrib/tensor_forest/python/kernel_tests/sample_inputs_op_test.py
index 9830651a5d..58f3e898ae 100644
--- a/tensorflow/contrib/tensor_forest/python/kernel_tests/sample_inputs_op_test.py
+++ b/tensorflow/contrib/tensor_forest/python/kernel_tests/sample_inputs_op_test.py
@@ -39,12 +39,14 @@ class SampleInputsTest(test_util.TensorFlowTestCase):
   def testSimple(self):
     with self.test_session():
       tf.initialize_all_variables().run()
-      indices, feature_updates, threshold_updates = (
-          self.ops.sample_inputs(
-              self.input_data, [], [], [],
-              self.node_map, self.leaves, self.split_features,
-              self.split_thresholds, split_initializations_per_input=1,
-              split_sampling_random_seed=3))
+      indices, feature_updates, threshold_updates = (self.ops.sample_inputs(
+          self.input_data, [], [], [], [],
+          self.node_map,
+          self.leaves,
+          self.split_features,
+          self.split_thresholds,
+          split_initializations_per_input=1,
+          split_sampling_random_seed=3))
       self.assertAllEqual([1, 0], indices.eval())
       self.assertAllEqual([[1, 0, 1], [0, 0, -1]],
                           feature_updates.eval())
@@ -64,27 +66,49 @@ class SampleInputsTest(test_util.TensorFlowTestCase):
 
     with self.test_session():
       tf.initialize_all_variables().run()
-      indices, feature_updates, threshold_updates = (
-          self.ops.sample_inputs(
-              [], sparse_indices, sparse_values, sparse_shape,
-              self.node_map, self.leaves, self.split_features,
-              self.split_thresholds, split_initializations_per_input=1,
-              split_sampling_random_seed=3))
+      indices, feature_updates, threshold_updates = (self.ops.sample_inputs(
+          [],
+          sparse_indices,
+          sparse_values,
+          sparse_shape, [],
+          self.node_map,
+          self.leaves,
+          self.split_features,
+          self.split_thresholds,
+          split_initializations_per_input=1,
+          split_sampling_random_seed=3))
       self.assertAllEqual([1, 0], indices.eval())
       self.assertAllEqual([[1, 0, 0], [4, 7, -1]],
                           feature_updates.eval())
       self.assertAllEqual([[5., -2., -2.], [-1., 6., 0.]],
                           threshold_updates.eval())
 
+  def testWeights(self):
+    with self.test_session():
+      tf.initialize_all_variables().run()
+      indices, feature_updates, threshold_updates = (self.ops.sample_inputs(
+          self.input_data, [], [], [], [0.5, 0.1, 0.8, 0.7],
+          self.node_map,
+          self.leaves,
+          self.split_features,
+          self.split_thresholds,
+          split_initializations_per_input=1,
+          split_sampling_random_seed=3))
+      self.assertAllEqual([1, 0], indices.eval())
+      self.assertAllEqual([[1, 0, 0], [-1, -1, -1]], feature_updates.eval())
+      self.assertAllEqual([[5., -2., 20.], [0., 0., 0.]],
+                          threshold_updates.eval())
+
   def testNoAccumulators(self):
     with self.test_session():
       tf.initialize_all_variables().run()
-      indices, feature_updates, threshold_updates = (
-          self.ops.sample_inputs(
-              self.input_data, [], [], [],
-              [-1] * 3, self.leaves, self.split_features,
-              self.split_thresholds, split_initializations_per_input=1,
-              split_sampling_random_seed=3))
+      indices, feature_updates, threshold_updates = (self.ops.sample_inputs(
+          self.input_data, [], [], [], [], [-1] * 3,
+          self.leaves,
+          self.split_features,
+          self.split_thresholds,
+          split_initializations_per_input=1,
+          split_sampling_random_seed=3))
       self.assertAllEqual([], indices.eval())
       self.assertAllEqual((0, 3), feature_updates.eval().shape)
       self.assertAllEqual((0, 3), threshold_updates.eval().shape)
@@ -96,9 +120,12 @@ class SampleInputsTest(test_util.TensorFlowTestCase):
       with self.assertRaisesOpError(
           'split_features and split_thresholds should be the same shape.'):
         indices, _, _ = self.ops.sample_inputs(
-            self.input_data, [], [], [],
-            self.node_map, self.leaves, self.split_features,
-            self.split_thresholds, split_initializations_per_input=1,
+            self.input_data, [], [], [], [],
+            self.node_map,
+            self.leaves,
+            self.split_features,
+            self.split_thresholds,
+            split_initializations_per_input=1,
             split_sampling_random_seed=3)
         self.assertAllEqual([], indices.eval())
 
diff --git a/tensorflow/contrib/tensor_forest/python/ops/training_ops.py b/tensorflow/contrib/tensor_forest/python/ops/training_ops.py
index 3994571074..b56ca58e07 100644
--- a/tensorflow/contrib/tensor_forest/python/ops/training_ops.py
+++ b/tensorflow/contrib/tensor_forest/python/ops/training_ops.py
@@ -69,7 +69,7 @@ def _CountExtremelyRandomStatsShape(op):
 @ops.RegisterShape('SampleInputs')
 def _SampleInputsShape(op):
   """Shape function for SampleInputs Op."""
-  num_splits = op.inputs[6].get_shape()[1].value
+  num_splits = op.inputs[7].get_shape()[1].value
   return [[None], [None, num_splits], [None, num_splits]]
 
 
diff --git a/tensorflow/contrib/tensor_forest/python/tensor_forest.py b/tensorflow/contrib/tensor_forest/python/tensor_forest.py
index 3e7d13d72d..c28bd4ff41 100644
--- a/tensorflow/contrib/tensor_forest/python/tensor_forest.py
+++ b/tensorflow/contrib/tensor_forest/python/tensor_forest.py
@@ -610,9 +610,14 @@ class RandomTreeGraphs(object):
     # Sample inputs.
     update_indices, feature_updates, threshold_updates = (
         self.training_ops.sample_inputs(
-            input_data, sparse_indices, sparse_values, sparse_shape,
+            input_data,
+            sparse_indices,
+            sparse_values,
+            sparse_shape,
+            input_weights,
             self.variables.node_to_accumulator_map,
-            input_leaves, self.variables.candidate_split_features,
+            input_leaves,
+            self.variables.candidate_split_features,
             self.variables.candidate_split_thresholds,
             split_initializations_per_input=(
                 self.params.split_initializations_per_input),
-- 
GitLab


From 397afd01201fce52a60da234b1e12bd7facaa4f4 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 26 Aug 2016 08:19:01 -0800
Subject: [PATCH 197/223] Update generated Python Op docs. Change: 131408174

---
 .../contrib.bayesflow.stochastic_graph.md     |    8 +-
 .../api_docs/python/contrib.distributions.md  | 6025 ++++++++++++-----
 ...low.stochastic_graph.DistributionTensor.md |    8 +-
 .../tf.contrib.distributions.Bernoulli.md     |  253 +-
 .../tf.contrib.distributions.Dirichlet.md     |  239 +-
 .../tf.contrib.distributions.Distribution.md  |  269 +-
 ...istributions.MultivariateNormalCholesky.md |  247 +-
 ...ib.distributions.MultivariateNormalDiag.md |  247 +-
 .../tf.contrib.distributions.StudentT.md      |  254 +-
 ...b.distributions.TransformedDistribution.md |  215 +-
 .../tf.contrib.distributions.Categorical.md   |  232 +-
 .../shard2/tf.contrib.distributions.Chi2.md   |  228 +-
 .../tf.contrib.distributions.Uniform.md       |  231 +-
 ...f.contrib.distributions.WishartCholesky.md |  270 +-
 .../tf.contrib.distributions.Binomial.md      |  237 +-
 ...trib.distributions.DirichletMultinomial.md |  265 +-
 .../tf.contrib.distributions.Exponential.md   |  226 +-
 .../shard3/tf.contrib.distributions.Gamma.md  |  228 +-
 .../tf.contrib.distributions.InverseGamma.md  |  240 +-
 .../tf.contrib.distributions.Multinomial.md   |  229 +-
 ...ibutions.MultivariateNormalDiagPlusVDVT.md |  247 +-
 .../shard6/tf.contrib.distributions.Beta.md   |  241 +-
 .../tf.contrib.distributions.Laplace.md       |  215 +-
 ....contrib.distributions.BaseDistribution.md |   41 +-
 ...ib.distributions.MultivariateNormalFull.md |  247 +-
 .../shard7/tf.contrib.distributions.Normal.md |  212 +-
 .../tf.contrib.distributions.Poisson.md       |  254 +-
 .../tf.contrib.distributions.WishartFull.md   |  270 +-
 28 files changed, 8478 insertions(+), 3400 deletions(-)

diff --git a/tensorflow/g3doc/api_docs/python/contrib.bayesflow.stochastic_graph.md b/tensorflow/g3doc/api_docs/python/contrib.bayesflow.stochastic_graph.md
index 31b4c876ee..903b86ab30 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.bayesflow.stochastic_graph.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.bayesflow.stochastic_graph.md
@@ -109,7 +109,7 @@ reparameterized distributions; it will also return None if the value type is
 ##### Args:
 
 
-*  <b>`dist_cls`</b>: a class deriving from `BaseDistribution`.
+*  <b>`dist_cls`</b>: a `Distribution` class.
 *  <b>`name`</b>: a name for this `DistributionTensor` and its ops.
 *  <b>`dist_value_type`</b>: a `_StochasticValueType`, which will determine what the
       `value` of this `DistributionTensor` will be. If not provided, the
@@ -123,6 +123,12 @@ reparameterized distributions; it will also return None if the value type is
 *  <b>`**dist_args`</b>: keyword arguments to be passed through to `dist_cls` on
       construction.
 
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `dist_cls` is not a `Distribution`.
+*  <b>`TypeError`</b>: if `loss_fn` is not `callable`.
+
 
 - - -
 
diff --git a/tensorflow/g3doc/api_docs/python/contrib.distributions.md b/tensorflow/g3doc/api_docs/python/contrib.distributions.md
index 148058d014..25a5a53141 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.distributions.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.distributions.md
@@ -16,22 +16,25 @@ initialized with parameters that define the distributions.
 
 ### `class tf.contrib.distributions.Distribution` {#Distribution}
 
-Fully-featured abstract base class for probability distributions.
+A generic probability distribution base class.
 
-This class defines the API for probability distributions. Users will only ever
-instantiate subclasses of `Distribution`.
+`Distribution` is a base class for constructing and organizing properties
+(e.g., mean, variance) of random variables (e.g, Bernoulli, Gaussian).
 
-### API
+### Subclassing
 
-The key methods for probability distributions are defined here.
+Subclasess are expected to implement a leading-underscore version of the
+same-named function.  The argument signature should be identical except for
+the omission of `name="..."`.  For example, to enable `log_prob(value,
+name="log_prob")` a subclass should implement `_log_prob(value)`.
 
-To keep ops generated by the distribution tied together by name, subclasses
-should override `name` and use it to prepend names of ops in other methods
-(see `cdf` for an example).
+Subclasses can rewrite/append to public-level docstrings. For example,
 
-Subclasses that wish to support `cdf` and `log_cdf` can override `log_cdf`
-and use the base class's implementation for `cdf`, or vice versa. The same
-goes for `log_prob` and `prob`.
+```python
+Subclass.prob.__func__.__doc__ += "Some other details."
+```
+
+would add the string "Some other details." to the `prob` function docstring.
 
 ### Broadcasting, batching, and shapes
 
@@ -120,16 +123,57 @@ dist.mean().eval()
 ```
 - - -
 
+#### `tf.contrib.distributions.Distribution.__init__(dtype=None, parameters=None, is_continuous=True, is_reparameterized=False, validate_args=True, allow_nan_stats=False, name=None)` {#Distribution.__init__}
+
+Constructs the `Distribution`.
+
+##### Args:
+
+
+*  <b>`dtype`</b>: The type of the event samples. `None` implies no type-enforcement.
+*  <b>`parameters`</b>: Python dictionary of parameters used by this `Distribution`.
+*  <b>`is_continuous`</b>: Python boolean, default `True`. If `True` this
+    `Distribution` is continuous over its supported domain.
+*  <b>`is_reparameterized`</b>: Python boolean, default `False`. If `True` this
+    `Distribution` can be reparameterized in terms of some standard
+    distribution with a function whose Jacobian is constant for the support
+    of the standard distribution.
+*  <b>`validate_args`</b>: Whether to validate input with asserts. If `validate_args`
+    is `False`, and the inputs are invalid, correct behavior is not
+    guaranteed.
+*  <b>`allow_nan_stats`</b>: Python boolean, default `False`. If `False`, raise an
+    exception if a statistic (e.g., mean, mode) is undefined for any batch
+    member. If True, batch members with valid parameters leading to
+    undefined statistics will return `NaN` for this statistic.
+*  <b>`name`</b>: A name for this distribution (optional).
+
+
+- - -
+
 #### `tf.contrib.distributions.Distribution.allow_nan_stats` {#Distribution.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.batch_shape(name='batch_shape')` {#Distribution.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -141,7 +185,8 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -150,26 +195,38 @@ independent distributions of this kind the instance represents.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.dtype` {#Distribution.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.entropy(name='entropy')` {#Distribution.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.event_shape(name='event_shape')` {#Distribution.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -178,7 +235,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -198,8 +256,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -242,19 +300,29 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Distribution.get_batch_shape()` {#Distribution.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
+
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.get_event_shape()` {#Distribution.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
+
 
 - - -
 
@@ -274,49 +342,107 @@ Same meaning as `event_shape`. May be only partially defined.
 
 #### `tf.contrib.distributions.Distribution.log_cdf(value, name='log_cdf')` {#Distribution.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.log_pdf(value, name='log_pdf')` {#Distribution.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.log_pmf(value, name='log_pmf')` {#Distribution.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.log_prob(value, name='log_prob')` {#Distribution.log_prob}
 
-Log of the probability density/mass function.
+Log probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.mean(name='mean')` {#Distribution.mean}
 
-Mean of the distribution.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.mode(name='mode')` {#Distribution.mode}
 
-Mode of the distribution.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.name` {#Distribution.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -361,48 +487,100 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Distribution.parameters` {#Distribution.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Distribution.pdf(value, name='pdf')` {#Distribution.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.pmf(value, name='pmf')` {#Distribution.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.prob(value, name='prob')` {#Distribution.prob}
 
-Probability density/mass function.
+Probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.sample(sample_shape=(), seed=None, name='sample')` {#Distribution.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
@@ -414,36 +592,41 @@ Generate `n` samples.
 ##### Args:
 
 
-*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.std(name='std')` {#Distribution.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.validate_args` {#Distribution.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.variance(name='variance')` {#Distribution.variance}
 
-Variance of the distribution.
+Variance.
 
 
 
@@ -550,14 +733,28 @@ dist = Binomial(n=[4., 5], p=[.1, .3])
 
 #### `tf.contrib.distributions.Binomial.allow_nan_stats` {#Binomial.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.batch_shape(name='batch_shape')` {#Binomial.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -569,7 +766,8 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -578,26 +776,38 @@ independent distributions of this kind the instance represents.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.dtype` {#Binomial.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.entropy(name='entropy')` {#Binomial.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.event_shape(name='event_shape')` {#Binomial.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -606,7 +816,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -626,8 +837,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -670,26 +881,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Binomial.get_batch_shape()` {#Binomial.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  batch shape
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.get_event_shape()` {#Binomial.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  event shape
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -710,46 +923,86 @@ Same meaning as `event_shape`. May be only partially defined.
 
 #### `tf.contrib.distributions.Binomial.log_cdf(value, name='log_cdf')` {#Binomial.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.log_pdf(value, name='log_pdf')` {#Binomial.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.log_pmf(value, name='log_pmf')` {#Binomial.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
-- - -
 
-#### `tf.contrib.distributions.Binomial.log_prob(counts, name='log_prob')` {#Binomial.log_prob}
+- - -
 
-`Log(P[counts])`, computed for every batch member.
+#### `tf.contrib.distributions.Binomial.log_prob(value, name='log_prob')` {#Binomial.log_prob}
 
-For each batch member of counts `k`, `P[counts]` is the probability that
-after sampling `n` draws from this Binomial distribution, the number of
-successes is `k`.  Note that different sequences of draws can result in the
-same counts, thus the probability includes a combinatorial coefficient.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`counts`</b>: Non-negative tensor with dtype `dtype` and whose shape can be
-    broadcast with `self.p` and `self.n`. `counts` is only legal if it is
-    less than or equal to `n` and its components are equal to integer
-    values.
-*  <b>`name`</b>: Name to give this Op, defaults to "log_prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Log probabilities for each record, shape `[N1,...,Nm]`.
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -763,27 +1016,14 @@ Log-odds.
 
 #### `tf.contrib.distributions.Binomial.mean(name='mean')` {#Binomial.mean}
 
-Mean of the distribution.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.mode(name='mode')` {#Binomial.mode}
 
-Mode of the distribution.
-
-Note that when `(n + 1) * p` is an integer, there are actually two modes.
-Namely, `(n + 1) * p` and `(n + 1) * p - 1` are both modes. Here we return
-only the larger of the two modes.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name for this op.
-
-##### Returns:
-
-  The mode of the Binomial distribution.
+Mode.
 
 
 - - -
@@ -797,7 +1037,7 @@ Number of trials.
 
 #### `tf.contrib.distributions.Binomial.name` {#Binomial.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -851,106 +1091,144 @@ param_shapes with static (i.e. TensorShape) shapes.
 
 - - -
 
-#### `tf.contrib.distributions.Binomial.pdf(value, name='pdf')` {#Binomial.pdf}
+#### `tf.contrib.distributions.Binomial.parameters` {#Binomial.parameters}
 
-The probability density function.
+Dictionary of parameters used by this `Distribution`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Binomial.pmf(value, name='pmf')` {#Binomial.pmf}
-
-The probability mass function.
-
+#### `tf.contrib.distributions.Binomial.pdf(value, name='pdf')` {#Binomial.pdf}
 
-- - -
+Probability density function.
 
-#### `tf.contrib.distributions.Binomial.prob(counts, name='prob')` {#Binomial.prob}
+##### Args:
 
-`P[counts]`, computed for every batch member.
 
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-For each batch member of counts `k`, `P[counts]` is the probability that
-after sampling `n` draws from this Binomial distribution, the number of
-successes is `k`.  Note that different sequences of draws can result in the
-same counts, thus the probability includes a combinatorial coefficient.
+##### Returns:
 
-##### Args:
 
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-*  <b>`counts`</b>: Non-negative tensor with dtype `dtype` and whose shape can be
-    broadcast with `self.p` and `self.n`. `counts` is only legal if it is
-    less than or equal to `n` and its components are equal to integer
-    values.
-*  <b>`name`</b>: Name to give this Op, defaults to "prob".
+##### Raises:
 
-##### Returns:
 
-  Probabilities for each record, shape `[N1,...,Nm]`.
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Binomial.sample(sample_shape=(), seed=None, name='sample')` {#Binomial.sample}
-
-Generate samples of the specified shape for each batched distribution.
+#### `tf.contrib.distributions.Binomial.pmf(value, name='pmf')` {#Binomial.pmf}
 
-Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+Probability mass function.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
-*  <b>`seed`</b>: Python integer seed for RNG
-*  <b>`name`</b>: name to give to the op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Binomial.sample_n(n, seed=None, name='sample_n')` {#Binomial.sample_n}
+#### `tf.contrib.distributions.Binomial.prob(value, name='prob')` {#Binomial.prob}
 
-Generate `n` samples.
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
-*  <b>`seed`</b>: Python integer seed for RNG
-*  <b>`name`</b>: name to give to the op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Binomial.std(name='std')` {#Binomial.std}
+#### `tf.contrib.distributions.Binomial.sample(sample_shape=(), seed=None, name='sample')` {#Binomial.sample}
+
+Generate samples of the specified shape.
+
+Note that a call to `sample()` without arguments will generate a single
+sample.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Binomial.sample_n(n, seed=None, name='sample_n')` {#Binomial.sample_n}
+
+Generate `n` samples.
+
+##### Args:
+
+
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
-Standard deviation of the distribution.
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
+
+
+- - -
+
+#### `tf.contrib.distributions.Binomial.std(name='std')` {#Binomial.std}
+
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.validate_args` {#Binomial.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.variance(name='variance')` {#Binomial.variance}
 
-Variance of the distribution.
+Variance.
 
 
 
@@ -997,14 +1275,41 @@ Construct Bernoulli distributions.
 
 #### `tf.contrib.distributions.Bernoulli.allow_nan_stats` {#Bernoulli.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.batch_shape(name='batch_shape')` {#Bernoulli.batch_shape}
 
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
 
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -1013,36 +1318,48 @@ Boolean describing behavior when a stat is undefined for batch member.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.dtype` {#Bernoulli.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.entropy(name='entropy')` {#Bernoulli.entropy}
 
-Entropy of the distribution.
-
-##### Args:
+Shanon entropy in nats.
 
 
-*  <b>`name`</b>: Name for the op.
+- - -
 
-##### Returns:
+#### `tf.contrib.distributions.Bernoulli.event_shape(name='event_shape')` {#Bernoulli.event_shape}
 
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
-*  <b>`entropy`</b>: `Tensor` of the same type and shape as `p`.
+##### Args:
 
 
-- - -
+*  <b>`name`</b>: name to give to the op
 
-#### `tf.contrib.distributions.Bernoulli.event_shape(name='event_shape')` {#Bernoulli.event_shape}
+##### Returns:
 
 
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -1062,8 +1379,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -1106,15 +1423,29 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Bernoulli.get_batch_shape()` {#Bernoulli.get_batch_shape}
 
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
 
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.get_event_shape()` {#Bernoulli.get_event_shape}
 
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
 
 
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
+
 
 - - -
 
@@ -1134,88 +1465,114 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Bernoulli.log_cdf(value, name='log_cdf')` {#Bernoulli.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.log_pdf(value, name='log_pdf')` {#Bernoulli.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
 
-#### `tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf')` {#Bernoulli.log_pmf}
 
-Log of the probability mass function.
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Bernoulli.log_prob(event, name='log_prob')` {#Bernoulli.log_prob}
+#### `tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf')` {#Bernoulli.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`event`</b>: `int32` or `int64` binary Tensor.
-*  <b>`name`</b>: A name for this operation (optional).
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The log-probabilities of the events.
-
 
-- - -
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-#### `tf.contrib.distributions.Bernoulli.logits` {#Bernoulli.logits}
+##### Raises:
 
 
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Bernoulli.mean(name='mean')` {#Bernoulli.mean}
+#### `tf.contrib.distributions.Bernoulli.log_prob(value, name='log_prob')` {#Bernoulli.log_prob}
 
-Mean of the distribution.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`name`</b>: Name for the op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`mean`</b>: `Tensor` of the same type and shape as `p`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Bernoulli.mode(name='mode')` {#Bernoulli.mode}
+#### `tf.contrib.distributions.Bernoulli.logits` {#Bernoulli.logits}
 
-Mode of the distribution.
 
-1 if p > 1-p. 0 otherwise.
 
-##### Args:
 
+- - -
+
+#### `tf.contrib.distributions.Bernoulli.mean(name='mean')` {#Bernoulli.mean}
+
+Mean.
 
-*  <b>`name`</b>: Name for the op.
 
-##### Returns:
+- - -
 
+#### `tf.contrib.distributions.Bernoulli.mode(name='mode')` {#Bernoulli.mode}
 
-*  <b>`mode`</b>: binary `Tensor` of type self.dtype.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.name` {#Bernoulli.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -1267,35 +1624,78 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Bernoulli.parameters` {#Bernoulli.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.pdf(value, name='pdf')` {#Bernoulli.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.pmf(value, name='pmf')` {#Bernoulli.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Bernoulli.prob(event, name='prob')` {#Bernoulli.prob}
+#### `tf.contrib.distributions.Bernoulli.prob(value, name='prob')` {#Bernoulli.prob}
 
-Probability mass function.
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`event`</b>: `int32` or `int64` binary Tensor; must be broadcastable with `p`.
-*  <b>`name`</b>: A name for this operation.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The probabilities of the events.
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -1309,23 +1709,22 @@ Probability mass function.
 
 #### `tf.contrib.distributions.Bernoulli.sample(sample_shape=(), seed=None, name='sample')` {#Bernoulli.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
@@ -1339,55 +1738,39 @@ Generate `n` samples.
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer seed for RNG.
+*  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape` with values of type
-      `self.dtype`.
-
-
-- - -
-
-#### `tf.contrib.distributions.Bernoulli.std(name='std')` {#Bernoulli.std}
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
 
-Standard deviation of the distribution.
+##### Raises:
 
-##### Args:
 
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
-*  <b>`name`</b>: Name for the op.
 
-##### Returns:
+- - -
 
+#### `tf.contrib.distributions.Bernoulli.std(name='std')` {#Bernoulli.std}
 
-*  <b>`std`</b>: `Tensor` of the same type and shape as `p`.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.validate_args` {#Bernoulli.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.variance(name='variance')` {#Bernoulli.variance}
 
-Variance of the distribution.
-
-##### Args:
-
-
-*  <b>`name`</b>: Name for the op.
-
-##### Returns:
-
-
-*  <b>`variance`</b>: `Tensor` of the same type and shape as `p`.
+Variance.
 
 
 
@@ -1499,11 +1882,32 @@ dist = Beta([1.0, 2.0], [4.0, 5.0])
 Shape parameter.
 
 
+- - -
+
+#### `tf.contrib.distributions.Beta.a_b_sum` {#Beta.a_b_sum}
+
+Sum of parameters.
+
+
 - - -
 
 #### `tf.contrib.distributions.Beta.allow_nan_stats` {#Beta.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -1517,7 +1921,7 @@ Shape parameter.
 
 #### `tf.contrib.distributions.Beta.batch_shape(name='batch_shape')` {#Beta.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -1529,35 +1933,48 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Beta.cdf(x, name='cdf')` {#Beta.cdf}
+#### `tf.contrib.distributions.Beta.cdf(value, name='cdf')` {#Beta.cdf}
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.Beta.dtype` {#Beta.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.entropy(name='entropy')` {#Beta.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.event_shape(name='event_shape')` {#Beta.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -1566,7 +1983,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -1586,8 +2004,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -1630,26 +2048,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Beta.get_batch_shape()` {#Beta.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  batch shape
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.get_event_shape()` {#Beta.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  event shape
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -1668,94 +2088,124 @@ Same meaning as `event_shape`. May be only partially defined.
 
 - - -
 
-#### `tf.contrib.distributions.Beta.log_cdf(x, name='log_cdf')` {#Beta.log_cdf}
+#### `tf.contrib.distributions.Beta.log_cdf(value, name='log_cdf')` {#Beta.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
 
+##### Args:
 
-- - -
-
-#### `tf.contrib.distributions.Beta.log_pdf(value, name='log_pdf')` {#Beta.log_pdf}
-
-Log of the probability density function.
 
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-- - -
+##### Returns:
 
-#### `tf.contrib.distributions.Beta.log_pmf(value, name='log_pmf')` {#Beta.log_pmf}
 
-Log of the probability mass function.
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Beta.log_prob(x, name='log_prob')` {#Beta.log_prob}
+#### `tf.contrib.distributions.Beta.log_pdf(value, name='log_pdf')` {#Beta.log_pdf}
 
-`Log(P[counts])`, computed for every batch member.
+Log probability density function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: Non-negative floating point tensor whose shape can
-    be broadcast with `self.a` and `self.b`.  For fixed leading
-    dimensions, the last dimension represents counts for the corresponding
-    Beta distribution in `self.a` and `self.b`. `x` is only legal if
-    0 < x < 1.
-*  <b>`name`</b>: Name to give this Op, defaults to "log_prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Log probabilities for each record, shape `[N1,...,Nm]`.
 
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-- - -
+##### Raises:
 
-#### `tf.contrib.distributions.Beta.mean(name='mean')` {#Beta.mean}
 
-Mean of the distribution.
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Beta.mode(name='mode')` {#Beta.mode}
-
-Mode of the distribution.
+#### `tf.contrib.distributions.Beta.log_pmf(value, name='log_pmf')` {#Beta.log_pmf}
 
-Note that the mode for the Beta distribution is only defined
-when `a > 1`, `b > 1`. This returns the mode when `a > 1` and `b > 1`,
-and NaN otherwise. If `self.allow_nan_stats` is `False`, an exception
-will be raised rather than returning `NaN`.
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`name`</b>: The name for this op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Mode of the Beta distribution.
 
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-- - -
+##### Raises:
 
-#### `tf.contrib.distributions.Beta.name` {#Beta.name}
 
-Name to prepend to all ops.
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Beta.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#Beta.param_shapes}
-
-Shapes of parameters given the desired shape of a call to `sample()`.
+#### `tf.contrib.distributions.Beta.log_prob(value, name='log_prob')` {#Beta.log_prob}
 
-Subclasses should override static method `_param_shapes`.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: `Tensor` or python list/tuple. Desired shape of a call to
-    `sample()`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Beta.mean(name='mean')` {#Beta.mean}
+
+Mean.
+
+
+- - -
+
+#### `tf.contrib.distributions.Beta.mode(name='mode')` {#Beta.mode}
+
+Mode.
+
+
+- - -
+
+#### `tf.contrib.distributions.Beta.name` {#Beta.name}
+
+Name prepended to all ops created by this `Distribution`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Beta.param_shapes(cls, sample_shape, name='DistributionParamShapes')` {#Beta.param_shapes}
+
+Shapes of parameters given the desired shape of a call to `sample()`.
+
+Subclasses should override static method `_param_shapes`.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: `Tensor` or python list/tuple. Desired shape of a call to
+    `sample()`.
 *  <b>`name`</b>: name to prepend ops with.
 
 ##### Returns:
@@ -1785,104 +2235,146 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Beta.parameters` {#Beta.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Beta.pdf(value, name='pdf')` {#Beta.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.pmf(value, name='pmf')` {#Beta.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Beta.prob(x, name='prob')` {#Beta.prob}
+#### `tf.contrib.distributions.Beta.prob(value, name='prob')` {#Beta.prob}
 
-`P[x]`, computed for every batch member.
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Non-negative floating point tensor whose shape can
-    be broadcast with `self.a` and `self.b`.  For fixed leading
-    dimensions, the last dimension represents x for the corresponding Beta
-    distribution in `self.a` and `self.b`. `x` is only legal if is
-    between 0 and 1.
-*  <b>`name`</b>: Name to give this Op, defaults to "pdf".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Probabilities for each record, shape `[N1,...,Nm]`.
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.sample(sample_shape=(), seed=None, name='sample')` {#Beta.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.sample_n(n, seed=None, name='sample_n')` {#Beta.sample_n}
 
-Sample `n` observations from the Beta Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the hyperparameters.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.std(name='std')` {#Beta.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.validate_args` {#Beta.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.variance(name='variance')` {#Beta.variance}
 
-Variance of the distribution.
+Variance.
 
 
 
@@ -1920,14 +2412,41 @@ Initialize Categorical distributions using class log-probabilities.
 
 #### `tf.contrib.distributions.Categorical.allow_nan_stats` {#Categorical.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.batch_shape(name='batch_shape')` {#Categorical.batch_shape}
 
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
 
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -1936,27 +2455,49 @@ Boolean describing behavior when a stat is undefined for batch member.
 
 Cumulative distribution function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Categorical.dtype` {#Categorical.dtype}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
 
 
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Categorical.entropy(name='sample')` {#Categorical.entropy}
+#### `tf.contrib.distributions.Categorical.dtype` {#Categorical.dtype}
+
+The `DType` of `Tensor`s handled by this `Distribution`.
+
+
+- - -
 
+#### `tf.contrib.distributions.Categorical.entropy(name='entropy')` {#Categorical.entropy}
 
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.event_shape(name='event_shape')` {#Categorical.event_shape}
 
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
 
 
+*  <b>`event_shape`</b>: `Tensor`.
+
 
 - - -
 
@@ -1975,8 +2516,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -2019,14 +2560,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Categorical.get_batch_shape()` {#Categorical.get_batch_shape}
 
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
 
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.get_event_shape()` {#Categorical.get_event_shape}
 
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
 
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -2047,39 +2602,86 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Categorical.log_cdf(value, name='log_cdf')` {#Categorical.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.log_pdf(value, name='log_pdf')` {#Categorical.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.log_pmf(value, name='log_pmf')` {#Categorical.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Categorical.log_prob(k, name='log_prob')` {#Categorical.log_prob}
+#### `tf.contrib.distributions.Categorical.log_prob(value, name='log_prob')` {#Categorical.log_prob}
 
-Log-probability of class `k`.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`k`</b>: `int32` or `int64` Tensor. Must be broadcastable with a `batch_shape`
-    `Tensor`.
-*  <b>`name`</b>: A name for this operation (optional).
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The log-probabilities of the classes indexed by `k`
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -2093,21 +2695,21 @@ Log-probability of class `k`.
 
 #### `tf.contrib.distributions.Categorical.mean(name='mean')` {#Categorical.mean}
 
-Mean of the distribution.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.mode(name='mode')` {#Categorical.mode}
 
-
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.name` {#Categorical.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -2159,98 +2761,146 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Categorical.parameters` {#Categorical.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Categorical.pdf(value, name='pdf')` {#Categorical.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.pmf(value, name='pmf')` {#Categorical.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Categorical.prob(k, name='prob')` {#Categorical.prob}
+#### `tf.contrib.distributions.Categorical.prob(value, name='prob')` {#Categorical.prob}
 
-Probability of class `k`.
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`k`</b>: `int32` or `int64` Tensor. Must be broadcastable with logits.
-*  <b>`name`</b>: A name for this operation (optional).
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The probabilities of the classes indexed by `k`
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.sample(sample_shape=(), seed=None, name='sample')` {#Categorical.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.sample_n(n, seed=None, name='sample_n')` {#Categorical.sample_n}
 
-Sample `n` observations from the Categorical distribution.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Random seed (optional).
-*  <b>`name`</b>: A name for this operation (optional).
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
-  An `int64` `Tensor` with shape `[n, batch_shape, event_shape]`
+
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.std(name='std')` {#Categorical.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.validate_args` {#Categorical.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.variance(name='variance')` {#Categorical.variance}
 
-Variance of the distribution.
+Variance.
 
 
 
@@ -2291,7 +2941,21 @@ Construct Chi2 distributions with parameter `df`.
 
 #### `tf.contrib.distributions.Chi2.allow_nan_stats` {#Chi2.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -2305,7 +2969,7 @@ Shape parameter.
 
 #### `tf.contrib.distributions.Chi2.batch_shape(name='batch_shape')` {#Chi2.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -2317,7 +2981,8 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -2329,20 +2994,21 @@ Inverse scale parameter.
 
 - - -
 
-#### `tf.contrib.distributions.Chi2.cdf(x, name='cdf')` {#Chi2.cdf}
+#### `tf.contrib.distributions.Chi2.cdf(value, name='cdf')` {#Chi2.cdf}
 
-CDF of observations `x` under these Gamma distribution(s).
+Cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -2356,40 +3022,21 @@ CDF of observations `x` under these Gamma distribution(s).
 
 #### `tf.contrib.distributions.Chi2.dtype` {#Chi2.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.entropy(name='entropy')` {#Chi2.entropy}
 
-The entropy of Gamma distribution(s).
-
-This is defined to be
-
-```
-entropy = alpha - log(beta) + log(Gamma(alpha))
-             + (1-alpha)digamma(alpha)
-```
-
-where digamma(alpha) is the digamma function.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.event_shape(name='event_shape')` {#Chi2.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -2398,7 +3045,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -2418,8 +3066,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -2462,26 +3110,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Chi2.get_batch_shape()` {#Chi2.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  `TensorShape` object.
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.get_event_shape()` {#Chi2.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  `TensorShape` object.
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -2500,91 +3150,109 @@ Same meaning as `event_shape`. May be only partially defined.
 
 - - -
 
-#### `tf.contrib.distributions.Chi2.log_cdf(x, name='log_cdf')` {#Chi2.log_cdf}
+#### `tf.contrib.distributions.Chi2.log_cdf(value, name='log_cdf')` {#Chi2.log_cdf}
 
-Log CDF of observations `x` under these Gamma distribution(s).
+Log cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.log_pdf(value, name='log_pdf')` {#Chi2.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Chi2.log_pmf(value, name='log_pmf')` {#Chi2.log_pmf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
 
-Log of the probability mass function.
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Chi2.log_prob(x, name='log_prob')` {#Chi2.log_prob}
+#### `tf.contrib.distributions.Chi2.log_pmf(value, name='log_pmf')` {#Chi2.log_pmf}
 
-Log prob of observations in `x` under these Gamma distribution(s).
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Chi2.mean(name='mean')` {#Chi2.mean}
+#### `tf.contrib.distributions.Chi2.log_prob(value, name='log_prob')` {#Chi2.log_prob}
 
-Mean of each batch member.
+Log probability density/mass function (depending on `is_continuous`).
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Chi2.mode(name='mode')` {#Chi2.mode}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-Mode of each batch member.
+##### Returns:
 
-The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`,
-and `NaN` otherwise.  If `self.allow_nan_stats` is `False`, an exception
-will be raised rather than returning `NaN`.
 
-##### Args:
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
-*  <b>`name`</b>: A name to give this op.
+- - -
 
-##### Returns:
+#### `tf.contrib.distributions.Chi2.mean(name='mean')` {#Chi2.mean}
+
+Mean.
+
+
+- - -
 
-  The mode for every batch member, a `Tensor` with same `dtype` as self.
+#### `tf.contrib.distributions.Chi2.mode(name='mode')` {#Chi2.mode}
+
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.name` {#Chi2.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -2631,106 +3299,144 @@ param_shapes with static (i.e. TensorShape) shapes.
 
 - - -
 
-#### `tf.contrib.distributions.Chi2.pdf(value, name='pdf')` {#Chi2.pdf}
+#### `tf.contrib.distributions.Chi2.parameters` {#Chi2.parameters}
 
-The probability density function.
+Dictionary of parameters used by this `Distribution`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Chi2.pmf(value, name='pmf')` {#Chi2.pmf}
+#### `tf.contrib.distributions.Chi2.pdf(value, name='pdf')` {#Chi2.pdf}
+
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
 
-The probability mass function.
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Chi2.prob(x, name='prob')` {#Chi2.prob}
+#### `tf.contrib.distributions.Chi2.pmf(value, name='pmf')` {#Chi2.pmf}
 
-Pdf of observations in `x` under these Gamma distribution(s).
+Probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the PDFs of `x`
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.prob(value, name='prob')` {#Chi2.prob}
+
+Probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.sample(sample_shape=(), seed=None, name='sample')` {#Chi2.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.sample_n(n, seed=None, name='sample_n')` {#Chi2.sample_n}
 
-Draws `n` samples from the Gamma distribution(s).
-
-See the doc for tf.random_gamma for further detail.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed for this operation.
-*  <b>`name`</b>: Optional name for the operation.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.std(name='std')` {#Chi2.std}
 
-Standard deviation of this distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.validate_args` {#Chi2.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.variance(name='variance')` {#Chi2.variance}
 
-Variance of each batch member.
+Variance.
 
 
 
@@ -2771,7 +3477,21 @@ Construct Exponential distribution with parameter `lam`.
 
 #### `tf.contrib.distributions.Exponential.allow_nan_stats` {#Exponential.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -2785,7 +3505,7 @@ Shape parameter.
 
 #### `tf.contrib.distributions.Exponential.batch_shape(name='batch_shape')` {#Exponential.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -2797,7 +3517,8 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -2809,60 +3530,42 @@ Inverse scale parameter.
 
 - - -
 
-#### `tf.contrib.distributions.Exponential.cdf(x, name='cdf')` {#Exponential.cdf}
+#### `tf.contrib.distributions.Exponential.cdf(value, name='cdf')` {#Exponential.cdf}
 
-CDF of observations `x` under these Gamma distribution(s).
+Cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.dtype` {#Exponential.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.entropy(name='entropy')` {#Exponential.entropy}
 
-The entropy of Gamma distribution(s).
-
-This is defined to be
-
-```
-entropy = alpha - log(beta) + log(Gamma(alpha))
-             + (1-alpha)digamma(alpha)
-```
-
-where digamma(alpha) is the digamma function.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.event_shape(name='event_shape')` {#Exponential.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -2871,7 +3574,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -2891,8 +3595,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -2935,26 +3639,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Exponential.get_batch_shape()` {#Exponential.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  `TensorShape` object.
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.get_event_shape()` {#Exponential.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  `TensorShape` object.
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -2980,91 +3686,109 @@ Same meaning as `event_shape`. May be only partially defined.
 
 - - -
 
-#### `tf.contrib.distributions.Exponential.log_cdf(x, name='log_cdf')` {#Exponential.log_cdf}
+#### `tf.contrib.distributions.Exponential.log_cdf(value, name='log_cdf')` {#Exponential.log_cdf}
 
-Log CDF of observations `x` under these Gamma distribution(s).
+Log cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.log_pdf(value, name='log_pdf')` {#Exponential.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
 
-#### `tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf')` {#Exponential.log_pmf}
 
-Log of the probability mass function.
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Exponential.log_prob(x, name='log_prob')` {#Exponential.log_prob}
+#### `tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf')` {#Exponential.log_pmf}
 
-Log prob of observations in `x` under these Gamma distribution(s).
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Exponential.mean(name='mean')` {#Exponential.mean}
+#### `tf.contrib.distributions.Exponential.log_prob(value, name='log_prob')` {#Exponential.log_prob}
 
-Mean of each batch member.
+Log probability density/mass function (depending on `is_continuous`).
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Exponential.mode(name='mode')` {#Exponential.mode}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-Mode of each batch member.
+##### Returns:
 
-The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`,
-and `NaN` otherwise.  If `self.allow_nan_stats` is `False`, an exception
-will be raised rather than returning `NaN`.
 
-##### Args:
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
-*  <b>`name`</b>: A name to give this op.
+- - -
 
-##### Returns:
+#### `tf.contrib.distributions.Exponential.mean(name='mean')` {#Exponential.mean}
+
+Mean.
 
-  The mode for every batch member, a `Tensor` with same `dtype` as self.
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.mode(name='mode')` {#Exponential.mode}
+
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.name` {#Exponential.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -3111,104 +3835,144 @@ param_shapes with static (i.e. TensorShape) shapes.
 
 - - -
 
-#### `tf.contrib.distributions.Exponential.pdf(value, name='pdf')` {#Exponential.pdf}
+#### `tf.contrib.distributions.Exponential.parameters` {#Exponential.parameters}
 
-The probability density function.
+Dictionary of parameters used by this `Distribution`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Exponential.pmf(value, name='pmf')` {#Exponential.pmf}
+#### `tf.contrib.distributions.Exponential.pdf(value, name='pdf')` {#Exponential.pdf}
 
-The probability mass function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Exponential.prob(x, name='prob')` {#Exponential.prob}
+#### `tf.contrib.distributions.Exponential.pmf(value, name='pmf')` {#Exponential.pmf}
 
-Pdf of observations in `x` under these Gamma distribution(s).
+Probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the PDFs of `x`
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.prob(value, name='prob')` {#Exponential.prob}
+
+Probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.sample(sample_shape=(), seed=None, name='sample')` {#Exponential.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.sample_n(n, seed=None, name='sample_n')` {#Exponential.sample_n}
 
-Sample `n` observations from the Exponential Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by the hyperparameters.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.std(name='std')` {#Exponential.std}
 
-Standard deviation of this distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.validate_args` {#Exponential.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.variance(name='variance')` {#Exponential.variance}
 
-Variance of each batch member.
+Variance.
 
 
 
@@ -3273,7 +4037,21 @@ broadcasting (e.g. `alpha + beta` is a valid operation).
 
 #### `tf.contrib.distributions.Gamma.allow_nan_stats` {#Gamma.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -3287,7 +4065,7 @@ Shape parameter.
 
 #### `tf.contrib.distributions.Gamma.batch_shape(name='batch_shape')` {#Gamma.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -3299,7 +4077,8 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -3311,60 +4090,42 @@ Inverse scale parameter.
 
 - - -
 
-#### `tf.contrib.distributions.Gamma.cdf(x, name='cdf')` {#Gamma.cdf}
+#### `tf.contrib.distributions.Gamma.cdf(value, name='cdf')` {#Gamma.cdf}
 
-CDF of observations `x` under these Gamma distribution(s).
+Cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.dtype` {#Gamma.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.entropy(name='entropy')` {#Gamma.entropy}
 
-The entropy of Gamma distribution(s).
-
-This is defined to be
-
-```
-entropy = alpha - log(beta) + log(Gamma(alpha))
-             + (1-alpha)digamma(alpha)
-```
-
-where digamma(alpha) is the digamma function.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.event_shape(name='event_shape')` {#Gamma.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -3373,7 +4134,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -3393,8 +4155,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -3437,26 +4199,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Gamma.get_batch_shape()` {#Gamma.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  `TensorShape` object.
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.get_event_shape()` {#Gamma.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  `TensorShape` object.
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -3475,91 +4239,109 @@ Same meaning as `event_shape`. May be only partially defined.
 
 - - -
 
-#### `tf.contrib.distributions.Gamma.log_cdf(x, name='log_cdf')` {#Gamma.log_cdf}
+#### `tf.contrib.distributions.Gamma.log_cdf(value, name='log_cdf')` {#Gamma.log_cdf}
 
-Log CDF of observations `x` under these Gamma distribution(s).
+Log cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.log_pdf(value, name='log_pdf')` {#Gamma.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf')` {#Gamma.log_pmf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
 
-Log of the probability mass function.
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Gamma.log_prob(x, name='log_prob')` {#Gamma.log_prob}
+#### `tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf')` {#Gamma.log_pmf}
 
-Log prob of observations in `x` under these Gamma distribution(s).
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Gamma.mean(name='mean')` {#Gamma.mean}
+#### `tf.contrib.distributions.Gamma.log_prob(value, name='log_prob')` {#Gamma.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
 
-Mean of each batch member.
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-#### `tf.contrib.distributions.Gamma.mode(name='mode')` {#Gamma.mode}
+##### Returns:
 
-Mode of each batch member.
 
-The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`,
-and `NaN` otherwise.  If `self.allow_nan_stats` is `False`, an exception
-will be raised rather than returning `NaN`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-##### Args:
 
+- - -
+
+#### `tf.contrib.distributions.Gamma.mean(name='mean')` {#Gamma.mean}
 
-*  <b>`name`</b>: A name to give this op.
+Mean.
 
-##### Returns:
 
-  The mode for every batch member, a `Tensor` with same `dtype` as self.
+- - -
+
+#### `tf.contrib.distributions.Gamma.mode(name='mode')` {#Gamma.mode}
+
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.name` {#Gamma.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -3606,106 +4388,144 @@ param_shapes with static (i.e. TensorShape) shapes.
 
 - - -
 
-#### `tf.contrib.distributions.Gamma.pdf(value, name='pdf')` {#Gamma.pdf}
-
-The probability density function.
-
-
-- - -
-
-#### `tf.contrib.distributions.Gamma.pmf(value, name='pmf')` {#Gamma.pmf}
+#### `tf.contrib.distributions.Gamma.parameters` {#Gamma.parameters}
 
-The probability mass function.
+Dictionary of parameters used by this `Distribution`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Gamma.prob(x, name='prob')` {#Gamma.prob}
+#### `tf.contrib.distributions.Gamma.pdf(value, name='pdf')` {#Gamma.pdf}
 
-Pdf of observations in `x` under these Gamma distribution(s).
+Probability density function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the PDFs of `x`
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.pmf(value, name='pmf')` {#Gamma.pmf}
+
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.prob(value, name='prob')` {#Gamma.prob}
+
+Probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.sample(sample_shape=(), seed=None, name='sample')` {#Gamma.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.sample_n(n, seed=None, name='sample_n')` {#Gamma.sample_n}
 
-Draws `n` samples from the Gamma distribution(s).
-
-See the doc for tf.random_gamma for further detail.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed for this operation.
-*  <b>`name`</b>: Optional name for the operation.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.std(name='std')` {#Gamma.std}
 
-Standard deviation of this distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.validate_args` {#Gamma.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.variance(name='variance')` {#Gamma.variance}
 
-Variance of each batch member.
+Variance.
 
 
 
@@ -3769,7 +4589,21 @@ broadcasting (e.g. `alpha + beta` is a valid operation).
 
 #### `tf.contrib.distributions.InverseGamma.allow_nan_stats` {#InverseGamma.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -3783,7 +4617,7 @@ Shape parameter.
 
 #### `tf.contrib.distributions.InverseGamma.batch_shape(name='batch_shape')` {#InverseGamma.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -3795,7 +4629,8 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -3807,60 +4642,42 @@ Scale parameter.
 
 - - -
 
-#### `tf.contrib.distributions.InverseGamma.cdf(x, name='cdf')` {#InverseGamma.cdf}
+#### `tf.contrib.distributions.InverseGamma.cdf(value, name='cdf')` {#InverseGamma.cdf}
 
-CDF of observations `x` under these InverseGamma distribution(s).
+Cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.dtype` {#InverseGamma.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.entropy(name='entropy')` {#InverseGamma.entropy}
 
-The entropy of these InverseGamma distribution(s).
-
-This is defined to be
-
-```
-entropy = alpha - log(beta) + log(Gamma(alpha))
-             + (1-alpha)digamma(alpha)
-```
-
-where digamma(alpha) is the digamma function.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.event_shape(name='event_shape')` {#InverseGamma.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -3869,7 +4686,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -3889,8 +4707,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -3933,26 +4751,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.InverseGamma.get_batch_shape()` {#InverseGamma.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  `TensorShape` object.
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.get_event_shape()` {#InverseGamma.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  `TensorShape` object.
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -3971,102 +4791,109 @@ Same meaning as `event_shape`. May be only partially defined.
 
 - - -
 
-#### `tf.contrib.distributions.InverseGamma.log_cdf(x, name='log_cdf')` {#InverseGamma.log_cdf}
+#### `tf.contrib.distributions.InverseGamma.log_cdf(value, name='log_cdf')` {#InverseGamma.log_cdf}
 
-Log CDF of observations `x` under these InverseGamma distribution(s).
+Log cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.log_pdf(value, name='log_pdf')` {#InverseGamma.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
 
-#### `tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf')` {#InverseGamma.log_pmf}
 
-Log of the probability mass function.
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.InverseGamma.log_prob(x, name='log_prob')` {#InverseGamma.log_prob}
+#### `tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf')` {#InverseGamma.log_pmf}
 
-Log prob of observations in `x` under these InverseGamma distribution(s).
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.InverseGamma.mean(name='mean')` {#InverseGamma.mean}
-
-Mean of each batch member.
+#### `tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob')` {#InverseGamma.log_prob}
 
-The mean of an inverse gamma distribution is `beta / (alpha - 1)`,
-when `alpha > 1`, and `NaN` otherwise.  If `self.allow_nan_stats` is
-`False`, an exception will be raised rather than returning `NaN`
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`name`</b>: A name to give this op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The mean for every batch member, a `Tensor` with same `dtype` as self.
 
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-- - -
-
-#### `tf.contrib.distributions.InverseGamma.mode(name='mode')` {#InverseGamma.mode}
 
-Mode of each batch member.
+- - -
 
-The mode of an inverse gamma distribution is `beta / (alpha + 1)`.
+#### `tf.contrib.distributions.InverseGamma.mean(name='mean')` {#InverseGamma.mean}
 
-##### Args:
+Mean.
 
 
-*  <b>`name`</b>: A name to give this op.
+- - -
 
-##### Returns:
+#### `tf.contrib.distributions.InverseGamma.mode(name='mode')` {#InverseGamma.mode}
 
-  The mode for every batch member, a `Tensor` with same `dtype` as self.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.name` {#InverseGamma.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -4113,119 +4940,144 @@ param_shapes with static (i.e. TensorShape) shapes.
 
 - - -
 
-#### `tf.contrib.distributions.InverseGamma.pdf(value, name='pdf')` {#InverseGamma.pdf}
+#### `tf.contrib.distributions.InverseGamma.parameters` {#InverseGamma.parameters}
 
-The probability density function.
+Dictionary of parameters used by this `Distribution`.
 
 
 - - -
 
-#### `tf.contrib.distributions.InverseGamma.pmf(value, name='pmf')` {#InverseGamma.pmf}
+#### `tf.contrib.distributions.InverseGamma.pdf(value, name='pdf')` {#InverseGamma.pdf}
+
+Probability density function.
+
+##### Args:
+
 
-The probability mass function.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.InverseGamma.prob(x, name='prob')` {#InverseGamma.prob}
+#### `tf.contrib.distributions.InverseGamma.pmf(value, name='pmf')` {#InverseGamma.pmf}
 
-Pdf of observations in `x` under these Gamma distribution(s).
+Probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the PDFs of `x`
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.InverseGamma.prob(value, name='prob')` {#InverseGamma.prob}
+
+Probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.sample(sample_shape=(), seed=None, name='sample')` {#InverseGamma.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.sample_n(n, seed=None, name='sample_n')` {#InverseGamma.sample_n}
 
-Draws `n` samples from these InverseGamma distribution(s).
-
-See the doc for tf.random_gamma for further details on sampling strategy.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed for this operation.
-*  <b>`name`</b>: Optional name for the operation.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.std(name='std')` {#InverseGamma.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.validate_args` {#InverseGamma.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.variance(name='variance')` {#InverseGamma.variance}
 
-Variance of each batch member.
-
-Variance for inverse gamma is defined only for `alpha > 2`. If
-`self.allow_nan_stats` is `False`, an exception will be raised rather
-than returning `NaN`.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name to give this op.
-
-##### Returns:
-
-  The variance for every batch member, a `Tensor` with same `dtype` as self.
+Variance.
 
 
 
@@ -4278,14 +5130,28 @@ broadcasting (e.g., `loc / scale` is a valid operation).
 
 #### `tf.contrib.distributions.Laplace.allow_nan_stats` {#Laplace.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.batch_shape(name='batch_shape')` {#Laplace.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -4293,69 +5159,62 @@ independent distributions of this kind the instance represents.
 ##### Args:
 
 
-*  <b>`name`</b>: name to give to the op.
+*  <b>`name`</b>: name to give to the op
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.cdf(x, name='cdf')` {#Laplace.cdf}
+#### `tf.contrib.distributions.Laplace.cdf(value, name='cdf')` {#Laplace.cdf}
 
-CDF of observations in `x` under the Laplace distribution(s).
+Cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.dtype` {#Laplace.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.entropy(name='entropy')` {#Laplace.entropy}
 
-The entropy of Laplace distribution(s).
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.event_shape(name='event_shape')` {#Laplace.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
 
-*  <b>`name`</b>: name to give to the op.
+*  <b>`name`</b>: name to give to the op
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -4375,8 +5234,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -4419,26 +5278,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Laplace.get_batch_shape()` {#Laplace.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  batch shape
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.get_event_shape()` {#Laplace.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  event shape
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -4464,80 +5325,109 @@ Distribution parameter for the location.
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.log_cdf(x, name='log_cdf')` {#Laplace.log_cdf}
+#### `tf.contrib.distributions.Laplace.log_cdf(value, name='log_cdf')` {#Laplace.log_cdf}
 
-Log CDF of observations `x` under the Laplace distribution(s).
+Log cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.log_pdf(value, name='log_pdf')` {#Laplace.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf')` {#Laplace.log_pmf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
 
-Log of the probability mass function.
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.log_prob(x, name='log_prob')` {#Laplace.log_prob}
+#### `tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf')` {#Laplace.log_pmf}
 
-Log prob of observations in `x` under these Laplace distribution(s).
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-probability of `x`.
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.mean(name='mean')` {#Laplace.mean}
+#### `tf.contrib.distributions.Laplace.log_prob(value, name='log_prob')` {#Laplace.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
 
-Mean of this distribution.
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.median(name='median')` {#Laplace.median}
+#### `tf.contrib.distributions.Laplace.mean(name='mean')` {#Laplace.mean}
 
-Median of this distribution.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.mode(name='mode')` {#Laplace.mode}
 
-Mode of this distribution.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.name` {#Laplace.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -4584,106 +5474,151 @@ param_shapes with static (i.e. TensorShape) shapes.
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.pdf(value, name='pdf')` {#Laplace.pdf}
-
-The probability density function.
-
-
-- - -
-
-#### `tf.contrib.distributions.Laplace.pmf(value, name='pmf')` {#Laplace.pmf}
+#### `tf.contrib.distributions.Laplace.parameters` {#Laplace.parameters}
 
-The probability mass function.
+Dictionary of parameters used by this `Distribution`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.prob(x, name='pdf')` {#Laplace.prob}
+#### `tf.contrib.distributions.Laplace.pdf(value, name='pdf')` {#Laplace.pdf}
 
-The prob of observations in `x` under the Laplace distribution(s).
+Probability density function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`pdf`</b>: tensor of dtype `dtype`, the pdf values of `x`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
+##### Raises:
 
-- - -
 
-#### `tf.contrib.distributions.Laplace.sample(sample_shape=(), seed=None, name='sample')` {#Laplace.sample}
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
-Generate samples of the specified shape for each batched distribution.
 
-Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+- - -
+
+#### `tf.contrib.distributions.Laplace.pmf(value, name='pmf')` {#Laplace.pmf}
+
+Probability mass function.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
-*  <b>`seed`</b>: Python integer seed for RNG
-*  <b>`name`</b>: name to give to the op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.sample_n(n, seed=None, name='sample_n')` {#Laplace.sample_n}
+#### `tf.contrib.distributions.Laplace.prob(value, name='prob')` {#Laplace.prob}
 
-Sample `n` observations from the Laplace Distributions.
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-    observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the parameters.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.scale` {#Laplace.scale}
+#### `tf.contrib.distributions.Laplace.sample(sample_shape=(), seed=None, name='sample')` {#Laplace.sample}
 
-Distribution parameter for scale.
+Generate samples of the specified shape.
+
+Note that a call to `sample()` without arguments will generate a single
+sample.
+
+##### Args:
+
+
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.sample_n(n, seed=None, name='sample_n')` {#Laplace.sample_n}
+
+Generate `n` samples.
+
+##### Args:
+
+
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
+
+
+- - -
+
+#### `tf.contrib.distributions.Laplace.scale` {#Laplace.scale}
+
+Distribution parameter for scale.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.std(name='std')` {#Laplace.std}
 
-Standard deviation of this distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.validate_args` {#Laplace.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.variance(name='variance')` {#Laplace.variance}
 
-Variance of this distribution.
+Variance.
 
 
 
@@ -4766,14 +5701,28 @@ broadcasting (e.g. `mu + sigma` is a valid operation).
 
 #### `tf.contrib.distributions.Normal.allow_nan_stats` {#Normal.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.batch_shape(name='batch_shape')` {#Normal.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -4781,69 +5730,62 @@ independent distributions of this kind the instance represents.
 ##### Args:
 
 
-*  <b>`name`</b>: name to give to the op.
+*  <b>`name`</b>: name to give to the op
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Normal.cdf(x, name='cdf')` {#Normal.cdf}
+#### `tf.contrib.distributions.Normal.cdf(value, name='cdf')` {#Normal.cdf}
 
-CDF of observations in `x` under these Normal distribution(s).
+Cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.dtype` {#Normal.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.entropy(name='entropy')` {#Normal.entropy}
 
-The entropy of Normal distribution(s).
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.event_shape(name='event_shape')` {#Normal.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
 
-*  <b>`name`</b>: name to give to the op.
+*  <b>`name`</b>: name to give to the op
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -4863,8 +5805,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -4907,26 +5849,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Normal.get_batch_shape()` {#Normal.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  batch shape
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.get_event_shape()` {#Normal.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  event shape
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -4945,66 +5889,102 @@ Same meaning as `event_shape`. May be only partially defined.
 
 - - -
 
-#### `tf.contrib.distributions.Normal.log_cdf(x, name='log_cdf')` {#Normal.log_cdf}
+#### `tf.contrib.distributions.Normal.log_cdf(value, name='log_cdf')` {#Normal.log_cdf}
 
-Log CDF of observations `x` under these Normal distribution(s).
+Log cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.log_pdf(value, name='log_pdf')` {#Normal.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.log_pmf(value, name='log_pmf')` {#Normal.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Normal.log_prob(x, name='log_prob')` {#Normal.log_prob}
+#### `tf.contrib.distributions.Normal.log_prob(value, name='log_prob')` {#Normal.log_prob}
 
-Log prob of observations in `x` under these Normal distribution(s).
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.mean(name='mean')` {#Normal.mean}
 
-Mean of this distribution.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.mode(name='mode')` {#Normal.mode}
 
-Mode of this distribution.
+Mode.
 
 
 - - -
@@ -5018,7 +5998,7 @@ Distribution parameter for the mean.
 
 #### `tf.contrib.distributions.Normal.name` {#Normal.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -5063,80 +6043,125 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Normal.parameters` {#Normal.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Normal.pdf(value, name='pdf')` {#Normal.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.pmf(value, name='pmf')` {#Normal.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Normal.prob(x, name='prob')` {#Normal.prob}
+#### `tf.contrib.distributions.Normal.prob(value, name='prob')` {#Normal.prob}
 
-The PDF of observations in `x` under these Normal distribution(s).
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the prob values of `x`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.sample(sample_shape=(), seed=None, name='sample')` {#Normal.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.sample_n(n, seed=None, name='sample_n')` {#Normal.sample_n}
 
-Sample `n` observations from the Normal Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the hyperparameters.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -5150,21 +6175,21 @@ Distribution parameter for standard deviation.
 
 #### `tf.contrib.distributions.Normal.std(name='std')` {#Normal.std}
 
-Standard deviation of this distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.validate_args` {#Normal.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.variance(name='variance')` {#Normal.variance}
 
-Variance of this distribution.
+Variance.
 
 
 
@@ -5208,53 +6233,91 @@ Construct Poisson distributions.
 
 #### `tf.contrib.distributions.Poisson.allow_nan_stats` {#Poisson.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Poisson.batch_shape(name='batch_shape')` {#Poisson.batch_shape}
 
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
 
 
+*  <b>`batch_shape`</b>: `Tensor`.
+
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.cdf(x, name='cdf')` {#Poisson.cdf}
+#### `tf.contrib.distributions.Poisson.cdf(value, name='cdf')` {#Poisson.cdf}
 
-Cumulative density function.
+Cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: Non-negative floating point tensor with dtype `dtype` and whose shape
-    can be broadcast with `self.lam`.
-*  <b>`name`</b>: A name for this operation.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The CDF of the events.
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Poisson.dtype` {#Poisson.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Poisson.entropy(name='entropy')` {#Poisson.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Poisson.event_shape(name='event_shape')` {#Poisson.event_shape}
 
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
 
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -5274,8 +6337,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -5318,14 +6381,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Poisson.get_batch_shape()` {#Poisson.get_batch_shape}
 
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
 
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Poisson.get_event_shape()` {#Poisson.get_event_shape}
 
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
 
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -5351,98 +6428,109 @@ Rate parameter.
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.log_cdf(x, name='log_cdf')` {#Poisson.log_cdf}
+#### `tf.contrib.distributions.Poisson.log_cdf(value, name='log_cdf')` {#Poisson.log_cdf}
 
-Log cumulative density function.
+Log cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: Non-negative floating point tensor with dtype `dtype` and whose shape
-    can be broadcast with `self.lam`.
-*  <b>`name`</b>: A name for this operation.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The Log CDF of the events.
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Poisson.log_pdf(value, name='log_pdf')` {#Poisson.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
 
-#### `tf.contrib.distributions.Poisson.log_pmf(value, name='log_pmf')` {#Poisson.log_pmf}
 
-Log of the probability mass function.
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.log_prob(x, name='log_prob')` {#Poisson.log_prob}
+#### `tf.contrib.distributions.Poisson.log_pmf(value, name='log_pmf')` {#Poisson.log_pmf}
 
 Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: Non-negative floating point tensor with dtype `dtype` and whose shape
-    can be broadcast with `self.lam`. `x` is only legal if it is
-    non-negative and its components are equal to integer values.
-*  <b>`name`</b>: A name for this operation (optional).
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The log-probabilities of the events.
+
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.mean(name='mean')` {#Poisson.mean}
+#### `tf.contrib.distributions.Poisson.log_prob(value, name='log_prob')` {#Poisson.log_prob}
 
-Mean of the distribution.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`name`</b>: Name for the op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`mean`</b>: `Tensor` of the same type and shape as `lam`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.mode(name='mode')` {#Poisson.mode}
-
-Mode of the distribution.
-
-Note that when `lam` is an integer, there are actually two modes.
-Namely, `lam` and `lam - 1` are both modes. Here we return
-only the larger of the two modes.
-
-##### Args:
+#### `tf.contrib.distributions.Poisson.mean(name='mean')` {#Poisson.mean}
 
+Mean.
 
-*  <b>`name`</b>: Name for the op.
 
-##### Returns:
+- - -
 
+#### `tf.contrib.distributions.Poisson.mode(name='mode')` {#Poisson.mode}
 
-*  <b>`mode`</b>: `Tensor` of the same type and shape as `lam`.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Poisson.name` {#Poisson.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -5489,119 +6577,144 @@ param_shapes with static (i.e. TensorShape) shapes.
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.pdf(value, name='pdf')` {#Poisson.pdf}
+#### `tf.contrib.distributions.Poisson.parameters` {#Poisson.parameters}
 
-The probability density function.
+Dictionary of parameters used by this `Distribution`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.pmf(value, name='pmf')` {#Poisson.pmf}
+#### `tf.contrib.distributions.Poisson.pdf(value, name='pdf')` {#Poisson.pdf}
 
-The probability mass function.
+Probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Poisson.prob(x, name='prob')` {#Poisson.prob}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-Probability mass function.
+##### Returns:
 
-##### Args:
 
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-*  <b>`x`</b>: Non-negative floating point tensor with dtype `dtype` and whose shape
-    can be broadcast with `self.lam`. `x` is only legal if it is
-    non-negative and its components are equal to integer values.
-*  <b>`name`</b>: A name for this operation.
+##### Raises:
 
-##### Returns:
 
-  The probabilities of the events.
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.sample(sample_shape=(), seed=None, name='sample')` {#Poisson.sample}
-
-Generate samples of the specified shape for each batched distribution.
+#### `tf.contrib.distributions.Poisson.pmf(value, name='pmf')` {#Poisson.pmf}
 
-Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+Probability mass function.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
-*  <b>`seed`</b>: Python integer seed for RNG
-*  <b>`name`</b>: name to give to the op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.sample_n(n, seed=None, name='sample_n')` {#Poisson.sample_n}
+#### `tf.contrib.distributions.Poisson.prob(value, name='prob')` {#Poisson.prob}
 
-Generate `n` samples.
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
-*  <b>`seed`</b>: Python integer seed for RNG
-*  <b>`name`</b>: name to give to the op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.std(name='std')` {#Poisson.std}
+#### `tf.contrib.distributions.Poisson.sample(sample_shape=(), seed=None, name='sample')` {#Poisson.sample}
 
-Standard deviation of the distribution.
+Generate samples of the specified shape.
+
+Note that a call to `sample()` without arguments will generate a single
+sample.
 
 ##### Args:
 
 
-*  <b>`name`</b>: Name for the op.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`std`</b>: `Tensor` of the same type and shape as `lam`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.validate_args` {#Poisson.validate_args}
+#### `tf.contrib.distributions.Poisson.sample_n(n, seed=None, name='sample_n')` {#Poisson.sample_n}
+
+Generate `n` samples.
+
+##### Args:
+
 
-Boolean describing behavior on invalid input.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.variance(name='variance')` {#Poisson.variance}
+#### `tf.contrib.distributions.Poisson.std(name='std')` {#Poisson.std}
 
-Variance of the distribution.
+Standard deviation.
 
-##### Args:
 
+- - -
+
+#### `tf.contrib.distributions.Poisson.validate_args` {#Poisson.validate_args}
 
-*  <b>`name`</b>: Name for the op.
+Python boolean indicated possibly expensive checks are enabled.
 
-##### Returns:
 
+- - -
 
-*  <b>`variance`</b>: `Tensor` of the same type and shape as `lam`.
+#### `tf.contrib.distributions.Poisson.variance(name='variance')` {#Poisson.variance}
+
+Variance.
 
 
 
@@ -5693,14 +6806,41 @@ broadcasting (e.g. `df + mu + sigma` is a valid operation).
 
 #### `tf.contrib.distributions.StudentT.allow_nan_stats` {#StudentT.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.batch_shape(name='batch_shape')` {#StudentT.batch_shape}
 
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
 
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -5709,6 +6849,18 @@ Boolean describing behavior when a stat is undefined for batch member.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
@@ -5721,31 +6873,31 @@ Degrees of freedom in these Student's t distribution(s).
 
 #### `tf.contrib.distributions.StudentT.dtype` {#StudentT.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.entropy(name='entropy')` {#StudentT.entropy}
 
-The entropy of Student t distribution(s).
-
-##### Args:
+Shanon entropy in nats.
 
 
-*  <b>`name`</b>: The name to give this op.
+- - -
 
-##### Returns:
+#### `tf.contrib.distributions.StudentT.event_shape(name='event_shape')` {#StudentT.event_shape}
 
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+##### Args:
 
 
-- - -
+*  <b>`name`</b>: name to give to the op
 
-#### `tf.contrib.distributions.StudentT.event_shape(name='event_shape')` {#StudentT.event_shape}
+##### Returns:
 
 
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -5765,8 +6917,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -5809,14 +6961,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.StudentT.get_batch_shape()` {#StudentT.get_batch_shape}
 
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
 
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.get_event_shape()` {#StudentT.get_event_shape}
 
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
 
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -5837,67 +7003,101 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.StudentT.log_cdf(value, name='log_cdf')` {#StudentT.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.log_pdf(value, name='log_pdf')` {#StudentT.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.StudentT.log_pmf(value, name='log_pmf')` {#StudentT.log_pmf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-Log of the probability mass function.
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.StudentT.log_prob(x, name='log_prob')` {#StudentT.log_prob}
+#### `tf.contrib.distributions.StudentT.log_pmf(value, name='log_pmf')` {#StudentT.log_pmf}
 
-Log prob of observations in `x` under these Student's t-distribution(s).
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `df`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
 
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
-- - -
 
-#### `tf.contrib.distributions.StudentT.mean(name='mean')` {#StudentT.mean}
+- - -
 
-Mean of the distribution.
+#### `tf.contrib.distributions.StudentT.log_prob(value, name='log_prob')` {#StudentT.log_prob}
 
-The mean of Student's T equals `mu` if `df > 1`, otherwise it is `NaN`.  If
-`self.allow_nan_stats=False`, then an exception will be raised rather than
-returning `NaN`.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`name`</b>: A name to give this op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The mean for every batch member, a `Tensor` with same `dtype` as self.
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.StudentT.mode(name='mode')` {#StudentT.mode}
+#### `tf.contrib.distributions.StudentT.mean(name='mean')` {#StudentT.mean}
 
+Mean.
 
 
+- - -
+
+#### `tf.contrib.distributions.StudentT.mode(name='mode')` {#StudentT.mode}
+
+Mode.
+
 
 - - -
 
@@ -5910,7 +7110,7 @@ Locations of these Student's t distribution(s).
 
 #### `tf.contrib.distributions.StudentT.name` {#StudentT.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -5955,81 +7155,125 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.StudentT.parameters` {#StudentT.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.StudentT.pdf(value, name='pdf')` {#StudentT.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.pmf(value, name='pmf')` {#StudentT.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.StudentT.prob(x, name='prob')` {#StudentT.prob}
+#### `tf.contrib.distributions.StudentT.prob(value, name='prob')` {#StudentT.prob}
 
-The PDF of observations in `x` under these Student's t distribution(s).
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `df`, `mu`, and
-    `sigma`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the prob values of `x`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.sample(sample_shape=(), seed=None, name='sample')` {#StudentT.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.sample_n(n, seed=None, name='sample_n')` {#StudentT.sample_n}
 
-Sample `n` observations from the Student t Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -6043,42 +7287,21 @@ Scaling factors of these Student's t distribution(s).
 
 #### `tf.contrib.distributions.StudentT.std(name='std')` {#StudentT.std}
 
-
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.validate_args` {#StudentT.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.variance(name='variance')` {#StudentT.variance}
 
-Variance of the distribution.
-
-Variance for Student's T equals
-
-```
-df / (df - 2), when df > 2
-infinity, when 1 < df <= 2
-NaN, when df <= 1
-```
-
-The NaN state occurs because mean is undefined for `df <= 1`, and if
-`self.allow_nan_stats` is `False`, an exception will be raised if any batch
-members fall into this state.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for this op.
-
-##### Returns:
-
-  The variance for every batch member, a `Tensor` with same `dtype` as self.
+Variance.
 
 
 
@@ -6146,7 +7369,21 @@ u1 = Uniform(3.0, [5.0, 6.0, 7.0])  # 3 distributions
 
 #### `tf.contrib.distributions.Uniform.allow_nan_stats` {#Uniform.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -6160,57 +7397,70 @@ Boolean describing behavior when a stat is undefined for batch member.
 
 #### `tf.contrib.distributions.Uniform.batch_shape(name='batch_shape')` {#Uniform.batch_shape}
 
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
 
 
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
+
 
 - - -
 
-#### `tf.contrib.distributions.Uniform.cdf(x, name='cdf')` {#Uniform.cdf}
+#### `tf.contrib.distributions.Uniform.cdf(value, name='cdf')` {#Uniform.cdf}
 
-CDF of observations in `x` under these Uniform distribution(s).
+Cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `a` and `b`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`. If `x` is `nan`, will
-      return `nan`.
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.dtype` {#Uniform.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.entropy(name='entropy')` {#Uniform.entropy}
 
-The entropy of Uniform distribution(s).
-
-##### Args:
+Shanon entropy in nats.
 
 
-*  <b>`name`</b>: The name to give this op.
+- - -
 
-##### Returns:
+#### `tf.contrib.distributions.Uniform.event_shape(name='event_shape')` {#Uniform.event_shape}
 
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+##### Args:
 
 
-- - -
+*  <b>`name`</b>: name to give to the op
 
-#### `tf.contrib.distributions.Uniform.event_shape(name='event_shape')` {#Uniform.event_shape}
+##### Returns:
 
 
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -6230,8 +7480,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -6270,81 +7520,153 @@ apply it externally and set `make_safe=False`.
     implemented directly for `cls`.
 
 
-- - -
+- - -
+
+#### `tf.contrib.distributions.Uniform.get_batch_shape()` {#Uniform.get_batch_shape}
+
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.get_event_shape()` {#Uniform.get_event_shape}
+
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.is_continuous` {#Uniform.is_continuous}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.is_reparameterized` {#Uniform.is_reparameterized}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.Uniform.log_cdf(value, name='log_cdf')` {#Uniform.log_cdf}
+
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-#### `tf.contrib.distributions.Uniform.get_batch_shape()` {#Uniform.get_batch_shape}
 
+- - -
 
+#### `tf.contrib.distributions.Uniform.log_pdf(value, name='log_pdf')` {#Uniform.log_pdf}
 
+Log probability density function.
 
-- - -
+##### Args:
 
-#### `tf.contrib.distributions.Uniform.get_event_shape()` {#Uniform.get_event_shape}
 
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
+##### Returns:
 
 
-- - -
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-#### `tf.contrib.distributions.Uniform.is_continuous` {#Uniform.is_continuous}
+##### Raises:
 
 
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Uniform.is_reparameterized` {#Uniform.is_reparameterized}
-
+#### `tf.contrib.distributions.Uniform.log_pmf(value, name='log_pmf')` {#Uniform.log_pmf}
 
+Log probability mass function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Uniform.log_cdf(x, name='log_cdf')` {#Uniform.log_cdf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
+##### Returns:
 
 
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-- - -
+##### Raises:
 
-#### `tf.contrib.distributions.Uniform.log_pdf(value, name='log_pdf')` {#Uniform.log_pdf}
 
-Log of the probability density function.
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Uniform.log_pmf(value, name='log_pmf')` {#Uniform.log_pmf}
+#### `tf.contrib.distributions.Uniform.log_prob(value, name='log_prob')` {#Uniform.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
 
-Log of the probability mass function.
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-#### `tf.contrib.distributions.Uniform.log_prob(x, name='log_prob')` {#Uniform.log_prob}
+##### Returns:
 
 
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.mean(name='mean')` {#Uniform.mean}
 
-
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.mode(name='mode')` {#Uniform.mode}
 
-Mode of the distribution.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.name` {#Uniform.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -6389,37 +7711,78 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Uniform.parameters` {#Uniform.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Uniform.pdf(value, name='pdf')` {#Uniform.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.pmf(value, name='pmf')` {#Uniform.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Uniform.prob(x, name='prob')` {#Uniform.prob}
+#### `tf.contrib.distributions.Uniform.prob(value, name='prob')` {#Uniform.prob}
 
-The PDF of observations in `x` under these Uniform distribution(s).
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `a` and `b`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the prob values of `x`. If `x` is `nan`,
-      will return `nan`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -6433,65 +7796,68 @@ The PDF of observations in `x` under these Uniform distribution(s).
 
 #### `tf.contrib.distributions.Uniform.sample(sample_shape=(), seed=None, name='sample')` {#Uniform.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.sample_n(n, seed=None, name='sample_n')` {#Uniform.sample_n}
 
-Sample `n` observations from the Uniform Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.std(name='std')` {#Uniform.std}
 
-
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.validate_args` {#Uniform.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.variance(name='variance')` {#Uniform.variance}
 
-
+Variance.
 
 
 
@@ -6585,14 +7951,41 @@ The mean of `X_i` is `mu[i]`, and the standard deviation is `diag_stdev[i]`.
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.allow_nan_stats` {#MultivariateNormalDiag.allow_nan_stats}
 
-`Boolean` describing behavior when stats are undefined.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.batch_shape(name='batch_shape')` {#MultivariateNormalDiag.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -6601,36 +7994,48 @@ Batch dimensions of this instance as a 1-D int32 `Tensor`.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.dtype` {#MultivariateNormalDiag.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.entropy(name='entropy')` {#MultivariateNormalDiag.entropy}
 
-The entropies of these Multivariate Normals.
+Shanon entropy in nats.
 
-##### Args:
 
+- - -
 
-*  <b>`name`</b>: The name to give this op.
+#### `tf.contrib.distributions.MultivariateNormalDiag.event_shape(name='event_shape')` {#MultivariateNormalDiag.event_shape}
 
-##### Returns:
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
+##### Args:
 
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropies.
 
+*  <b>`name`</b>: name to give to the op
 
-- - -
+##### Returns:
 
-#### `tf.contrib.distributions.MultivariateNormalDiag.event_shape(name='event_shape')` {#MultivariateNormalDiag.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -6650,8 +8055,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -6694,14 +8099,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.get_batch_shape()` {#MultivariateNormalDiag.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.get_event_shape()` {#MultivariateNormalDiag.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -6722,48 +8141,86 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiag.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiag.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiag.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.MultivariateNormalDiag.log_prob(x, name='log_prob')` {#MultivariateNormalDiag.log_prob}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-Log prob of observations `x` given these Multivariate Normals.
+##### Returns:
 
-`x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-shape can be broadcast up to either:
 
-````
-self.batch_shape + self.event_shape
-OR
-[M1,...,Mm] + self.batch_shape + self.event_shape
-```
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalDiag.log_prob(value, name='log_prob')` {#MultivariateNormalDiag.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Compatible batch vector with same `dtype` as this distribution.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -6777,14 +8234,14 @@ Log of determinant of covariance matrix.
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.mean(name='mean')` {#MultivariateNormalDiag.mean}
 
-Mean of each batch member.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.mode(name='mode')` {#MultivariateNormalDiag.mode}
 
-Mode of each batch member.
+Mode.
 
 
 - - -
@@ -6798,7 +8255,7 @@ Mode of each batch member.
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.name` {#MultivariateNormalDiag.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -6843,89 +8300,125 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalDiag.parameters` {#MultivariateNormalDiag.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.pdf(value, name='pdf')` {#MultivariateNormalDiag.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.pmf(value, name='pmf')` {#MultivariateNormalDiag.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-#### `tf.contrib.distributions.MultivariateNormalDiag.prob(x, name='prob')` {#MultivariateNormalDiag.prob}
+##### Returns:
 
-The PDF of observations `x` under these Multivariate Normals.
 
-`x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-shape can be broadcast up to either:
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-````
-self.batch_shape + self.event_shape
-OR
-[M1,...,Mm] + self.batch_shape + self.event_shape
-```
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalDiag.prob(value, name='prob')` {#MultivariateNormalDiag.prob}
+
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Compatible batch vector with same `dtype` as this distribution.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the prob values of `x`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiag.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalDiag.sample_n}
 
-Sample `n` observations from the Multivariate Normal Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the hyperparameters.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -6946,21 +8439,21 @@ Determinant of covariance matrix.
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.std(name='std')` {#MultivariateNormalDiag.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.validate_args` {#MultivariateNormalDiag.validate_args}
 
-`Boolean` describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.variance(name='variance')` {#MultivariateNormalDiag.variance}
 
-Variance of each batch member.
+Variance.
 
 
 
@@ -7041,14 +8534,41 @@ User must provide means `mu` and `sigma`, the mean and covariance.
 
 #### `tf.contrib.distributions.MultivariateNormalFull.allow_nan_stats` {#MultivariateNormalFull.allow_nan_stats}
 
-`Boolean` describing behavior when stats are undefined.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.batch_shape(name='batch_shape')` {#MultivariateNormalFull.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -7057,36 +8577,48 @@ Batch dimensions of this instance as a 1-D int32 `Tensor`.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.dtype` {#MultivariateNormalFull.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.entropy(name='entropy')` {#MultivariateNormalFull.entropy}
 
-The entropies of these Multivariate Normals.
+Shanon entropy in nats.
 
-##### Args:
 
+- - -
 
-*  <b>`name`</b>: The name to give this op.
+#### `tf.contrib.distributions.MultivariateNormalFull.event_shape(name='event_shape')` {#MultivariateNormalFull.event_shape}
 
-##### Returns:
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
+##### Args:
 
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropies.
 
+*  <b>`name`</b>: name to give to the op
 
-- - -
+##### Returns:
 
-#### `tf.contrib.distributions.MultivariateNormalFull.event_shape(name='event_shape')` {#MultivariateNormalFull.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -7106,8 +8638,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -7150,14 +8682,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.MultivariateNormalFull.get_batch_shape()` {#MultivariateNormalFull.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.get_event_shape()` {#MultivariateNormalFull.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -7178,48 +8724,86 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.MultivariateNormalFull.log_cdf(value, name='log_cdf')` {#MultivariateNormalFull.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.log_pdf(value, name='log_pdf')` {#MultivariateNormalFull.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.log_pmf(value, name='log_pmf')` {#MultivariateNormalFull.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-#### `tf.contrib.distributions.MultivariateNormalFull.log_prob(x, name='log_prob')` {#MultivariateNormalFull.log_prob}
+##### Returns:
 
-Log prob of observations `x` given these Multivariate Normals.
 
-`x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-shape can be broadcast up to either:
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-````
-self.batch_shape + self.event_shape
-OR
-[M1,...,Mm] + self.batch_shape + self.event_shape
-```
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalFull.log_prob(value, name='log_prob')` {#MultivariateNormalFull.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Compatible batch vector with same `dtype` as this distribution.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -7233,14 +8817,14 @@ Log of determinant of covariance matrix.
 
 #### `tf.contrib.distributions.MultivariateNormalFull.mean(name='mean')` {#MultivariateNormalFull.mean}
 
-Mean of each batch member.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.mode(name='mode')` {#MultivariateNormalFull.mode}
 
-Mode of each batch member.
+Mode.
 
 
 - - -
@@ -7254,7 +8838,7 @@ Mode of each batch member.
 
 #### `tf.contrib.distributions.MultivariateNormalFull.name` {#MultivariateNormalFull.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -7299,89 +8883,125 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalFull.parameters` {#MultivariateNormalFull.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.pdf(value, name='pdf')` {#MultivariateNormalFull.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.pmf(value, name='pmf')` {#MultivariateNormalFull.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
 
-#### `tf.contrib.distributions.MultivariateNormalFull.prob(x, name='prob')` {#MultivariateNormalFull.prob}
 
-The PDF of observations `x` under these Multivariate Normals.
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-`x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-shape can be broadcast up to either:
+##### Raises:
 
-````
-self.batch_shape + self.event_shape
-OR
-[M1,...,Mm] + self.batch_shape + self.event_shape
-```
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalFull.prob(value, name='prob')` {#MultivariateNormalFull.prob}
+
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Compatible batch vector with same `dtype` as this distribution.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the prob values of `x`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalFull.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalFull.sample_n}
 
-Sample `n` observations from the Multivariate Normal Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the hyperparameters.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -7402,21 +9022,21 @@ Determinant of covariance matrix.
 
 #### `tf.contrib.distributions.MultivariateNormalFull.std(name='std')` {#MultivariateNormalFull.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.validate_args` {#MultivariateNormalFull.validate_args}
 
-`Boolean` describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.variance(name='variance')` {#MultivariateNormalFull.variance}
 
-Variance of each batch member.
+Variance.
 
 
 
@@ -7506,14 +9126,41 @@ factors, such that the covariance of each batch member is `chol chol^T`.
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.allow_nan_stats` {#MultivariateNormalCholesky.allow_nan_stats}
 
-`Boolean` describing behavior when stats are undefined.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.batch_shape(name='batch_shape')` {#MultivariateNormalCholesky.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -7522,36 +9169,48 @@ Batch dimensions of this instance as a 1-D int32 `Tensor`.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.dtype` {#MultivariateNormalCholesky.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.entropy(name='entropy')` {#MultivariateNormalCholesky.entropy}
 
-The entropies of these Multivariate Normals.
+Shanon entropy in nats.
 
-##### Args:
 
+- - -
 
-*  <b>`name`</b>: The name to give this op.
+#### `tf.contrib.distributions.MultivariateNormalCholesky.event_shape(name='event_shape')` {#MultivariateNormalCholesky.event_shape}
 
-##### Returns:
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
+##### Args:
 
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropies.
 
+*  <b>`name`</b>: name to give to the op
 
-- - -
+##### Returns:
 
-#### `tf.contrib.distributions.MultivariateNormalCholesky.event_shape(name='event_shape')` {#MultivariateNormalCholesky.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -7571,8 +9230,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -7615,14 +9274,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.get_batch_shape()` {#MultivariateNormalCholesky.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.get_event_shape()` {#MultivariateNormalCholesky.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -7643,48 +9316,86 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.log_cdf(value, name='log_cdf')` {#MultivariateNormalCholesky.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.log_pdf(value, name='log_pdf')` {#MultivariateNormalCholesky.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.log_pmf(value, name='log_pmf')` {#MultivariateNormalCholesky.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
 
-#### `tf.contrib.distributions.MultivariateNormalCholesky.log_prob(x, name='log_prob')` {#MultivariateNormalCholesky.log_prob}
 
-Log prob of observations `x` given these Multivariate Normals.
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-`x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-shape can be broadcast up to either:
+##### Raises:
 
-````
-self.batch_shape + self.event_shape
-OR
-[M1,...,Mm] + self.batch_shape + self.event_shape
-```
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob')` {#MultivariateNormalCholesky.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Compatible batch vector with same `dtype` as this distribution.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -7698,14 +9409,14 @@ Log of determinant of covariance matrix.
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.mean(name='mean')` {#MultivariateNormalCholesky.mean}
 
-Mean of each batch member.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.mode(name='mode')` {#MultivariateNormalCholesky.mode}
 
-Mode of each batch member.
+Mode.
 
 
 - - -
@@ -7719,7 +9430,7 @@ Mode of each batch member.
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.name` {#MultivariateNormalCholesky.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -7764,89 +9475,125 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalCholesky.parameters` {#MultivariateNormalCholesky.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.pdf(value, name='pdf')` {#MultivariateNormalCholesky.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.pmf(value, name='pmf')` {#MultivariateNormalCholesky.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-#### `tf.contrib.distributions.MultivariateNormalCholesky.prob(x, name='prob')` {#MultivariateNormalCholesky.prob}
+##### Returns:
 
-The PDF of observations `x` under these Multivariate Normals.
 
-`x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-shape can be broadcast up to either:
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
-````
-self.batch_shape + self.event_shape
-OR
-[M1,...,Mm] + self.batch_shape + self.event_shape
-```
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalCholesky.prob(value, name='prob')` {#MultivariateNormalCholesky.prob}
+
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Compatible batch vector with same `dtype` as this distribution.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the prob values of `x`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalCholesky.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalCholesky.sample_n}
 
-Sample `n` observations from the Multivariate Normal Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
-*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
-    observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
+
+##### Returns:
+
+
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
 
-##### Returns:
+##### Raises:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the hyperparameters.
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -7867,21 +9614,21 @@ Determinant of covariance matrix.
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.std(name='std')` {#MultivariateNormalCholesky.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.validate_args` {#MultivariateNormalCholesky.validate_args}
 
-`Boolean` describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.variance(name='variance')` {#MultivariateNormalCholesky.variance}
 
-Variance of each batch member.
+Variance.
 
 
 
@@ -8047,7 +9794,21 @@ dist = Dirichlet([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
 
 #### `tf.contrib.distributions.Dirichlet.allow_nan_stats` {#Dirichlet.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -8057,11 +9818,18 @@ Boolean describing behavior when a stat is undefined for batch member.
 Shape parameter.
 
 
+- - -
+
+#### `tf.contrib.distributions.Dirichlet.alpha_sum` {#Dirichlet.alpha_sum}
+
+Sum of shape parameter.
+
+
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.batch_shape(name='batch_shape')` {#Dirichlet.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -8073,35 +9841,48 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Dirichlet.cdf(x, name='cdf')` {#Dirichlet.cdf}
+#### `tf.contrib.distributions.Dirichlet.cdf(value, name='cdf')` {#Dirichlet.cdf}
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.dtype` {#Dirichlet.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.entropy(name='entropy')` {#Dirichlet.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.event_shape(name='event_shape')` {#Dirichlet.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -8110,7 +9891,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -8130,8 +9912,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -8174,26 +9956,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Dirichlet.get_batch_shape()` {#Dirichlet.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  batch shape
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.get_event_shape()` {#Dirichlet.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  event shape
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -8212,78 +9996,109 @@ Same meaning as `event_shape`. May be only partially defined.
 
 - - -
 
-#### `tf.contrib.distributions.Dirichlet.log_cdf(x, name='log_cdf')` {#Dirichlet.log_cdf}
+#### `tf.contrib.distributions.Dirichlet.log_cdf(value, name='log_cdf')` {#Dirichlet.log_cdf}
+
+Log cumulative distribution function.
 
-Log CDF.
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.log_pdf(value, name='log_pdf')` {#Dirichlet.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Dirichlet.log_pmf(value, name='log_pmf')` {#Dirichlet.log_pmf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
 
-Log of the probability mass function.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Dirichlet.log_prob(x, name='log_prob')` {#Dirichlet.log_prob}
+#### `tf.contrib.distributions.Dirichlet.log_pmf(value, name='log_pmf')` {#Dirichlet.log_pmf}
 
-`Log(P[counts])`, computed for every batch member.
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: Non-negative tensor with dtype `dtype` and whose shape can
-    be broadcast with `self.alpha`.  For fixed leading dimensions, the last
-    dimension represents counts for the corresponding Dirichlet distribution
-    in `self.alpha`. `x` is only legal if it sums up to one.
-*  <b>`name`</b>: Name to give this Op, defaults to "log_prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Log probabilities for each record, shape `[N1,...,Nm]`.
 
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-- - -
+##### Raises:
 
-#### `tf.contrib.distributions.Dirichlet.mean(name='mean')` {#Dirichlet.mean}
 
-Mean of the distribution.
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Dirichlet.mode(name='mode')` {#Dirichlet.mode}
-
-Mode of the distribution.
+#### `tf.contrib.distributions.Dirichlet.log_prob(value, name='log_prob')` {#Dirichlet.log_prob}
 
-Note that the mode for the Beta distribution is only defined
-when `alpha > 1`. This returns the mode when `alpha > 1`,
-and NaN otherwise. If `self.allow_nan_stats` is `False`, an exception
-will be raised rather than returning `NaN`.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`name`</b>: The name for this op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Mode of the Dirichlet distribution.
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Dirichlet.mean(name='mean')` {#Dirichlet.mean}
+
+Mean.
+
+
+- - -
+
+#### `tf.contrib.distributions.Dirichlet.mode(name='mode')` {#Dirichlet.mode}
+
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.name` {#Dirichlet.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -8328,103 +10143,146 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Dirichlet.parameters` {#Dirichlet.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.pdf(value, name='pdf')` {#Dirichlet.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.pmf(value, name='pmf')` {#Dirichlet.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Dirichlet.prob(x, name='prob')` {#Dirichlet.prob}
+#### `tf.contrib.distributions.Dirichlet.prob(value, name='prob')` {#Dirichlet.prob}
 
-`P[x]`, computed for every batch member.
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Non-negative tensor with dtype `dtype` and whose shape can
-    be broadcast with `self.alpha`.  For fixed leading dimensions, the last
-    dimension represents x for the corresponding Dirichlet distribution in
-    `self.alpha` and `self.beta`. `x` is only legal if it sums up to one.
-*  <b>`name`</b>: Name to give this Op, defaults to "prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Probabilities for each record, shape `[N1,...,Nm]`.
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.sample(sample_shape=(), seed=None, name='sample')` {#Dirichlet.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.sample_n(n, seed=None, name='sample_n')` {#Dirichlet.sample_n}
 
-Sample `n` observations from the distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the hyperparameters.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.std(name='std')` {#Dirichlet.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.validate_args` {#Dirichlet.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.variance(name='variance')` {#Dirichlet.variance}
 
-Variance of the distribution.
+Variance.
 
 
 
@@ -8543,7 +10401,21 @@ dist = DirichletMultinomial([3., 4], [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
 
 #### `tf.contrib.distributions.DirichletMultinomial.allow_nan_stats` {#DirichletMultinomial.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -8553,11 +10425,18 @@ Boolean describing behavior when a stat is undefined for batch member.
 Parameter defining this distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.alpha_sum` {#DirichletMultinomial.alpha_sum}
+
+Summation of alpha parameter.
+
+
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.batch_shape(name='batch_shape')` {#DirichletMultinomial.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -8569,35 +10448,48 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.cdf(x, name='cdf')` {#DirichletMultinomial.cdf}
+#### `tf.contrib.distributions.DirichletMultinomial.cdf(value, name='cdf')` {#DirichletMultinomial.cdf}
+
+Cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
 
 
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.dtype` {#DirichletMultinomial.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.entropy(name='entropy')` {#DirichletMultinomial.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.event_shape(name='event_shape')` {#DirichletMultinomial.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -8606,7 +10498,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -8626,8 +10519,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -8670,26 +10563,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.DirichletMultinomial.get_batch_shape()` {#DirichletMultinomial.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  batch shape
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.get_event_shape()` {#DirichletMultinomial.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  event shape
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -8708,64 +10603,102 @@ Same meaning as `event_shape`. May be only partially defined.
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.log_cdf(x, name='log_cdf')` {#DirichletMultinomial.log_cdf}
+#### `tf.contrib.distributions.DirichletMultinomial.log_cdf(value, name='log_cdf')` {#DirichletMultinomial.log_cdf}
+
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
 
 
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.log_pdf(value, name='log_pdf')` {#DirichletMultinomial.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.log_pmf(value, name='log_pmf')` {#DirichletMultinomial.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
 
-#### `tf.contrib.distributions.DirichletMultinomial.log_prob(counts, name='log_prob')` {#DirichletMultinomial.log_prob}
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
 
-`Log(P[counts])`, computed for every batch member.
+#### `tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob')` {#DirichletMultinomial.log_prob}
 
-For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
-that after sampling `n` draws from this Dirichlet Multinomial
-distribution, the number of draws falling in class `j` is `n_j`.  Note that
-different sequences of draws can result in the same counts, thus the
-probability includes a combinatorial coefficient.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`counts`</b>: Non-negative tensor with dtype `dtype` and whose shape can be
-    broadcast with `self.alpha`.  For fixed leading dimensions, the last
-    dimension represents counts for the corresponding Dirichlet Multinomial
-    distribution in `self.alpha`. `counts` is only legal if it sums up to
-    `n` and its components are equal to integer values.
-*  <b>`name`</b>: Name to give this Op, defaults to "log_prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Log probabilities for each record, shape `[N1,...,Nn]`.
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.mean(name='mean')` {#DirichletMultinomial.mean}
 
-Class means for every batch member.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.mode(name='mode')` {#DirichletMultinomial.mode}
 
-Mode of the distribution.
+Mode.
 
 
 - - -
@@ -8779,7 +10712,7 @@ Parameter defining this distribution.
 
 #### `tf.contrib.distributions.DirichletMultinomial.name` {#DirichletMultinomial.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -8824,68 +10757,100 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.parameters` {#DirichletMultinomial.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.pdf(value, name='pdf')` {#DirichletMultinomial.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.pmf(value, name='pmf')` {#DirichletMultinomial.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
 
-#### `tf.contrib.distributions.DirichletMultinomial.prob(counts, name='prob')` {#DirichletMultinomial.prob}
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
-`P[counts]`, computed for every batch member.
 
-For each batch of counts `[c_1,...,c_k]`, `P[counts]` is the probability
-that after sampling `sum_j c_j` draws from this Dirichlet Multinomial
-distribution, the number of draws falling in class `j` is `c_j`.  Note that
-different sequences of draws can result in the same counts, thus the
-probability includes a combinatorial coefficient.
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.prob(value, name='prob')` {#DirichletMultinomial.prob}
+
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`counts`</b>: Non-negative tensor with dtype `dtype` and whose shape can be
-    broadcast with `self.alpha`.  For fixed leading dimensions, the last
-    dimension represents counts for the corresponding Dirichlet Multinomial
-    distribution in `self.alpha`. `counts` is only legal if it sums up to
-    `n` and its components are equal to integer values.
-*  <b>`name`</b>: Name to give this Op, defaults to "prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Probabilities for each record, shape `[N1,...,Nn]`.
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.sample(sample_shape=(), seed=None, name='sample')` {#DirichletMultinomial.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
@@ -8897,61 +10862,41 @@ Generate `n` samples.
 ##### Args:
 
 
-*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
-
-
-- - -
-
-#### `tf.contrib.distributions.DirichletMultinomial.std(name='std')` {#DirichletMultinomial.std}
-
-Standard deviation of the distribution.
-
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
 
-- - -
+##### Raises:
 
-#### `tf.contrib.distributions.DirichletMultinomial.validate_args` {#DirichletMultinomial.validate_args}
 
-Boolean describing behavior on invalid input.
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.variance(name='mean')` {#DirichletMultinomial.variance}
-
-Class variances for every batch member.
-
-The variance for each batch member is defined as the following:
-
-```
-Var(X_j) = n * alpha_j / alpha_0 * (1 - alpha_j / alpha_0) *
-  (n + alpha_0) / (1 + alpha_0)
-```
+#### `tf.contrib.distributions.DirichletMultinomial.std(name='std')` {#DirichletMultinomial.std}
 
-where `alpha_0 = sum_j alpha_j`.
+Standard deviation.
 
-The covariance between elements in a batch is defined as:
 
-```
-Cov(X_i, X_j) = -n * alpha_i * alpha_j / alpha_0 ** 2 *
-  (n + alpha_0) / (1 + alpha_0)
-```
+- - -
 
-##### Args:
+#### `tf.contrib.distributions.DirichletMultinomial.validate_args` {#DirichletMultinomial.validate_args}
 
+Python boolean indicated possibly expensive checks are enabled.
 
-*  <b>`name`</b>: The name for this op.
 
-##### Returns:
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.variance(name='variance')` {#DirichletMultinomial.variance}
 
-  A `Tensor` representing the variances for each batch member.
+Variance.
 
 
 
@@ -9066,14 +11011,28 @@ dist = Multinomial(n=[4., 5], p=[[.1, .3, .6], [.4, .05, .55]])
 
 #### `tf.contrib.distributions.Multinomial.allow_nan_stats` {#Multinomial.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.batch_shape(name='batch_shape')` {#Multinomial.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -9085,7 +11044,8 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -9094,26 +11054,38 @@ independent distributions of this kind the instance represents.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.dtype` {#Multinomial.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.entropy(name='entropy')` {#Multinomial.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.event_shape(name='event_shape')` {#Multinomial.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -9122,7 +11094,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -9142,8 +11115,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -9186,26 +11159,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Multinomial.get_batch_shape()` {#Multinomial.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  batch shape
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.get_event_shape()` {#Multinomial.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  event shape
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -9226,48 +11201,86 @@ Same meaning as `event_shape`. May be only partially defined.
 
 #### `tf.contrib.distributions.Multinomial.log_cdf(value, name='log_cdf')` {#Multinomial.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.log_pdf(value, name='log_pdf')` {#Multinomial.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.log_pmf(value, name='log_pmf')` {#Multinomial.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
 
-#### `tf.contrib.distributions.Multinomial.log_prob(counts, name='log_prob')` {#Multinomial.log_prob}
 
-`Log(P[counts])`, computed for every batch member.
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
-For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
-that after sampling `n` draws from this Multinomial distribution, the
-number of draws falling in class `j` is `n_j`.  Note that different
-sequences of draws can result in the same counts, thus the probability
-includes a combinatorial coefficient.
+
+- - -
+
+#### `tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob')` {#Multinomial.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`counts`</b>: Non-negative tensor with dtype `dtype` and whose shape can
-    be broadcast with `self.p` and `self.n`.  For fixed leading dimensions,
-    the last dimension represents counts for the corresponding Multinomial
-    distribution in `self.p`. `counts` is only legal if it sums up to `n`
-    and its components are equal to integer values.
-*  <b>`name`</b>: Name to give this Op, defaults to "log_prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Log probabilities for each record, shape `[N1,...,Nm]`.
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -9281,14 +11294,14 @@ Log-odds.
 
 #### `tf.contrib.distributions.Multinomial.mean(name='mean')` {#Multinomial.mean}
 
-Mean of the distribution.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.mode(name='mode')` {#Multinomial.mode}
 
-Mode of the distribution.
+Mode.
 
 
 - - -
@@ -9302,7 +11315,7 @@ Number of trials.
 
 #### `tf.contrib.distributions.Multinomial.name` {#Multinomial.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -9354,68 +11367,100 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Multinomial.parameters` {#Multinomial.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Multinomial.pdf(value, name='pdf')` {#Multinomial.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.pmf(value, name='pmf')` {#Multinomial.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-#### `tf.contrib.distributions.Multinomial.prob(counts, name='prob')` {#Multinomial.prob}
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
-`P[counts]`, computed for every batch member.
 
-For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
-that after sampling `n` draws from this Multinomial distribution, the
-number of draws falling in class `j` is `n_j`.  Note that different
-sequences of draws can result in the same counts, thus the probability
-includes a combinatorial coefficient.
+- - -
+
+#### `tf.contrib.distributions.Multinomial.prob(value, name='prob')` {#Multinomial.prob}
+
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`counts`</b>: Non-negative tensor with dtype `dtype` and whose shape can
-    be broadcast with `self.p` and `self.n`.  For fixed leading dimensions,
-    the last dimension represents counts for the corresponding Multinomial
-    distribution in `self.p`. `counts` is only legal if it sums up to `n`
-    and its components are equal to integer values.
-*  <b>`name`</b>: Name to give this Op, defaults to "prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Probabilities for each record, shape `[N1,...,Nm]`.
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.sample(sample_shape=(), seed=None, name='sample')` {#Multinomial.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
@@ -9427,36 +11472,41 @@ Generate `n` samples.
 ##### Args:
 
 
-*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.std(name='std')` {#Multinomial.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.validate_args` {#Multinomial.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.variance(name='variance')` {#Multinomial.variance}
 
-Variance of the distribution.
+Variance.
 
 
 
@@ -9525,7 +11575,7 @@ dist.pdf(x)  # Shape is [2, 2].
 ```
 - - -
 
-#### `tf.contrib.distributions.WishartCholesky.__init__(df, scale, cholesky_input_output_matrices=False, allow_nan_stats=False, validate_args=True, name='Wishart')` {#WishartCholesky.__init__}
+#### `tf.contrib.distributions.WishartCholesky.__init__(df, scale, cholesky_input_output_matrices=False, validate_args=True, allow_nan_stats=False, name='WishartCholesky')` {#WishartCholesky.__init__}
 
 Construct Wishart distributions.
 
@@ -9541,13 +11591,13 @@ Construct Wishart distributions.
     Cholesky factored matrix. Example`log_pdf` input takes a Cholesky and
     `sample_n` returns a Cholesky when
     `cholesky_input_output_matrices=True`.
+*  <b>`validate_args`</b>: Whether to validate input with asserts. If `validate_args`
+    is `False`, and the inputs are invalid, correct behavior is not
+    guaranteed.
 *  <b>`allow_nan_stats`</b>: `Boolean`, default `False`. If `False`, raise an
     exception if a statistic (e.g., mean, mode) is undefined for any batch
     member. If True, batch members with valid parameters leading to
     undefined statistics will return `NaN` for this statistic.
-*  <b>`validate_args`</b>: Whether to validate input with asserts. If `validate_args`
-    is `False`, and the inputs are invalid, correct behavior is not
-    guaranteed.
 *  <b>`name`</b>: The name scope to give class member ops.
 
 
@@ -9555,14 +11605,41 @@ Construct Wishart distributions.
 
 #### `tf.contrib.distributions.WishartCholesky.allow_nan_stats` {#WishartCholesky.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.batch_shape(name='batch_shape')` {#WishartCholesky.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -9571,6 +11648,18 @@ Batch dimensions of this instance as a 1-D int32 `Tensor`.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
@@ -9597,21 +11686,31 @@ Dimension of underlying vector space. The `p` in `R^(p*p)`.
 
 #### `tf.contrib.distributions.WishartCholesky.dtype` {#WishartCholesky.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.entropy(name='entropy')` {#WishartCholesky.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.event_shape(name='event_shape')` {#WishartCholesky.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -9631,8 +11730,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -9675,33 +11774,40 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.WishartCholesky.get_batch_shape()` {#WishartCholesky.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.get_event_shape()` {#WishartCholesky.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
+Same meaning as `event_shape`. May be only partially defined.
 
-- - -
+##### Returns:
 
-#### `tf.contrib.distributions.WishartCholesky.inputs` {#WishartCholesky.inputs}
 
-Dictionary of inputs provided at initialization.
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartCholesky.is_continuous()` {#WishartCholesky.is_continuous}
+#### `tf.contrib.distributions.WishartCholesky.is_continuous` {#WishartCholesky.is_continuous}
 
 
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartCholesky.is_reparameterized()` {#WishartCholesky.is_reparameterized}
+#### `tf.contrib.distributions.WishartCholesky.is_reparameterized` {#WishartCholesky.is_reparameterized}
 
 
 
@@ -9710,7 +11816,19 @@ Dictionary of inputs provided at initialization.
 
 #### `tf.contrib.distributions.WishartCholesky.log_cdf(value, name='log_cdf')` {#WishartCholesky.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -9724,26 +11842,60 @@ Computes the log normalizing constant, log(Z).
 
 #### `tf.contrib.distributions.WishartCholesky.log_pdf(value, name='log_pdf')` {#WishartCholesky.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.log_pmf(value, name='log_pmf')` {#WishartCholesky.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartCholesky.log_prob(x, name='log_prob')` {#WishartCholesky.log_prob}
+#### `tf.contrib.distributions.WishartCholesky.log_prob(value, name='log_prob')` {#WishartCholesky.log_prob}
 
-Log of the probability density/mass function.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: `float` or `double` `Tensor`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
@@ -9757,7 +11909,7 @@ Log of the probability density/mass function.
 
 #### `tf.contrib.distributions.WishartCholesky.mean(name='mean')` {#WishartCholesky.mean}
 
-Mean of the distribution.
+Mean.
 
 
 - - -
@@ -9771,14 +11923,14 @@ Computes E[log(det(X))] under this Wishart distribution.
 
 #### `tf.contrib.distributions.WishartCholesky.mode(name='mode')` {#WishartCholesky.mode}
 
-Mode of the distribution.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.name` {#WishartCholesky.name}
 
-Name prepended to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -9815,84 +11967,133 @@ param_shapes with static (i.e. TensorShape) shapes.
 
 ##### Returns:
 
-  `dict` of parameter name to `TensorShape`.
+  `dict` of parameter name to `TensorShape`.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
+
+
+- - -
+
+#### `tf.contrib.distributions.WishartCholesky.parameters` {#WishartCholesky.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
+- - -
+
+#### `tf.contrib.distributions.WishartCholesky.pdf(value, name='pdf')` {#WishartCholesky.pdf}
+
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.WishartCholesky.pmf(value, name='pmf')` {#WishartCholesky.pmf}
+
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartCholesky.pdf(value, name='pdf')` {#WishartCholesky.pdf}
-
-The probability density function.
-
+#### `tf.contrib.distributions.WishartCholesky.prob(value, name='prob')` {#WishartCholesky.prob}
 
-- - -
+Probability density/mass function (depending on `is_continuous`).
 
-#### `tf.contrib.distributions.WishartCholesky.pmf(value, name='pmf')` {#WishartCholesky.pmf}
+##### Args:
 
-The probability mass function.
 
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-- - -
+##### Returns:
 
-#### `tf.contrib.distributions.WishartCholesky.prob(value, name='prob')` {#WishartCholesky.prob}
 
-Probability density/mass function.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.sample(sample_shape=(), seed=None, name='sample')` {#WishartCholesky.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartCholesky.sample_n(n, seed=None, name='sample')` {#WishartCholesky.sample_n}
+#### `tf.contrib.distributions.WishartCholesky.sample_n(n, seed=None, name='sample_n')` {#WishartCholesky.sample_n}
 
 Generate `n` samples.
 
-Complexity: O(nbk^3)
-
-The sampling procedure is based on the [Bartlett decomposition](
-https://en.wikipedia.org/wiki/Wishart_distribution#Bartlett_decomposition)
-and [using a Gamma distribution to generate Chi2 random variates](
-https://en.wikipedia.org/wiki/Chi-squared_distribution#Gamma.2C_exponential.2C_and_related_distributions).
-
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer; random number generator seed.
-*  <b>`name`</b>: The name of this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -9913,40 +12114,21 @@ Wishart distribution scale matrix as an OperatorPD.
 
 #### `tf.contrib.distributions.WishartCholesky.std(name='std')` {#WishartCholesky.std}
 
-Standard deviation of the Wishart distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.validate_args` {#WishartCholesky.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.variance(name='variance')` {#WishartCholesky.variance}
 
-Variance of the Wishart distribution.
-
-This function should not be confused with the covariance of the Wishart. The
-covariance matrix would have shape `q x q` where,
-`q = dimension * (dimension+1) / 2`
-and having elements corresponding to some mapping from a lower-triangular
-matrix to a vector-space.
-
-This function returns the diagonal of the Covariance matrix but shaped
-as a `dimension x dimension` matrix.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name of this op.
-
-##### Returns:
-
-
-*  <b>`variance`</b>: `Tensor` of dtype `self.dtype`.
+Variance.
 
 
 
@@ -10011,7 +12193,7 @@ dist.pdf(x)  # Shape is [2, 2].
 ```
 - - -
 
-#### `tf.contrib.distributions.WishartFull.__init__(df, scale, cholesky_input_output_matrices=False, allow_nan_stats=False, validate_args=True, name='Wishart')` {#WishartFull.__init__}
+#### `tf.contrib.distributions.WishartFull.__init__(df, scale, cholesky_input_output_matrices=False, validate_args=True, allow_nan_stats=False, name='WishartFull')` {#WishartFull.__init__}
 
 Construct Wishart distributions.
 
@@ -10027,13 +12209,13 @@ Construct Wishart distributions.
     Cholesky factored matrix. Example`log_pdf` input takes a Cholesky and
     `sample_n` returns a Cholesky when
     `cholesky_input_output_matrices=True`.
+*  <b>`validate_args`</b>: Whether to validate input with asserts. If `validate_args`
+    is `False`, and the inputs are invalid, correct behavior is not
+    guaranteed.
 *  <b>`allow_nan_stats`</b>: `Boolean`, default `False`. If `False`, raise an
     exception if a statistic (e.g., mean, mode) is undefined for any batch
     member. If True, batch members with valid parameters leading to
     undefined statistics will return `NaN` for this statistic.
-*  <b>`validate_args`</b>: Whether to validate input with asserts. If `validate_args`
-    is `False`, and the inputs are invalid, correct behavior is not
-    guaranteed.
 *  <b>`name`</b>: The name scope to give class member ops.
 
 
@@ -10041,14 +12223,41 @@ Construct Wishart distributions.
 
 #### `tf.contrib.distributions.WishartFull.allow_nan_stats` {#WishartFull.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.batch_shape(name='batch_shape')` {#WishartFull.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -10057,6 +12266,18 @@ Batch dimensions of this instance as a 1-D int32 `Tensor`.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
@@ -10083,21 +12304,31 @@ Dimension of underlying vector space. The `p` in `R^(p*p)`.
 
 #### `tf.contrib.distributions.WishartFull.dtype` {#WishartFull.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.entropy(name='entropy')` {#WishartFull.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.event_shape(name='event_shape')` {#WishartFull.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -10117,8 +12348,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -10161,33 +12392,40 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.WishartFull.get_batch_shape()` {#WishartFull.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.get_event_shape()` {#WishartFull.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
+Same meaning as `event_shape`. May be only partially defined.
 
-- - -
+##### Returns:
 
-#### `tf.contrib.distributions.WishartFull.inputs` {#WishartFull.inputs}
 
-Dictionary of inputs provided at initialization.
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartFull.is_continuous()` {#WishartFull.is_continuous}
+#### `tf.contrib.distributions.WishartFull.is_continuous` {#WishartFull.is_continuous}
 
 
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartFull.is_reparameterized()` {#WishartFull.is_reparameterized}
+#### `tf.contrib.distributions.WishartFull.is_reparameterized` {#WishartFull.is_reparameterized}
 
 
 
@@ -10196,7 +12434,19 @@ Dictionary of inputs provided at initialization.
 
 #### `tf.contrib.distributions.WishartFull.log_cdf(value, name='log_cdf')` {#WishartFull.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -10210,26 +12460,60 @@ Computes the log normalizing constant, log(Z).
 
 #### `tf.contrib.distributions.WishartFull.log_pdf(value, name='log_pdf')` {#WishartFull.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.log_pmf(value, name='log_pmf')` {#WishartFull.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartFull.log_prob(x, name='log_prob')` {#WishartFull.log_prob}
+#### `tf.contrib.distributions.WishartFull.log_prob(value, name='log_prob')` {#WishartFull.log_prob}
 
-Log of the probability density/mass function.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: `float` or `double` `Tensor`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
@@ -10243,7 +12527,7 @@ Log of the probability density/mass function.
 
 #### `tf.contrib.distributions.WishartFull.mean(name='mean')` {#WishartFull.mean}
 
-Mean of the distribution.
+Mean.
 
 
 - - -
@@ -10257,14 +12541,14 @@ Computes E[log(det(X))] under this Wishart distribution.
 
 #### `tf.contrib.distributions.WishartFull.mode(name='mode')` {#WishartFull.mode}
 
-Mode of the distribution.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.name` {#WishartFull.name}
 
-Name prepended to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -10309,76 +12593,125 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.WishartFull.parameters` {#WishartFull.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.WishartFull.pdf(value, name='pdf')` {#WishartFull.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.pmf(value, name='pmf')` {#WishartFull.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.prob(value, name='prob')` {#WishartFull.prob}
 
-Probability density/mass function.
+Probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.sample(sample_shape=(), seed=None, name='sample')` {#WishartFull.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartFull.sample_n(n, seed=None, name='sample')` {#WishartFull.sample_n}
+#### `tf.contrib.distributions.WishartFull.sample_n(n, seed=None, name='sample_n')` {#WishartFull.sample_n}
 
 Generate `n` samples.
 
-Complexity: O(nbk^3)
-
-The sampling procedure is based on the [Bartlett decomposition](
-https://en.wikipedia.org/wiki/Wishart_distribution#Bartlett_decomposition)
-and [using a Gamma distribution to generate Chi2 random variates](
-https://en.wikipedia.org/wiki/Chi-squared_distribution#Gamma.2C_exponential.2C_and_related_distributions).
-
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer; random number generator seed.
-*  <b>`name`</b>: The name of this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -10399,40 +12732,21 @@ Wishart distribution scale matrix as an OperatorPD.
 
 #### `tf.contrib.distributions.WishartFull.std(name='std')` {#WishartFull.std}
 
-Standard deviation of the Wishart distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.validate_args` {#WishartFull.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.variance(name='variance')` {#WishartFull.variance}
 
-Variance of the Wishart distribution.
-
-This function should not be confused with the covariance of the Wishart. The
-covariance matrix would have shape `q x q` where,
-`q = dimension * (dimension+1) / 2`
-and having elements corresponding to some mapping from a lower-triangular
-matrix to a vector-space.
-
-This function returns the diagonal of the Covariance matrix but shaped
-as a `dimension x dimension` matrix.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name of this op.
-
-##### Returns:
-
-
-*  <b>`variance`</b>: `Tensor` of dtype `self.dtype`.
+Variance.
 
 
 
@@ -10509,7 +12823,21 @@ Construct a Transformed Distribution.
 
 #### `tf.contrib.distributions.TransformedDistribution.allow_nan_stats` {#TransformedDistribution.allow_nan_stats}
 
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
 
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -10523,7 +12851,7 @@ Base distribution, p(x).
 
 #### `tf.contrib.distributions.TransformedDistribution.batch_shape(name='batch_shape')` {#TransformedDistribution.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -10531,11 +12859,12 @@ independent distributions of this kind the instance represents.
 ##### Args:
 
 
-*  <b>`name`</b>: name to give to the op.
+*  <b>`name`</b>: name to give to the op
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -10544,35 +12873,48 @@ independent distributions of this kind the instance represents.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.dtype` {#TransformedDistribution.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.entropy(name='entropy')` {#TransformedDistribution.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.event_shape(name='event_shape')` {#TransformedDistribution.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
 
-*  <b>`name`</b>: name to give to the op.
+*  <b>`name`</b>: name to give to the op
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -10592,8 +12934,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -10636,26 +12978,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.TransformedDistribution.get_batch_shape()` {#TransformedDistribution.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  batch shape
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.get_event_shape()` {#TransformedDistribution.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  event shape
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -10667,91 +13011,130 @@ Inverse function of transform, y => x.
 
 - - -
 
-#### `tf.contrib.distributions.TransformedDistribution.is_continuous` {#TransformedDistribution.is_continuous}
-
+#### `tf.contrib.distributions.TransformedDistribution.is_continuous` {#TransformedDistribution.is_continuous}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.TransformedDistribution.is_reparameterized` {#TransformedDistribution.is_reparameterized}
+
+
+
+
+- - -
+
+#### `tf.contrib.distributions.TransformedDistribution.log_cdf(value, name='log_cdf')` {#TransformedDistribution.log_cdf}
+
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+
+- - -
+
+#### `tf.contrib.distributions.TransformedDistribution.log_det_jacobian` {#TransformedDistribution.log_det_jacobian}
+
+Function computing the log determinant of the Jacobian of transform.
+
+
+- - -
+
+#### `tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf')` {#TransformedDistribution.log_pdf}
 
+Log probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.TransformedDistribution.is_reparameterized` {#TransformedDistribution.is_reparameterized}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
+##### Returns:
 
 
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-- - -
+##### Raises:
 
-#### `tf.contrib.distributions.TransformedDistribution.log_cdf(value, name='log_cdf')` {#TransformedDistribution.log_cdf}
 
-Log CDF.
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.TransformedDistribution.log_det_jacobian` {#TransformedDistribution.log_det_jacobian}
+#### `tf.contrib.distributions.TransformedDistribution.log_pmf(value, name='log_pmf')` {#TransformedDistribution.log_pmf}
 
-Function computing the log determinant of the Jacobian of transform.
+Log probability mass function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf')` {#TransformedDistribution.log_pdf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-Log of the probability density function.
+##### Returns:
 
 
-- - -
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-#### `tf.contrib.distributions.TransformedDistribution.log_pmf(value, name='log_pmf')` {#TransformedDistribution.log_pmf}
+##### Raises:
 
-Log of the probability mass function.
 
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
-- - -
 
-#### `tf.contrib.distributions.TransformedDistribution.log_prob(y, name='log_prob')` {#TransformedDistribution.log_prob}
+- - -
 
-Log prob of observations in `y`.
+#### `tf.contrib.distributions.TransformedDistribution.log_prob(value, name='log_prob')` {#TransformedDistribution.log_prob}
 
-`log ( p(g(y)) / det|J(g(y))| )`, where `g` is the inverse of `transform`.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`y`</b>: tensor of dtype `dtype`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `y`.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if `inverse` was not provided to the distribution and `y` was
-      not returned from `sample`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.mean(name='mean')` {#TransformedDistribution.mean}
 
-Mean of the distribution.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.mode(name='mode')` {#TransformedDistribution.mode}
 
-Mode of the distribution.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.name` {#TransformedDistribution.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -10796,90 +13179,132 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.TransformedDistribution.parameters` {#TransformedDistribution.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.pdf(value, name='pdf')` {#TransformedDistribution.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.pmf(value, name='pmf')` {#TransformedDistribution.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
-#### `tf.contrib.distributions.TransformedDistribution.prob(y, name='prob')` {#TransformedDistribution.prob}
 
-The prob of observations in `y`.
+- - -
+
+#### `tf.contrib.distributions.TransformedDistribution.prob(value, name='prob')` {#TransformedDistribution.prob}
 
-`p(g(y)) / det|J(g(y))|`, where `g` is the inverse of `transform`.
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`y`</b>: `Tensor` of dtype `dtype`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`pdf`</b>: `Tensor` of dtype `dtype`, the pdf values of `y`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.sample(sample_shape=(), seed=None, name='sample')` {#TransformedDistribution.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.sample_n(n, seed=None, name='sample_n')` {#TransformedDistribution.sample_n}
 
-Sample `n` observations.
-
-Samples from the base distribution and then passes through the transform.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.std(name='std')` {#TransformedDistribution.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
@@ -10893,14 +13318,14 @@ Function transforming x => y.
 
 #### `tf.contrib.distributions.TransformedDistribution.validate_args` {#TransformedDistribution.validate_args}
 
-
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.variance(name='variance')` {#TransformedDistribution.variance}
 
-Variance of the distribution.
+Variance.
 
 
 
@@ -11098,48 +13523,24 @@ that want to fulfill a simpler distribution contract.
 
 #### `tf.contrib.distributions.BaseDistribution.log_prob(value, name='log_prob')` {#BaseDistribution.log_prob}
 
-Log of the probability density/mass function.
-
-
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.name` {#BaseDistribution.name}
-
-Name to prepend to all ops.
-
-
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.prob(value, name='prob')` {#BaseDistribution.prob}
-
-Probability density/mass function.
-
-
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.sample(sample_shape=(), seed=None, name='sample')` {#BaseDistribution.sample}
-
-Generate samples of the specified shape.
-
-Note that a call to `sample()` without arguments will generate a single
-sample.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
-*  <b>`seed`</b>: Python integer seed for RNG
-*  <b>`name`</b>: name to give to the op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.BaseDistribution.sample_n(n, seed=None, name='sample_n')` {#BaseDistribution.sample_n}
+#### `tf.contrib.distributions.BaseDistribution.sample_n(n, seed=None, name='sample')` {#BaseDistribution.sample_n}
 
 Generate `n` samples.
 
@@ -11156,6 +13557,11 @@ Generate `n` samples.
 
 *  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
 
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
+
 
 
 - - -
@@ -11270,14 +13676,41 @@ D = is diagonal (r x r), optional (defaults to identity).
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.allow_nan_stats` {#MultivariateNormalDiagPlusVDVT.allow_nan_stats}
 
-`Boolean` describing behavior when stats are undefined.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.batch_shape(name='batch_shape')` {#MultivariateNormalDiagPlusVDVT.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -11286,36 +13719,48 @@ Batch dimensions of this instance as a 1-D int32 `Tensor`.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.dtype` {#MultivariateNormalDiagPlusVDVT.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.entropy(name='entropy')` {#MultivariateNormalDiagPlusVDVT.entropy}
 
-The entropies of these Multivariate Normals.
+Shanon entropy in nats.
 
-##### Args:
 
+- - -
 
-*  <b>`name`</b>: The name to give this op.
+#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.event_shape(name='event_shape')` {#MultivariateNormalDiagPlusVDVT.event_shape}
 
-##### Returns:
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
+##### Args:
 
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropies.
 
+*  <b>`name`</b>: name to give to the op
 
-- - -
+##### Returns:
 
-#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.event_shape(name='event_shape')` {#MultivariateNormalDiagPlusVDVT.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -11335,8 +13780,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -11379,14 +13824,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.get_batch_shape()` {#MultivariateNormalDiagPlusVDVT.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.get_event_shape()` {#MultivariateNormalDiagPlusVDVT.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -11407,48 +13866,86 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiagPlusVDVT.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiagPlusVDVT.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiagPlusVDVT.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(x, name='log_prob')` {#MultivariateNormalDiagPlusVDVT.log_prob}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-Log prob of observations `x` given these Multivariate Normals.
+##### Returns:
 
-`x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-shape can be broadcast up to either:
 
-````
-self.batch_shape + self.event_shape
-OR
-[M1,...,Mm] + self.batch_shape + self.event_shape
-```
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob')` {#MultivariateNormalDiagPlusVDVT.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Compatible batch vector with same `dtype` as this distribution.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -11462,14 +13959,14 @@ Log of determinant of covariance matrix.
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mean(name='mean')` {#MultivariateNormalDiagPlusVDVT.mean}
 
-Mean of each batch member.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mode(name='mode')` {#MultivariateNormalDiagPlusVDVT.mode}
 
-Mode of each batch member.
+Mode.
 
 
 - - -
@@ -11483,7 +13980,7 @@ Mode of each batch member.
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.name` {#MultivariateNormalDiagPlusVDVT.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -11528,89 +14025,125 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.parameters` {#MultivariateNormalDiagPlusVDVT.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pdf(value, name='pdf')` {#MultivariateNormalDiagPlusVDVT.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pmf(value, name='pmf')` {#MultivariateNormalDiagPlusVDVT.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(x, name='prob')` {#MultivariateNormalDiagPlusVDVT.prob}
+##### Returns:
 
-The PDF of observations `x` under these Multivariate Normals.
 
-`x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-shape can be broadcast up to either:
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-````
-self.batch_shape + self.event_shape
-OR
-[M1,...,Mm] + self.batch_shape + self.event_shape
-```
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob')` {#MultivariateNormalDiagPlusVDVT.prob}
+
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Compatible batch vector with same `dtype` as this distribution.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the prob values of `x`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiagPlusVDVT.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalDiagPlusVDVT.sample_n}
 
-Sample `n` observations from the Multivariate Normal Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the hyperparameters.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -11631,21 +14164,21 @@ Determinant of covariance matrix.
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.std(name='std')` {#MultivariateNormalDiagPlusVDVT.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.validate_args` {#MultivariateNormalDiagPlusVDVT.validate_args}
 
-`Boolean` describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.variance(name='variance')` {#MultivariateNormalDiagPlusVDVT.variance}
 
-Variance of each batch member.
+Variance.
 
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.bayesflow.stochastic_graph.DistributionTensor.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.bayesflow.stochastic_graph.DistributionTensor.md
index 4f8e8d87d1..f1c599588c 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.bayesflow.stochastic_graph.DistributionTensor.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.bayesflow.stochastic_graph.DistributionTensor.md
@@ -26,7 +26,7 @@ reparameterized distributions; it will also return None if the value type is
 ##### Args:
 
 
-*  <b>`dist_cls`</b>: a class deriving from `BaseDistribution`.
+*  <b>`dist_cls`</b>: a `Distribution` class.
 *  <b>`name`</b>: a name for this `DistributionTensor` and its ops.
 *  <b>`dist_value_type`</b>: a `_StochasticValueType`, which will determine what the
       `value` of this `DistributionTensor` will be. If not provided, the
@@ -40,6 +40,12 @@ reparameterized distributions; it will also return None if the value type is
 *  <b>`**dist_args`</b>: keyword arguments to be passed through to `dist_cls` on
       construction.
 
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `dist_cls` is not a `Distribution`.
+*  <b>`TypeError`</b>: if `loss_fn` is not `callable`.
+
 
 - - -
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md
index 98ba7f6071..235aac6beb 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md
@@ -37,14 +37,41 @@ Construct Bernoulli distributions.
 
 #### `tf.contrib.distributions.Bernoulli.allow_nan_stats` {#Bernoulli.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.batch_shape(name='batch_shape')` {#Bernoulli.batch_shape}
 
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
 
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -53,36 +80,48 @@ Boolean describing behavior when a stat is undefined for batch member.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.dtype` {#Bernoulli.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.entropy(name='entropy')` {#Bernoulli.entropy}
 
-Entropy of the distribution.
-
-##### Args:
+Shanon entropy in nats.
 
 
-*  <b>`name`</b>: Name for the op.
+- - -
 
-##### Returns:
+#### `tf.contrib.distributions.Bernoulli.event_shape(name='event_shape')` {#Bernoulli.event_shape}
 
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
-*  <b>`entropy`</b>: `Tensor` of the same type and shape as `p`.
+##### Args:
 
 
-- - -
+*  <b>`name`</b>: name to give to the op
 
-#### `tf.contrib.distributions.Bernoulli.event_shape(name='event_shape')` {#Bernoulli.event_shape}
+##### Returns:
 
 
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -102,8 +141,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -146,15 +185,29 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Bernoulli.get_batch_shape()` {#Bernoulli.get_batch_shape}
 
+Shape of a single sample from a single event index as a `TensorShape`.
 
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.get_event_shape()` {#Bernoulli.get_event_shape}
 
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
 
 
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
+
 
 - - -
 
@@ -174,88 +227,114 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Bernoulli.log_cdf(value, name='log_cdf')` {#Bernoulli.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.log_pdf(value, name='log_pdf')` {#Bernoulli.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf')` {#Bernoulli.log_pmf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
 
-Log of the probability mass function.
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Bernoulli.log_prob(event, name='log_prob')` {#Bernoulli.log_prob}
+#### `tf.contrib.distributions.Bernoulli.log_pmf(value, name='log_pmf')` {#Bernoulli.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`event`</b>: `int32` or `int64` binary Tensor.
-*  <b>`name`</b>: A name for this operation (optional).
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The log-probabilities of the events.
 
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-- - -
-
-#### `tf.contrib.distributions.Bernoulli.logits` {#Bernoulli.logits}
+##### Raises:
 
 
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Bernoulli.mean(name='mean')` {#Bernoulli.mean}
+#### `tf.contrib.distributions.Bernoulli.log_prob(value, name='log_prob')` {#Bernoulli.log_prob}
 
-Mean of the distribution.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`name`</b>: Name for the op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`mean`</b>: `Tensor` of the same type and shape as `p`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Bernoulli.mode(name='mode')` {#Bernoulli.mode}
+#### `tf.contrib.distributions.Bernoulli.logits` {#Bernoulli.logits}
 
-Mode of the distribution.
 
-1 if p > 1-p. 0 otherwise.
 
-##### Args:
 
+- - -
+
+#### `tf.contrib.distributions.Bernoulli.mean(name='mean')` {#Bernoulli.mean}
+
+Mean.
 
-*  <b>`name`</b>: Name for the op.
 
-##### Returns:
+- - -
 
+#### `tf.contrib.distributions.Bernoulli.mode(name='mode')` {#Bernoulli.mode}
 
-*  <b>`mode`</b>: binary `Tensor` of type self.dtype.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.name` {#Bernoulli.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -307,35 +386,78 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Bernoulli.parameters` {#Bernoulli.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.pdf(value, name='pdf')` {#Bernoulli.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.pmf(value, name='pmf')` {#Bernoulli.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Bernoulli.prob(event, name='prob')` {#Bernoulli.prob}
+#### `tf.contrib.distributions.Bernoulli.prob(value, name='prob')` {#Bernoulli.prob}
 
-Probability mass function.
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`event`</b>: `int32` or `int64` binary Tensor; must be broadcastable with `p`.
-*  <b>`name`</b>: A name for this operation.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The probabilities of the events.
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -349,23 +471,22 @@ Probability mass function.
 
 #### `tf.contrib.distributions.Bernoulli.sample(sample_shape=(), seed=None, name='sample')` {#Bernoulli.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
@@ -379,54 +500,38 @@ Generate `n` samples.
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer seed for RNG.
+*  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape` with values of type
-      `self.dtype`.
-
-
-- - -
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
 
-#### `tf.contrib.distributions.Bernoulli.std(name='std')` {#Bernoulli.std}
-
-Standard deviation of the distribution.
+##### Raises:
 
-##### Args:
 
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
-*  <b>`name`</b>: Name for the op.
 
-##### Returns:
+- - -
 
+#### `tf.contrib.distributions.Bernoulli.std(name='std')` {#Bernoulli.std}
 
-*  <b>`std`</b>: `Tensor` of the same type and shape as `p`.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.validate_args` {#Bernoulli.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.variance(name='variance')` {#Bernoulli.variance}
 
-Variance of the distribution.
-
-##### Args:
-
-
-*  <b>`name`</b>: Name for the op.
-
-##### Returns:
-
-
-*  <b>`variance`</b>: `Tensor` of the same type and shape as `p`.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md
index 9964b0115a..0da51e79e2 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Dirichlet.md
@@ -97,7 +97,21 @@ dist = Dirichlet([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
 
 #### `tf.contrib.distributions.Dirichlet.allow_nan_stats` {#Dirichlet.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -107,11 +121,18 @@ Boolean describing behavior when a stat is undefined for batch member.
 Shape parameter.
 
 
+- - -
+
+#### `tf.contrib.distributions.Dirichlet.alpha_sum` {#Dirichlet.alpha_sum}
+
+Sum of shape parameter.
+
+
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.batch_shape(name='batch_shape')` {#Dirichlet.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -123,35 +144,48 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Dirichlet.cdf(x, name='cdf')` {#Dirichlet.cdf}
+#### `tf.contrib.distributions.Dirichlet.cdf(value, name='cdf')` {#Dirichlet.cdf}
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.dtype` {#Dirichlet.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.entropy(name='entropy')` {#Dirichlet.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.event_shape(name='event_shape')` {#Dirichlet.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -160,7 +194,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -180,8 +215,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -224,26 +259,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Dirichlet.get_batch_shape()` {#Dirichlet.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  batch shape
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.get_event_shape()` {#Dirichlet.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  event shape
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -262,78 +299,109 @@ Same meaning as `event_shape`. May be only partially defined.
 
 - - -
 
-#### `tf.contrib.distributions.Dirichlet.log_cdf(x, name='log_cdf')` {#Dirichlet.log_cdf}
+#### `tf.contrib.distributions.Dirichlet.log_cdf(value, name='log_cdf')` {#Dirichlet.log_cdf}
+
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-Log CDF.
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.log_pdf(value, name='log_pdf')` {#Dirichlet.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Dirichlet.log_pmf(value, name='log_pmf')` {#Dirichlet.log_pmf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
 
-Log of the probability mass function.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Dirichlet.log_prob(x, name='log_prob')` {#Dirichlet.log_prob}
+#### `tf.contrib.distributions.Dirichlet.log_pmf(value, name='log_pmf')` {#Dirichlet.log_pmf}
 
-`Log(P[counts])`, computed for every batch member.
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: Non-negative tensor with dtype `dtype` and whose shape can
-    be broadcast with `self.alpha`.  For fixed leading dimensions, the last
-    dimension represents counts for the corresponding Dirichlet distribution
-    in `self.alpha`. `x` is only legal if it sums up to one.
-*  <b>`name`</b>: Name to give this Op, defaults to "log_prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Log probabilities for each record, shape `[N1,...,Nm]`.
 
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-- - -
+##### Raises:
 
-#### `tf.contrib.distributions.Dirichlet.mean(name='mean')` {#Dirichlet.mean}
 
-Mean of the distribution.
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Dirichlet.mode(name='mode')` {#Dirichlet.mode}
-
-Mode of the distribution.
+#### `tf.contrib.distributions.Dirichlet.log_prob(value, name='log_prob')` {#Dirichlet.log_prob}
 
-Note that the mode for the Beta distribution is only defined
-when `alpha > 1`. This returns the mode when `alpha > 1`,
-and NaN otherwise. If `self.allow_nan_stats` is `False`, an exception
-will be raised rather than returning `NaN`.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`name`</b>: The name for this op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Mode of the Dirichlet distribution.
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Dirichlet.mean(name='mean')` {#Dirichlet.mean}
+
+Mean.
+
+
+- - -
+
+#### `tf.contrib.distributions.Dirichlet.mode(name='mode')` {#Dirichlet.mode}
+
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.name` {#Dirichlet.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -378,102 +446,145 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Dirichlet.parameters` {#Dirichlet.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.pdf(value, name='pdf')` {#Dirichlet.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.pmf(value, name='pmf')` {#Dirichlet.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Dirichlet.prob(x, name='prob')` {#Dirichlet.prob}
+#### `tf.contrib.distributions.Dirichlet.prob(value, name='prob')` {#Dirichlet.prob}
 
-`P[x]`, computed for every batch member.
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Non-negative tensor with dtype `dtype` and whose shape can
-    be broadcast with `self.alpha`.  For fixed leading dimensions, the last
-    dimension represents x for the corresponding Dirichlet distribution in
-    `self.alpha` and `self.beta`. `x` is only legal if it sums up to one.
-*  <b>`name`</b>: Name to give this Op, defaults to "prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Probabilities for each record, shape `[N1,...,Nm]`.
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.sample(sample_shape=(), seed=None, name='sample')` {#Dirichlet.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.sample_n(n, seed=None, name='sample_n')` {#Dirichlet.sample_n}
 
-Sample `n` observations from the distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the hyperparameters.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.std(name='std')` {#Dirichlet.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.validate_args` {#Dirichlet.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Dirichlet.variance(name='variance')` {#Dirichlet.variance}
 
-Variance of the distribution.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md
index 10c613a163..80ae0062e6 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Distribution.md
@@ -1,19 +1,22 @@
-Fully-featured abstract base class for probability distributions.
+A generic probability distribution base class.
 
-This class defines the API for probability distributions. Users will only ever
-instantiate subclasses of `Distribution`.
+`Distribution` is a base class for constructing and organizing properties
+(e.g., mean, variance) of random variables (e.g, Bernoulli, Gaussian).
 
-### API
+### Subclassing
 
-The key methods for probability distributions are defined here.
+Subclasess are expected to implement a leading-underscore version of the
+same-named function.  The argument signature should be identical except for
+the omission of `name="..."`.  For example, to enable `log_prob(value,
+name="log_prob")` a subclass should implement `_log_prob(value)`.
 
-To keep ops generated by the distribution tied together by name, subclasses
-should override `name` and use it to prepend names of ops in other methods
-(see `cdf` for an example).
+Subclasses can rewrite/append to public-level docstrings. For example,
 
-Subclasses that wish to support `cdf` and `log_cdf` can override `log_cdf`
-and use the base class's implementation for `cdf`, or vice versa. The same
-goes for `log_prob` and `prob`.
+```python
+Subclass.prob.__func__.__doc__ += "Some other details."
+```
+
+would add the string "Some other details." to the `prob` function docstring.
 
 ### Broadcasting, batching, and shapes
 
@@ -102,16 +105,57 @@ dist.mean().eval()
 ```
 - - -
 
+#### `tf.contrib.distributions.Distribution.__init__(dtype=None, parameters=None, is_continuous=True, is_reparameterized=False, validate_args=True, allow_nan_stats=False, name=None)` {#Distribution.__init__}
+
+Constructs the `Distribution`.
+
+##### Args:
+
+
+*  <b>`dtype`</b>: The type of the event samples. `None` implies no type-enforcement.
+*  <b>`parameters`</b>: Python dictionary of parameters used by this `Distribution`.
+*  <b>`is_continuous`</b>: Python boolean, default `True`. If `True` this
+    `Distribution` is continuous over its supported domain.
+*  <b>`is_reparameterized`</b>: Python boolean, default `False`. If `True` this
+    `Distribution` can be reparameterized in terms of some standard
+    distribution with a function whose Jacobian is constant for the support
+    of the standard distribution.
+*  <b>`validate_args`</b>: Whether to validate input with asserts. If `validate_args`
+    is `False`, and the inputs are invalid, correct behavior is not
+    guaranteed.
+*  <b>`allow_nan_stats`</b>: Python boolean, default `False`. If `False`, raise an
+    exception if a statistic (e.g., mean, mode) is undefined for any batch
+    member. If True, batch members with valid parameters leading to
+    undefined statistics will return `NaN` for this statistic.
+*  <b>`name`</b>: A name for this distribution (optional).
+
+
+- - -
+
 #### `tf.contrib.distributions.Distribution.allow_nan_stats` {#Distribution.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.batch_shape(name='batch_shape')` {#Distribution.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -123,7 +167,8 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -132,26 +177,38 @@ independent distributions of this kind the instance represents.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.dtype` {#Distribution.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.entropy(name='entropy')` {#Distribution.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.event_shape(name='event_shape')` {#Distribution.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -160,7 +217,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -180,8 +238,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -224,19 +282,29 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Distribution.get_batch_shape()` {#Distribution.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
+
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.get_event_shape()` {#Distribution.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
+
 
 - - -
 
@@ -256,49 +324,107 @@ Same meaning as `event_shape`. May be only partially defined.
 
 #### `tf.contrib.distributions.Distribution.log_cdf(value, name='log_cdf')` {#Distribution.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.log_pdf(value, name='log_pdf')` {#Distribution.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.log_pmf(value, name='log_pmf')` {#Distribution.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.log_prob(value, name='log_prob')` {#Distribution.log_prob}
 
-Log of the probability density/mass function.
+Log probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.mean(name='mean')` {#Distribution.mean}
 
-Mean of the distribution.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.mode(name='mode')` {#Distribution.mode}
 
-Mode of the distribution.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.name` {#Distribution.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -343,48 +469,100 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Distribution.parameters` {#Distribution.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Distribution.pdf(value, name='pdf')` {#Distribution.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.pmf(value, name='pmf')` {#Distribution.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.prob(value, name='prob')` {#Distribution.prob}
 
-Probability density/mass function.
+Probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.sample(sample_shape=(), seed=None, name='sample')` {#Distribution.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
@@ -396,35 +574,40 @@ Generate `n` samples.
 ##### Args:
 
 
-*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.std(name='std')` {#Distribution.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.validate_args` {#Distribution.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Distribution.variance(name='variance')` {#Distribution.variance}
 
-Variance of the distribution.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md
index c34b12543a..085d7a660e 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.MultivariateNormalCholesky.md
@@ -80,14 +80,41 @@ factors, such that the covariance of each batch member is `chol chol^T`.
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.allow_nan_stats` {#MultivariateNormalCholesky.allow_nan_stats}
 
-`Boolean` describing behavior when stats are undefined.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.batch_shape(name='batch_shape')` {#MultivariateNormalCholesky.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -96,36 +123,48 @@ Batch dimensions of this instance as a 1-D int32 `Tensor`.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.dtype` {#MultivariateNormalCholesky.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.entropy(name='entropy')` {#MultivariateNormalCholesky.entropy}
 
-The entropies of these Multivariate Normals.
+Shanon entropy in nats.
 
-##### Args:
 
+- - -
 
-*  <b>`name`</b>: The name to give this op.
+#### `tf.contrib.distributions.MultivariateNormalCholesky.event_shape(name='event_shape')` {#MultivariateNormalCholesky.event_shape}
 
-##### Returns:
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
+##### Args:
 
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropies.
 
+*  <b>`name`</b>: name to give to the op
 
-- - -
+##### Returns:
 
-#### `tf.contrib.distributions.MultivariateNormalCholesky.event_shape(name='event_shape')` {#MultivariateNormalCholesky.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -145,8 +184,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -189,14 +228,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.get_batch_shape()` {#MultivariateNormalCholesky.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.get_event_shape()` {#MultivariateNormalCholesky.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -217,48 +270,86 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.log_cdf(value, name='log_cdf')` {#MultivariateNormalCholesky.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.log_pdf(value, name='log_pdf')` {#MultivariateNormalCholesky.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.log_pmf(value, name='log_pmf')` {#MultivariateNormalCholesky.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.MultivariateNormalCholesky.log_prob(x, name='log_prob')` {#MultivariateNormalCholesky.log_prob}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-Log prob of observations `x` given these Multivariate Normals.
+##### Returns:
 
-`x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-shape can be broadcast up to either:
 
-````
-self.batch_shape + self.event_shape
-OR
-[M1,...,Mm] + self.batch_shape + self.event_shape
-```
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalCholesky.log_prob(value, name='log_prob')` {#MultivariateNormalCholesky.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Compatible batch vector with same `dtype` as this distribution.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -272,14 +363,14 @@ Log of determinant of covariance matrix.
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.mean(name='mean')` {#MultivariateNormalCholesky.mean}
 
-Mean of each batch member.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.mode(name='mode')` {#MultivariateNormalCholesky.mode}
 
-Mode of each batch member.
+Mode.
 
 
 - - -
@@ -293,7 +384,7 @@ Mode of each batch member.
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.name` {#MultivariateNormalCholesky.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -338,89 +429,125 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalCholesky.parameters` {#MultivariateNormalCholesky.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.pdf(value, name='pdf')` {#MultivariateNormalCholesky.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.pmf(value, name='pmf')` {#MultivariateNormalCholesky.pmf}
 
-The probability mass function.
+Probability mass function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.MultivariateNormalCholesky.prob(x, name='prob')` {#MultivariateNormalCholesky.prob}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-The PDF of observations `x` under these Multivariate Normals.
+##### Returns:
 
-`x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-shape can be broadcast up to either:
 
-````
-self.batch_shape + self.event_shape
-OR
-[M1,...,Mm] + self.batch_shape + self.event_shape
-```
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalCholesky.prob(value, name='prob')` {#MultivariateNormalCholesky.prob}
+
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Compatible batch vector with same `dtype` as this distribution.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the prob values of `x`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalCholesky.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalCholesky.sample_n}
 
-Sample `n` observations from the Multivariate Normal Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the hyperparameters.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -441,20 +568,20 @@ Determinant of covariance matrix.
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.std(name='std')` {#MultivariateNormalCholesky.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.validate_args` {#MultivariateNormalCholesky.validate_args}
 
-`Boolean` describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalCholesky.variance(name='variance')` {#MultivariateNormalCholesky.variance}
 
-Variance of each batch member.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md
index fb6fff957b..bc8fb0e828 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.MultivariateNormalDiag.md
@@ -79,14 +79,41 @@ The mean of `X_i` is `mu[i]`, and the standard deviation is `diag_stdev[i]`.
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.allow_nan_stats` {#MultivariateNormalDiag.allow_nan_stats}
 
-`Boolean` describing behavior when stats are undefined.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.batch_shape(name='batch_shape')` {#MultivariateNormalDiag.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -95,36 +122,48 @@ Batch dimensions of this instance as a 1-D int32 `Tensor`.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.dtype` {#MultivariateNormalDiag.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.entropy(name='entropy')` {#MultivariateNormalDiag.entropy}
 
-The entropies of these Multivariate Normals.
+Shanon entropy in nats.
 
-##### Args:
 
+- - -
 
-*  <b>`name`</b>: The name to give this op.
+#### `tf.contrib.distributions.MultivariateNormalDiag.event_shape(name='event_shape')` {#MultivariateNormalDiag.event_shape}
 
-##### Returns:
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
+##### Args:
 
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropies.
 
+*  <b>`name`</b>: name to give to the op
 
-- - -
+##### Returns:
 
-#### `tf.contrib.distributions.MultivariateNormalDiag.event_shape(name='event_shape')` {#MultivariateNormalDiag.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -144,8 +183,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -188,14 +227,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.get_batch_shape()` {#MultivariateNormalDiag.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.get_event_shape()` {#MultivariateNormalDiag.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -216,48 +269,86 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiag.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiag.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiag.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.MultivariateNormalDiag.log_prob(x, name='log_prob')` {#MultivariateNormalDiag.log_prob}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-Log prob of observations `x` given these Multivariate Normals.
+##### Returns:
 
-`x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-shape can be broadcast up to either:
 
-````
-self.batch_shape + self.event_shape
-OR
-[M1,...,Mm] + self.batch_shape + self.event_shape
-```
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalDiag.log_prob(value, name='log_prob')` {#MultivariateNormalDiag.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Compatible batch vector with same `dtype` as this distribution.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -271,14 +362,14 @@ Log of determinant of covariance matrix.
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.mean(name='mean')` {#MultivariateNormalDiag.mean}
 
-Mean of each batch member.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.mode(name='mode')` {#MultivariateNormalDiag.mode}
 
-Mode of each batch member.
+Mode.
 
 
 - - -
@@ -292,7 +383,7 @@ Mode of each batch member.
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.name` {#MultivariateNormalDiag.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -337,89 +428,125 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalDiag.parameters` {#MultivariateNormalDiag.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.pdf(value, name='pdf')` {#MultivariateNormalDiag.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.pmf(value, name='pmf')` {#MultivariateNormalDiag.pmf}
 
-The probability mass function.
+Probability mass function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.MultivariateNormalDiag.prob(x, name='prob')` {#MultivariateNormalDiag.prob}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-The PDF of observations `x` under these Multivariate Normals.
+##### Returns:
 
-`x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-shape can be broadcast up to either:
 
-````
-self.batch_shape + self.event_shape
-OR
-[M1,...,Mm] + self.batch_shape + self.event_shape
-```
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalDiag.prob(value, name='prob')` {#MultivariateNormalDiag.prob}
+
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Compatible batch vector with same `dtype` as this distribution.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the prob values of `x`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiag.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalDiag.sample_n}
 
-Sample `n` observations from the Multivariate Normal Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the hyperparameters.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -440,20 +567,20 @@ Determinant of covariance matrix.
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.std(name='std')` {#MultivariateNormalDiag.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.validate_args` {#MultivariateNormalDiag.validate_args}
 
-`Boolean` describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiag.variance(name='variance')` {#MultivariateNormalDiag.variance}
 
-Variance of each batch member.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md
index 427199e5f1..3109e3e4ac 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md
@@ -82,14 +82,41 @@ broadcasting (e.g. `df + mu + sigma` is a valid operation).
 
 #### `tf.contrib.distributions.StudentT.allow_nan_stats` {#StudentT.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.batch_shape(name='batch_shape')` {#StudentT.batch_shape}
 
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -98,6 +125,18 @@ Boolean describing behavior when a stat is undefined for batch member.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
@@ -110,31 +149,31 @@ Degrees of freedom in these Student's t distribution(s).
 
 #### `tf.contrib.distributions.StudentT.dtype` {#StudentT.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.entropy(name='entropy')` {#StudentT.entropy}
 
-The entropy of Student t distribution(s).
-
-##### Args:
+Shanon entropy in nats.
 
 
-*  <b>`name`</b>: The name to give this op.
+- - -
 
-##### Returns:
+#### `tf.contrib.distributions.StudentT.event_shape(name='event_shape')` {#StudentT.event_shape}
 
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+##### Args:
 
 
-- - -
+*  <b>`name`</b>: name to give to the op
 
-#### `tf.contrib.distributions.StudentT.event_shape(name='event_shape')` {#StudentT.event_shape}
+##### Returns:
 
 
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -154,8 +193,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -198,14 +237,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.StudentT.get_batch_shape()` {#StudentT.get_batch_shape}
 
+Shape of a single sample from a single event index as a `TensorShape`.
 
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.get_event_shape()` {#StudentT.get_event_shape}
 
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
 
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -226,66 +279,100 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.StudentT.log_cdf(value, name='log_cdf')` {#StudentT.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.log_pdf(value, name='log_pdf')` {#StudentT.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.StudentT.log_pmf(value, name='log_pmf')` {#StudentT.log_pmf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
 
-Log of the probability mass function.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.StudentT.log_prob(x, name='log_prob')` {#StudentT.log_prob}
+#### `tf.contrib.distributions.StudentT.log_pmf(value, name='log_pmf')` {#StudentT.log_pmf}
 
-Log prob of observations in `x` under these Student's t-distribution(s).
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `df`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
+##### Raises:
 
-- - -
 
-#### `tf.contrib.distributions.StudentT.mean(name='mean')` {#StudentT.mean}
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
-Mean of the distribution.
 
-The mean of Student's T equals `mu` if `df > 1`, otherwise it is `NaN`.  If
-`self.allow_nan_stats=False`, then an exception will be raised rather than
-returning `NaN`.
+- - -
+
+#### `tf.contrib.distributions.StudentT.log_prob(value, name='log_prob')` {#StudentT.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`name`</b>: A name to give this op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The mean for every batch member, a `Tensor` with same `dtype` as self.
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.StudentT.mode(name='mode')` {#StudentT.mode}
+#### `tf.contrib.distributions.StudentT.mean(name='mean')` {#StudentT.mean}
+
+Mean.
 
 
+- - -
+
+#### `tf.contrib.distributions.StudentT.mode(name='mode')` {#StudentT.mode}
+
+Mode.
 
 
 - - -
@@ -299,7 +386,7 @@ Locations of these Student's t distribution(s).
 
 #### `tf.contrib.distributions.StudentT.name` {#StudentT.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -344,81 +431,125 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.StudentT.parameters` {#StudentT.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.StudentT.pdf(value, name='pdf')` {#StudentT.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.pmf(value, name='pmf')` {#StudentT.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.StudentT.prob(x, name='prob')` {#StudentT.prob}
+#### `tf.contrib.distributions.StudentT.prob(value, name='prob')` {#StudentT.prob}
 
-The PDF of observations in `x` under these Student's t distribution(s).
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `df`, `mu`, and
-    `sigma`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the prob values of `x`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.sample(sample_shape=(), seed=None, name='sample')` {#StudentT.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.sample_n(n, seed=None, name='sample_n')` {#StudentT.sample_n}
 
-Sample `n` observations from the Student t Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -432,41 +563,20 @@ Scaling factors of these Student's t distribution(s).
 
 #### `tf.contrib.distributions.StudentT.std(name='std')` {#StudentT.std}
 
-
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.validate_args` {#StudentT.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.StudentT.variance(name='variance')` {#StudentT.variance}
 
-Variance of the distribution.
-
-Variance for Student's T equals
-
-```
-df / (df - 2), when df > 2
-infinity, when 1 < df <= 2
-NaN, when df <= 1
-```
-
-The NaN state occurs because mean is undefined for `df <= 1`, and if
-`self.allow_nan_stats` is `False`, an exception will be raised if any batch
-members fall into this state.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name for this op.
-
-##### Returns:
-
-  The variance for every batch member, a `Tensor` with same `dtype` as self.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md
index cbc87efd29..6cc092faad 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.TransformedDistribution.md
@@ -64,7 +64,21 @@ Construct a Transformed Distribution.
 
 #### `tf.contrib.distributions.TransformedDistribution.allow_nan_stats` {#TransformedDistribution.allow_nan_stats}
 
+Python boolean describing behavior when a stat is undefined.
 
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -78,7 +92,7 @@ Base distribution, p(x).
 
 #### `tf.contrib.distributions.TransformedDistribution.batch_shape(name='batch_shape')` {#TransformedDistribution.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -86,11 +100,12 @@ independent distributions of this kind the instance represents.
 ##### Args:
 
 
-*  <b>`name`</b>: name to give to the op.
+*  <b>`name`</b>: name to give to the op
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -99,35 +114,48 @@ independent distributions of this kind the instance represents.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.dtype` {#TransformedDistribution.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.entropy(name='entropy')` {#TransformedDistribution.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.event_shape(name='event_shape')` {#TransformedDistribution.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
 
-*  <b>`name`</b>: name to give to the op.
+*  <b>`name`</b>: name to give to the op
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -147,8 +175,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -191,26 +219,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.TransformedDistribution.get_batch_shape()` {#TransformedDistribution.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  batch shape
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.get_event_shape()` {#TransformedDistribution.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  event shape
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -238,7 +268,19 @@ Inverse function of transform, y => x.
 
 #### `tf.contrib.distributions.TransformedDistribution.log_cdf(value, name='log_cdf')` {#TransformedDistribution.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -252,61 +294,88 @@ Function computing the log determinant of the Jacobian of transform.
 
 #### `tf.contrib.distributions.TransformedDistribution.log_pdf(value, name='log_pdf')` {#TransformedDistribution.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.TransformedDistribution.log_pmf(value, name='log_pmf')` {#TransformedDistribution.log_pmf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
 
-Log of the probability mass function.
 
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-- - -
+##### Raises:
 
-#### `tf.contrib.distributions.TransformedDistribution.log_prob(y, name='log_prob')` {#TransformedDistribution.log_prob}
 
-Log prob of observations in `y`.
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
-`log ( p(g(y)) / det|J(g(y))| )`, where `g` is the inverse of `transform`.
+
+- - -
+
+#### `tf.contrib.distributions.TransformedDistribution.log_pmf(value, name='log_pmf')` {#TransformedDistribution.log_pmf}
+
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`y`</b>: tensor of dtype `dtype`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_pdf`</b>: tensor of dtype `dtype`, the log-PDFs of `y`.
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`ValueError`</b>: if `inverse` was not provided to the distribution and `y` was
-      not returned from `sample`.
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.TransformedDistribution.log_prob(value, name='log_prob')` {#TransformedDistribution.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.mean(name='mean')` {#TransformedDistribution.mean}
 
-Mean of the distribution.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.mode(name='mode')` {#TransformedDistribution.mode}
 
-Mode of the distribution.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.name` {#TransformedDistribution.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -351,90 +420,132 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.TransformedDistribution.parameters` {#TransformedDistribution.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.pdf(value, name='pdf')` {#TransformedDistribution.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.pmf(value, name='pmf')` {#TransformedDistribution.pmf}
 
-The probability mass function.
+Probability mass function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
 
-- - -
 
-#### `tf.contrib.distributions.TransformedDistribution.prob(y, name='prob')` {#TransformedDistribution.prob}
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
-The prob of observations in `y`.
 
-`p(g(y)) / det|J(g(y))|`, where `g` is the inverse of `transform`.
+- - -
+
+#### `tf.contrib.distributions.TransformedDistribution.prob(value, name='prob')` {#TransformedDistribution.prob}
+
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`y`</b>: `Tensor` of dtype `dtype`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`pdf`</b>: `Tensor` of dtype `dtype`, the pdf values of `y`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.sample(sample_shape=(), seed=None, name='sample')` {#TransformedDistribution.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.sample_n(n, seed=None, name='sample_n')` {#TransformedDistribution.sample_n}
 
-Sample `n` observations.
-
-Samples from the base distribution and then passes through the transform.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.std(name='std')` {#TransformedDistribution.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
@@ -448,13 +559,13 @@ Function transforming x => y.
 
 #### `tf.contrib.distributions.TransformedDistribution.validate_args` {#TransformedDistribution.validate_args}
 
-
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.TransformedDistribution.variance(name='variance')` {#TransformedDistribution.variance}
 
-Variance of the distribution.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md
index 701ab200ed..bd3b960af8 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md
@@ -28,14 +28,41 @@ Initialize Categorical distributions using class log-probabilities.
 
 #### `tf.contrib.distributions.Categorical.allow_nan_stats` {#Categorical.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.batch_shape(name='batch_shape')` {#Categorical.batch_shape}
 
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
 
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -44,27 +71,49 @@ Boolean describing behavior when a stat is undefined for batch member.
 
 Cumulative distribution function.
 
+##### Args:
+
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-#### `tf.contrib.distributions.Categorical.dtype` {#Categorical.dtype}
+##### Returns:
 
 
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Categorical.entropy(name='sample')` {#Categorical.entropy}
+#### `tf.contrib.distributions.Categorical.dtype` {#Categorical.dtype}
+
+The `DType` of `Tensor`s handled by this `Distribution`.
+
 
+- - -
+
+#### `tf.contrib.distributions.Categorical.entropy(name='entropy')` {#Categorical.entropy}
 
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.event_shape(name='event_shape')` {#Categorical.event_shape}
 
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
+
+##### Args:
 
 
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `Tensor`.
+
 
 - - -
 
@@ -83,8 +132,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -127,15 +176,29 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Categorical.get_batch_shape()` {#Categorical.get_batch_shape}
 
+Shape of a single sample from a single event index as a `TensorShape`.
 
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.get_event_shape()` {#Categorical.get_event_shape}
 
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
 
 
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
+
 
 - - -
 
@@ -155,39 +218,86 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Categorical.log_cdf(value, name='log_cdf')` {#Categorical.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.log_pdf(value, name='log_pdf')` {#Categorical.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.log_pmf(value, name='log_pmf')` {#Categorical.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Categorical.log_prob(k, name='log_prob')` {#Categorical.log_prob}
+#### `tf.contrib.distributions.Categorical.log_prob(value, name='log_prob')` {#Categorical.log_prob}
 
-Log-probability of class `k`.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`k`</b>: `int32` or `int64` Tensor. Must be broadcastable with a `batch_shape`
-    `Tensor`.
-*  <b>`name`</b>: A name for this operation (optional).
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The log-probabilities of the classes indexed by `k`
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -201,21 +311,21 @@ Log-probability of class `k`.
 
 #### `tf.contrib.distributions.Categorical.mean(name='mean')` {#Categorical.mean}
 
-Mean of the distribution.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.mode(name='mode')` {#Categorical.mode}
 
-
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.name` {#Categorical.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -267,97 +377,145 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Categorical.parameters` {#Categorical.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Categorical.pdf(value, name='pdf')` {#Categorical.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.pmf(value, name='pmf')` {#Categorical.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Categorical.prob(k, name='prob')` {#Categorical.prob}
+#### `tf.contrib.distributions.Categorical.prob(value, name='prob')` {#Categorical.prob}
 
-Probability of class `k`.
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`k`</b>: `int32` or `int64` Tensor. Must be broadcastable with logits.
-*  <b>`name`</b>: A name for this operation (optional).
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The probabilities of the classes indexed by `k`
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.sample(sample_shape=(), seed=None, name='sample')` {#Categorical.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.sample_n(n, seed=None, name='sample_n')` {#Categorical.sample_n}
 
-Sample `n` observations from the Categorical distribution.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Random seed (optional).
-*  <b>`name`</b>: A name for this operation (optional).
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
-  An `int64` `Tensor` with shape `[n, batch_shape, event_shape]`
+
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.std(name='std')` {#Categorical.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.validate_args` {#Categorical.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Categorical.variance(name='variance')` {#Categorical.variance}
 
-Variance of the distribution.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md
index fb4d719a40..9734c0420f 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md
@@ -31,7 +31,21 @@ Construct Chi2 distributions with parameter `df`.
 
 #### `tf.contrib.distributions.Chi2.allow_nan_stats` {#Chi2.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -45,7 +59,7 @@ Shape parameter.
 
 #### `tf.contrib.distributions.Chi2.batch_shape(name='batch_shape')` {#Chi2.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -57,7 +71,8 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -69,20 +84,21 @@ Inverse scale parameter.
 
 - - -
 
-#### `tf.contrib.distributions.Chi2.cdf(x, name='cdf')` {#Chi2.cdf}
+#### `tf.contrib.distributions.Chi2.cdf(value, name='cdf')` {#Chi2.cdf}
 
-CDF of observations `x` under these Gamma distribution(s).
+Cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -96,40 +112,21 @@ CDF of observations `x` under these Gamma distribution(s).
 
 #### `tf.contrib.distributions.Chi2.dtype` {#Chi2.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.entropy(name='entropy')` {#Chi2.entropy}
 
-The entropy of Gamma distribution(s).
-
-This is defined to be
-
-```
-entropy = alpha - log(beta) + log(Gamma(alpha))
-             + (1-alpha)digamma(alpha)
-```
-
-where digamma(alpha) is the digamma function.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.event_shape(name='event_shape')` {#Chi2.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -138,7 +135,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -158,8 +156,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -202,26 +200,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Chi2.get_batch_shape()` {#Chi2.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  `TensorShape` object.
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.get_event_shape()` {#Chi2.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  `TensorShape` object.
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -240,91 +240,109 @@ Same meaning as `event_shape`. May be only partially defined.
 
 - - -
 
-#### `tf.contrib.distributions.Chi2.log_cdf(x, name='log_cdf')` {#Chi2.log_cdf}
+#### `tf.contrib.distributions.Chi2.log_cdf(value, name='log_cdf')` {#Chi2.log_cdf}
 
-Log CDF of observations `x` under these Gamma distribution(s).
+Log cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.log_pdf(value, name='log_pdf')` {#Chi2.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Chi2.log_pmf(value, name='log_pmf')` {#Chi2.log_pmf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
 
-Log of the probability mass function.
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Chi2.log_prob(x, name='log_prob')` {#Chi2.log_prob}
+#### `tf.contrib.distributions.Chi2.log_pmf(value, name='log_pmf')` {#Chi2.log_pmf}
 
-Log prob of observations in `x` under these Gamma distribution(s).
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Chi2.mean(name='mean')` {#Chi2.mean}
+#### `tf.contrib.distributions.Chi2.log_prob(value, name='log_prob')` {#Chi2.log_prob}
 
-Mean of each batch member.
+Log probability density/mass function (depending on `is_continuous`).
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Chi2.mode(name='mode')` {#Chi2.mode}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-Mode of each batch member.
+##### Returns:
 
-The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`,
-and `NaN` otherwise.  If `self.allow_nan_stats` is `False`, an exception
-will be raised rather than returning `NaN`.
 
-##### Args:
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
-*  <b>`name`</b>: A name to give this op.
+- - -
 
-##### Returns:
+#### `tf.contrib.distributions.Chi2.mean(name='mean')` {#Chi2.mean}
 
-  The mode for every batch member, a `Tensor` with same `dtype` as self.
+Mean.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.mode(name='mode')` {#Chi2.mode}
+
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.name` {#Chi2.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -371,105 +389,143 @@ param_shapes with static (i.e. TensorShape) shapes.
 
 - - -
 
-#### `tf.contrib.distributions.Chi2.pdf(value, name='pdf')` {#Chi2.pdf}
+#### `tf.contrib.distributions.Chi2.parameters` {#Chi2.parameters}
 
-The probability density function.
+Dictionary of parameters used by this `Distribution`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Chi2.pmf(value, name='pmf')` {#Chi2.pmf}
+#### `tf.contrib.distributions.Chi2.pdf(value, name='pdf')` {#Chi2.pdf}
+
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
 
-The probability mass function.
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Chi2.prob(x, name='prob')` {#Chi2.prob}
+#### `tf.contrib.distributions.Chi2.pmf(value, name='pmf')` {#Chi2.pmf}
 
-Pdf of observations in `x` under these Gamma distribution(s).
+Probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the PDFs of `x`
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Chi2.prob(value, name='prob')` {#Chi2.prob}
+
+Probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.sample(sample_shape=(), seed=None, name='sample')` {#Chi2.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.sample_n(n, seed=None, name='sample_n')` {#Chi2.sample_n}
 
-Draws `n` samples from the Gamma distribution(s).
-
-See the doc for tf.random_gamma for further detail.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed for this operation.
-*  <b>`name`</b>: Optional name for the operation.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.std(name='std')` {#Chi2.std}
 
-Standard deviation of this distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.validate_args` {#Chi2.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Chi2.variance(name='variance')` {#Chi2.variance}
 
-Variance of each batch member.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md
index 457b15e39c..c40c7637d6 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md
@@ -58,7 +58,21 @@ u1 = Uniform(3.0, [5.0, 6.0, 7.0])  # 3 distributions
 
 #### `tf.contrib.distributions.Uniform.allow_nan_stats` {#Uniform.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -72,57 +86,70 @@ Boolean describing behavior when a stat is undefined for batch member.
 
 #### `tf.contrib.distributions.Uniform.batch_shape(name='batch_shape')` {#Uniform.batch_shape}
 
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
 
 
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
+
 
 - - -
 
-#### `tf.contrib.distributions.Uniform.cdf(x, name='cdf')` {#Uniform.cdf}
+#### `tf.contrib.distributions.Uniform.cdf(value, name='cdf')` {#Uniform.cdf}
 
-CDF of observations in `x` under these Uniform distribution(s).
+Cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `a` and `b`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`. If `x` is `nan`, will
-      return `nan`.
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.dtype` {#Uniform.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.entropy(name='entropy')` {#Uniform.entropy}
 
-The entropy of Uniform distribution(s).
+Shanon entropy in nats.
 
-##### Args:
 
+- - -
 
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
+#### `tf.contrib.distributions.Uniform.event_shape(name='event_shape')` {#Uniform.event_shape}
 
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+##### Args:
 
 
-- - -
+*  <b>`name`</b>: name to give to the op
 
-#### `tf.contrib.distributions.Uniform.event_shape(name='event_shape')` {#Uniform.event_shape}
+##### Returns:
 
 
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -142,8 +169,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -186,15 +213,29 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Uniform.get_batch_shape()` {#Uniform.get_batch_shape}
 
+Shape of a single sample from a single event index as a `TensorShape`.
 
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.get_event_shape()` {#Uniform.get_event_shape}
 
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
 
 
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
+
 
 - - -
 
@@ -212,51 +253,109 @@ apply it externally and set `make_safe=False`.
 
 - - -
 
-#### `tf.contrib.distributions.Uniform.log_cdf(x, name='log_cdf')` {#Uniform.log_cdf}
+#### `tf.contrib.distributions.Uniform.log_cdf(value, name='log_cdf')` {#Uniform.log_cdf}
+
+Log cumulative distribution function.
+
+##### Args:
 
 
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.log_pdf(value, name='log_pdf')` {#Uniform.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.log_pmf(value, name='log_pmf')` {#Uniform.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Uniform.log_prob(x, name='log_prob')` {#Uniform.log_prob}
+#### `tf.contrib.distributions.Uniform.log_prob(value, name='log_prob')` {#Uniform.log_prob}
 
+Log probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
 
 
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.mean(name='mean')` {#Uniform.mean}
 
-
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.mode(name='mode')` {#Uniform.mode}
 
-Mode of the distribution.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.name` {#Uniform.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -301,37 +400,78 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Uniform.parameters` {#Uniform.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Uniform.pdf(value, name='pdf')` {#Uniform.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.pmf(value, name='pmf')` {#Uniform.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Uniform.prob(x, name='prob')` {#Uniform.prob}
+#### `tf.contrib.distributions.Uniform.prob(value, name='prob')` {#Uniform.prob}
 
-The PDF of observations in `x` under these Uniform distribution(s).
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `a` and `b`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the prob values of `x`. If `x` is `nan`,
-      will return `nan`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -345,64 +485,67 @@ The PDF of observations in `x` under these Uniform distribution(s).
 
 #### `tf.contrib.distributions.Uniform.sample(sample_shape=(), seed=None, name='sample')` {#Uniform.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.sample_n(n, seed=None, name='sample_n')` {#Uniform.sample_n}
 
-Sample `n` observations from the Uniform Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.std(name='std')` {#Uniform.std}
 
-
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.validate_args` {#Uniform.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Uniform.variance(name='variance')` {#Uniform.variance}
 
-
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.WishartCholesky.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.WishartCholesky.md
index e7dd32ef8b..fd5a7b705f 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.WishartCholesky.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.WishartCholesky.md
@@ -59,7 +59,7 @@ dist.pdf(x)  # Shape is [2, 2].
 ```
 - - -
 
-#### `tf.contrib.distributions.WishartCholesky.__init__(df, scale, cholesky_input_output_matrices=False, allow_nan_stats=False, validate_args=True, name='Wishart')` {#WishartCholesky.__init__}
+#### `tf.contrib.distributions.WishartCholesky.__init__(df, scale, cholesky_input_output_matrices=False, validate_args=True, allow_nan_stats=False, name='WishartCholesky')` {#WishartCholesky.__init__}
 
 Construct Wishart distributions.
 
@@ -75,13 +75,13 @@ Construct Wishart distributions.
     Cholesky factored matrix. Example`log_pdf` input takes a Cholesky and
     `sample_n` returns a Cholesky when
     `cholesky_input_output_matrices=True`.
+*  <b>`validate_args`</b>: Whether to validate input with asserts. If `validate_args`
+    is `False`, and the inputs are invalid, correct behavior is not
+    guaranteed.
 *  <b>`allow_nan_stats`</b>: `Boolean`, default `False`. If `False`, raise an
     exception if a statistic (e.g., mean, mode) is undefined for any batch
     member. If True, batch members with valid parameters leading to
     undefined statistics will return `NaN` for this statistic.
-*  <b>`validate_args`</b>: Whether to validate input with asserts. If `validate_args`
-    is `False`, and the inputs are invalid, correct behavior is not
-    guaranteed.
 *  <b>`name`</b>: The name scope to give class member ops.
 
 
@@ -89,14 +89,41 @@ Construct Wishart distributions.
 
 #### `tf.contrib.distributions.WishartCholesky.allow_nan_stats` {#WishartCholesky.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.batch_shape(name='batch_shape')` {#WishartCholesky.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -105,6 +132,18 @@ Batch dimensions of this instance as a 1-D int32 `Tensor`.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
@@ -131,21 +170,31 @@ Dimension of underlying vector space. The `p` in `R^(p*p)`.
 
 #### `tf.contrib.distributions.WishartCholesky.dtype` {#WishartCholesky.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.entropy(name='entropy')` {#WishartCholesky.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.event_shape(name='event_shape')` {#WishartCholesky.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -165,8 +214,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -209,33 +258,40 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.WishartCholesky.get_batch_shape()` {#WishartCholesky.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.get_event_shape()` {#WishartCholesky.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
+Same meaning as `event_shape`. May be only partially defined.
 
-- - -
+##### Returns:
 
-#### `tf.contrib.distributions.WishartCholesky.inputs` {#WishartCholesky.inputs}
 
-Dictionary of inputs provided at initialization.
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartCholesky.is_continuous()` {#WishartCholesky.is_continuous}
+#### `tf.contrib.distributions.WishartCholesky.is_continuous` {#WishartCholesky.is_continuous}
 
 
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartCholesky.is_reparameterized()` {#WishartCholesky.is_reparameterized}
+#### `tf.contrib.distributions.WishartCholesky.is_reparameterized` {#WishartCholesky.is_reparameterized}
 
 
 
@@ -244,7 +300,19 @@ Dictionary of inputs provided at initialization.
 
 #### `tf.contrib.distributions.WishartCholesky.log_cdf(value, name='log_cdf')` {#WishartCholesky.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -258,26 +326,60 @@ Computes the log normalizing constant, log(Z).
 
 #### `tf.contrib.distributions.WishartCholesky.log_pdf(value, name='log_pdf')` {#WishartCholesky.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.log_pmf(value, name='log_pmf')` {#WishartCholesky.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartCholesky.log_prob(x, name='log_prob')` {#WishartCholesky.log_prob}
+#### `tf.contrib.distributions.WishartCholesky.log_prob(value, name='log_prob')` {#WishartCholesky.log_prob}
 
-Log of the probability density/mass function.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: `float` or `double` `Tensor`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
@@ -291,7 +393,7 @@ Log of the probability density/mass function.
 
 #### `tf.contrib.distributions.WishartCholesky.mean(name='mean')` {#WishartCholesky.mean}
 
-Mean of the distribution.
+Mean.
 
 
 - - -
@@ -305,14 +407,14 @@ Computes E[log(det(X))] under this Wishart distribution.
 
 #### `tf.contrib.distributions.WishartCholesky.mode(name='mode')` {#WishartCholesky.mode}
 
-Mode of the distribution.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.name` {#WishartCholesky.name}
 
-Name prepended to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -357,76 +459,125 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.WishartCholesky.parameters` {#WishartCholesky.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.pdf(value, name='pdf')` {#WishartCholesky.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.pmf(value, name='pmf')` {#WishartCholesky.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.prob(value, name='prob')` {#WishartCholesky.prob}
 
-Probability density/mass function.
+Probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.sample(sample_shape=(), seed=None, name='sample')` {#WishartCholesky.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartCholesky.sample_n(n, seed=None, name='sample')` {#WishartCholesky.sample_n}
+#### `tf.contrib.distributions.WishartCholesky.sample_n(n, seed=None, name='sample_n')` {#WishartCholesky.sample_n}
 
 Generate `n` samples.
 
-Complexity: O(nbk^3)
-
-The sampling procedure is based on the [Bartlett decomposition](
-https://en.wikipedia.org/wiki/Wishart_distribution#Bartlett_decomposition)
-and [using a Gamma distribution to generate Chi2 random variates](
-https://en.wikipedia.org/wiki/Chi-squared_distribution#Gamma.2C_exponential.2C_and_related_distributions).
-
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer; random number generator seed.
-*  <b>`name`</b>: The name of this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -447,39 +598,20 @@ Wishart distribution scale matrix as an OperatorPD.
 
 #### `tf.contrib.distributions.WishartCholesky.std(name='std')` {#WishartCholesky.std}
 
-Standard deviation of the Wishart distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.validate_args` {#WishartCholesky.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartCholesky.variance(name='variance')` {#WishartCholesky.variance}
 
-Variance of the Wishart distribution.
-
-This function should not be confused with the covariance of the Wishart. The
-covariance matrix would have shape `q x q` where,
-`q = dimension * (dimension+1) / 2`
-and having elements corresponding to some mapping from a lower-triangular
-matrix to a vector-space.
-
-This function returns the diagonal of the Covariance matrix but shaped
-as a `dimension x dimension` matrix.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name of this op.
-
-##### Returns:
-
-
-*  <b>`variance`</b>: `Tensor` of dtype `self.dtype`.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Binomial.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Binomial.md
index 22c7c07aa5..1c7d889946 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Binomial.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Binomial.md
@@ -94,14 +94,28 @@ dist = Binomial(n=[4., 5], p=[.1, .3])
 
 #### `tf.contrib.distributions.Binomial.allow_nan_stats` {#Binomial.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.batch_shape(name='batch_shape')` {#Binomial.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -113,7 +127,8 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -122,26 +137,38 @@ independent distributions of this kind the instance represents.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.dtype` {#Binomial.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.entropy(name='entropy')` {#Binomial.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.event_shape(name='event_shape')` {#Binomial.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -150,7 +177,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -170,8 +198,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -214,26 +242,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Binomial.get_batch_shape()` {#Binomial.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  batch shape
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.get_event_shape()` {#Binomial.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  event shape
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -254,46 +284,86 @@ Same meaning as `event_shape`. May be only partially defined.
 
 #### `tf.contrib.distributions.Binomial.log_cdf(value, name='log_cdf')` {#Binomial.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.log_pdf(value, name='log_pdf')` {#Binomial.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.log_pmf(value, name='log_pmf')` {#Binomial.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Binomial.log_prob(counts, name='log_prob')` {#Binomial.log_prob}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
 
-`Log(P[counts])`, computed for every batch member.
 
-For each batch member of counts `k`, `P[counts]` is the probability that
-after sampling `n` draws from this Binomial distribution, the number of
-successes is `k`.  Note that different sequences of draws can result in the
-same counts, thus the probability includes a combinatorial coefficient.
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Binomial.log_prob(value, name='log_prob')` {#Binomial.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`counts`</b>: Non-negative tensor with dtype `dtype` and whose shape can be
-    broadcast with `self.p` and `self.n`. `counts` is only legal if it is
-    less than or equal to `n` and its components are equal to integer
-    values.
-*  <b>`name`</b>: Name to give this Op, defaults to "log_prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Log probabilities for each record, shape `[N1,...,Nm]`.
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -307,27 +377,14 @@ Log-odds.
 
 #### `tf.contrib.distributions.Binomial.mean(name='mean')` {#Binomial.mean}
 
-Mean of the distribution.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.mode(name='mode')` {#Binomial.mode}
 
-Mode of the distribution.
-
-Note that when `(n + 1) * p` is an integer, there are actually two modes.
-Namely, `(n + 1) * p` and `(n + 1) * p - 1` are both modes. Here we return
-only the larger of the two modes.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name for this op.
-
-##### Returns:
-
-  The mode of the Binomial distribution.
+Mode.
 
 
 - - -
@@ -341,7 +398,7 @@ Number of trials.
 
 #### `tf.contrib.distributions.Binomial.name` {#Binomial.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -393,67 +450,100 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Binomial.parameters` {#Binomial.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Binomial.pdf(value, name='pdf')` {#Binomial.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.pmf(value, name='pmf')` {#Binomial.pmf}
 
-The probability mass function.
+Probability mass function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Binomial.prob(counts, name='prob')` {#Binomial.prob}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
 
-`P[counts]`, computed for every batch member.
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
+##### Raises:
 
-For each batch member of counts `k`, `P[counts]` is the probability that
-after sampling `n` draws from this Binomial distribution, the number of
-successes is `k`.  Note that different sequences of draws can result in the
-same counts, thus the probability includes a combinatorial coefficient.
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Binomial.prob(value, name='prob')` {#Binomial.prob}
+
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`counts`</b>: Non-negative tensor with dtype `dtype` and whose shape can be
-    broadcast with `self.p` and `self.n`. `counts` is only legal if it is
-    less than or equal to `n` and its components are equal to integer
-    values.
-*  <b>`name`</b>: Name to give this Op, defaults to "prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Probabilities for each record, shape `[N1,...,Nm]`.
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.sample(sample_shape=(), seed=None, name='sample')` {#Binomial.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
@@ -465,35 +555,40 @@ Generate `n` samples.
 ##### Args:
 
 
-*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.std(name='std')` {#Binomial.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.validate_args` {#Binomial.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Binomial.variance(name='variance')` {#Binomial.variance}
 
-Variance of the distribution.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md
index aba16da6f9..1a9d3c28b1 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md
@@ -109,7 +109,21 @@ dist = DirichletMultinomial([3., 4], [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
 
 #### `tf.contrib.distributions.DirichletMultinomial.allow_nan_stats` {#DirichletMultinomial.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -119,11 +133,18 @@ Boolean describing behavior when a stat is undefined for batch member.
 Parameter defining this distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.alpha_sum` {#DirichletMultinomial.alpha_sum}
+
+Summation of alpha parameter.
+
+
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.batch_shape(name='batch_shape')` {#DirichletMultinomial.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -135,35 +156,48 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.cdf(x, name='cdf')` {#DirichletMultinomial.cdf}
+#### `tf.contrib.distributions.DirichletMultinomial.cdf(value, name='cdf')` {#DirichletMultinomial.cdf}
 
+Cumulative distribution function.
+
+##### Args:
 
 
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.dtype` {#DirichletMultinomial.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.entropy(name='entropy')` {#DirichletMultinomial.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.event_shape(name='event_shape')` {#DirichletMultinomial.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -172,7 +206,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -192,8 +227,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -236,26 +271,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.DirichletMultinomial.get_batch_shape()` {#DirichletMultinomial.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  batch shape
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.get_event_shape()` {#DirichletMultinomial.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  event shape
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -274,64 +311,102 @@ Same meaning as `event_shape`. May be only partially defined.
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.log_cdf(x, name='log_cdf')` {#DirichletMultinomial.log_cdf}
+#### `tf.contrib.distributions.DirichletMultinomial.log_cdf(value, name='log_cdf')` {#DirichletMultinomial.log_cdf}
 
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
 
 
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.log_pdf(value, name='log_pdf')` {#DirichletMultinomial.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.log_pmf(value, name='log_pmf')` {#DirichletMultinomial.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.log_prob(counts, name='log_prob')` {#DirichletMultinomial.log_prob}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
 
-`Log(P[counts])`, computed for every batch member.
+#### `tf.contrib.distributions.DirichletMultinomial.log_prob(value, name='log_prob')` {#DirichletMultinomial.log_prob}
 
-For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
-that after sampling `n` draws from this Dirichlet Multinomial
-distribution, the number of draws falling in class `j` is `n_j`.  Note that
-different sequences of draws can result in the same counts, thus the
-probability includes a combinatorial coefficient.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`counts`</b>: Non-negative tensor with dtype `dtype` and whose shape can be
-    broadcast with `self.alpha`.  For fixed leading dimensions, the last
-    dimension represents counts for the corresponding Dirichlet Multinomial
-    distribution in `self.alpha`. `counts` is only legal if it sums up to
-    `n` and its components are equal to integer values.
-*  <b>`name`</b>: Name to give this Op, defaults to "log_prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Log probabilities for each record, shape `[N1,...,Nn]`.
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.mean(name='mean')` {#DirichletMultinomial.mean}
 
-Class means for every batch member.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.mode(name='mode')` {#DirichletMultinomial.mode}
 
-Mode of the distribution.
+Mode.
 
 
 - - -
@@ -345,7 +420,7 @@ Parameter defining this distribution.
 
 #### `tf.contrib.distributions.DirichletMultinomial.name` {#DirichletMultinomial.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -390,68 +465,100 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.parameters` {#DirichletMultinomial.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.pdf(value, name='pdf')` {#DirichletMultinomial.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.pmf(value, name='pmf')` {#DirichletMultinomial.pmf}
 
-The probability mass function.
+Probability mass function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
 
-- - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.prob(counts, name='prob')` {#DirichletMultinomial.prob}
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
-`P[counts]`, computed for every batch member.
 
-For each batch of counts `[c_1,...,c_k]`, `P[counts]` is the probability
-that after sampling `sum_j c_j` draws from this Dirichlet Multinomial
-distribution, the number of draws falling in class `j` is `c_j`.  Note that
-different sequences of draws can result in the same counts, thus the
-probability includes a combinatorial coefficient.
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.prob(value, name='prob')` {#DirichletMultinomial.prob}
+
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`counts`</b>: Non-negative tensor with dtype `dtype` and whose shape can be
-    broadcast with `self.alpha`.  For fixed leading dimensions, the last
-    dimension represents counts for the corresponding Dirichlet Multinomial
-    distribution in `self.alpha`. `counts` is only legal if it sums up to
-    `n` and its components are equal to integer values.
-*  <b>`name`</b>: Name to give this Op, defaults to "prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Probabilities for each record, shape `[N1,...,Nn]`.
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.sample(sample_shape=(), seed=None, name='sample')` {#DirichletMultinomial.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
@@ -463,60 +570,40 @@ Generate `n` samples.
 ##### Args:
 
 
-*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
-
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
 
-- - -
+##### Raises:
 
-#### `tf.contrib.distributions.DirichletMultinomial.std(name='std')` {#DirichletMultinomial.std}
 
-Standard deviation of the distribution.
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.validate_args` {#DirichletMultinomial.validate_args}
+#### `tf.contrib.distributions.DirichletMultinomial.std(name='std')` {#DirichletMultinomial.std}
 
-Boolean describing behavior on invalid input.
+Standard deviation.
 
 
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.variance(name='mean')` {#DirichletMultinomial.variance}
-
-Class variances for every batch member.
-
-The variance for each batch member is defined as the following:
-
-```
-Var(X_j) = n * alpha_j / alpha_0 * (1 - alpha_j / alpha_0) *
-  (n + alpha_0) / (1 + alpha_0)
-```
-
-where `alpha_0 = sum_j alpha_j`.
-
-The covariance between elements in a batch is defined as:
-
-```
-Cov(X_i, X_j) = -n * alpha_i * alpha_j / alpha_0 ** 2 *
-  (n + alpha_0) / (1 + alpha_0)
-```
+#### `tf.contrib.distributions.DirichletMultinomial.validate_args` {#DirichletMultinomial.validate_args}
 
-##### Args:
+Python boolean indicated possibly expensive checks are enabled.
 
 
-*  <b>`name`</b>: The name for this op.
+- - -
 
-##### Returns:
+#### `tf.contrib.distributions.DirichletMultinomial.variance(name='variance')` {#DirichletMultinomial.variance}
 
-  A `Tensor` representing the variances for each batch member.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md
index 9ce971c182..2c4cb44aaf 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md
@@ -31,7 +31,21 @@ Construct Exponential distribution with parameter `lam`.
 
 #### `tf.contrib.distributions.Exponential.allow_nan_stats` {#Exponential.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -45,7 +59,7 @@ Shape parameter.
 
 #### `tf.contrib.distributions.Exponential.batch_shape(name='batch_shape')` {#Exponential.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -57,7 +71,8 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -69,60 +84,42 @@ Inverse scale parameter.
 
 - - -
 
-#### `tf.contrib.distributions.Exponential.cdf(x, name='cdf')` {#Exponential.cdf}
+#### `tf.contrib.distributions.Exponential.cdf(value, name='cdf')` {#Exponential.cdf}
 
-CDF of observations `x` under these Gamma distribution(s).
+Cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.dtype` {#Exponential.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.entropy(name='entropy')` {#Exponential.entropy}
 
-The entropy of Gamma distribution(s).
-
-This is defined to be
-
-```
-entropy = alpha - log(beta) + log(Gamma(alpha))
-             + (1-alpha)digamma(alpha)
-```
-
-where digamma(alpha) is the digamma function.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.event_shape(name='event_shape')` {#Exponential.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -131,7 +128,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -151,8 +149,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -195,26 +193,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Exponential.get_batch_shape()` {#Exponential.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  `TensorShape` object.
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.get_event_shape()` {#Exponential.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  `TensorShape` object.
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -240,91 +240,109 @@ Same meaning as `event_shape`. May be only partially defined.
 
 - - -
 
-#### `tf.contrib.distributions.Exponential.log_cdf(x, name='log_cdf')` {#Exponential.log_cdf}
+#### `tf.contrib.distributions.Exponential.log_cdf(value, name='log_cdf')` {#Exponential.log_cdf}
 
-Log CDF of observations `x` under these Gamma distribution(s).
+Log cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.log_pdf(value, name='log_pdf')` {#Exponential.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf')` {#Exponential.log_pmf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
 
-Log of the probability mass function.
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Exponential.log_prob(x, name='log_prob')` {#Exponential.log_prob}
+#### `tf.contrib.distributions.Exponential.log_pmf(value, name='log_pmf')` {#Exponential.log_pmf}
 
-Log prob of observations in `x` under these Gamma distribution(s).
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Exponential.mean(name='mean')` {#Exponential.mean}
+#### `tf.contrib.distributions.Exponential.log_prob(value, name='log_prob')` {#Exponential.log_prob}
 
-Mean of each batch member.
+Log probability density/mass function (depending on `is_continuous`).
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Exponential.mode(name='mode')` {#Exponential.mode}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-Mode of each batch member.
+##### Returns:
 
-The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`,
-and `NaN` otherwise.  If `self.allow_nan_stats` is `False`, an exception
-will be raised rather than returning `NaN`.
 
-##### Args:
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
-*  <b>`name`</b>: A name to give this op.
+- - -
 
-##### Returns:
+#### `tf.contrib.distributions.Exponential.mean(name='mean')` {#Exponential.mean}
 
-  The mode for every batch member, a `Tensor` with same `dtype` as self.
+Mean.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.mode(name='mode')` {#Exponential.mode}
+
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.name` {#Exponential.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -371,103 +389,143 @@ param_shapes with static (i.e. TensorShape) shapes.
 
 - - -
 
-#### `tf.contrib.distributions.Exponential.pdf(value, name='pdf')` {#Exponential.pdf}
+#### `tf.contrib.distributions.Exponential.parameters` {#Exponential.parameters}
 
-The probability density function.
+Dictionary of parameters used by this `Distribution`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Exponential.pmf(value, name='pmf')` {#Exponential.pmf}
+#### `tf.contrib.distributions.Exponential.pdf(value, name='pdf')` {#Exponential.pdf}
+
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
 
-The probability mass function.
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Exponential.prob(x, name='prob')` {#Exponential.prob}
+#### `tf.contrib.distributions.Exponential.pmf(value, name='pmf')` {#Exponential.pmf}
 
-Pdf of observations in `x` under these Gamma distribution(s).
+Probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the PDFs of `x`
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Exponential.prob(value, name='prob')` {#Exponential.prob}
+
+Probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.sample(sample_shape=(), seed=None, name='sample')` {#Exponential.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.sample_n(n, seed=None, name='sample_n')` {#Exponential.sample_n}
 
-Sample `n` observations from the Exponential Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by the hyperparameters.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.std(name='std')` {#Exponential.std}
 
-Standard deviation of this distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.validate_args` {#Exponential.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Exponential.variance(name='variance')` {#Exponential.variance}
 
-Variance of each batch member.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md
index 173ad43326..8c045924d3 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md
@@ -55,7 +55,21 @@ broadcasting (e.g. `alpha + beta` is a valid operation).
 
 #### `tf.contrib.distributions.Gamma.allow_nan_stats` {#Gamma.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -69,7 +83,7 @@ Shape parameter.
 
 #### `tf.contrib.distributions.Gamma.batch_shape(name='batch_shape')` {#Gamma.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -81,7 +95,8 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -93,60 +108,42 @@ Inverse scale parameter.
 
 - - -
 
-#### `tf.contrib.distributions.Gamma.cdf(x, name='cdf')` {#Gamma.cdf}
+#### `tf.contrib.distributions.Gamma.cdf(value, name='cdf')` {#Gamma.cdf}
 
-CDF of observations `x` under these Gamma distribution(s).
+Cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.dtype` {#Gamma.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.entropy(name='entropy')` {#Gamma.entropy}
 
-The entropy of Gamma distribution(s).
-
-This is defined to be
-
-```
-entropy = alpha - log(beta) + log(Gamma(alpha))
-             + (1-alpha)digamma(alpha)
-```
-
-where digamma(alpha) is the digamma function.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.event_shape(name='event_shape')` {#Gamma.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -155,7 +152,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -175,8 +173,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -219,26 +217,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Gamma.get_batch_shape()` {#Gamma.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  `TensorShape` object.
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.get_event_shape()` {#Gamma.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  `TensorShape` object.
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -257,91 +257,109 @@ Same meaning as `event_shape`. May be only partially defined.
 
 - - -
 
-#### `tf.contrib.distributions.Gamma.log_cdf(x, name='log_cdf')` {#Gamma.log_cdf}
+#### `tf.contrib.distributions.Gamma.log_cdf(value, name='log_cdf')` {#Gamma.log_cdf}
 
-Log CDF of observations `x` under these Gamma distribution(s).
+Log cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.log_pdf(value, name='log_pdf')` {#Gamma.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf')` {#Gamma.log_pmf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
 
-Log of the probability mass function.
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Gamma.log_prob(x, name='log_prob')` {#Gamma.log_prob}
+#### `tf.contrib.distributions.Gamma.log_pmf(value, name='log_pmf')` {#Gamma.log_pmf}
 
-Log prob of observations in `x` under these Gamma distribution(s).
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Gamma.mean(name='mean')` {#Gamma.mean}
+#### `tf.contrib.distributions.Gamma.log_prob(value, name='log_prob')` {#Gamma.log_prob}
 
-Mean of each batch member.
+Log probability density/mass function (depending on `is_continuous`).
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Gamma.mode(name='mode')` {#Gamma.mode}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-Mode of each batch member.
+##### Returns:
 
-The mode of a gamma distribution is `(alpha - 1) / beta` when `alpha > 1`,
-and `NaN` otherwise.  If `self.allow_nan_stats` is `False`, an exception
-will be raised rather than returning `NaN`.
 
-##### Args:
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
-*  <b>`name`</b>: A name to give this op.
+- - -
 
-##### Returns:
+#### `tf.contrib.distributions.Gamma.mean(name='mean')` {#Gamma.mean}
 
-  The mode for every batch member, a `Tensor` with same `dtype` as self.
+Mean.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.mode(name='mode')` {#Gamma.mode}
+
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.name` {#Gamma.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -388,105 +406,143 @@ param_shapes with static (i.e. TensorShape) shapes.
 
 - - -
 
-#### `tf.contrib.distributions.Gamma.pdf(value, name='pdf')` {#Gamma.pdf}
+#### `tf.contrib.distributions.Gamma.parameters` {#Gamma.parameters}
 
-The probability density function.
+Dictionary of parameters used by this `Distribution`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Gamma.pmf(value, name='pmf')` {#Gamma.pmf}
+#### `tf.contrib.distributions.Gamma.pdf(value, name='pdf')` {#Gamma.pdf}
+
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
 
-The probability mass function.
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Gamma.prob(x, name='prob')` {#Gamma.prob}
+#### `tf.contrib.distributions.Gamma.pmf(value, name='pmf')` {#Gamma.pmf}
 
-Pdf of observations in `x` under these Gamma distribution(s).
+Probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the PDFs of `x`
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Gamma.prob(value, name='prob')` {#Gamma.prob}
+
+Probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.sample(sample_shape=(), seed=None, name='sample')` {#Gamma.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.sample_n(n, seed=None, name='sample_n')` {#Gamma.sample_n}
 
-Draws `n` samples from the Gamma distribution(s).
-
-See the doc for tf.random_gamma for further detail.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed for this operation.
-*  <b>`name`</b>: Optional name for the operation.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.std(name='std')` {#Gamma.std}
 
-Standard deviation of this distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.validate_args` {#Gamma.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Gamma.variance(name='variance')` {#Gamma.variance}
 
-Variance of each batch member.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md
index d9dd80bcee..45c2201d61 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.InverseGamma.md
@@ -54,7 +54,21 @@ broadcasting (e.g. `alpha + beta` is a valid operation).
 
 #### `tf.contrib.distributions.InverseGamma.allow_nan_stats` {#InverseGamma.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -68,7 +82,7 @@ Shape parameter.
 
 #### `tf.contrib.distributions.InverseGamma.batch_shape(name='batch_shape')` {#InverseGamma.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -80,7 +94,8 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -92,60 +107,42 @@ Scale parameter.
 
 - - -
 
-#### `tf.contrib.distributions.InverseGamma.cdf(x, name='cdf')` {#InverseGamma.cdf}
+#### `tf.contrib.distributions.InverseGamma.cdf(value, name='cdf')` {#InverseGamma.cdf}
 
-CDF of observations `x` under these InverseGamma distribution(s).
+Cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.dtype` {#InverseGamma.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.entropy(name='entropy')` {#InverseGamma.entropy}
 
-The entropy of these InverseGamma distribution(s).
-
-This is defined to be
-
-```
-entropy = alpha - log(beta) + log(Gamma(alpha))
-             + (1-alpha)digamma(alpha)
-```
-
-where digamma(alpha) is the digamma function.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.event_shape(name='event_shape')` {#InverseGamma.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -154,7 +151,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -174,8 +172,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -218,26 +216,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.InverseGamma.get_batch_shape()` {#InverseGamma.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  `TensorShape` object.
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.get_event_shape()` {#InverseGamma.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  `TensorShape` object.
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -256,102 +256,109 @@ Same meaning as `event_shape`. May be only partially defined.
 
 - - -
 
-#### `tf.contrib.distributions.InverseGamma.log_cdf(x, name='log_cdf')` {#InverseGamma.log_cdf}
+#### `tf.contrib.distributions.InverseGamma.log_cdf(value, name='log_cdf')` {#InverseGamma.log_cdf}
 
-Log CDF of observations `x` under these InverseGamma distribution(s).
+Log cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.log_pdf(value, name='log_pdf')` {#InverseGamma.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf')` {#InverseGamma.log_pmf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
 
-Log of the probability mass function.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.InverseGamma.log_prob(x, name='log_prob')` {#InverseGamma.log_prob}
+#### `tf.contrib.distributions.InverseGamma.log_pmf(value, name='log_pmf')` {#InverseGamma.log_pmf}
 
-Log prob of observations in `x` under these InverseGamma distribution(s).
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.InverseGamma.mean(name='mean')` {#InverseGamma.mean}
+#### `tf.contrib.distributions.InverseGamma.log_prob(value, name='log_prob')` {#InverseGamma.log_prob}
 
-Mean of each batch member.
-
-The mean of an inverse gamma distribution is `beta / (alpha - 1)`,
-when `alpha > 1`, and `NaN` otherwise.  If `self.allow_nan_stats` is
-`False`, an exception will be raised rather than returning `NaN`
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`name`</b>: A name to give this op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The mean for every batch member, a `Tensor` with same `dtype` as self.
-
 
-- - -
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-#### `tf.contrib.distributions.InverseGamma.mode(name='mode')` {#InverseGamma.mode}
 
-Mode of each batch member.
+- - -
 
-The mode of an inverse gamma distribution is `beta / (alpha + 1)`.
+#### `tf.contrib.distributions.InverseGamma.mean(name='mean')` {#InverseGamma.mean}
 
-##### Args:
+Mean.
 
 
-*  <b>`name`</b>: A name to give this op.
+- - -
 
-##### Returns:
+#### `tf.contrib.distributions.InverseGamma.mode(name='mode')` {#InverseGamma.mode}
 
-  The mode for every batch member, a `Tensor` with same `dtype` as self.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.name` {#InverseGamma.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -398,118 +405,143 @@ param_shapes with static (i.e. TensorShape) shapes.
 
 - - -
 
-#### `tf.contrib.distributions.InverseGamma.pdf(value, name='pdf')` {#InverseGamma.pdf}
+#### `tf.contrib.distributions.InverseGamma.parameters` {#InverseGamma.parameters}
 
-The probability density function.
+Dictionary of parameters used by this `Distribution`.
 
 
 - - -
 
-#### `tf.contrib.distributions.InverseGamma.pmf(value, name='pmf')` {#InverseGamma.pmf}
+#### `tf.contrib.distributions.InverseGamma.pdf(value, name='pdf')` {#InverseGamma.pdf}
+
+Probability density function.
+
+##### Args:
 
-The probability mass function.
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.InverseGamma.prob(x, name='prob')` {#InverseGamma.prob}
+#### `tf.contrib.distributions.InverseGamma.pmf(value, name='pmf')` {#InverseGamma.pmf}
 
-Pdf of observations in `x` under these Gamma distribution(s).
+Probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `alpha` and `beta`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the PDFs of `x`
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 ##### Raises:
 
 
-*  <b>`TypeError`</b>: if `x` and `alpha` are different dtypes.
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.InverseGamma.prob(value, name='prob')` {#InverseGamma.prob}
+
+Probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.sample(sample_shape=(), seed=None, name='sample')` {#InverseGamma.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.sample_n(n, seed=None, name='sample_n')` {#InverseGamma.sample_n}
 
-Draws `n` samples from these InverseGamma distribution(s).
-
-See the doc for tf.random_gamma for further details on sampling strategy.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed for this operation.
-*  <b>`name`</b>: Optional name for the operation.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.std(name='std')` {#InverseGamma.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.validate_args` {#InverseGamma.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.InverseGamma.variance(name='variance')` {#InverseGamma.variance}
 
-Variance of each batch member.
-
-Variance for inverse gamma is defined only for `alpha > 2`. If
-`self.allow_nan_stats` is `False`, an exception will be raised rather
-than returning `NaN`.
-
-##### Args:
-
-
-*  <b>`name`</b>: A name to give this op.
-
-##### Returns:
-
-  The variance for every batch member, a `Tensor` with same `dtype` as self.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Multinomial.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Multinomial.md
index ff1317a83d..59a3dfa4de 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Multinomial.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Multinomial.md
@@ -105,14 +105,28 @@ dist = Multinomial(n=[4., 5], p=[[.1, .3, .6], [.4, .05, .55]])
 
 #### `tf.contrib.distributions.Multinomial.allow_nan_stats` {#Multinomial.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.batch_shape(name='batch_shape')` {#Multinomial.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -124,7 +138,8 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -133,26 +148,38 @@ independent distributions of this kind the instance represents.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.dtype` {#Multinomial.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.entropy(name='entropy')` {#Multinomial.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.event_shape(name='event_shape')` {#Multinomial.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -161,7 +188,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -181,8 +209,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -225,26 +253,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Multinomial.get_batch_shape()` {#Multinomial.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  batch shape
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.get_event_shape()` {#Multinomial.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  event shape
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -265,48 +295,86 @@ Same meaning as `event_shape`. May be only partially defined.
 
 #### `tf.contrib.distributions.Multinomial.log_cdf(value, name='log_cdf')` {#Multinomial.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.log_pdf(value, name='log_pdf')` {#Multinomial.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.log_pmf(value, name='log_pmf')` {#Multinomial.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
 
-#### `tf.contrib.distributions.Multinomial.log_prob(counts, name='log_prob')` {#Multinomial.log_prob}
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-`Log(P[counts])`, computed for every batch member.
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
 
-For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
-that after sampling `n` draws from this Multinomial distribution, the
-number of draws falling in class `j` is `n_j`.  Note that different
-sequences of draws can result in the same counts, thus the probability
-includes a combinatorial coefficient.
+#### `tf.contrib.distributions.Multinomial.log_prob(value, name='log_prob')` {#Multinomial.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`counts`</b>: Non-negative tensor with dtype `dtype` and whose shape can
-    be broadcast with `self.p` and `self.n`.  For fixed leading dimensions,
-    the last dimension represents counts for the corresponding Multinomial
-    distribution in `self.p`. `counts` is only legal if it sums up to `n`
-    and its components are equal to integer values.
-*  <b>`name`</b>: Name to give this Op, defaults to "log_prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Log probabilities for each record, shape `[N1,...,Nm]`.
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -320,14 +388,14 @@ Log-odds.
 
 #### `tf.contrib.distributions.Multinomial.mean(name='mean')` {#Multinomial.mean}
 
-Mean of the distribution.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.mode(name='mode')` {#Multinomial.mode}
 
-Mode of the distribution.
+Mode.
 
 
 - - -
@@ -341,7 +409,7 @@ Number of trials.
 
 #### `tf.contrib.distributions.Multinomial.name` {#Multinomial.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -393,68 +461,100 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Multinomial.parameters` {#Multinomial.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Multinomial.pdf(value, name='pdf')` {#Multinomial.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.pmf(value, name='pmf')` {#Multinomial.pmf}
 
-The probability mass function.
+Probability mass function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Multinomial.prob(counts, name='prob')` {#Multinomial.prob}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
 
-`P[counts]`, computed for every batch member.
 
-For each batch of counts `[n_1,...,n_k]`, `P[counts]` is the probability
-that after sampling `n` draws from this Multinomial distribution, the
-number of draws falling in class `j` is `n_j`.  Note that different
-sequences of draws can result in the same counts, thus the probability
-includes a combinatorial coefficient.
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Multinomial.prob(value, name='prob')` {#Multinomial.prob}
+
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`counts`</b>: Non-negative tensor with dtype `dtype` and whose shape can
-    be broadcast with `self.p` and `self.n`.  For fixed leading dimensions,
-    the last dimension represents counts for the corresponding Multinomial
-    distribution in `self.p`. `counts` is only legal if it sums up to `n`
-    and its components are equal to integer values.
-*  <b>`name`</b>: Name to give this Op, defaults to "prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Probabilities for each record, shape `[N1,...,Nm]`.
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.sample(sample_shape=(), seed=None, name='sample')` {#Multinomial.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
@@ -466,35 +566,40 @@ Generate `n` samples.
 ##### Args:
 
 
-*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.std(name='std')` {#Multinomial.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.validate_args` {#Multinomial.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Multinomial.variance(name='variance')` {#Multinomial.variance}
 
-Variance of the distribution.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.md
index 17b9b0a145..977fd67739 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.md
@@ -106,14 +106,41 @@ D = is diagonal (r x r), optional (defaults to identity).
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.allow_nan_stats` {#MultivariateNormalDiagPlusVDVT.allow_nan_stats}
 
-`Boolean` describing behavior when stats are undefined.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.batch_shape(name='batch_shape')` {#MultivariateNormalDiagPlusVDVT.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -122,36 +149,48 @@ Batch dimensions of this instance as a 1-D int32 `Tensor`.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.dtype` {#MultivariateNormalDiagPlusVDVT.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.entropy(name='entropy')` {#MultivariateNormalDiagPlusVDVT.entropy}
 
-The entropies of these Multivariate Normals.
+Shanon entropy in nats.
 
-##### Args:
 
+- - -
 
-*  <b>`name`</b>: The name to give this op.
+#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.event_shape(name='event_shape')` {#MultivariateNormalDiagPlusVDVT.event_shape}
 
-##### Returns:
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
+##### Args:
 
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropies.
 
+*  <b>`name`</b>: name to give to the op
 
-- - -
+##### Returns:
 
-#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.event_shape(name='event_shape')` {#MultivariateNormalDiagPlusVDVT.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -171,8 +210,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -215,14 +254,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.get_batch_shape()` {#MultivariateNormalDiagPlusVDVT.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.get_event_shape()` {#MultivariateNormalDiagPlusVDVT.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -243,48 +296,86 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf')` {#MultivariateNormalDiagPlusVDVT.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pdf(value, name='log_pdf')` {#MultivariateNormalDiagPlusVDVT.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_pmf(value, name='log_pmf')` {#MultivariateNormalDiagPlusVDVT.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(x, name='log_prob')` {#MultivariateNormalDiagPlusVDVT.log_prob}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-Log prob of observations `x` given these Multivariate Normals.
+##### Returns:
 
-`x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-shape can be broadcast up to either:
 
-````
-self.batch_shape + self.event_shape
-OR
-[M1,...,Mm] + self.batch_shape + self.event_shape
-```
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_prob(value, name='log_prob')` {#MultivariateNormalDiagPlusVDVT.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Compatible batch vector with same `dtype` as this distribution.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -298,14 +389,14 @@ Log of determinant of covariance matrix.
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mean(name='mean')` {#MultivariateNormalDiagPlusVDVT.mean}
 
-Mean of each batch member.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mode(name='mode')` {#MultivariateNormalDiagPlusVDVT.mode}
 
-Mode of each batch member.
+Mode.
 
 
 - - -
@@ -319,7 +410,7 @@ Mode of each batch member.
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.name` {#MultivariateNormalDiagPlusVDVT.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -364,89 +455,125 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.parameters` {#MultivariateNormalDiagPlusVDVT.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pdf(value, name='pdf')` {#MultivariateNormalDiagPlusVDVT.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.pmf(value, name='pmf')` {#MultivariateNormalDiagPlusVDVT.pmf}
 
-The probability mass function.
+Probability mass function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(x, name='prob')` {#MultivariateNormalDiagPlusVDVT.prob}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-The PDF of observations `x` under these Multivariate Normals.
+##### Returns:
 
-`x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-shape can be broadcast up to either:
 
-````
-self.batch_shape + self.event_shape
-OR
-[M1,...,Mm] + self.batch_shape + self.event_shape
-```
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.prob(value, name='prob')` {#MultivariateNormalDiagPlusVDVT.prob}
+
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Compatible batch vector with same `dtype` as this distribution.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the prob values of `x`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalDiagPlusVDVT.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalDiagPlusVDVT.sample_n}
 
-Sample `n` observations from the Multivariate Normal Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the hyperparameters.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -467,20 +594,20 @@ Determinant of covariance matrix.
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.std(name='std')` {#MultivariateNormalDiagPlusVDVT.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.validate_args` {#MultivariateNormalDiagPlusVDVT.validate_args}
 
-`Boolean` describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.variance(name='variance')` {#MultivariateNormalDiagPlusVDVT.variance}
 
-Variance of each batch member.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md
index de784991b2..f7a6f0c8a8 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Beta.md
@@ -102,11 +102,32 @@ dist = Beta([1.0, 2.0], [4.0, 5.0])
 Shape parameter.
 
 
+- - -
+
+#### `tf.contrib.distributions.Beta.a_b_sum` {#Beta.a_b_sum}
+
+Sum of parameters.
+
+
 - - -
 
 #### `tf.contrib.distributions.Beta.allow_nan_stats` {#Beta.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
@@ -120,7 +141,7 @@ Shape parameter.
 
 #### `tf.contrib.distributions.Beta.batch_shape(name='batch_shape')` {#Beta.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -132,35 +153,48 @@ independent distributions of this kind the instance represents.
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Beta.cdf(x, name='cdf')` {#Beta.cdf}
+#### `tf.contrib.distributions.Beta.cdf(value, name='cdf')` {#Beta.cdf}
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.Beta.dtype` {#Beta.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.entropy(name='entropy')` {#Beta.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.event_shape(name='event_shape')` {#Beta.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
@@ -169,7 +203,8 @@ Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -189,8 +224,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -233,26 +268,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Beta.get_batch_shape()` {#Beta.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  batch shape
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.get_event_shape()` {#Beta.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  event shape
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -271,79 +308,109 @@ Same meaning as `event_shape`. May be only partially defined.
 
 - - -
 
-#### `tf.contrib.distributions.Beta.log_cdf(x, name='log_cdf')` {#Beta.log_cdf}
+#### `tf.contrib.distributions.Beta.log_cdf(value, name='log_cdf')` {#Beta.log_cdf}
+
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-Log CDF.
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.log_pdf(value, name='log_pdf')` {#Beta.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Beta.log_pmf(value, name='log_pmf')` {#Beta.log_pmf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
 
-Log of the probability mass function.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Beta.log_prob(x, name='log_prob')` {#Beta.log_prob}
+#### `tf.contrib.distributions.Beta.log_pmf(value, name='log_pmf')` {#Beta.log_pmf}
 
-`Log(P[counts])`, computed for every batch member.
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: Non-negative floating point tensor whose shape can
-    be broadcast with `self.a` and `self.b`.  For fixed leading
-    dimensions, the last dimension represents counts for the corresponding
-    Beta distribution in `self.a` and `self.b`. `x` is only legal if
-    0 < x < 1.
-*  <b>`name`</b>: Name to give this Op, defaults to "log_prob".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Log probabilities for each record, shape `[N1,...,Nm]`.
 
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-- - -
+##### Raises:
 
-#### `tf.contrib.distributions.Beta.mean(name='mean')` {#Beta.mean}
 
-Mean of the distribution.
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Beta.mode(name='mode')` {#Beta.mode}
-
-Mode of the distribution.
+#### `tf.contrib.distributions.Beta.log_prob(value, name='log_prob')` {#Beta.log_prob}
 
-Note that the mode for the Beta distribution is only defined
-when `a > 1`, `b > 1`. This returns the mode when `a > 1` and `b > 1`,
-and NaN otherwise. If `self.allow_nan_stats` is `False`, an exception
-will be raised rather than returning `NaN`.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`name`</b>: The name for this op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Mode of the Beta distribution.
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+
+- - -
+
+#### `tf.contrib.distributions.Beta.mean(name='mean')` {#Beta.mean}
+
+Mean.
+
+
+- - -
+
+#### `tf.contrib.distributions.Beta.mode(name='mode')` {#Beta.mode}
+
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.name` {#Beta.name}
 
-Name to prepend to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -388,103 +455,145 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Beta.parameters` {#Beta.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Beta.pdf(value, name='pdf')` {#Beta.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.pmf(value, name='pmf')` {#Beta.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Beta.prob(x, name='prob')` {#Beta.prob}
+#### `tf.contrib.distributions.Beta.prob(value, name='prob')` {#Beta.prob}
 
-`P[x]`, computed for every batch member.
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Non-negative floating point tensor whose shape can
-    be broadcast with `self.a` and `self.b`.  For fixed leading
-    dimensions, the last dimension represents x for the corresponding Beta
-    distribution in `self.a` and `self.b`. `x` is only legal if is
-    between 0 and 1.
-*  <b>`name`</b>: Name to give this Op, defaults to "pdf".
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  Probabilities for each record, shape `[N1,...,Nm]`.
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.sample(sample_shape=(), seed=None, name='sample')` {#Beta.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.sample_n(n, seed=None, name='sample_n')` {#Beta.sample_n}
 
-Sample `n` observations from the Beta Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the hyperparameters.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.std(name='std')` {#Beta.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.validate_args` {#Beta.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Beta.variance(name='variance')` {#Beta.variance}
 
-Variance of the distribution.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md
index eca8801302..fd57c8369d 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.Laplace.md
@@ -43,14 +43,28 @@ broadcasting (e.g., `loc / scale` is a valid operation).
 
 #### `tf.contrib.distributions.Laplace.allow_nan_stats` {#Laplace.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.batch_shape(name='batch_shape')` {#Laplace.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -58,69 +72,62 @@ independent distributions of this kind the instance represents.
 ##### Args:
 
 
-*  <b>`name`</b>: name to give to the op.
+*  <b>`name`</b>: name to give to the op
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.cdf(x, name='cdf')` {#Laplace.cdf}
+#### `tf.contrib.distributions.Laplace.cdf(value, name='cdf')` {#Laplace.cdf}
 
-CDF of observations in `x` under the Laplace distribution(s).
+Cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.dtype` {#Laplace.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.entropy(name='entropy')` {#Laplace.entropy}
 
-The entropy of Laplace distribution(s).
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.event_shape(name='event_shape')` {#Laplace.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
 
-*  <b>`name`</b>: name to give to the op.
+*  <b>`name`</b>: name to give to the op
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -140,8 +147,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -184,26 +191,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Laplace.get_batch_shape()` {#Laplace.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  batch shape
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.get_event_shape()` {#Laplace.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  event shape
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -229,80 +238,109 @@ Distribution parameter for the location.
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.log_cdf(x, name='log_cdf')` {#Laplace.log_cdf}
+#### `tf.contrib.distributions.Laplace.log_cdf(value, name='log_cdf')` {#Laplace.log_cdf}
 
-Log CDF of observations `x` under the Laplace distribution(s).
+Log cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.log_pdf(value, name='log_pdf')` {#Laplace.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
 
 
-- - -
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-#### `tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf')` {#Laplace.log_pmf}
+##### Returns:
 
-Log of the probability mass function.
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.log_prob(x, name='log_prob')` {#Laplace.log_prob}
+#### `tf.contrib.distributions.Laplace.log_pmf(value, name='log_pmf')` {#Laplace.log_pmf}
 
-Log prob of observations in `x` under these Laplace distribution(s).
+Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-probability of `x`.
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.mean(name='mean')` {#Laplace.mean}
+#### `tf.contrib.distributions.Laplace.log_prob(value, name='log_prob')` {#Laplace.log_prob}
 
-Mean of this distribution.
+Log probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.median(name='median')` {#Laplace.median}
+#### `tf.contrib.distributions.Laplace.mean(name='mean')` {#Laplace.mean}
 
-Median of this distribution.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.mode(name='mode')` {#Laplace.mode}
 
-Mode of this distribution.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.name` {#Laplace.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -347,80 +385,125 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Laplace.parameters` {#Laplace.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Laplace.pdf(value, name='pdf')` {#Laplace.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.pmf(value, name='pmf')` {#Laplace.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Laplace.prob(x, name='pdf')` {#Laplace.prob}
+#### `tf.contrib.distributions.Laplace.prob(value, name='prob')` {#Laplace.prob}
 
-The prob of observations in `x` under the Laplace distribution(s).
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `loc` and `scale`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`pdf`</b>: tensor of dtype `dtype`, the pdf values of `x`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.sample(sample_shape=(), seed=None, name='sample')` {#Laplace.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.sample_n(n, seed=None, name='sample_n')` {#Laplace.sample_n}
 
-Sample `n` observations from the Laplace Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the parameters.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -434,20 +517,20 @@ Distribution parameter for scale.
 
 #### `tf.contrib.distributions.Laplace.std(name='std')` {#Laplace.std}
 
-Standard deviation of this distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.validate_args` {#Laplace.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Laplace.variance(name='variance')` {#Laplace.variance}
 
-Variance of this distribution.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.BaseDistribution.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.BaseDistribution.md
index 92c712e827..f864de22ff 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.BaseDistribution.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.BaseDistribution.md
@@ -7,48 +7,24 @@ that want to fulfill a simpler distribution contract.
 
 #### `tf.contrib.distributions.BaseDistribution.log_prob(value, name='log_prob')` {#BaseDistribution.log_prob}
 
-Log of the probability density/mass function.
-
-
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.name` {#BaseDistribution.name}
-
-Name to prepend to all ops.
-
-
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.prob(value, name='prob')` {#BaseDistribution.prob}
-
-Probability density/mass function.
-
-
-- - -
-
-#### `tf.contrib.distributions.BaseDistribution.sample(sample_shape=(), seed=None, name='sample')` {#BaseDistribution.sample}
-
-Generate samples of the specified shape.
-
-Note that a call to `sample()` without arguments will generate a single
-sample.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
-*  <b>`seed`</b>: Python integer seed for RNG
-*  <b>`name`</b>: name to give to the op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.BaseDistribution.sample_n(n, seed=None, name='sample_n')` {#BaseDistribution.sample_n}
+#### `tf.contrib.distributions.BaseDistribution.sample_n(n, seed=None, name='sample')` {#BaseDistribution.sample_n}
 
 Generate `n` samples.
 
@@ -65,4 +41,9 @@ Generate `n` samples.
 
 *  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
 
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
+
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md
index 5a9b3c686f..714c457a24 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.MultivariateNormalFull.md
@@ -71,14 +71,41 @@ User must provide means `mu` and `sigma`, the mean and covariance.
 
 #### `tf.contrib.distributions.MultivariateNormalFull.allow_nan_stats` {#MultivariateNormalFull.allow_nan_stats}
 
-`Boolean` describing behavior when stats are undefined.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.batch_shape(name='batch_shape')` {#MultivariateNormalFull.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -87,36 +114,48 @@ Batch dimensions of this instance as a 1-D int32 `Tensor`.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.dtype` {#MultivariateNormalFull.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.entropy(name='entropy')` {#MultivariateNormalFull.entropy}
 
-The entropies of these Multivariate Normals.
+Shanon entropy in nats.
 
-##### Args:
 
+- - -
 
-*  <b>`name`</b>: The name to give this op.
+#### `tf.contrib.distributions.MultivariateNormalFull.event_shape(name='event_shape')` {#MultivariateNormalFull.event_shape}
 
-##### Returns:
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
+##### Args:
 
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropies.
 
+*  <b>`name`</b>: name to give to the op
 
-- - -
+##### Returns:
 
-#### `tf.contrib.distributions.MultivariateNormalFull.event_shape(name='event_shape')` {#MultivariateNormalFull.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -136,8 +175,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -180,14 +219,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.MultivariateNormalFull.get_batch_shape()` {#MultivariateNormalFull.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.get_event_shape()` {#MultivariateNormalFull.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -208,48 +261,86 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.MultivariateNormalFull.log_cdf(value, name='log_cdf')` {#MultivariateNormalFull.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.log_pdf(value, name='log_pdf')` {#MultivariateNormalFull.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.log_pmf(value, name='log_pmf')` {#MultivariateNormalFull.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.MultivariateNormalFull.log_prob(x, name='log_prob')` {#MultivariateNormalFull.log_prob}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-Log prob of observations `x` given these Multivariate Normals.
+##### Returns:
 
-`x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-shape can be broadcast up to either:
 
-````
-self.batch_shape + self.event_shape
-OR
-[M1,...,Mm] + self.batch_shape + self.event_shape
-```
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalFull.log_prob(value, name='log_prob')` {#MultivariateNormalFull.log_prob}
+
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Compatible batch vector with same `dtype` as this distribution.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -263,14 +354,14 @@ Log of determinant of covariance matrix.
 
 #### `tf.contrib.distributions.MultivariateNormalFull.mean(name='mean')` {#MultivariateNormalFull.mean}
 
-Mean of each batch member.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.mode(name='mode')` {#MultivariateNormalFull.mode}
 
-Mode of each batch member.
+Mode.
 
 
 - - -
@@ -284,7 +375,7 @@ Mode of each batch member.
 
 #### `tf.contrib.distributions.MultivariateNormalFull.name` {#MultivariateNormalFull.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -329,89 +420,125 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalFull.parameters` {#MultivariateNormalFull.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.pdf(value, name='pdf')` {#MultivariateNormalFull.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.pmf(value, name='pmf')` {#MultivariateNormalFull.pmf}
 
-The probability mass function.
+Probability mass function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.MultivariateNormalFull.prob(x, name='prob')` {#MultivariateNormalFull.prob}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
-The PDF of observations `x` under these Multivariate Normals.
+##### Returns:
 
-`x` is a batch vector with compatible shape if `x` is a `Tensor` whose
-shape can be broadcast up to either:
 
-````
-self.batch_shape + self.event_shape
-OR
-[M1,...,Mm] + self.batch_shape + self.event_shape
-```
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
+
+
+- - -
+
+#### `tf.contrib.distributions.MultivariateNormalFull.prob(value, name='prob')` {#MultivariateNormalFull.prob}
+
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: Compatible batch vector with same `dtype` as this distribution.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the prob values of `x`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.sample(sample_shape=(), seed=None, name='sample')` {#MultivariateNormalFull.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.sample_n(n, seed=None, name='sample_n')` {#MultivariateNormalFull.sample_n}
 
-Sample `n` observations from the Multivariate Normal Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the hyperparameters.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -432,20 +559,20 @@ Determinant of covariance matrix.
 
 #### `tf.contrib.distributions.MultivariateNormalFull.std(name='std')` {#MultivariateNormalFull.std}
 
-Standard deviation of the distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.validate_args` {#MultivariateNormalFull.validate_args}
 
-`Boolean` describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.MultivariateNormalFull.variance(name='variance')` {#MultivariateNormalFull.variance}
 
-Variance of each batch member.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md
index 2962678742..af4a07e521 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md
@@ -73,14 +73,28 @@ broadcasting (e.g. `mu + sigma` is a valid operation).
 
 #### `tf.contrib.distributions.Normal.allow_nan_stats` {#Normal.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.batch_shape(name='batch_shape')` {#Normal.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
 
 The product of the dimensions of the `batch_shape` is the number of
 independent distributions of this kind the instance represents.
@@ -88,69 +102,62 @@ independent distributions of this kind the instance represents.
 ##### Args:
 
 
-*  <b>`name`</b>: name to give to the op.
+*  <b>`name`</b>: name to give to the op
 
 ##### Returns:
 
-  `Tensor` `batch_shape`
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Normal.cdf(x, name='cdf')` {#Normal.cdf}
+#### `tf.contrib.distributions.Normal.cdf(value, name='cdf')` {#Normal.cdf}
 
-CDF of observations in `x` under these Normal distribution(s).
+Cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`cdf`</b>: tensor of dtype `dtype`, the CDFs of `x`.
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.dtype` {#Normal.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.entropy(name='entropy')` {#Normal.entropy}
 
-The entropy of Normal distribution(s).
-
-##### Args:
-
-
-*  <b>`name`</b>: The name to give this op.
-
-##### Returns:
-
-
-*  <b>`entropy`</b>: tensor of dtype `dtype`, the entropy.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.event_shape(name='event_shape')` {#Normal.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
 
 ##### Args:
 
 
-*  <b>`name`</b>: name to give to the op.
+*  <b>`name`</b>: name to give to the op
 
 ##### Returns:
 
-  `Tensor` `event_shape`
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -170,8 +177,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -214,26 +221,28 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Normal.get_batch_shape()` {#Normal.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
 
 Same meaning as `batch_shape`. May be only partially defined.
 
 ##### Returns:
 
-  batch shape
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.get_event_shape()` {#Normal.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
 Same meaning as `event_shape`. May be only partially defined.
 
 ##### Returns:
 
-  event shape
+
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
@@ -252,66 +261,102 @@ Same meaning as `event_shape`. May be only partially defined.
 
 - - -
 
-#### `tf.contrib.distributions.Normal.log_cdf(x, name='log_cdf')` {#Normal.log_cdf}
+#### `tf.contrib.distributions.Normal.log_cdf(value, name='log_cdf')` {#Normal.log_cdf}
 
-Log CDF of observations `x` under these Normal distribution(s).
+Log cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_cdf`</b>: tensor of dtype `dtype`, the log-CDFs of `x`.
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.log_pdf(value, name='log_pdf')` {#Normal.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.log_pmf(value, name='log_pmf')` {#Normal.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Normal.log_prob(x, name='log_prob')` {#Normal.log_prob}
+#### `tf.contrib.distributions.Normal.log_prob(value, name='log_prob')` {#Normal.log_prob}
 
-Log prob of observations in `x` under these Normal distribution(s).
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`log_prob`</b>: tensor of dtype `dtype`, the log-PDFs of `x`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.mean(name='mean')` {#Normal.mean}
 
-Mean of this distribution.
+Mean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.mode(name='mode')` {#Normal.mode}
 
-Mode of this distribution.
+Mode.
 
 
 - - -
@@ -325,7 +370,7 @@ Distribution parameter for the mean.
 
 #### `tf.contrib.distributions.Normal.name` {#Normal.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -370,80 +415,125 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.Normal.parameters` {#Normal.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.Normal.pdf(value, name='pdf')` {#Normal.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.pmf(value, name='pmf')` {#Normal.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Normal.prob(x, name='prob')` {#Normal.prob}
+#### `tf.contrib.distributions.Normal.prob(value, name='prob')` {#Normal.prob}
 
-The PDF of observations in `x` under these Normal distribution(s).
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: tensor of dtype `dtype`, must be broadcastable with `mu` and `sigma`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`prob`</b>: tensor of dtype `dtype`, the prob values of `x`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.sample(sample_shape=(), seed=None, name='sample')` {#Normal.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.sample_n(n, seed=None, name='sample_n')` {#Normal.sample_n}
 
-Sample `n` observations from the Normal Distributions.
+Generate `n` samples.
 
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer, the random seed.
-*  <b>`name`</b>: The name to give this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: `[n, ...]`, a `Tensor` of `n` samples for each
-    of the distributions determined by broadcasting the hyperparameters.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -457,20 +547,20 @@ Distribution parameter for standard deviation.
 
 #### `tf.contrib.distributions.Normal.std(name='std')` {#Normal.std}
 
-Standard deviation of this distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.validate_args` {#Normal.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.Normal.variance(name='variance')` {#Normal.variance}
 
-Variance of this distribution.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Poisson.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Poisson.md
index fda570b290..080df4c657 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Poisson.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.Poisson.md
@@ -34,54 +34,92 @@ Construct Poisson distributions.
 
 #### `tf.contrib.distributions.Poisson.allow_nan_stats` {#Poisson.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.Poisson.batch_shape(name='batch_shape')` {#Poisson.batch_shape}
 
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
 
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.cdf(x, name='cdf')` {#Poisson.cdf}
+#### `tf.contrib.distributions.Poisson.cdf(value, name='cdf')` {#Poisson.cdf}
 
-Cumulative density function.
+Cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: Non-negative floating point tensor with dtype `dtype` and whose shape
-    can be broadcast with `self.lam`.
-*  <b>`name`</b>: A name for this operation.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The CDF of the events.
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Poisson.dtype` {#Poisson.dtype}
 
-
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Poisson.entropy(name='entropy')` {#Poisson.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.Poisson.event_shape(name='event_shape')` {#Poisson.event_shape}
 
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
 
 
+*  <b>`event_shape`</b>: `Tensor`.
+
 
 - - -
 
@@ -100,8 +138,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -144,15 +182,29 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.Poisson.get_batch_shape()` {#Poisson.get_batch_shape}
 
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
 
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.Poisson.get_event_shape()` {#Poisson.get_event_shape}
 
+Shape of a single sample from a single batch as a `TensorShape`.
+
+Same meaning as `event_shape`. May be only partially defined.
+
+##### Returns:
 
 
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
+
 
 - - -
 
@@ -177,98 +229,109 @@ Rate parameter.
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.log_cdf(x, name='log_cdf')` {#Poisson.log_cdf}
+#### `tf.contrib.distributions.Poisson.log_cdf(value, name='log_cdf')` {#Poisson.log_cdf}
 
-Log cumulative density function.
+Log cumulative distribution function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: Non-negative floating point tensor with dtype `dtype` and whose shape
-    can be broadcast with `self.lam`.
-*  <b>`name`</b>: A name for this operation.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The Log CDF of the events.
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.Poisson.log_pdf(value, name='log_pdf')` {#Poisson.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
 
+##### Args:
 
-- - -
 
-#### `tf.contrib.distributions.Poisson.log_pmf(value, name='log_pmf')` {#Poisson.log_pmf}
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
 
-Log of the probability mass function.
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.log_prob(x, name='log_prob')` {#Poisson.log_prob}
+#### `tf.contrib.distributions.Poisson.log_pmf(value, name='log_pmf')` {#Poisson.log_pmf}
 
 Log probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: Non-negative floating point tensor with dtype `dtype` and whose shape
-    can be broadcast with `self.lam`. `x` is only legal if it is
-    non-negative and its components are equal to integer values.
-*  <b>`name`</b>: A name for this operation (optional).
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The log-probabilities of the events.
+
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.mean(name='mean')` {#Poisson.mean}
+#### `tf.contrib.distributions.Poisson.log_prob(value, name='log_prob')` {#Poisson.log_prob}
 
-Mean of the distribution.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`name`</b>: Name for the op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`mean`</b>: `Tensor` of the same type and shape as `lam`.
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.mode(name='mode')` {#Poisson.mode}
-
-Mode of the distribution.
-
-Note that when `lam` is an integer, there are actually two modes.
-Namely, `lam` and `lam - 1` are both modes. Here we return
-only the larger of the two modes.
-
-##### Args:
+#### `tf.contrib.distributions.Poisson.mean(name='mean')` {#Poisson.mean}
 
+Mean.
 
-*  <b>`name`</b>: Name for the op.
 
-##### Returns:
+- - -
 
+#### `tf.contrib.distributions.Poisson.mode(name='mode')` {#Poisson.mode}
 
-*  <b>`mode`</b>: `Tensor` of the same type and shape as `lam`.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.Poisson.name` {#Poisson.name}
 
-
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -315,118 +378,143 @@ param_shapes with static (i.e. TensorShape) shapes.
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.pdf(value, name='pdf')` {#Poisson.pdf}
+#### `tf.contrib.distributions.Poisson.parameters` {#Poisson.parameters}
 
-The probability density function.
+Dictionary of parameters used by this `Distribution`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.pmf(value, name='pmf')` {#Poisson.pmf}
+#### `tf.contrib.distributions.Poisson.pdf(value, name='pdf')` {#Poisson.pdf}
+
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
 
-The probability mass function.
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.prob(x, name='prob')` {#Poisson.prob}
+#### `tf.contrib.distributions.Poisson.pmf(value, name='pmf')` {#Poisson.pmf}
 
 Probability mass function.
 
 ##### Args:
 
 
-*  <b>`x`</b>: Non-negative floating point tensor with dtype `dtype` and whose shape
-    can be broadcast with `self.lam`. `x` is only legal if it is
-    non-negative and its components are equal to integer values.
-*  <b>`name`</b>: A name for this operation.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
-  The probabilities of the events.
 
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
-- - -
+##### Raises:
 
-#### `tf.contrib.distributions.Poisson.sample(sample_shape=(), seed=None, name='sample')` {#Poisson.sample}
 
-Generate samples of the specified shape for each batched distribution.
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
-Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+
+- - -
+
+#### `tf.contrib.distributions.Poisson.prob(value, name='prob')` {#Poisson.prob}
+
+Probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
-*  <b>`seed`</b>: Python integer seed for RNG
-*  <b>`name`</b>: name to give to the op.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.sample_n(n, seed=None, name='sample_n')` {#Poisson.sample_n}
+#### `tf.contrib.distributions.Poisson.sample(sample_shape=(), seed=None, name='sample')` {#Poisson.sample}
 
-Generate `n` samples.
+Generate samples of the specified shape.
+
+Note that a call to `sample()` without arguments will generate a single
+sample.
 
 ##### Args:
 
 
-*  <b>`n`</b>: scalar. Number of samples to draw from each distribution.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.std(name='std')` {#Poisson.std}
+#### `tf.contrib.distributions.Poisson.sample_n(n, seed=None, name='sample_n')` {#Poisson.sample_n}
 
-Standard deviation of the distribution.
+Generate `n` samples.
 
 ##### Args:
 
 
-*  <b>`name`</b>: Name for the op.
+*  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
+    observations to sample.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`std`</b>: `Tensor` of the same type and shape as `lam`.
-
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
 
-- - -
+##### Raises:
 
-#### `tf.contrib.distributions.Poisson.validate_args` {#Poisson.validate_args}
 
-Boolean describing behavior on invalid input.
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
 
-#### `tf.contrib.distributions.Poisson.variance(name='variance')` {#Poisson.variance}
+#### `tf.contrib.distributions.Poisson.std(name='std')` {#Poisson.std}
 
-Variance of the distribution.
+Standard deviation.
 
-##### Args:
 
+- - -
 
-*  <b>`name`</b>: Name for the op.
+#### `tf.contrib.distributions.Poisson.validate_args` {#Poisson.validate_args}
+
+Python boolean indicated possibly expensive checks are enabled.
 
-##### Returns:
 
+- - -
+
+#### `tf.contrib.distributions.Poisson.variance(name='variance')` {#Poisson.variance}
 
-*  <b>`variance`</b>: `Tensor` of the same type and shape as `lam`.
+Variance.
 
 
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.WishartFull.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.WishartFull.md
index d4dfd7719b..96b878fcfe 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.WishartFull.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard9/tf.contrib.distributions.WishartFull.md
@@ -55,7 +55,7 @@ dist.pdf(x)  # Shape is [2, 2].
 ```
 - - -
 
-#### `tf.contrib.distributions.WishartFull.__init__(df, scale, cholesky_input_output_matrices=False, allow_nan_stats=False, validate_args=True, name='Wishart')` {#WishartFull.__init__}
+#### `tf.contrib.distributions.WishartFull.__init__(df, scale, cholesky_input_output_matrices=False, validate_args=True, allow_nan_stats=False, name='WishartFull')` {#WishartFull.__init__}
 
 Construct Wishart distributions.
 
@@ -71,13 +71,13 @@ Construct Wishart distributions.
     Cholesky factored matrix. Example`log_pdf` input takes a Cholesky and
     `sample_n` returns a Cholesky when
     `cholesky_input_output_matrices=True`.
+*  <b>`validate_args`</b>: Whether to validate input with asserts. If `validate_args`
+    is `False`, and the inputs are invalid, correct behavior is not
+    guaranteed.
 *  <b>`allow_nan_stats`</b>: `Boolean`, default `False`. If `False`, raise an
     exception if a statistic (e.g., mean, mode) is undefined for any batch
     member. If True, batch members with valid parameters leading to
     undefined statistics will return `NaN` for this statistic.
-*  <b>`validate_args`</b>: Whether to validate input with asserts. If `validate_args`
-    is `False`, and the inputs are invalid, correct behavior is not
-    guaranteed.
 *  <b>`name`</b>: The name scope to give class member ops.
 
 
@@ -85,14 +85,41 @@ Construct Wishart distributions.
 
 #### `tf.contrib.distributions.WishartFull.allow_nan_stats` {#WishartFull.allow_nan_stats}
 
-Boolean describing behavior when a stat is undefined for batch member.
+Python boolean describing behavior when a stat is undefined.
+
+Stats return +/- infinity when it makes sense.  E.g., the variance
+of a Cauchy distribution is infinity.  However, sometimes the
+statistic is undefined, e.g., if a distribution's pdf does not achieve a
+maximum within the support of the distribution, the mode is undefined.
+If the mean is undefined, then by definition the variance is undefined.
+E.g. the mean for Student's T for df = 1 is undefined (no clear way to say
+it is either + or - infinity), so the variance = E[(X - mean)^2] is also
+undefined.
+
+##### Returns:
+
+
+*  <b>`allow_nan_stats`</b>: Python boolean.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.batch_shape(name='batch_shape')` {#WishartFull.batch_shape}
 
-Batch dimensions of this instance as a 1-D int32 `Tensor`.
+Shape of a single sample from a single event index as a 1-D `Tensor`.
+
+The product of the dimensions of the `batch_shape` is the number of
+independent distributions of this kind the instance represents.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `Tensor`.
 
 
 - - -
@@ -101,6 +128,18 @@ Batch dimensions of this instance as a 1-D int32 `Tensor`.
 
 Cumulative distribution function.
 
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`cdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
 
 - - -
 
@@ -127,21 +166,31 @@ Dimension of underlying vector space. The `p` in `R^(p*p)`.
 
 #### `tf.contrib.distributions.WishartFull.dtype` {#WishartFull.dtype}
 
-dtype of samples from this distribution.
+The `DType` of `Tensor`s handled by this `Distribution`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.entropy(name='entropy')` {#WishartFull.entropy}
 
-Entropy of the distribution in nats.
+Shanon entropy in nats.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.event_shape(name='event_shape')` {#WishartFull.event_shape}
 
-Shape of a sample from a single distribution as a 1-D int32 `Tensor`.
+Shape of a single sample from a single batch as a 1-D int32 `Tensor`.
+
+##### Args:
+
+
+*  <b>`name`</b>: name to give to the op
+
+##### Returns:
+
+
+*  <b>`event_shape`</b>: `Tensor`.
 
 
 - - -
@@ -161,8 +210,8 @@ shapes = MultiVariateNormalDiag.param_shapes([batch_size, 10])
 
 # shapes has a Tensor shape for mu and sigma
 # shapes == {
-#   'mu': tf.constant([batch_size, 10]),
-#   'sigma': tf.constant([batch_size, 10]),
+#   "mu": tf.constant([batch_size, 10]),
+#   "sigma": tf.constant([batch_size, 10]),
 # }
 
 # Here we parameterize mu and sigma with the output of a linear
@@ -205,33 +254,40 @@ apply it externally and set `make_safe=False`.
 
 #### `tf.contrib.distributions.WishartFull.get_batch_shape()` {#WishartFull.get_batch_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single event index as a `TensorShape`.
+
+Same meaning as `batch_shape`. May be only partially defined.
+
+##### Returns:
+
+
+*  <b>`batch_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.get_event_shape()` {#WishartFull.get_event_shape}
 
-`TensorShape` available at graph construction time.
+Shape of a single sample from a single batch as a `TensorShape`.
 
+Same meaning as `event_shape`. May be only partially defined.
 
-- - -
+##### Returns:
 
-#### `tf.contrib.distributions.WishartFull.inputs` {#WishartFull.inputs}
 
-Dictionary of inputs provided at initialization.
+*  <b>`event_shape`</b>: `TensorShape`, possibly unknown.
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartFull.is_continuous()` {#WishartFull.is_continuous}
+#### `tf.contrib.distributions.WishartFull.is_continuous` {#WishartFull.is_continuous}
 
 
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartFull.is_reparameterized()` {#WishartFull.is_reparameterized}
+#### `tf.contrib.distributions.WishartFull.is_reparameterized` {#WishartFull.is_reparameterized}
 
 
 
@@ -240,7 +296,19 @@ Dictionary of inputs provided at initialization.
 
 #### `tf.contrib.distributions.WishartFull.log_cdf(value, name='log_cdf')` {#WishartFull.log_cdf}
 
-Log CDF.
+Log cumulative distribution function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`logcdf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
@@ -254,26 +322,60 @@ Computes the log normalizing constant, log(Z).
 
 #### `tf.contrib.distributions.WishartFull.log_pdf(value, name='log_pdf')` {#WishartFull.log_pdf}
 
-Log of the probability density function.
+Log probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.log_pmf(value, name='log_pmf')` {#WishartFull.log_pmf}
 
-Log of the probability mass function.
+Log probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`log_pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartFull.log_prob(x, name='log_prob')` {#WishartFull.log_prob}
+#### `tf.contrib.distributions.WishartFull.log_prob(value, name='log_prob')` {#WishartFull.log_prob}
 
-Log of the probability density/mass function.
+Log probability density/mass function (depending on `is_continuous`).
 
 ##### Args:
 
 
-*  <b>`x`</b>: `float` or `double` `Tensor`.
+*  <b>`value`</b>: `float` or `double` `Tensor`.
 *  <b>`name`</b>: The name to give this op.
 
 ##### Returns:
@@ -287,7 +389,7 @@ Log of the probability density/mass function.
 
 #### `tf.contrib.distributions.WishartFull.mean(name='mean')` {#WishartFull.mean}
 
-Mean of the distribution.
+Mean.
 
 
 - - -
@@ -301,14 +403,14 @@ Computes E[log(det(X))] under this Wishart distribution.
 
 #### `tf.contrib.distributions.WishartFull.mode(name='mode')` {#WishartFull.mode}
 
-Mode of the distribution.
+Mode.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.name` {#WishartFull.name}
 
-Name prepended to all ops.
+Name prepended to all ops created by this `Distribution`.
 
 
 - - -
@@ -353,76 +455,125 @@ param_shapes with static (i.e. TensorShape) shapes.
 *  <b>`ValueError`</b>: if `sample_shape` is a `TensorShape` and is not fully defined.
 
 
+- - -
+
+#### `tf.contrib.distributions.WishartFull.parameters` {#WishartFull.parameters}
+
+Dictionary of parameters used by this `Distribution`.
+
+
 - - -
 
 #### `tf.contrib.distributions.WishartFull.pdf(value, name='pdf')` {#WishartFull.pdf}
 
-The probability density function.
+Probability density function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if not `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.pmf(value, name='pmf')` {#WishartFull.pmf}
 
-The probability mass function.
+Probability mass function.
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`pmf`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
+
+##### Raises:
+
+
+*  <b>`AttributeError`</b>: if `is_continuous`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.prob(value, name='prob')` {#WishartFull.prob}
 
-Probability density/mass function.
+Probability density/mass function (depending on `is_continuous`).
+
+##### Args:
+
+
+*  <b>`value`</b>: `float` or `double` `Tensor`.
+*  <b>`name`</b>: The name to give this op.
+
+##### Returns:
+
+
+*  <b>`prob`</b>: a `Tensor` of shape `sample_shape(x) + self.batch_shape` with
+    values of type `self.dtype`.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.sample(sample_shape=(), seed=None, name='sample')` {#WishartFull.sample}
 
-Generate samples of the specified shape for each batched distribution.
+Generate samples of the specified shape.
 
 Note that a call to `sample()` without arguments will generate a single
-sample per batched distribution.
+sample.
 
 ##### Args:
 
 
-*  <b>`sample_shape`</b>: Rank 1 `int32` `Tensor`. Shape of the generated samples.
+*  <b>`sample_shape`</b>: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
 *  <b>`seed`</b>: Python integer seed for RNG
 *  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of dtype `self.dtype` and shape
-      `sample_shape + self.batch_shape + self.event_shape`.
+*  <b>`samples`</b>: a `Tensor` with prepended dimensions `sample_shape`.
 
 
 - - -
 
-#### `tf.contrib.distributions.WishartFull.sample_n(n, seed=None, name='sample')` {#WishartFull.sample_n}
+#### `tf.contrib.distributions.WishartFull.sample_n(n, seed=None, name='sample_n')` {#WishartFull.sample_n}
 
 Generate `n` samples.
 
-Complexity: O(nbk^3)
-
-The sampling procedure is based on the [Bartlett decomposition](
-https://en.wikipedia.org/wiki/Wishart_distribution#Bartlett_decomposition)
-and [using a Gamma distribution to generate Chi2 random variates](
-https://en.wikipedia.org/wiki/Chi-squared_distribution#Gamma.2C_exponential.2C_and_related_distributions).
-
 ##### Args:
 
 
 *  <b>`n`</b>: `Scalar` `Tensor` of type `int32` or `int64`, the number of
     observations to sample.
-*  <b>`seed`</b>: Python integer; random number generator seed.
-*  <b>`name`</b>: The name of this op.
+*  <b>`seed`</b>: Python integer seed for RNG
+*  <b>`name`</b>: name to give to the op.
 
 ##### Returns:
 
 
-*  <b>`samples`</b>: a `Tensor` of shape `(n,) + self.batch_shape + self.event_shape`
-      with values of type `self.dtype`.
+*  <b>`samples`</b>: a `Tensor` with a prepended dimension (n,).
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if `n` is not an integer type.
 
 
 - - -
@@ -443,39 +594,20 @@ Wishart distribution scale matrix as an OperatorPD.
 
 #### `tf.contrib.distributions.WishartFull.std(name='std')` {#WishartFull.std}
 
-Standard deviation of the Wishart distribution.
+Standard deviation.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.validate_args` {#WishartFull.validate_args}
 
-Boolean describing behavior on invalid input.
+Python boolean indicated possibly expensive checks are enabled.
 
 
 - - -
 
 #### `tf.contrib.distributions.WishartFull.variance(name='variance')` {#WishartFull.variance}
 
-Variance of the Wishart distribution.
-
-This function should not be confused with the covariance of the Wishart. The
-covariance matrix would have shape `q x q` where,
-`q = dimension * (dimension+1) / 2`
-and having elements corresponding to some mapping from a lower-triangular
-matrix to a vector-space.
-
-This function returns the diagonal of the Covariance matrix but shaped
-as a `dimension x dimension` matrix.
-
-##### Args:
-
-
-*  <b>`name`</b>: The name of this op.
-
-##### Returns:
-
-
-*  <b>`variance`</b>: `Tensor` of dtype `self.dtype`.
+Variance.
 
 
-- 
GitLab


From c72554035d49284d2d90d370f83bcb53693db119 Mon Sep 17 00:00:00 2001
From: Shanqing Cai <cais@google.com>
Date: Fri, 26 Aug 2016 08:34:18 -0800
Subject: [PATCH 198/223] Remove use of deprecated flag parser_num_threads in
 graph_io_test Change: 131409218

---
 .../learn/python/learn/learn_io/graph_io_test.py       | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/tensorflow/contrib/learn/python/learn/learn_io/graph_io_test.py b/tensorflow/contrib/learn/python/learn/learn_io/graph_io_test.py
index e14048d04c..c1b1c40945 100644
--- a/tensorflow/contrib/learn/python/learn/learn_io/graph_io_test.py
+++ b/tensorflow/contrib/learn/python/learn/learn_io/graph_io_test.py
@@ -121,9 +121,13 @@ class GraphIOTest(tf.test.TestCase):
 
     with tf.Graph().as_default() as g, self.test_session(graph=g) as sess:
       features = tf.contrib.learn.io.read_batch_record_features(
-          _VALID_FILE_PATTERN, batch_size, features, randomize_input=False,
-          queue_capacity=queue_capacity, reader_num_threads=2,
-          parser_num_threads=2, name=name)
+          _VALID_FILE_PATTERN,
+          batch_size,
+          features,
+          randomize_input=False,
+          queue_capacity=queue_capacity,
+          reader_num_threads=2,
+          name=name)
       self.assertEqual("%s/fifo_queue_1_Dequeue:0" % name,
                        features["feature"].name)
       file_name_queue_name = "%s/file_name_queue" % name
-- 
GitLab


From 8faa6f04f341a749e56f6475404c82cc704d08e2 Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Fri, 26 Aug 2016 08:47:39 -0800
Subject: [PATCH 199/223] Automated rollback of change 131356339 Change:
 131410487

---
 .../core/common_runtime/gpu/gpu_device.cc     | 66 +++++++++++++++----
 .../core/common_runtime/gpu/gpu_device.h      |  7 +-
 tensorflow/core/protobuf/config.proto         | 17 +++++
 3 files changed, 78 insertions(+), 12 deletions(-)

diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc
index c08abc5689..38a759dbb7 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc
@@ -597,7 +597,8 @@ Status BaseGPUDeviceFactory::CreateDevices(const SessionOptions& options,
     n = iter->second;
   }
   std::vector<int> valid_gpu_ids;
-  GetValidDeviceIds(&valid_gpu_ids);
+  TF_RETURN_IF_ERROR(GetValidDeviceIds(
+      options.config.gpu_options().visible_device_list(), &valid_gpu_ids));
   if (static_cast<size_t>(n) > valid_gpu_ids.size()) {
     n = valid_gpu_ids.size();
   }
@@ -810,21 +811,60 @@ std::vector<CudaVersion> GetSupportedCudaComputeCapabilities() {
 
 }  // namespace
 
-void BaseGPUDeviceFactory::GetValidDeviceIds(std::vector<int>* ids) {
+Status BaseGPUDeviceFactory::GetValidDeviceIds(
+    const string& visible_device_list, std::vector<int>* ids) {
   gpu::Platform* gpu_manager = GPUMachineManager();
   if (gpu_manager == nullptr) {
-    return;
+    return Status::OK();
+  }
+
+  // If there are no GPUs visible, do nothing.
+  if (gpu_manager->VisibleDeviceCount() <= 0) {
+    return Status::OK();
   }
 
   auto cuda_supported_capabilities = GetSupportedCudaComputeCapabilities();
-  CHECK(!cuda_supported_capabilities.empty());
+  if (cuda_supported_capabilities.empty()) {
+    return errors::FailedPrecondition(
+        "No supported cuda capabilities in binary.");
+  }
   CudaVersion min_supported_capability = *std::min_element(
       cuda_supported_capabilities.begin(), cuda_supported_capabilities.end());
 
   int min_gpu_core_count = GetMinGPUMultiprocessorCount(gpu_manager);
 
-  for (int i = 0; i < gpu_manager->VisibleDeviceCount(); ++i) {
-    auto exec_status = gpu_manager->ExecutorForDevice(i);
+  // If the user wants to remap the visible to virtual GPU mapping,
+  // check for that here.
+  std::vector<int> visible_gpu_order;
+  if (visible_device_list.empty()) {
+    visible_gpu_order.resize(gpu_manager->VisibleDeviceCount());
+    // By default, visible to virtual mapping is unchanged.
+    std::iota(visible_gpu_order.begin(), visible_gpu_order.end(), 0);
+  } else {
+    std::vector<string> order_str = str_util::Split(visible_device_list, ',');
+    for (int i = 0; i < order_str.size(); ++i) {
+      const string& gpu_id_str = order_str[i];
+      int32 gpu_id;
+      if (!strings::safe_strto32(gpu_id_str, &gpu_id)) {
+        return errors::InvalidArgument(
+            "Could not parse entry in 'visible_device_list': '", gpu_id_str,
+            "'.  visible_device_list = ", visible_device_list);
+      }
+
+      if (gpu_id < 0 || gpu_id >= gpu_manager->VisibleDeviceCount()) {
+        return errors::InvalidArgument(
+            "'visible_device_list' listed an invalid GPU id '", gpu_id,
+            "' but visible device count is ",
+            gpu_manager->VisibleDeviceCount());
+      }
+
+      visible_gpu_order.push_back(gpu_id);
+    }
+  }
+
+  for (int i = 0; i < visible_gpu_order.size(); ++i) {
+    const int32 visible_gpu_id = visible_gpu_order[i];
+    auto exec_status = gpu_manager->ExecutorForDevice(visible_gpu_id);
     if (!exec_status.ok()) {
       continue;
     }
@@ -838,8 +878,9 @@ void BaseGPUDeviceFactory::GetValidDeviceIds(std::vector<int>* ids) {
     // Only GPUs with no less than the minimum supported compute capability is
     // accepted.
     if (device_capability < min_supported_capability) {
-      LOG(INFO) << "Ignoring gpu device "
-                << "(" << GetShortDeviceDescription(i, desc) << ") "
+      LOG(INFO) << "Ignoring physical gpu device "
+                << "(" << GetShortDeviceDescription(visible_gpu_id, desc)
+                << ") "
                 << "with Cuda compute capability " << device_capability
                 << ". The minimum required Cuda capability is "
                 << min_supported_capability << ".";
@@ -852,7 +893,8 @@ void BaseGPUDeviceFactory::GetValidDeviceIds(std::vector<int>* ids) {
     // variable is set, its value will be used to filter out GPUs.
     if (desc.core_count() < min_gpu_core_count) {
       LOG(INFO) << "Ignoring gpu device "
-                << "(" << GetShortDeviceDescription(i, desc) << ") "
+                << "(" << GetShortDeviceDescription(visible_gpu_id, desc)
+                << ") "
                 << "with Cuda multiprocessor count: " << desc.core_count()
                 << ". The minimum required count is " << min_gpu_core_count
                 << ". You can adjust this requirement with the env var "
@@ -861,11 +903,13 @@ void BaseGPUDeviceFactory::GetValidDeviceIds(std::vector<int>* ids) {
     }
 
     int new_id = ids->size();
-    ids->push_back(i);
+    ids->push_back(visible_gpu_id);
 
     LOG(INFO) << "Creating TensorFlow device (/gpu:" << new_id << ") -> "
-              << "(" << GetShortDeviceDescription(i, desc) << ")";
+              << "(" << GetShortDeviceDescription(visible_gpu_id, desc) << ")";
   }
+
+  return Status::OK();
 }
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.h b/tensorflow/core/common_runtime/gpu/gpu_device.h
index 03090aa537..abe7c0f687 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.h
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.h
@@ -123,7 +123,12 @@ class BaseGPUDeviceFactory : public DeviceFactory {
                                          Allocator* gpu_allocator,
                                          Allocator* cpu_allocator) = 0;
 
-  void GetValidDeviceIds(std::vector<int>* ids);
+  // Returns into 'ids' the list of valid GPU ids, in the order that
+  // they should map to logical gpu ids "/gpu:0", "/gpu:1", etc, based
+  // upon 'visible_device_list', a comma-separated list of 'visible
+  // gpu ids'.
+  Status GetValidDeviceIds(const string& visible_device_list,
+                           std::vector<int>* ids);
 };
 
 }  // namespace tensorflow
diff --git a/tensorflow/core/protobuf/config.proto b/tensorflow/core/protobuf/config.proto
index cea68d43d4..f0ec90d781 100644
--- a/tensorflow/core/protobuf/config.proto
+++ b/tensorflow/core/protobuf/config.proto
@@ -35,6 +35,23 @@ message GPUOptions {
   // If true, the allocator does not pre-allocate the entire specified
   // GPU memory region, instead starting small and growing as needed.
   bool allow_growth = 4;
+
+  // A comma-separated list of GPU ids that determines the 'visible'
+  // to 'virtual' mapping of GPU devices.  For example, if TensorFlow
+  // can see 8 GPU devices in the process, and one wanted to map
+  // visible GPU devices 5 and 3 as "/gpu:0", and "/gpu:1", then one
+  // would specify this field as "5,3".  This field is similar in
+  // spirit to the CUDA_VISIBLE_DEVICES environment variable, except
+  // it applies to the visible GPU devices in the process.
+  //
+  // NOTE: The GPU driver provides the process with the visible GPUs
+  // in an order which is not guaranteed to have any correlation to
+  // the *physical* GPU id in the machine.  This field is used for
+  // remapping "visible" to "virtual", which means this operates only
+  // after the process starts.  Users are required to use vendor
+  // specific mechanisms (e.g., CUDA_VISIBLE_DEVICES) to control the
+  // physical to visible device mapping prior to invoking TensorFlow.
+  string visible_device_list = 5;
 };
 
 // Options passed to the graph optimizer
-- 
GitLab


From 2f3328b03f2ef261781b02ca3571688356a7915c Mon Sep 17 00:00:00 2001
From: Zongheng Yang <zongheng@google.com>
Date: Fri, 26 Aug 2016 09:09:34 -0800
Subject: [PATCH 200/223] io::InputBuffer: add ReadVarint32(). Change:
 131412838

---
 tensorflow/core/lib/core/coding.h          |  3 ++
 tensorflow/core/lib/io/inputbuffer.cc      | 14 ++++++++
 tensorflow/core/lib/io/inputbuffer.h       | 25 +++++++++++++-
 tensorflow/core/lib/io/inputbuffer_test.cc | 39 ++++++++++++++++++++++
 4 files changed, 80 insertions(+), 1 deletion(-)

diff --git a/tensorflow/core/lib/core/coding.h b/tensorflow/core/lib/core/coding.h
index 94779f9f29..77d52a909b 100644
--- a/tensorflow/core/lib/core/coding.h
+++ b/tensorflow/core/lib/core/coding.h
@@ -28,6 +28,9 @@ limitations under the License.
 namespace tensorflow {
 namespace core {
 
+// Maximum number of bytes occupied by a varint32.
+static const int kMaxVarint32Bytes = 5;
+
 // Lower-level versions of Put... that write directly into a character buffer
 // REQUIRES: dst has enough space for the value being written
 extern void EncodeFixed16(char* dst, uint16 value);
diff --git a/tensorflow/core/lib/io/inputbuffer.cc b/tensorflow/core/lib/io/inputbuffer.cc
index eedca47ce5..9cff1d349e 100644
--- a/tensorflow/core/lib/io/inputbuffer.cc
+++ b/tensorflow/core/lib/io/inputbuffer.cc
@@ -113,6 +113,20 @@ Status InputBuffer::ReadNBytes(int64 bytes_to_read, char* result,
   return status;
 }
 
+Status InputBuffer::ReadVarint32Fallback(uint32* result) {
+  uint8 scratch = 0;
+  char* p = reinterpret_cast<char*>(&scratch);
+  size_t unused_bytes_read = 0;
+
+  *result = 0;
+  for (int shift = 0; shift <= 28; shift += 7) {
+    TF_RETURN_IF_ERROR(ReadNBytes(1, p, &unused_bytes_read));
+    *result |= (scratch & 127) << shift;
+    if (!(scratch & 128)) return Status::OK();
+  }
+  return errors::DataLoss("Stored data is too large to be a varint32.");
+}
+
 Status InputBuffer::SkipNBytes(int64 bytes_to_skip) {
   if (bytes_to_skip < 0) {
     return errors::InvalidArgument("Can only skip forward, not ",
diff --git a/tensorflow/core/lib/io/inputbuffer.h b/tensorflow/core/lib/io/inputbuffer.h
index e19cb7ba33..02dfda74e3 100644
--- a/tensorflow/core/lib/io/inputbuffer.h
+++ b/tensorflow/core/lib/io/inputbuffer.h
@@ -17,6 +17,7 @@ limitations under the License.
 #define TENSORFLOW_LIB_IO_INPUTBUFFER_H_
 
 #include <string>
+#include "tensorflow/core/lib/core/coding.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/platform/env.h"
 #include "tensorflow/core/platform/macros.h"
@@ -52,10 +53,13 @@ class InputBuffer {
   Status ReadNBytes(int64 bytes_to_read, string* result);
 
   // An overload that writes to char*.  Caller must ensure result[0,
-  // bytes_to_read) is valid to be overwritten.  Returns OK, iff "*bytes_read ==
+  // bytes_to_read) is valid to be overwritten.  Returns OK iff "*bytes_read ==
   // bytes_to_read".
   Status ReadNBytes(int64 bytes_to_read, char* result, size_t* bytes_read);
 
+  // Reads a single varint32.
+  Status ReadVarint32(uint32* result);
+
   // Like ReadNBytes() without returning the bytes read.
   Status SkipNBytes(int64 bytes_to_skip);
 
@@ -72,6 +76,9 @@ class InputBuffer {
  private:
   Status FillBuffer();
 
+  // Internal slow-path routine used by ReadVarint32().
+  Status ReadVarint32Fallback(uint32* result);
+
   RandomAccessFile* file_;  // Not owned
   int64 file_pos_;          // Next position to read from in "file_"
   size_t size_;             // Size of "buf_"
@@ -83,6 +90,22 @@ class InputBuffer {
   TF_DISALLOW_COPY_AND_ASSIGN(InputBuffer);
 };
 
+// Implementation details.
+
+// Inlined for performance.
+inline Status InputBuffer::ReadVarint32(uint32* result) {
+  if (pos_ + core::kMaxVarint32Bytes <= limit_) {
+    // Fast path: directly parse from buffered data.
+    // Reads strictly from the range [pos_, limit_).
+    const char* offset = core::GetVarint32Ptr(pos_, limit_, result);
+    if (offset == nullptr) return errors::OutOfRange("Parsed past limit.");
+    pos_ = const_cast<char*>(offset);
+    return Status::OK();
+  } else {
+    return ReadVarint32Fallback(result);
+  }
+}
+
 }  // namespace io
 }  // namespace tensorflow
 
diff --git a/tensorflow/core/lib/io/inputbuffer_test.cc b/tensorflow/core/lib/io/inputbuffer_test.cc
index 5dc71c731c..31fa96bf76 100644
--- a/tensorflow/core/lib/io/inputbuffer_test.cc
+++ b/tensorflow/core/lib/io/inputbuffer_test.cc
@@ -18,6 +18,7 @@ limitations under the License.
 #include <vector>
 #include "tensorflow/core/platform/env.h"
 
+#include "tensorflow/core/lib/core/coding.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/core/status_test_util.h"
@@ -287,5 +288,43 @@ TEST(InputBuffer, Seek) {
   }
 }
 
+TEST(InputBuffer, ReadVarint32) {
+  Env* env = Env::Default();
+  string fname = testing::TmpDir() + "/inputbuffer_test";
+
+  // Generates data.
+  std::vector<uint32> data;
+  uint32 i = 0;
+  for (; i < (1 << 10); i += 1) data.push_back(i);
+  for (; i < (1 << 15); i += 5) data.push_back(i);
+  for (; i < (1 << 31); i += 132817) data.push_back(i);
+  data.push_back(std::numeric_limits<uint32>::max());
+
+  // Writes the varints.
+  {
+    std::unique_ptr<WritableFile> file;
+    TF_CHECK_OK(env->NewWritableFile(fname, &file));
+    string varint;
+    for (uint32 number : data) {
+      varint.clear();
+      core::PutVarint32(&varint, number);
+      TF_CHECK_OK(file->Append(StringPiece(varint)));
+    }
+  }
+
+  for (auto buf_size : BufferSizes()) {
+    std::unique_ptr<RandomAccessFile> file;
+    TF_CHECK_OK(env->NewRandomAccessFile(fname, &file));
+    io::InputBuffer in(file.get(), buf_size);
+    uint32 result = 0;
+
+    for (uint32 expected : data) {
+      TF_ASSERT_OK(in.ReadVarint32(&result));
+      EXPECT_EQ(expected, result);
+    }
+    EXPECT_TRUE(errors::IsOutOfRange(in.ReadVarint32(&result)));
+  }
+}
+
 }  // namespace
 }  // namespace tensorflow
-- 
GitLab


From 46248932c3b0e75c37ceacce0e34a5e5c84f5046 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 26 Aug 2016 09:10:11 -0800
Subject: [PATCH 201/223] Fix build breakage of iOS makefile build - updated
 download url for gmock - removed repeated node_def.proto inclusion Change:
 131412912

---
 tensorflow/contrib/makefile/download_dependencies.sh | 4 ++++
 tensorflow/contrib/makefile/tf_proto_files.txt       | 1 -
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/makefile/download_dependencies.sh b/tensorflow/contrib/makefile/download_dependencies.sh
index 43cd9f4fbc..e145021065 100755
--- a/tensorflow/contrib/makefile/download_dependencies.sh
+++ b/tensorflow/contrib/makefile/download_dependencies.sh
@@ -63,4 +63,8 @@ git clone https://github.com/google/gemmlowp.git gemmlowp
 git clone https://github.com/google/protobuf.git protobuf
 git clone https://github.com/google/googletest.git googletest
 
+# TODO(satok): Remove this once protobuf/autogen.sh is fixed.
+replace_by_sed 's#https://googlemock.googlecode.com/files/gmock-1.7.0.zip#http://download.tensorflow.org/deps/gmock-1.7.0.zip#' \
+protobuf/autogen.sh
+
 echo "download_dependencies.sh completed successfully."
diff --git a/tensorflow/contrib/makefile/tf_proto_files.txt b/tensorflow/contrib/makefile/tf_proto_files.txt
index 1e048bc637..1b85c50060 100644
--- a/tensorflow/contrib/makefile/tf_proto_files.txt
+++ b/tensorflow/contrib/makefile/tf_proto_files.txt
@@ -23,7 +23,6 @@ tensorflow/core/framework/node_def.proto
 tensorflow/core/framework/log_memory.proto
 tensorflow/core/framework/kernel_def.proto
 tensorflow/core/framework/graph.proto
-tensorflow/core/framework/node_def.proto
 tensorflow/core/framework/function.proto
 tensorflow/core/framework/device_attributes.proto
 tensorflow/core/framework/cost_graph.proto
-- 
GitLab


From fab34d35f0a6821c71fcd05b59184135af845ad7 Mon Sep 17 00:00:00 2001
From: Damien Martin-Guillerez <damienmg@users.noreply.github.com>
Date: Fri, 26 Aug 2016 19:30:27 +0200
Subject: [PATCH 202/223] Deprecate parameters from tf_workspace (#4055)

There is no need for either of those parameters, use Bazel label
resolution instead.
---
 BUILD                    |  0
 tensorflow/workspace.bzl | 40 +++++++++++++++++++++-------------------
 2 files changed, 21 insertions(+), 19 deletions(-)
 create mode 100644 BUILD

diff --git a/BUILD b/BUILD
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index d47c446bbe..f95d2a6217 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -2,12 +2,14 @@
 
 load("//third_party/gpus:cuda_configure.bzl", "cuda_configure")
 
-# If TensorFlow is linked as a submodule, path_prefix is TensorFlow's directory
-# within the workspace (e.g. "tensorflow/"), and tf_repo_name is the name of the
-# local_repository rule (e.g. "@tf").
+# If TensorFlow is linked as a submodule.
+# path_prefix and tf_repo_name are no longer used.
 def tf_workspace(path_prefix = "", tf_repo_name = ""):
   cuda_configure(name = "local_config_cuda")
-
+  if path_prefix:
+    print("path_prefix was specified to tf_workspace but is no longer used and will be removed in the future.")
+  if tf_repo_name:
+    print("tf_repo_name was specified to tf_workspace but is no longer used and will be removed in the future.")
   # These lines need to be changed when updating Eigen. They are parsed from
   # this file by the cmake and make builds to determine the eigen version and hash.
   eigen_version = "9e1b48c333aa"
@@ -18,7 +20,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
     url = "https://bitbucket.org/eigen/eigen/get/" + eigen_version + ".tar.gz",
     sha256 = eigen_sha256,
     strip_prefix = "eigen-eigen-" + eigen_version,
-    build_file = path_prefix + "eigen.BUILD",
+    build_file = str(Label("//:eigen.BUILD")),
   )
 
   native.git_repository(
@@ -37,7 +39,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
     name = "farmhash_archive",
     url = "https://github.com/google/farmhash/archive/34c13ddfab0e35422f4c3979f360635a8c050260.zip",
     sha256 = "e3d37a59101f38fd58fb799ed404d630f0eee18bfc2a2433910977cc8fea9c28",
-    build_file = path_prefix + "farmhash.BUILD",
+    build_file = str(Label("//:farmhash.BUILD")),
   )
 
   native.bind(
@@ -56,28 +58,28 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
     name = "jpeg_archive",
     url = "http://www.ijg.org/files/jpegsrc.v9a.tar.gz",
     sha256 = "3a753ea48d917945dd54a2d97de388aa06ca2eb1066cbfdc6652036349fe05a7",
-    build_file = path_prefix + "jpeg.BUILD",
+    build_file = str(Label("//:jpeg.BUILD")),
   )
 
   native.new_http_archive(
     name = "png_archive",
     url = "https://github.com/glennrp/libpng/archive/v1.2.53.zip",
     sha256 = "c35bcc6387495ee6e757507a68ba036d38ad05b415c2553b3debe2a57647a692",
-    build_file = path_prefix + "png.BUILD",
+    build_file = str(Label("//:png.BUILD")),
   )
 
   native.new_http_archive(
     name = "gif_archive",
     url = "http://ufpr.dl.sourceforge.net/project/giflib/giflib-5.1.4.tar.gz",
     sha256 = "34a7377ba834397db019e8eb122e551a49c98f49df75ec3fcc92b9a794a4f6d1",
-    build_file = path_prefix + "gif.BUILD",
+    build_file = str(Label("//:gif.BUILD")),
   )
 
   native.new_http_archive(
     name = "six_archive",
     url = "https://pypi.python.org/packages/source/s/six/six-1.10.0.tar.gz#md5=34eed507548117b2ab523ab14b2f8b55",
     sha256 = "105f8d68616f8248e24bf0e9372ef04d3cc10104f1980f54d57b2ce73a5ad56a",
-    build_file = path_prefix + "six.BUILD",
+    build_file = str(Label("//:six.BUILD")),
   )
 
   native.bind(
@@ -95,7 +97,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
     name = "gmock_archive",
     url = "http://pkgs.fedoraproject.org/repo/pkgs/gmock/gmock-1.7.0.zip/073b984d8798ea1594f5e44d85b20d66/gmock-1.7.0.zip",
     sha256 = "26fcbb5925b74ad5fc8c26b0495dfc96353f4d553492eb97e85a8a6d2f43095b",
-    build_file = path_prefix + "gmock.BUILD",
+    build_file = str(Label("//:gmock.BUILD")),
   )
 
   native.bind(
@@ -110,7 +112,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
 
   native.bind(
     name = "python_headers",
-    actual = tf_repo_name + "//util/python:python_headers",
+    actual = str(Label("//util/python:python_headers")),
   )
 
   # grpc expects //external:protobuf_clib and //external:protobuf_compiler
@@ -130,7 +132,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
     commit = "d7ff4ff40071d2b486a052183e3e9f9382afb745",
     init_submodules = True,
     remote = "https://github.com/grpc/grpc.git",
-    build_file = path_prefix + "grpc.BUILD",
+    build_file = str(Label("//:grpc.BUILD")),
   )
 
   # protobuf expects //external:grpc_cpp_plugin to point to grpc's
@@ -149,7 +151,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
     name = "jsoncpp_git",
     remote = "https://github.com/open-source-parsers/jsoncpp.git",
     commit = "11086dd6a7eba04289944367ca82cea71299ed70",
-    build_file = path_prefix + "jsoncpp.BUILD",
+    build_file = str(Label("//:jsoncpp.BUILD")),
   )
 
   native.bind(
@@ -167,7 +169,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
     name = "nanopb_git",
     commit = "1251fa1",
     remote = "https://github.com/nanopb/nanopb.git",
-    build_file = path_prefix + "nanopb.BUILD",
+    build_file = str(Label("//:nanopb.BUILD")),
   )
 
   native.bind(
@@ -179,26 +181,26 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
     name = "avro_archive",
     url = "http://www-us.apache.org/dist/avro/avro-1.8.0/cpp/avro-cpp-1.8.0.tar.gz",
     sha256 = "ec6e2ec957e95ca07f70cc25f02f5c416f47cb27bd987a6ec770dcbe72527368",
-    build_file = path_prefix + "avro.BUILD",
+    build_file = str(Label("//:avro.BUILD")),
   )
 
   native.new_http_archive(
     name = "boost_archive",
     url = "http://pilotfiber.dl.sourceforge.net/project/boost/boost/1.61.0/boost_1_61_0.tar.gz",
     sha256 = "a77c7cc660ec02704c6884fbb20c552d52d60a18f26573c9cee0788bf00ed7e6",
-    build_file = path_prefix + "boost.BUILD",
+    build_file = str(Label("//:boost.BUILD")),
   )
 
   native.new_http_archive(
     name = "bzip2_archive",
     url = "http://www.bzip.org/1.0.6/bzip2-1.0.6.tar.gz",
     sha256 = "a2848f34fcd5d6cf47def00461fcb528a0484d8edef8208d6d2e2909dc61d9cd",
-    build_file = path_prefix + "bzip2.BUILD",
+    build_file = str(Label("//:bzip2.BUILD")),
   )
 
   native.new_http_archive(
     name = "zlib_archive",
     url = "http://zlib.net/zlib-1.2.8.tar.gz",
     sha256 = "36658cb768a54c1d4dec43c3116c27ed893e88b02ecfcb44f2166f9c0b7f2a0d",
-    build_file = path_prefix + "zlib.BUILD",
+    build_file = str(Label("//:zlib.BUILD")),
   )
-- 
GitLab


From dd770849b363520d30e3db981186120254f93421 Mon Sep 17 00:00:00 2001
From: Brandon Amos <bdamos@vt.edu>
Date: Fri, 26 Aug 2016 13:30:51 -0400
Subject: [PATCH 203/223] Fix stddev computation in TensorBoard examples.
 (#4054)

---
 tensorflow/examples/image_retraining/retrain.py             | 2 +-
 tensorflow/examples/tutorials/mnist/mnist_with_summaries.py | 2 +-
 tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index 056664778d..acd3005c48 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -703,7 +703,7 @@ def variable_summaries(var, name):
     mean = tf.reduce_mean(var)
     tf.scalar_summary('mean/' + name, mean)
     with tf.name_scope('stddev'):
-      stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
+      stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
     tf.scalar_summary('sttdev/' + name, stddev)
     tf.scalar_summary('max/' + name, tf.reduce_max(var))
     tf.scalar_summary('min/' + name, tf.reduce_min(var))
diff --git a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
index c83239217a..23492e5122 100644
--- a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
+++ b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
@@ -75,7 +75,7 @@ def train():
       mean = tf.reduce_mean(var)
       tf.scalar_summary('mean/' + name, mean)
       with tf.name_scope('stddev'):
-        stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
+        stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
       tf.scalar_summary('sttdev/' + name, stddev)
       tf.scalar_summary('max/' + name, tf.reduce_max(var))
       tf.scalar_summary('min/' + name, tf.reduce_min(var))
diff --git a/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md b/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md
index 8183cdf024..e487d8f04c 100644
--- a/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md
+++ b/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md
@@ -86,7 +86,7 @@ def variable_summaries(var, name):
     mean = tf.reduce_mean(var)
     tf.scalar_summary('mean/' + name, mean)
     with tf.name_scope('stddev'):
-      stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
+      stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
     tf.scalar_summary('sttdev/' + name, stddev)
     tf.scalar_summary('max/' + name, tf.reduce_max(var))
     tf.scalar_summary('min/' + name, tf.reduce_min(var))
-- 
GitLab


From d36dd2fcde3e5a95ade152b85268f686173866c5 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 26 Aug 2016 09:15:50 -0800
Subject: [PATCH 204/223] Added test for copy_with_input_replacements and
 graph_replace. Change: 131413482

---
 tensorflow/contrib/graph_editor/__init__.py   | 24 +++++-----
 .../graph_editor/tests/transform_test.py      | 45 ++++++++++++++++++-
 tensorflow/contrib/graph_editor/transform.py  |  9 ++--
 3 files changed, 62 insertions(+), 16 deletions(-)

diff --git a/tensorflow/contrib/graph_editor/__init__.py b/tensorflow/contrib/graph_editor/__init__.py
index dad4600b9a..71d89f192c 100644
--- a/tensorflow/contrib/graph_editor/__init__.py
+++ b/tensorflow/contrib/graph_editor/__init__.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""# TensorFlow Graph Editor.
+"""TensorFlow Graph Editor.
 
 The TensorFlow Graph Editor library allows for modification of an existing
 tf.Graph instance in-place.
@@ -61,7 +61,7 @@ Note that this procedure is very costly because a new session must be created
 after any modifications. Among other things, it takes time because the entire
 graph state must be saved and restored again.
 
-### Sub-graph
+## Sub-graph
 
 Most of the functions in the Graph Editor library operate on *sub-graph*.
 More precisely, they take as input arguments instances of the SubGraphView class
@@ -94,7 +94,7 @@ to avoid any confusion, the default graph is never used and the graph on
 which to operate must always be explicitely given. This is the reason why
 *graph=tf.get_default_graph()* is used in the code snippets above.
 
-### Modules overview
+## Modules overview
 
 * util: utility functions.
 * select: various selection methods of TensorFlow tensors and operations.
@@ -109,7 +109,7 @@ which to operate must always be explicitely given. This is the reason why
 * transform: the Transformer class, which enables transforming
   (or simply copying) a subgraph into another one.
 
-### Module: util
+## Module: util
 
 @@make_list_of_op
 @@get_tensors
@@ -121,7 +121,7 @@ which to operate must always be explicitely given. This is the reason why
 @@make_placeholder_from_tensor
 @@make_placeholder_from_dtype_and_shape
 
-### Module: select
+## Module: select
 
 @@filter_ts
 @@filter_ts_from_regex
@@ -140,13 +140,13 @@ which to operate must always be explicitely given. This is the reason why
 @@select_ts
 @@select_ops_and_ts
 
-### Module: subgraph
+## Module: subgraph
 
 @@SubGraphView
 @@make_view
 @@make_view_from_scope
 
-### Module: reroute
+## Module: reroute
 
 @@swap_ts
 @@reroute_a2b_ts
@@ -163,7 +163,7 @@ which to operate must always be explicitely given. This is the reason why
 @@remove_control_inputs
 @@add_control_inputs
 
-### Module: edit
+## Module: edit
 
 @@detach_control_inputs
 @@detach_control_outputs
@@ -173,7 +173,7 @@ which to operate must always be explicitely given. This is the reason why
 @@connect
 @@bypass
 
-### Module: transform
+## Module: transform
 
 @@replace_t_with_placeholder_handler
 @@keep_t_if_possible_handler
@@ -183,13 +183,15 @@ which to operate must always be explicitely given. This is the reason why
 @@transform_op_in_place
 @@Transformer
 @@copy
+@@copy_with_input_replacements
+@@graph_replace
 
-### Module: match
+## Module: match
 
 @@op_type
 @@OpMatcher
 
-### Useful aliases
+## Useful aliases
 
 @@ph
 @@sgv
diff --git a/tensorflow/contrib/graph_editor/tests/transform_test.py b/tensorflow/contrib/graph_editor/tests/transform_test.py
index e88f83e3f8..0b09584ec0 100644
--- a/tensorflow/contrib/graph_editor/tests/transform_test.py
+++ b/tensorflow/contrib/graph_editor/tests/transform_test.py
@@ -18,9 +18,13 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
 import tensorflow as tf
 from tensorflow.contrib import graph_editor as ge
 
+# Precision tolerance for floating-point value tests.
+ERROR_TOLERANCE = 1e-3
+
 
 class TransformTest(tf.test.TestCase):
 
@@ -35,9 +39,23 @@ class TransformTest(tf.test.TestCase):
 
   def test_copy(self):
     graph = tf.Graph()
-    ge.copy(self.graph, graph)
+    _, info = ge.copy(self.graph, graph)
     self.assertEqual(set(op.name for op in self.graph.get_operations()),
                      set(op.name for op in graph.get_operations()))
+    src_ops = self.graph.get_operations()
+    dst_ops = graph.get_operations()
+    for op in src_ops:
+      op_ = info.transformed(op)
+      self.assertTrue(op_ in dst_ops)
+      self.assertEqual(op.name, op_.name)
+      self.assertEqual(info.original(op_), op)
+    src_ts = ge.util.get_tensors(self.graph)
+    dst_ts = ge.util.get_tensors(graph)
+    for t in src_ts:
+      t_ = info.transformed(t)
+      self.assertTrue(t_ in dst_ts)
+      self.assertEqual(t.name, t_.name)
+      self.assertEqual(info.original(t_), t)
 
   def test_transform(self):
     transformer = ge.Transformer()
@@ -93,5 +111,30 @@ class TransformTest(tf.test.TestCase):
     top = ge.select_ops("^AddNoise_2$", graph=self.graph)[0]
     self.assertTrue(matcher2(top))
 
+  def test_copy_with_input_replacements(self):
+    with self.graph.as_default():
+      ten = tf.constant(10.0, shape=[10], name="Input")
+      sgv, _ = ge.copy_with_input_replacements(self.o.op,
+                                               {self.o.op.inputs[1]: ten})
+      with tf.Session() as sess:
+        val = sess.run(sgv.outputs[0])
+      self.assertNear(np.linalg.norm(val - np.array([11])),
+                      0.0, ERROR_TOLERANCE)
+
+  def test_graph_replace(self):
+    tf.reset_default_graph()
+    a = tf.constant(1.0, name="a")
+    b = tf.Variable(1.0, name="b")
+    eps = tf.constant(0.001, name="eps")
+    c = tf.identity(a + b + eps, name="c")
+    a_new = tf.constant(2.0, name="a_new")
+    c_new = ge.graph_replace(c, {a: a_new})
+    with tf.Session() as sess:
+      sess.run(tf.initialize_all_variables())
+      c_val, c_new_val = sess.run([c, c_new])
+    self.assertNear(c_val, 2.001, ERROR_TOLERANCE)
+    self.assertNear(c_new_val, 3.001, ERROR_TOLERANCE)
+
+
 if __name__ == "__main__":
   tf.test.main()
diff --git a/tensorflow/contrib/graph_editor/transform.py b/tensorflow/contrib/graph_editor/transform.py
index a33a85ad3f..584318891c 100644
--- a/tensorflow/contrib/graph_editor/transform.py
+++ b/tensorflow/contrib/graph_editor/transform.py
@@ -20,9 +20,10 @@ from __future__ import division
 from __future__ import print_function
 
 from copy import deepcopy
-import types
 
 from six import iteritems
+from six import iterkeys
+from six import string_types
 from six import StringIO
 
 from tensorflow.contrib.graph_editor import edit
@@ -274,7 +275,7 @@ class Transformer(object):
         the transformed tensor/operation (or None if no match is found).
       """
       transformed_map = self._get_transformed_map(original_top)
-      if isinstance(original_top, str):
+      if isinstance(original_top, string_types):
         for original, transformed in iteritems(transformed_map):
           if original.name == original_top:
             return transformed
@@ -293,7 +294,7 @@ class Transformer(object):
         the original tensor/operation (or None if no match is found).
       """
       transformed_map = self._get_transformed_map(transformed_top)
-      if isinstance(transformed_top, types.StringTypes):
+      if isinstance(transformed_top, string_types):
         finder = lambda transformed: transformed.name == transformed_top
       else:
         finder = lambda transformed: transformed == transformed_top
@@ -686,7 +687,7 @@ def graph_replace(target_ts, replacement_ts, dst_scope="",
   # control dependencies.
   graph = util.get_unique_graph(flatten_target_ts, check_types=(tf_ops.Tensor))
   control_ios = util.ControlOutputs(graph)
-  ops = select.get_walks_intersection_ops(replacement_ts.keys(),
+  ops = select.get_walks_intersection_ops(list(iterkeys(replacement_ts)),
                                           flatten_target_ts,
                                           control_ios=control_ios)
   if not ops:
-- 
GitLab


From 19e8d26dda94bc4dc0439ca5fddaed8fba4adf1b Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 26 Aug 2016 09:18:21 -0800
Subject: [PATCH 205/223] Add a way to link and call hexagon shared library
 APIs Change: 131413727

---
 tensorflow/contrib/makefile/Makefile                  |  1 +
 tensorflow/contrib/quantization/Makefile.in           |  1 +
 .../hexagon/quantized_matmul_op_for_hexagon_test.cc   |  8 +++++---
 tensorflow/core/platform/hexagon/gemm_wrapper.h       | 11 ++++++++---
 4 files changed, 15 insertions(+), 6 deletions(-)

diff --git a/tensorflow/contrib/makefile/Makefile b/tensorflow/contrib/makefile/Makefile
index 6530d32fee..e68e8dc1bb 100644
--- a/tensorflow/contrib/makefile/Makefile
+++ b/tensorflow/contrib/makefile/Makefile
@@ -264,6 +264,7 @@ ifeq ($(TARGET),ANDROID)
 		INCLUDES += -I$(HEXAGON_INCLUDE)
 		LIBS += -lgemm_wrapper
 		LDFLAGS += -L$(HEXAGON_LIBS)
+		CXXFLAGS += -DUSE_HEXAGON_LIBS
 	endif
 
 endif  # ANDROID
diff --git a/tensorflow/contrib/quantization/Makefile.in b/tensorflow/contrib/quantization/Makefile.in
index 656f1d00ea..563639e5d7 100644
--- a/tensorflow/contrib/quantization/Makefile.in
+++ b/tensorflow/contrib/quantization/Makefile.in
@@ -39,6 +39,7 @@ tensorflow/contrib/quantization/kernels/quantize_op.cc \
 tensorflow/contrib/quantization/kernels/quantized_conv_ops.cc \
 tensorflow/contrib/quantization/kernels/quantized_matmul_op.cc \
 tensorflow/contrib/quantization/kernels/quantized_matmul_op_test.cc \
+tensorflow/contrib/quantization/kernels/hexagon/quantized_matmul_op_for_hexagon_test.cc \
 tensorflow/contrib/makefile/test/test_main.cc
 
 QUANTIZATION_TEST_OBJS := $(addprefix $(OBJDIR), $(QUANTIZATION_TEST_SRCS:.cc=.o))
diff --git a/tensorflow/contrib/quantization/kernels/hexagon/quantized_matmul_op_for_hexagon_test.cc b/tensorflow/contrib/quantization/kernels/hexagon/quantized_matmul_op_for_hexagon_test.cc
index 1fa4ba0163..9c452fe39c 100644
--- a/tensorflow/contrib/quantization/kernels/hexagon/quantized_matmul_op_for_hexagon_test.cc
+++ b/tensorflow/contrib/quantization/kernels/hexagon/quantized_matmul_op_for_hexagon_test.cc
@@ -30,7 +30,7 @@ limitations under the License.
 #include "tensorflow/core/platform/test.h"
 
 #ifdef USE_HEXAGON_LIBS
-#include "gemm_wrapper.h"
+#include "tensorflow/core/platform/hexagon/gemm_wrapper.h"
 #endif
 
 namespace tensorflow {
@@ -39,8 +39,10 @@ class QuantizedMatMulOpForHexagonTest : public OpsTestBase {
  protected:
   void SetUp() final {
 #ifdef USE_HEXAGON_LIBS
-    LOG(INFO) << "Hexagon libs are linked (version = "
-              << hexagon_gemm_wrapper_GetVersion() << ")";
+    LOG(INFO) << "Hexagon libs are linked (wrapper version = "
+              << hexagon_gemm_wrapper_GetWrapperVersion()
+              << ", hexagon binary version = "
+              << hexagon_gemm_wrapper_GetHexagonBinaryVersion() << ")";
 #else
     LOG(WARNING) << "Hexagon libs are not linked.";
 #endif
diff --git a/tensorflow/core/platform/hexagon/gemm_wrapper.h b/tensorflow/core/platform/hexagon/gemm_wrapper.h
index 30890d4655..b1c22bafdb 100644
--- a/tensorflow/core/platform/hexagon/gemm_wrapper.h
+++ b/tensorflow/core/platform/hexagon/gemm_wrapper.h
@@ -23,9 +23,14 @@ limitations under the License.
 #ifdef __cplusplus
 extern "C" {
 #endif  // __cplusplus
-// Returns the version of loaded hexagon shared library. Assert if the version
-// matches the expected version.
-int hexagon_gemm_wrapper_GetVersion();
+// Returns the version of loaded hexagon wrapper shared library.
+// You should assert that the version matches the expected version before
+// calling APIs defined in this header.
+int hexagon_gemm_wrapper_GetWrapperVersion();
+// Returns the version of hexagon binary.
+// You should assert that the version matches the expected version before
+// calling APIs defined in this header.
+int hexagon_gemm_wrapper_GetHexagonBinaryVersion();
 // TODO(satok): Support gemm APIs via RPC
 #ifdef __cplusplus
 }
-- 
GitLab


From a559f91041f144820266fc0a751e09267acf4428 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 26 Aug 2016 09:25:26 -0800
Subject: [PATCH 206/223] TF_SetAttrTensorShapeProto should take a "const
 void*", not a "void*" argument.

This makes it consistent with the other TF_SetAttr* functions and also more
clearly conveys that it is read-only.
Change: 131414593
---
 tensorflow/c/c_api.cc | 2 +-
 tensorflow/c/c_api.h  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc
index b081dff36a..3928e9e09f 100644
--- a/tensorflow/c/c_api.cc
+++ b/tensorflow/c/c_api.cc
@@ -824,7 +824,7 @@ void TF_SetAttrShapeList(TF_OperationDescription* desc, const char* attr_name,
 }
 
 void TF_SetAttrTensorShapeProto(TF_OperationDescription* desc,
-                                const char* attr_name, void* proto,
+                                const char* attr_name, const void* proto,
                                 int proto_len, TF_Status* status) {
   TensorShapeProto shape;
   if (shape.ParseFromArray(proto, proto_len)) {
diff --git a/tensorflow/c/c_api.h b/tensorflow/c/c_api.h
index e02c2f1620..c31b57f79f 100644
--- a/tensorflow/c/c_api.h
+++ b/tensorflow/c/c_api.h
@@ -369,7 +369,7 @@ extern void TF_SetAttrShapeList(TF_OperationDescription* desc,
 // `proto` must point to an array of `proto_len` bytes representing a
 // binary-serialized TensorShapeProto.
 extern void TF_SetAttrTensorShapeProto(TF_OperationDescription* desc,
-                                       const char* attr_name, void* proto,
+                                       const char* attr_name, const void* proto,
                                        int proto_len, TF_Status* status);
 // `protos` and `proto_lens` must point to arrays of length `num_shapes`.
 // `protos[i]` must point to an array of `proto_lens[i]` bytes
-- 
GitLab


From 60154a9f3d7c26cb50d165e4dd8d6e402603a73a Mon Sep 17 00:00:00 2001
From: Georg Nebehay <gnebehay@gmail.com>
Date: Fri, 26 Aug 2016 19:33:40 +0200
Subject: [PATCH 207/223] Consider that arg keep_prob to dropout might be
 non-float number (#4015)

---
 tensorflow/python/ops/nn_ops.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py
index 1ac5314ad2..11ca3ff0d7 100644
--- a/tensorflow/python/ops/nn_ops.py
+++ b/tensorflow/python/ops/nn_ops.py
@@ -19,6 +19,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numbers
+
 import numpy as np
 
 from tensorflow.python.framework import common_shapes
@@ -1131,7 +1133,7 @@ def dropout(x, keep_prob, noise_shape=None, seed=None, name=None):
   """
   with ops.name_scope(name, "dropout", [x]) as name:
     x = ops.convert_to_tensor(x, name="x")
-    if isinstance(keep_prob, float) and not 0 < keep_prob <= 1:
+    if isinstance(keep_prob, numbers.Real) and not 0 < keep_prob <= 1:
       raise ValueError("keep_prob must be a scalar tensor or a float in the "
                        "range (0, 1], got %g" % keep_prob)
     keep_prob = ops.convert_to_tensor(keep_prob,
-- 
GitLab


From 912863b4a7bcbf223099d93940944f5244ba1253 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 26 Aug 2016 09:53:06 -0800
Subject: [PATCH 208/223] Update generated Python Op docs. Change: 131417929

---
 .../api_docs/python/contrib.graph_editor.md   | 162 +++++++++---------
 1 file changed, 80 insertions(+), 82 deletions(-)

diff --git a/tensorflow/g3doc/api_docs/python/contrib.graph_editor.md b/tensorflow/g3doc/api_docs/python/contrib.graph_editor.md
index 68e9619fc7..a1efb75613 100644
--- a/tensorflow/g3doc/api_docs/python/contrib.graph_editor.md
+++ b/tensorflow/g3doc/api_docs/python/contrib.graph_editor.md
@@ -3,7 +3,7 @@
 # Graph Editor (contrib)
 [TOC]
 
-# TensorFlow Graph Editor.
+TensorFlow Graph Editor.
 
 The TensorFlow Graph Editor library allows for modification of an existing
 tf.Graph instance in-place.
@@ -52,7 +52,7 @@ Note that this procedure is very costly because a new session must be created
 after any modifications. Among other things, it takes time because the entire
 graph state must be saved and restored again.
 
-### Sub-graph
+## Sub-graph
 
 Most of the functions in the Graph Editor library operate on *sub-graph*.
 More precisely, they take as input arguments instances of the SubGraphView class
@@ -85,7 +85,7 @@ to avoid any confusion, the default graph is never used and the graph on
 which to operate must always be explicitely given. This is the reason why
 *graph=tf.get_default_graph()* is used in the code snippets above.
 
-### Modules overview
+## Modules overview
 
 * util: utility functions.
 * select: various selection methods of TensorFlow tensors and operations.
@@ -100,7 +100,7 @@ which to operate must always be explicitely given. This is the reason why
 * transform: the Transformer class, which enables transforming
   (or simply copying) a subgraph into another one.
 
-### Module: util
+## Module: util
 
 - - -
 
@@ -348,7 +348,7 @@ tensor argument).
 
 
 
-### Module: select
+## Module: select
 
 - - -
 
@@ -836,7 +836,7 @@ Helper to select operations and tensors.
 
 
 
-### Module: subgraph
+## Module: subgraph
 
 - - -
 
@@ -1280,7 +1280,7 @@ Make a subgraph from a name scope.
 
 
 
-### Module: reroute
+## Module: reroute
 
 - - -
 
@@ -1490,7 +1490,7 @@ Warning: this function is directly manipulating the internals of the tf.Graph.
 
 
 
-### Module: edit
+## Module: edit
 
 - - -
 
@@ -1665,7 +1665,7 @@ Bypass the given subgraph by connecting its inputs to its outputs.
 
 
 
-### Module: transform
+## Module: transform
 
 - - -
 
@@ -1875,8 +1875,77 @@ Copy a subgraph.
     the same rules than the function subgraph.make_view.
 
 
+- - -
+
+### `tf.contrib.graph_editor.copy_with_input_replacements(sgv, replacement_ts, dst_graph=None, dst_scope='', src_scope='', reuse_dst_scope=False)` {#copy_with_input_replacements}
+
+Copy a subgraph, replacing some of its inputs.
+
+Note a replacement only happens if the tensor to be replaced
+is an input of the given subgraph. The inputs of a subgraph can
+be queried using sgv.inputs.
+
+##### Args:
+
+
+*  <b>`sgv`</b>: the source subgraph-view. This argument is converted to a subgraph
+    using the same rules as the function subgraph.make_view.
+*  <b>`replacement_ts`</b>: dictionary mapping from original tensors to the
+    replaced one.
+*  <b>`dst_graph`</b>: the destination graph.
+*  <b>`dst_scope`</b>: the destination scope.
+*  <b>`src_scope`</b>: the source scope.
+*  <b>`reuse_dst_scope`</b>: if True the dst_scope is re-used if it already exists.
+    Otherwise, the scope is given a unique name based on the one given
+    by appending an underscore followed by a digit (default).
+
+##### Returns:
+
+  A tuple `(sgv, info)` where:
+    `sgv` is the transformed subgraph view;
+    `info` is an instance of Transformer.ResultInfo containing
+    information about the transform, including mapping between
+    original and transformed tensors and operations.
+
+##### Raises:
+
+
+*  <b>`TypeError`</b>: if dst_graph is not a tf.Graph.
+*  <b>`StandardError`</b>: if sgv cannot be converted to a SubGraphView using
+    the same rules as the function subgraph.make_view.
 
-### Module: match
+
+- - -
+
+### `tf.contrib.graph_editor.graph_replace(target_ts, replacement_ts, dst_scope='', src_scope='', reuse_dst_scope=False)` {#graph_replace}
+
+Create a new graph which compute the targets from the replaced Tensors.
+
+##### Args:
+
+
+*  <b>`target_ts`</b>: a single tf.Tensor or an iterabble of tf.Tensor.
+*  <b>`replacement_ts`</b>: dictionary mapping from original tensors to replaced tensors
+*  <b>`dst_scope`</b>: the destination scope.
+*  <b>`src_scope`</b>: the source scope.
+*  <b>`reuse_dst_scope`</b>: if True the dst_scope is re-used if it already exists.
+    Otherwise, the scope is given a unique name based on the one given
+    by appending an underscore followed by a digit (default).
+
+##### Returns:
+
+  A single tf.Tensor or a list of target tf.Tensor, depending on
+  the type of the input argument `target_ts`.
+  The returned tensors are recomputed using the tensors from replacement_ts.
+
+##### Raises:
+
+
+*  <b>`ValueError`</b>: if the targets are not connected to replacement_ts.
+
+
+
+## Module: match
 
 - - -
 
@@ -1932,7 +2001,7 @@ Add output matches.
 
 
 
-### Useful aliases
+## Useful aliases
 
 - - -
 
@@ -2102,74 +2171,3 @@ Add output matches.
 
 
 
-
-## Other Functions and Classes
-- - -
-
-### `tf.contrib.graph_editor.copy_with_input_replacements(sgv, replacement_ts, dst_graph=None, dst_scope='', src_scope='', reuse_dst_scope=False)` {#copy_with_input_replacements}
-
-Copy a subgraph, replacing some of its inputs.
-
-Note a replacement only happens if the tensor to be replaced
-is an input of the given subgraph. The inputs of a subgraph can
-be queried using sgv.inputs.
-
-##### Args:
-
-
-*  <b>`sgv`</b>: the source subgraph-view. This argument is converted to a subgraph
-    using the same rules as the function subgraph.make_view.
-*  <b>`replacement_ts`</b>: dictionary mapping from original tensors to the
-    replaced one.
-*  <b>`dst_graph`</b>: the destination graph.
-*  <b>`dst_scope`</b>: the destination scope.
-*  <b>`src_scope`</b>: the source scope.
-*  <b>`reuse_dst_scope`</b>: if True the dst_scope is re-used if it already exists.
-    Otherwise, the scope is given a unique name based on the one given
-    by appending an underscore followed by a digit (default).
-
-##### Returns:
-
-  A tuple `(sgv, info)` where:
-    `sgv` is the transformed subgraph view;
-    `info` is an instance of Transformer.ResultInfo containing
-    information about the transform, including mapping between
-    original and transformed tensors and operations.
-
-##### Raises:
-
-
-*  <b>`TypeError`</b>: if dst_graph is not a tf.Graph.
-*  <b>`StandardError`</b>: if sgv cannot be converted to a SubGraphView using
-    the same rules as the function subgraph.make_view.
-
-
-- - -
-
-### `tf.contrib.graph_editor.graph_replace(target_ts, replacement_ts, dst_scope='', src_scope='', reuse_dst_scope=False)` {#graph_replace}
-
-Create a new graph which compute the targets from the replaced Tensors.
-
-##### Args:
-
-
-*  <b>`target_ts`</b>: a single tf.Tensor or an iterabble of tf.Tensor.
-*  <b>`replacement_ts`</b>: dictionary mapping from original tensors to replaced tensors
-*  <b>`dst_scope`</b>: the destination scope.
-*  <b>`src_scope`</b>: the source scope.
-*  <b>`reuse_dst_scope`</b>: if True the dst_scope is re-used if it already exists.
-    Otherwise, the scope is given a unique name based on the one given
-    by appending an underscore followed by a digit (default).
-
-##### Returns:
-
-  A single tf.Tensor or a list of target tf.Tensor, depending on
-  the type of the input argument `target_ts`.
-  The returned tensors are recomputed using the tensors from replacement_ts.
-
-##### Raises:
-
-
-*  <b>`ValueError`</b>: if the targets are not connected to replacement_ts.
-
-
-- 
GitLab


From d19c7ec962cfa264a6208abf11262b66cf22a675 Mon Sep 17 00:00:00 2001
From: Maniteja Nandana <manitejanmt@gmail.com>
Date: Fri, 26 Aug 2016 23:57:11 +0530
Subject: [PATCH 209/223] Add sparse_transpose wrapper (#3031)

* Add sparse_transpose wrapper

* Replace index packing using gather

* Add generalised tests for sparse transpose
---
 .../python/kernel_tests/sparse_ops_test.py    | 23 +++++++++
 tensorflow/python/ops/sparse_ops.py           | 47 +++++++++++++++++++
 2 files changed, 70 insertions(+)

diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py
index 29b57e8094..d945af0081 100644
--- a/tensorflow/python/kernel_tests/sparse_ops_test.py
+++ b/tensorflow/python/kernel_tests/sparse_ops_test.py
@@ -743,5 +743,28 @@ class SparseMinimumMaximumTest(test_util.TensorFlowTestCase):
         tf.sparse_maximum(sp_zero, sp_one).eval()
 
 
+class SparseTransposeTest(tf.test.TestCase):
+
+  def _SparseTensorPlaceholder(self):
+    return tf.SparseTensor(
+        tf.placeholder(tf.int64),
+        tf.placeholder(tf.float64),
+        tf.placeholder(tf.int64))
+
+  def testTranspose(self):
+    with self.test_session(use_gpu=False) as sess:
+      np.random.seed(1618)
+      shapes = [np.random.randint(1, 10, size=rank) for rank in range(1, 6)]
+      for shape in shapes:
+        for dtype in [np.int32, np.int64, np.float32, np.float64]:
+          dn_input = np.random.randn(*shape).astype(dtype)
+          rank = tf.rank(dn_input).eval()
+          perm = np.random.choice(rank, rank, False)
+          sp_input, unused_a_nnz = _sparsify(dn_input)
+          sp_trans = tf.sparse_transpose(sp_input, perm=perm)
+          dn_trans = tf.sparse_tensor_to_dense(sp_trans).eval()
+          expected_trans = tf.transpose(dn_input, perm=perm).eval()
+          self.assertAllEqual(dn_trans, expected_trans)
+
 if __name__ == "__main__":
   googletest.main()
diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py
index c42da9af5d..198ccab021 100644
--- a/tensorflow/python/ops/sparse_ops.py
+++ b/tensorflow/python/ops/sparse_ops.py
@@ -40,6 +40,7 @@ dimension, and dense along all other dimensions.
 @@sparse_retain
 @@sparse_reset_shape
 @@sparse_fill_empty_rows
+@@sparse_transpose
 
 ## Reduction
 @@sparse_reduce_sum
@@ -1582,3 +1583,49 @@ def _SparseSparseMaximumMinimumShape(op):  # pylint: disable=invalid-name
   op.inputs[4].get_shape().assert_has_rank(1)  # b_values
   op.inputs[5].get_shape().assert_has_rank(1)  # b_shape
   return [tensor_shape.unknown_shape(2), tensor_shape.unknown_shape(1)]
+
+
+def sparse_transpose(sp_input, perm=None, name=None):
+  """Transposes a `SparseTensor`
+
+  The returned tensor's dimension i will correspond to the input dimension
+  `perm[i]`. If `perm` is not given, it is set to (n-1...0), where n is
+  the rank of the input tensor. Hence by default, this operation performs a
+  regular matrix transpose on 2-D input Tensors.
+
+  For example, if `sp_input` has shape `[4, 5]` and `indices` / `values`:
+
+      [0, 3]: b
+      [0, 1]: a
+      [3, 1]: d
+      [2, 0]: c
+
+  then the output will be a `SparseTensor` of shape `[5, 4]` and
+  `indices` / `values`:
+
+      [0, 2]: c
+      [1, 0]: a
+      [1, 3]: d
+      [3, 0]: b
+
+  Args:
+    sp_input: The input `SparseTensor`.
+    perm: A permutation of the dimensions of `sp_input`.
+    name: A name prefix for the returned tensors (optional)
+  Returns:
+    A transposed `SparseTensor`.
+
+  Raises:
+    TypeError: If `sp_input` is not a `SparseTensor`.
+  """
+  with ops.op_scope([sp_input], name, "SparseTranspose") as name:
+    if perm is None:
+      rank = array_ops.rank(sp_input)
+      perm = (rank - 1) - math_ops.range(0, rank, 1)
+    indices = sp_input.indices
+    transposed_indices = array_ops.transpose(array_ops.gather(array_ops.transpose(indices), perm))
+    dense_shape = sp_input.shape
+    transposed_dense_shape = array_ops.gather(dense_shape, perm)
+    transposed_st = ops.SparseTensor(transposed_indices, sp_input.values, transposed_dense_shape)
+    transposed_st = sparse_reorder(transposed_st)
+    return transposed_st
-- 
GitLab


From d697b66e4ef0d569b3627724cebd53aa1ce0c1b1 Mon Sep 17 00:00:00 2001
From: Dan Smilkov <smilkov@google.com>
Date: Fri, 26 Aug 2016 10:38:50 -0800
Subject: [PATCH 210/223] Moving the frontend of the embedding projector to
 TensorBoard.

This is just code move and build integration. There are no UI/logic changes to TensorBoard.
Change: 131423744
---
 WORKSPACE                                     |   30 +-
 bower.BUILD                                   |  334 ++++
 tensorflow/BUILD                              |    1 +
 tensorflow/tensorboard/bower.json             |   16 +-
 tensorflow/tensorboard/bower/BUILD            |    4 +
 .../tensorboard/components/vz-projector/BUILD |   19 +
 .../components/vz-projector/async.ts          |   51 +
 .../components/vz-projector/bh_tsne.ts        |  472 ++++++
 .../components/vz-projector/data.ts           |  324 ++++
 .../components/vz-projector/data_test.ts      |   66 +
 .../components/vz-projector/demo/index.html   |   73 +
 .../components/vz-projector/external.d.ts     |   25 +
 .../components/vz-projector/heap.ts           |  146 ++
 .../components/vz-projector/knn.ts            |  223 +++
 .../components/vz-projector/label.ts          |  151 ++
 .../components/vz-projector/scatter.ts        |  135 ++
 .../components/vz-projector/scatterWebGL.ts   | 1510 +++++++++++++++++
 .../components/vz-projector/sptree.ts         |  189 +++
 .../components/vz-projector/sptree_test.ts    |  106 ++
 .../components/vz-projector/util.ts           |   43 +
 .../components/vz-projector/vector.ts         |  270 +++
 .../vz-projector-data-loader.html             |  144 ++
 .../vz-projector/vz-projector-data-loader.ts  |  500 ++++++
 .../vz-projector/vz-projector-util.ts         |   33 +
 .../components/vz-projector/vz-projector.html |  628 +++++++
 .../components/vz-projector/vz-projector.ts   |  762 +++++++++
 tensorflow/tensorboard/gulp_tasks/compile.js  |   55 +-
 tensorflow/tensorboard/package.json           |   16 +-
 tensorflow/tensorboard/tsconfig.json          |    3 +-
 tensorflow/tensorboard/typings.json           |    1 +
 30 files changed, 6314 insertions(+), 16 deletions(-)
 create mode 100644 tensorflow/tensorboard/components/vz-projector/BUILD
 create mode 100644 tensorflow/tensorboard/components/vz-projector/async.ts
 create mode 100644 tensorflow/tensorboard/components/vz-projector/bh_tsne.ts
 create mode 100644 tensorflow/tensorboard/components/vz-projector/data.ts
 create mode 100644 tensorflow/tensorboard/components/vz-projector/data_test.ts
 create mode 100644 tensorflow/tensorboard/components/vz-projector/demo/index.html
 create mode 100644 tensorflow/tensorboard/components/vz-projector/external.d.ts
 create mode 100644 tensorflow/tensorboard/components/vz-projector/heap.ts
 create mode 100644 tensorflow/tensorboard/components/vz-projector/knn.ts
 create mode 100644 tensorflow/tensorboard/components/vz-projector/label.ts
 create mode 100644 tensorflow/tensorboard/components/vz-projector/scatter.ts
 create mode 100644 tensorflow/tensorboard/components/vz-projector/scatterWebGL.ts
 create mode 100644 tensorflow/tensorboard/components/vz-projector/sptree.ts
 create mode 100644 tensorflow/tensorboard/components/vz-projector/sptree_test.ts
 create mode 100644 tensorflow/tensorboard/components/vz-projector/util.ts
 create mode 100644 tensorflow/tensorboard/components/vz-projector/vector.ts
 create mode 100644 tensorflow/tensorboard/components/vz-projector/vz-projector-data-loader.html
 create mode 100644 tensorflow/tensorboard/components/vz-projector/vz-projector-data-loader.ts
 create mode 100644 tensorflow/tensorboard/components/vz-projector/vz-projector-util.ts
 create mode 100644 tensorflow/tensorboard/components/vz-projector/vz-projector.html
 create mode 100644 tensorflow/tensorboard/components/vz-projector/vz-projector.ts

diff --git a/WORKSPACE b/WORKSPACE
index c0d148094f..7991061d1e 100644
--- a/WORKSPACE
+++ b/WORKSPACE
@@ -227,6 +227,13 @@ new_git_repository(
   tag = "v1.2.2",
 )
 
+new_git_repository(
+  name = "numericjs",
+  build_file = "bower.BUILD",
+  remote = "https://github.com/sloisel/numeric.git",
+  tag = "v1.2.6",
+)
+
 new_git_repository(
   name = "paper_behaviors",
   build_file = "bower.BUILD",
@@ -297,6 +304,13 @@ new_git_repository(
   tag = "v1.1.4",
 )
 
+new_git_repository(
+  name = "paper_listbox",
+  build_file = "bower.BUILD",
+  remote = "https://github.com/polymerelements/paper-listbox.git",
+  tag = "v1.1.2",
+)
+
 new_git_repository(
   name = "paper_material",
   build_file = "bower.BUILD",
@@ -399,7 +413,7 @@ new_git_repository(
   name = "polymer",
   build_file = "bower.BUILD",
   remote = "https://github.com/polymer/polymer.git",
-  tag = "v1.6.0",
+  tag = "v1.6.1",
 )
 
 new_git_repository(
@@ -409,6 +423,13 @@ new_git_repository(
   tag = "v1.0.0",
 )
 
+new_git_repository(
+  name = "three_js",
+  build_file = "bower.BUILD",
+  remote = "https://github.com/mrdoob/three.js.git",
+  tag = "r77",
+)
+
 new_git_repository(
   name = "web_animations_js",
   build_file = "bower.BUILD",
@@ -422,3 +443,10 @@ new_git_repository(
   remote = "https://github.com/webcomponents/webcomponentsjs.git",
   tag = "v0.7.22",
 )
+
+new_git_repository(
+  name = "weblas",
+  build_file = "bower.BUILD",
+  remote = "https://github.com/waylonflinn/weblas.git",
+  tag = "v0.9.0",
+)
diff --git a/bower.BUILD b/bower.BUILD
index cb3309a36e..e01fb6d7e9 100644
--- a/bower.BUILD
+++ b/bower.BUILD
@@ -302,6 +302,41 @@ filegroup(
     ],
 )
 
+filegroup(
+    name = "numericjs",
+    srcs = [
+        "benchmark.html",
+        "benchmark2.html",
+        "demo.html",
+        "documentation.html",
+        "myworker.js",
+        "resources/style.css",
+        "resources/style-ie.css",
+        "src/documentation.html",
+        "src/numeric.js",
+        "src/quadprog.js",
+        "src/seedrandom.js",
+        "src/sparse2.js",
+        "src/svd.js",
+        "tools/XMLHttpRequest.js",
+        "tools/closurelib.js",
+        "tools/excanvas.min.js",
+        "tools/goog-require.js",
+        "tools/jquery.flot.image.js",
+        "tools/jquery.flot.image.min.js",
+        "tools/jquery.flot.js",
+        "tools/jquery.flot.min.js",
+        "tools/jquery-1.7.1.js",
+        "tools/jquery-1.7.1.min.js",
+        "tools/json2.js",
+        "tools/megalib.js",
+        "tools/mytest.html",
+        "tools/sylvester.js",
+        "tools/unit2.js",
+        "tools/workshop.html",
+    ],
+)
+
 filegroup(
     name = "paper_behaviors",
     srcs = [
@@ -404,6 +439,14 @@ filegroup(
     ],
 )
 
+filegroup(
+    name = "paper_listbox",
+    srcs = [
+        "index.html",
+        "paper-listbox.html",
+    ],
+)
+
 filegroup(
     name = "paper_material",
     srcs = [
@@ -555,6 +598,275 @@ filegroup(
     ],
 )
 
+filegroup(
+    name = "three_js",
+    srcs = [
+        "build/three.js",
+        "build/three.min.js",
+        "examples/js/AnimationClipCreator.js",
+        "examples/js/BlendCharacter.js",
+        "examples/js/BlendCharacterGui.js",
+        "examples/js/BufferGeometryUtils.js",
+        "examples/js/Car.js",
+        "examples/js/Cloth.js",
+        "examples/js/CurveExtras.js",
+        "examples/js/Detector.js",
+        "examples/js/Encodings.js",
+        "examples/js/GPUParticleSystem.js",
+        "examples/js/Gyroscope.js",
+        "examples/js/Half.js",
+        "examples/js/ImprovedNoise.js",
+        "examples/js/MD2Character.js",
+        "examples/js/MD2CharacterComplex.js",
+        "examples/js/MarchingCubes.js",
+        "examples/js/Mirror.js",
+        "examples/js/MorphAnimMesh.js",
+        "examples/js/MorphAnimation.js",
+        "examples/js/Ocean.js",
+        "examples/js/Octree.js",
+        "examples/js/PRNG.js",
+        "examples/js/ParametricGeometries.js",
+        "examples/js/RollerCoaster.js",
+        "examples/js/ShaderGodRays.js",
+        "examples/js/ShaderSkin.js",
+        "examples/js/ShaderTerrain.js",
+        "examples/js/ShaderToon.js",
+        "examples/js/SimplexNoise.js",
+        "examples/js/SimulationRenderer.js",
+        "examples/js/SkyShader.js",
+        "examples/js/TimelinerController.js",
+        "examples/js/TypedArrayUtils.js",
+        "examples/js/UCSCharacter.js",
+        "examples/js/Volume.js",
+        "examples/js/VolumeSlice.js",
+        "examples/js/WaterShader.js",
+        "examples/js/WebVR.js",
+        "examples/js/animation/CCDIKSolver.js",
+        "examples/js/animation/MMDPhysics.js",
+        "examples/js/cameras/CinematicCamera.js",
+        "examples/js/cameras/CombinedCamera.js",
+        "examples/js/controls/DeviceOrientationControls.js",
+        "examples/js/controls/DragControls.js",
+        "examples/js/controls/EditorControls.js",
+        "examples/js/controls/FirstPersonControls.js",
+        "examples/js/controls/FlyControls.js",
+        "examples/js/controls/MouseControls.js",
+        "examples/js/controls/OrbitControls.js",
+        "examples/js/controls/OrthographicTrackballControls.js",
+        "examples/js/controls/PointerLockControls.js",
+        "examples/js/controls/TrackballControls.js",
+        "examples/js/controls/TransformControls.js",
+        "examples/js/controls/VRControls.js",
+        "examples/js/crossfade/gui.js",
+        "examples/js/crossfade/scenes.js",
+        "examples/js/crossfade/transition.js",
+        "examples/js/curves/NURBSCurve.js",
+        "examples/js/curves/NURBSSurface.js",
+        "examples/js/curves/NURBSUtils.js",
+        "examples/js/effects/AnaglyphEffect.js",
+        "examples/js/effects/AsciiEffect.js",
+        "examples/js/effects/ParallaxBarrierEffect.js",
+        "examples/js/effects/PeppersGhostEffect.js",
+        "examples/js/effects/StereoEffect.js",
+        "examples/js/effects/VREffect.js",
+        "examples/js/exporters/OBJExporter.js",
+        "examples/js/exporters/STLBinaryExporter.js",
+        "examples/js/exporters/STLExporter.js",
+        "examples/js/exporters/TypedGeometryExporter.js",
+        "examples/js/geometries/ConvexGeometry.js",
+        "examples/js/geometries/DecalGeometry.js",
+        "examples/js/geometries/TeapotBufferGeometry.js",
+        "examples/js/geometries/hilbert2D.js",
+        "examples/js/geometries/hilbert3D.js",
+        "examples/js/libs/ammo.js",
+        "examples/js/libs/charsetencoder.min.js",
+        "examples/js/libs/dat.gui.min.js",
+        "examples/js/libs/earcut.js",
+        "examples/js/libs/inflate.min.js",
+        "examples/js/libs/jszip.min.js",
+        "examples/js/libs/msgpack-js.js",
+        "examples/js/libs/pnltri.min.js",
+        "examples/js/libs/stats.min.js",
+        "examples/js/libs/system.min.js",
+        "examples/js/libs/timeliner_gui.min.js",
+        "examples/js/libs/tween.min.js",
+        "examples/js/libs/zlib_and_gzip.min.js",
+        "examples/js/loaders/3MFLoader.js",
+        "examples/js/loaders/AMFLoader.js",
+        "examples/js/loaders/AWDLoader.js",
+        "examples/js/loaders/AssimpJSONLoader.js",
+        "examples/js/loaders/BabylonLoader.js",
+        "examples/js/loaders/BinaryLoader.js",
+        "examples/js/loaders/ColladaLoader.js",
+        "examples/js/loaders/ColladaLoader2.js",
+        "examples/js/loaders/DDSLoader.js",
+        "examples/js/loaders/FBXLoader.js",
+        "examples/js/loaders/HDRCubeTextureLoader.js",
+        "examples/js/loaders/KMZLoader.js",
+        "examples/js/loaders/MD2Loader.js",
+        "examples/js/loaders/MMDLoader.js",
+        "examples/js/loaders/MTLLoader.js",
+        "examples/js/loaders/NRRDLoader.js",
+        "examples/js/loaders/OBJLoader.js",
+        "examples/js/loaders/PCDLoader.js",
+        "examples/js/loaders/PDBLoader.js",
+        "examples/js/loaders/PLYLoader.js",
+        "examples/js/loaders/PVRLoader.js",
+        "examples/js/loaders/PlayCanvasLoader.js",
+        "examples/js/loaders/RGBELoader.js",
+        "examples/js/loaders/STLLoader.js",
+        "examples/js/loaders/SVGLoader.js",
+        "examples/js/loaders/TGALoader.js",
+        "examples/js/loaders/UTF8Loader.js",
+        "examples/js/loaders/VRMLLoader.js",
+        "examples/js/loaders/VTKLoader.js",
+        "examples/js/loaders/collada/Animation.js",
+        "examples/js/loaders/collada/AnimationHandler.js",
+        "examples/js/loaders/collada/KeyFrameAnimation.js",
+        "examples/js/loaders/ctm/CTMLoader.js",
+        "examples/js/loaders/ctm/CTMWorker.js",
+        "examples/js/loaders/ctm/ctm.js",
+        "examples/js/loaders/ctm/lzma.js",
+        "examples/js/loaders/deprecated/SceneLoader.js",
+        "examples/js/loaders/gltf/glTF-parser.js",
+        "examples/js/loaders/gltf/glTFAnimation.js",
+        "examples/js/loaders/gltf/glTFLoader.js",
+        "examples/js/loaders/gltf/glTFLoaderUtils.js",
+        "examples/js/loaders/gltf/glTFShaders.js",
+        "examples/js/loaders/gltf/gltfUtilities.js",
+        "examples/js/loaders/sea3d/SEA3D.js",
+        "examples/js/loaders/sea3d/SEA3DDeflate.js",
+        "examples/js/loaders/sea3d/SEA3DLZMA.js",
+        "examples/js/loaders/sea3d/SEA3DLegacy.js",
+        "examples/js/loaders/sea3d/SEA3DLoader.js",
+        "examples/js/math/ColorConverter.js",
+        "examples/js/math/Lut.js",
+        "examples/js/modifiers/BufferSubdivisionModifier.js",
+        "examples/js/modifiers/ExplodeModifier.js",
+        "examples/js/modifiers/SubdivisionModifier.js",
+        "examples/js/modifiers/TessellateModifier.js",
+        "examples/js/nodes/BuilderNode.js",
+        "examples/js/nodes/ConstNode.js",
+        "examples/js/nodes/FunctionCallNode.js",
+        "examples/js/nodes/FunctionNode.js",
+        "examples/js/nodes/GLNode.js",
+        "examples/js/nodes/InputNode.js",
+        "examples/js/nodes/NodeLib.js",
+        "examples/js/nodes/NodeMaterial.js",
+        "examples/js/nodes/RawNode.js",
+        "examples/js/nodes/TempNode.js",
+        "examples/js/nodes/accessors/CameraNode.js",
+        "examples/js/nodes/accessors/ColorsNode.js",
+        "examples/js/nodes/accessors/LightNode.js",
+        "examples/js/nodes/accessors/NormalNode.js",
+        "examples/js/nodes/accessors/PositionNode.js",
+        "examples/js/nodes/accessors/ReflectNode.js",
+        "examples/js/nodes/accessors/ScreenUVNode.js",
+        "examples/js/nodes/accessors/UVNode.js",
+        "examples/js/nodes/inputs/ColorNode.js",
+        "examples/js/nodes/inputs/CubeTextureNode.js",
+        "examples/js/nodes/inputs/FloatNode.js",
+        "examples/js/nodes/inputs/IntNode.js",
+        "examples/js/nodes/inputs/Matrix4Node.js",
+        "examples/js/nodes/inputs/MirrorNode.js",
+        "examples/js/nodes/inputs/ScreenNode.js",
+        "examples/js/nodes/inputs/TextureNode.js",
+        "examples/js/nodes/inputs/Vector2Node.js",
+        "examples/js/nodes/inputs/Vector3Node.js",
+        "examples/js/nodes/inputs/Vector4Node.js",
+        "examples/js/nodes/materials/PhongNode.js",
+        "examples/js/nodes/materials/PhongNodeMaterial.js",
+        "examples/js/nodes/materials/StandardNode.js",
+        "examples/js/nodes/materials/StandardNodeMaterial.js",
+        "examples/js/nodes/math/Math1Node.js",
+        "examples/js/nodes/math/Math2Node.js",
+        "examples/js/nodes/math/Math3Node.js",
+        "examples/js/nodes/math/OperatorNode.js",
+        "examples/js/nodes/postprocessing/NodePass.js",
+        "examples/js/nodes/utils/ColorAdjustmentNode.js",
+        "examples/js/nodes/utils/JoinNode.js",
+        "examples/js/nodes/utils/LuminanceNode.js",
+        "examples/js/nodes/utils/NoiseNode.js",
+        "examples/js/nodes/utils/NormalMapNode.js",
+        "examples/js/nodes/utils/ResolutionNode.js",
+        "examples/js/nodes/utils/RoughnessToBlinnExponentNode.js",
+        "examples/js/nodes/utils/SwitchNode.js",
+        "examples/js/nodes/utils/TimerNode.js",
+        "examples/js/nodes/utils/VelocityNode.js",
+        "examples/js/objects/ShadowMesh.js",
+        "examples/js/pmrem/PMREMCubeUVPacker.js",
+        "examples/js/pmrem/PMREMGenerator.js",
+        "examples/js/postprocessing/AdaptiveToneMappingPass.js",
+        "examples/js/postprocessing/BloomPass.js",
+        "examples/js/postprocessing/BokehPass.js",
+        "examples/js/postprocessing/ClearPass.js",
+        "examples/js/postprocessing/DotScreenPass.js",
+        "examples/js/postprocessing/EffectComposer.js",
+        "examples/js/postprocessing/FilmPass.js",
+        "examples/js/postprocessing/GlitchPass.js",
+        "examples/js/postprocessing/ManualMSAARenderPass.js",
+        "examples/js/postprocessing/MaskPass.js",
+        "examples/js/postprocessing/RenderPass.js",
+        "examples/js/postprocessing/SMAAPass.js",
+        "examples/js/postprocessing/SavePass.js",
+        "examples/js/postprocessing/ShaderPass.js",
+        "examples/js/postprocessing/TAARenderPass.js",
+        "examples/js/postprocessing/TexturePass.js",
+        "examples/js/renderers/CSS2DRenderer.js",
+        "examples/js/renderers/CSS3DRenderer.js",
+        "examples/js/renderers/CanvasRenderer.js",
+        "examples/js/renderers/Projector.js",
+        "examples/js/renderers/RaytracingRenderer.js",
+        "examples/js/renderers/RaytracingWorker.js",
+        "examples/js/renderers/SVGRenderer.js",
+        "examples/js/renderers/SoftwareRenderer.js",
+        "examples/js/shaders/BasicShader.js",
+        "examples/js/shaders/BleachBypassShader.js",
+        "examples/js/shaders/BlendShader.js",
+        "examples/js/shaders/BokehShader.js",
+        "examples/js/shaders/BokehShader2.js",
+        "examples/js/shaders/BrightnessContrastShader.js",
+        "examples/js/shaders/ColorCorrectionShader.js",
+        "examples/js/shaders/ColorifyShader.js",
+        "examples/js/shaders/ConvolutionShader.js",
+        "examples/js/shaders/CopyShader.js",
+        "examples/js/shaders/DOFMipMapShader.js",
+        "examples/js/shaders/DigitalGlitch.js",
+        "examples/js/shaders/DotScreenShader.js",
+        "examples/js/shaders/EdgeShader.js",
+        "examples/js/shaders/EdgeShader2.js",
+        "examples/js/shaders/FXAAShader.js",
+        "examples/js/shaders/FilmShader.js",
+        "examples/js/shaders/FocusShader.js",
+        "examples/js/shaders/FresnelShader.js",
+        "examples/js/shaders/GammaCorrectionShader.js",
+        "examples/js/shaders/HorizontalBlurShader.js",
+        "examples/js/shaders/HorizontalTiltShiftShader.js",
+        "examples/js/shaders/HueSaturationShader.js",
+        "examples/js/shaders/KaleidoShader.js",
+        "examples/js/shaders/LuminosityShader.js",
+        "examples/js/shaders/MirrorShader.js",
+        "examples/js/shaders/NormalMapShader.js",
+        "examples/js/shaders/OceanShaders.js",
+        "examples/js/shaders/ParallaxShader.js",
+        "examples/js/shaders/RGBShiftShader.js",
+        "examples/js/shaders/SMAAShader.js",
+        "examples/js/shaders/SSAOShader.js",
+        "examples/js/shaders/SepiaShader.js",
+        "examples/js/shaders/TechnicolorShader.js",
+        "examples/js/shaders/ToneMapShader.js",
+        "examples/js/shaders/TriangleBlurShader.js",
+        "examples/js/shaders/UnpackDepthRGBAShader.js",
+        "examples/js/shaders/VerticalBlurShader.js",
+        "examples/js/shaders/VerticalTiltShiftShader.js",
+        "examples/js/shaders/VignetteShader.js",
+        "examples/js/utils/GeometryUtils.js",
+        "examples/js/utils/ImageUtils.js",
+        "examples/js/utils/ShadowMapViewer.js",
+        "examples/js/utils/UVsDebug.js",
+    ],
+)
+
 filegroup(
     name = "web_animations_js",
     srcs = [
@@ -582,3 +894,25 @@ filegroup(
         "webcomponents-lite.min.js",
     ],
 )
+
+filegroup(
+    name = "weblas",
+    srcs = [
+        "benchmark.html",
+        "benchmark/sgemm.js",
+        "dist/weblas.js",
+        "index.js",
+        "lib/globals.js",
+        "lib/pipeline.js",
+        "lib/saxpycalculator.js",
+        "lib/sclmpcalculator.js",
+        "lib/sdwnscalculator.js",
+        "lib/sgemmcalculator.js",
+        "lib/sscalcalculator.js",
+        "lib/tensor.js",
+        "lib/test.js",
+        "lib/webgl.js",
+        "test.html",
+        "test/data/generate.js",
+    ],
+)
diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index add5d97169..2221d26c37 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -148,6 +148,7 @@ filegroup(
         "//tensorflow/tensorboard/app:all_files",
         "//tensorflow/tensorboard/backend:all_files",
         "//tensorflow/tensorboard/components:all_files",
+        "//tensorflow/tensorboard/components/vz-projector:all_files",
         "//tensorflow/tensorboard/lib:all_files",
         "//tensorflow/tensorboard/lib/python:all_files",
         "//tensorflow/tensorboard/scripts:all_files",
diff --git a/tensorflow/tensorboard/bower.json b/tensorflow/tensorboard/bower.json
index 20095d9275..368b0f8abd 100644
--- a/tensorflow/tensorboard/bower.json
+++ b/tensorflow/tensorboard/bower.json
@@ -60,6 +60,7 @@
     "iron-validatable-behavior": "PolymerElements/iron-validatable-behavior#1.1.1",
     "lodash": "3.8.0",
     "neon-animation": "PolymerElements/neon-animation#1.2.2",
+    "numericjs": "1.2.6",
     "paper-behaviors": "PolymerElements/paper-behaviors#1.0.11",
     "paper-button": "PolymerElements/paper-button#1.0.11",
     "paper-checkbox": "PolymerElements/paper-checkbox#1.1.3",
@@ -70,6 +71,7 @@
     "paper-icon-button": "PolymerElements/paper-icon-button#1.1.1",
     "paper-input": "PolymerElements/paper-input#1.1.14",
     "paper-item": "PolymerElements/paper-item#1.1.4",
+    "paper-listbox": "PolymerElements/paper-listbox#1.1.2",
     "paper-material": "PolymerElements/paper-material#1.0.6",
     "paper-menu": "PolymerElements/paper-menu#1.2.2",
     "paper-menu-button": "PolymerElements/paper-menu-button#1.5.0",
@@ -84,10 +86,12 @@
     "paper-toolbar": "PolymerElements/paper-toolbar#1.1.4",
     "paper-tooltip": "PolymerElements/paper-tooltip#1.1.2",
     "plottable": "1.16.1",
-    "polymer": "1.6.0",
+    "polymer": "1.6.1",
     "promise-polyfill": "polymerlabs/promise-polyfill#1.0.0",
+    "three.js": "threejs#r77",
     "web-animations-js": "web-animations/web-animations-js#2.2.1",
-    "webcomponentsjs": "webcomponents/webcomponentsjs#0.7.22"
+    "webcomponentsjs": "webcomponents/webcomponentsjs#0.7.22",
+    "weblas": "0.9.0"
   },
   "description": "TensorBoard: Visualizations for TensorFlow",
   "devDependencies": {
@@ -136,6 +140,7 @@
     "iron-validatable-behavior": "1.1.1",
     "lodash": "3.8.0",
     "neon-animation": "1.2.2",
+    "numericjs": "1.2.6",
     "paper-behaviors": "1.0.11",
     "paper-button": "1.0.11",
     "paper-checkbox": "1.1.3",
@@ -146,6 +151,7 @@
     "paper-icon-button": "1.1.1",
     "paper-input": "1.1.14",
     "paper-item": "1.1.4",
+    "paper-listbox": "1.1.2",
     "paper-material": "1.0.6",
     "paper-menu": "1.2.2",
     "paper-menu-button": "1.5.0",
@@ -160,10 +166,12 @@
     "paper-toolbar": "1.1.4",
     "paper-tooltip": "1.1.2",
     "plottable": "1.16.1",
-    "polymer": "1.6.0",
+    "polymer": "1.6.1",
     "promise-polyfill": "1.0.0",
+    "three.js": "threejs#r77",
     "web-animations-js": "2.2.1",
-    "webcomponentsjs": "0.7.22"
+    "webcomponentsjs": "0.7.22",
+    "weblas": "0.9.0"
   },
   "version": "0.0.0"
 }
diff --git a/tensorflow/tensorboard/bower/BUILD b/tensorflow/tensorboard/bower/BUILD
index 4b43b55844..88b9314080 100644
--- a/tensorflow/tensorboard/bower/BUILD
+++ b/tensorflow/tensorboard/bower/BUILD
@@ -34,6 +34,7 @@ filegroup(
         "@iron_validatable_behavior//:iron_validatable_behavior",
         "@lodash//:lodash",
         "@neon_animation//:neon_animation",
+        "@numericjs//:numericjs",
         "@paper_behaviors//:paper_behaviors",
         "@paper_button//:paper_button",
         "@paper_checkbox//:paper_checkbox",
@@ -44,6 +45,7 @@ filegroup(
         "@paper_icon_button//:paper_icon_button",
         "@paper_input//:paper_input",
         "@paper_item//:paper_item",
+        "@paper_listbox//:paper_listbox",
         "@paper_material//:paper_material",
         "@paper_menu//:paper_menu",
         "@paper_menu_button//:paper_menu_button",
@@ -60,7 +62,9 @@ filegroup(
         "@plottable//:plottable",
         "@polymer//:polymer",
         "@promise_polyfill//:promise_polyfill",
+        "@three_js//:three_js",
         "@web_animations_js//:web_animations_js",
         "@webcomponentsjs//:webcomponentsjs",
+        "@weblas//:weblas",
     ],
 )
diff --git a/tensorflow/tensorboard/components/vz-projector/BUILD b/tensorflow/tensorboard/components/vz-projector/BUILD
new file mode 100644
index 0000000000..8c222be10e
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/BUILD
@@ -0,0 +1,19 @@
+# Description:
+# Package for the Embedding Projector component.
+package(default_visibility = ["//tensorflow:internal"])
+
+licenses(["notice"])  # Apache 2.0
+
+exports_files(["LICENSE"])
+
+filegroup(
+    name = "all_files",
+    srcs = glob(
+        ["**/*"],
+        exclude = [
+            "**/METADATA",
+            "**/OWNERS",
+        ],
+    ),
+    visibility = ["//tensorflow:__subpackages__"],
+)
diff --git a/tensorflow/tensorboard/components/vz-projector/async.ts b/tensorflow/tensorboard/components/vz-projector/async.ts
new file mode 100644
index 0000000000..9ee6746b8d
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/async.ts
@@ -0,0 +1,51 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+/** Delay for running async tasks, in milliseconds. */
+const ASYNC_DELAY = 15;
+
+/**
+ * Runs an expensive task asynchronously with some delay
+ * so that it doesn't block the UI thread immediately.
+ */
+export function runAsyncTask<T>(message: string, task: () => T): Promise<T> {
+  updateMessage(message);
+  return new Promise<T>((resolve, reject) => {
+    d3.timer(() => {
+      try {
+        let result = task();
+        // Clearing the old message.
+        updateMessage();
+        resolve(result);
+      } catch (ex) {
+        updateMessage('Error: ' + ex.message);
+        reject(ex);
+      }
+      return true;
+    }, ASYNC_DELAY);
+  });
+}
+
+/**
+ * Updates the user message at the top of the page. If the provided msg is
+ * null, the message box is hidden from the user.
+ */
+export function updateMessage(msg?: string): void {
+  if (msg == null) {
+    d3.select('#notify-msg').style('display', 'none');
+  } else {
+    d3.select('#notify-msg').style('display', 'block').text(msg);
+  }
+}
diff --git a/tensorflow/tensorboard/components/vz-projector/bh_tsne.ts b/tensorflow/tensorboard/components/vz-projector/bh_tsne.ts
new file mode 100644
index 0000000000..95c207ec6c
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/bh_tsne.ts
@@ -0,0 +1,472 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+/**
+ * This is a fork of the Karpathy's TSNE.js (original license below).
+ * This fork implements Barnes-Hut approximation and runs in O(NlogN)
+ * time, as opposed to the Karpathy's O(N^2) version.
+ *
+ * @author smilkov@google.com (Daniel Smilkov)
+ */
+
+/**
+ * The MIT License (MIT)
+ * Copyright (c) 2015 Andrej Karpathy
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+
+import {SPNode, SPTree} from './sptree';
+
+type AugmSPNode = SPNode&{numCells: number, yCell: number[], rCell: number};
+
+/**
+ * Barnes-hut approximation level. Higher means more approximation and faster
+ * results. Recommended value mentioned in the paper is 0.8.
+ */
+const THETA = 0.8;
+
+// Variables used for memorizing the second random number since running
+// gaussRandom() generates two random numbers at the cost of 1 atomic
+// computation. This optimization results in 2X speed-up of the generator.
+let return_v = false;
+let v_val = 0.0;
+
+/** Returns a vector filled with zeros */
+function zerosArray(length: number): number[] {
+  let result = new Array(length);
+  for (let i = 0; i < length; ++i) {
+    result[i] = 0;
+  }
+  return result;
+}
+
+
+/** Returns the square euclidean distance between two vectors. */
+export function dist2(a: number[], b: number[]): number {
+  if (a.length != b.length) {
+    throw new Error('Vectors a and b must be of same length');
+  }
+
+  let result = 0;
+  for (let i = 0; i < a.length; ++i) {
+    let diff = a[i] - b[i];
+    result += diff * diff;
+  }
+  return result;
+}
+
+/** Returns the square euclidean distance between two 2D points. */
+export function dist2_2D(a: number[], b: number[]): number {
+  let dX = a[0] - b[0];
+  let dY = a[1] - b[1];
+  return dX * dX + dY * dY;
+}
+
+/** Returns the square euclidean distance between two 3D points. */
+export function dist2_3D(a: number[], b: number[]): number {
+  let dX = a[0] - b[0];
+  let dY = a[1] - b[1];
+  let dZ = a[2] - b[2];
+  return dX * dX + dY * dY + dZ * dZ;
+}
+
+function gaussRandom(rng: () => number): number {
+  if (return_v) {
+    return_v = false;
+    return v_val;
+  }
+  let u = 2 * rng() - 1;
+  let v = 2 * rng() - 1;
+  let r = u * u + v * v;
+  if (r == 0 || r > 1) {
+    return gaussRandom(rng);
+  }
+  let c = Math.sqrt(-2 * Math.log(r) / r);
+  v_val = v * c;  // cache this for next function call for efficiency
+  return_v = true;
+  return u * c;
+};
+
+// return random normal number
+function randn(rng: () => number, mu: number, std: number) {
+  return mu + gaussRandom(rng) * std;
+};
+
+// utilitity that creates contiguous vector of zeros of size n
+function zeros(n: number): Float64Array {
+  return new Float64Array(n);
+};
+
+// utility that returns a matrix filled with random numbers
+// generated by the provided generator.
+function randnMatrix(n: number, d: number, rng: () => number) {
+  let nd = n * d;
+  let x = zeros(nd);
+  for (let i = 0; i < nd; ++i) {
+    x[i] = randn(rng, 0.0, 1E-4);
+  }
+  return x;
+};
+
+// utility that returns a matrix filled with the provided value.
+function arrayofs(n: number, d: number, val: number) {
+  let x: number[][] = [];
+  for (let i = 0; i < n; ++i) {
+    let row = new Array(d);
+    for (let j = 0; j < d; ++j) {
+      row[j] = val;
+    }
+    x.push(row);
+  }
+  return x;
+};
+
+// compute (p_{i|j} + p_{j|i})/(2n)
+function nearest2P(
+    nearest: {index: number, dist: number}[][], perplexity: number,
+    tol: number) {
+  let N = nearest.length;
+  let Htarget = Math.log(perplexity);  // target entropy of distribution
+  let P = zeros(N * N);                // temporary probability matrix
+  let K = nearest[0].length;
+  let pRow: number[] = new Array(K);  // pij[].
+
+  for (let i = 0; i < N; ++i) {
+    let neighbors = nearest[i];
+    let betaMin = -Infinity;
+    let betaMax = Infinity;
+    let beta = 1;  // initial value of precision
+    let maxTries = 50;
+
+    // perform binary search to find a suitable precision beta
+    // so that the entropy of the distribution is appropriate
+    let numTries = 0;
+    while (true) {
+      // compute entropy and kernel row with beta precision
+      let psum = 0.0;
+      for (let k = 0; k < neighbors.length; ++k) {
+        let neighbor = neighbors[k];
+        let pij = (i == neighbor.index) ? 0 : Math.exp(-neighbor.dist * beta);
+        pRow[k] = pij;
+        psum += pij;
+      }
+      // normalize p and compute entropy
+      let Hhere = 0.0;
+      for (let k = 0; k < pRow.length; ++k) {
+        pRow[k] /= psum;
+        let pij = pRow[k];
+        if (pij > 1E-7) {
+          Hhere -= pij * Math.log(pij);
+        };
+      }
+
+      // adjust beta based on result
+      if (Hhere > Htarget) {
+        // entropy was too high (distribution too diffuse)
+        // so we need to increase the precision for more peaky distribution
+        betaMin = beta;  // move up the bounds
+        if (betaMax === Infinity) {
+          beta = beta * 2;
+        } else {
+          beta = (beta + betaMax) / 2;
+        }
+
+      } else {
+        // converse case. make distrubtion less peaky
+        betaMax = beta;
+        if (betaMin === -Infinity) {
+          beta = beta / 2;
+        } else {
+          beta = (beta + betaMin) / 2;
+        }
+      }
+      numTries++;
+      // stopping conditions: too many tries or got a good precision
+      if (numTries >= maxTries || Math.abs(Hhere - Htarget) < tol) {
+        break;
+      }
+    }
+
+    // copy over the final prow to P at row i
+    for (let k = 0; k < pRow.length; ++k) {
+      let pij = pRow[k];
+      let j = neighbors[k].index;
+      P[i * N + j] = pij;
+    }
+  }  // end loop over examples i
+
+  // symmetrize P and normalize it to sum to 1 over all ij
+  let N2 = N * 2;
+  for (let i = 0; i < N; ++i) {
+    for (let j = i + 1; j < N; ++j) {
+      let i_j = i * N + j;
+      let j_i = j * N + i;
+      let value = (P[i_j] + P[j_i]) / N2;
+      P[i_j] = value;
+      P[j_i] = value;
+    }
+  }
+  return P;
+};
+
+// helper function
+function sign(x: number) {
+  return x > 0 ? 1 : x < 0 ? -1 : 0;
+}
+
+export interface TSNEOptions {
+  /** How many dimensions. */
+  dim: number;
+  /** Roughly how many neighbors each point influences. */
+  perplexity?: number;
+  /** Learning rate. */
+  epsilon?: number;
+  /** A random number generator. */
+  rng?: () => number;
+}
+
+export class TSNE {
+  private perplexity: number;
+  private epsilon: number;
+  /** Random generator */
+  private rng: () => number;
+  private iter = 0;
+  private Y: Float64Array;
+  private N: number;
+  private P: Float64Array;
+  private gains: number[][];
+  private ystep: number[][];
+  private nearest: {index: number, dist: number}[][];
+  private dim: number;
+  private dist2: (a: number[], b: number[]) => number;
+
+  constructor(opt: TSNEOptions) {
+    opt = opt || {dim: 2};
+    this.perplexity = opt.perplexity || 30;
+    this.epsilon = opt.epsilon || 10;
+    this.rng = opt.rng || Math.random;
+    this.dim = opt.dim;
+    if (opt.dim == 2) {
+      this.dist2 = dist2_2D;
+    } else if (opt.dim == 3) {
+      this.dist2 = dist2_3D;
+    } else {
+      this.dist2 = dist2;
+    }
+  }
+
+  // this function takes a fattened distance matrix and creates
+  // matrix P from them.
+  // D is assumed to be provided as an array of size N^2.
+  initDataDist(nearest: {index: number, dist: number}[][]) {
+    let N = nearest.length;
+    this.nearest = nearest;
+    this.P = nearest2P(nearest, this.perplexity, 1E-4);
+    this.N = N;
+    this.initSolution();  // refresh this
+  }
+
+  // (re)initializes the solution to random
+  initSolution() {
+    // generate random solution to t-SNE
+    this.Y = randnMatrix(this.N, this.dim, this.rng);  // the solution
+    this.gains = arrayofs(this.N, this.dim, 1.0);      // step gains
+    // to accelerate progress in unchanging directions
+    this.ystep = arrayofs(this.N, this.dim, 0.0);  // momentum accumulator
+    this.iter = 0;
+  }
+
+  // return pointer to current solution
+  getSolution() { return this.Y; }
+
+  // perform a single step of optimization to improve the embedding
+  step() {
+    this.iter += 1;
+    let N = this.N;
+
+    let grad = this.costGrad(this.Y);  // evaluate gradient
+
+    // perform gradient step
+    let ymean = zerosArray(this.dim);
+    for (let i = 0; i < N; ++i) {
+      for (let d = 0; d < this.dim; ++d) {
+        let gid = grad[i][d];
+        let sid = this.ystep[i][d];
+        let gainid = this.gains[i][d];
+
+        // compute gain update
+        let newgain = sign(gid) === sign(sid) ? gainid * 0.8 : gainid + 0.2;
+        if (newgain < 0.01) {
+          newgain = 0.01;  // clamp
+        }
+        this.gains[i][d] = newgain;  // store for next turn
+
+        // compute momentum step direction
+        let momval = this.iter < 250 ? 0.5 : 0.8;
+        let newsid = momval * sid - this.epsilon * newgain * grad[i][d];
+        this.ystep[i][d] = newsid;  // remember the step we took
+
+        // step!
+        let i_d = i * this.dim + d;
+        this.Y[i_d] += newsid;
+        ymean[d] += this.Y[i_d];  // accumulate mean so that we
+                                  // can center later
+      }
+    }
+
+    // reproject Y to be zero mean
+    for (let i = 0; i < N; ++i) {
+      for (let d = 0; d < this.dim; ++d) {
+        this.Y[i * this.dim + d] -= ymean[d] / N;
+      }
+    }
+  }
+
+  // return cost and gradient, given an arrangement
+  costGrad(Y: Float64Array): number[][] {
+    let N = this.N;
+    let P = this.P;
+
+    // Trick that helps with local optima.
+    let alpha = this.iter < 100 ? 4 : 1;
+
+    // Make data for the SP tree.
+    let points: number[][] = new Array(N);  // (x, y)[]
+    for (let i = 0; i < N; ++i) {
+      let iTimesD = i * this.dim;
+      let row = new Array(this.dim);
+      for (let d = 0; d < this.dim; ++d) {
+        row[d] = Y[iTimesD + d];
+      }
+      points[i] = row;
+    }
+
+    // Make a tree.
+    let tree = new SPTree(points, 1);
+    let root = tree.root as AugmSPNode;
+    // Annotate the tree.
+
+    let annotateTree =
+        (node: AugmSPNode): {numCells: number, yCell: number[]} => {
+          let numCells = node.points ? node.points.length : 0;
+          if (node.children == null) {
+            // Update the current node and tell the parent.
+            node.numCells = numCells;
+            // TODO(smilkov): yCell should be average across all points.
+            node.yCell = node.points[0];
+            return {numCells, yCell: node.yCell};
+          }
+          // TODO(smilkov): yCell should be average across all points.
+          let yCell =
+              node.points ? node.points[0].slice() : zerosArray(this.dim);
+          for (let i = 0; i < node.children.length; ++i) {
+            let child = node.children[i];
+            if (child == null) {
+              continue;
+            }
+            let result = annotateTree(child as AugmSPNode);
+            numCells += result.numCells;
+            for (let d = 0; d < this.dim; ++d) {
+              yCell[d] += result.yCell[d];
+            }
+          }
+          // Update the node and tell the parent.
+          node.numCells = numCells;
+          node.yCell = yCell.map(v => v / numCells);
+          return {numCells, yCell};
+        };
+
+    // Augment the tree with more info.
+    annotateTree(root);
+    tree.visit((node: AugmSPNode, low: number[], high: number[]) => {
+      node.rCell = high[0] - low[0];
+      return false;
+    });
+    // compute current Q distribution, unnormalized first
+    let grad: number[][] = [];
+    let Z = 0;
+    let forces: [number[], number[]][] = new Array(N);
+    for (let i = 0; i < N; ++i) {
+      let pointI = points[i];
+      // Compute the positive forces for the i-th node.
+      let Fpos = zerosArray(this.dim);
+      let neighbors = this.nearest[i];
+      for (let k = 0; k < neighbors.length; ++k) {
+        let j = neighbors[k].index;
+        let pij = P[i * N + j];
+        let pointJ = points[j];
+        let squaredDistItoJ = this.dist2(pointI, pointJ);
+        let premult = pij / (1 + squaredDistItoJ);
+        for (let d = 0; d < this.dim; ++d) {
+          Fpos[d] += premult * (pointI[d] - pointJ[d]);
+        }
+      }
+      // Compute the negative forces for the i-th node.
+      let FnegZ = zerosArray(this.dim);
+      tree.visit((node: AugmSPNode) => {
+        let squaredDistToCell = this.dist2(pointI, node.yCell);
+        // Squared distance from point i to cell.
+        if (node.children == null ||
+            (node.rCell / Math.sqrt(squaredDistToCell) < THETA)) {
+          let qijZ = 1 / (1 + squaredDistToCell);
+          let dZ = node.numCells * qijZ;
+          Z += dZ;
+          dZ *= qijZ;
+          for (let d = 0; d < this.dim; ++d) {
+            FnegZ[d] += dZ * (pointI[d] - node.yCell[d]);
+          }
+          return true;
+        }
+        if (node.points != null) {
+          // TODO(smilkov): Iterate over all points.
+          let squaredDistToPoint = this.dist2(pointI, node.points[0]);
+          let qijZ = 1 / (1 + squaredDistToPoint);
+          Z += qijZ;
+          qijZ *= qijZ;
+          for (let d = 0; d < this.dim; ++d) {
+            FnegZ[d] += qijZ * (pointI[d] - node.points[0][d]);
+          }
+        }
+        return false;
+      }, true);
+      forces[i] = [Fpos, FnegZ];
+    }
+    // Normalize the negative forces and compute the gradient.
+    for (let i = 0; i < N; ++i) {
+      let [FPos, FNegZ] = forces[i];
+      let gsum = new Array(this.dim);
+      for (let d = 0; d < this.dim; ++d) {
+        gsum[d] = 4 * (alpha * FPos[d] - FNegZ[d] / Z);
+      }
+      grad.push(gsum);
+    }
+    return grad;
+  }
+}
diff --git a/tensorflow/tensorboard/components/vz-projector/data.ts b/tensorflow/tensorboard/components/vz-projector/data.ts
new file mode 100644
index 0000000000..dc23defb4a
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/data.ts
@@ -0,0 +1,324 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+import {runAsyncTask} from './async';
+import {TSNE} from './bh_tsne';
+import * as knn from './knn';
+import * as scatter from './scatter';
+import {shuffle} from './util';
+import * as vector from './vector';
+
+
+/**
+ * A DataSource is our ground truth data. The original parsed data should never
+ * be modified, only copied out.
+ */
+export class DataSource {
+  originalDataSet: DataSet;
+  spriteImage: HTMLImageElement;
+  metadata: DatasetMetadata;
+
+  /** A shallow-copy constructor. */
+  makeShallowCopy(): DataSource {
+    let copy = new DataSource();
+    copy.originalDataSet = this.originalDataSet;
+    copy.spriteImage = this.spriteImage;
+    copy.metadata = this.metadata;
+    return copy;
+  }
+
+  /** Returns a new dataset. */
+  getDataSet(subset?: number[]): DataSet {
+    let pointsSubset = subset ?
+        subset.map(i => this.originalDataSet.points[i]) :
+        this.originalDataSet.points;
+    return new DataSet(pointsSubset);
+  }
+}
+
+export interface DataPoint extends scatter.DataPoint {
+  /** The point in the original space. */
+  vector: number[];
+
+  /*
+   * Metadata for each point. Each metadata is a set of key/value pairs
+   * where the value can be a string or a number.
+   */
+  metadata: {[key: string]: number | string};
+
+  /** This is where the calculated projections space are cached */
+  projections: {[key: string]: number};
+}
+
+/** Checks to see if the browser supports webgl. */
+function hasWebGLSupport(): boolean {
+  try {
+    let c = document.createElement('canvas');
+    let gl = c.getContext('webgl') || c.getContext('experimental-webgl');
+    return gl != null && typeof weblas !== 'undefined';
+  } catch (e) {
+    return false;
+  }
+}
+
+const WEBGL_SUPPORT = hasWebGLSupport();
+const MAX_TSNE_ITERS = 500;
+/**
+ * Sampling is used when computing expensive operations such as PCA, or T-SNE.
+ */
+const SAMPLE_SIZE = 10000;
+/** Number of dimensions to sample when doing approximate PCA. */
+const PCA_SAMPLE_DIM = 100;
+/** Number of pca components to compute. */
+const NUM_PCA_COMPONENTS = 10;
+/** Reserved metadata attribute used for trace information. */
+const TRACE_METADATA_ATTR = '__next__';
+
+/**
+ * Dataset contains a DataPoints array that should be treated as immutable. This
+ * acts as a working subset of the original data, with cached properties
+ * from computationally expensive operations. Because creating a subset
+ * requires normalizing and shifting the vector space, we make a copy of the
+ * data so we can still always create new subsets based on the original data.
+ */
+export class DataSet implements scatter.DataSet {
+  points: DataPoint[];
+  traces: scatter.DataTrace[];
+
+  sampledDataIndices: number[] = [];
+
+  /**
+   * This keeps a list of all current projections so you can easily test to see
+   * if it's been calculated already.
+   */
+  projections = d3.set();
+  nearest: knn.NearestEntry[][];
+  nearestK: number;
+  tSNEShouldStop = true;
+  dim = [0, 0];
+  private tsne: TSNE;
+
+  /**
+   * Creates a new Dataset by copying out data from an array of datapoints.
+   * We make a copy because we have to modify the vectors by normalizing them.
+   */
+  constructor(points: DataPoint[]) {
+    // Keep a list of indices seen so we don't compute traces for a given
+    // point twice.
+    let indicesSeen: boolean[] = [];
+
+    this.points = [];
+    points.forEach(dp => {
+      this.points.push({
+        metadata: dp.metadata,
+        dataSourceIndex: dp.dataSourceIndex,
+        vector: dp.vector.slice(),
+        projectedPoint: {
+          x: 0,
+          y: 0,
+          z: 0,
+        },
+        projections: {}
+      });
+      indicesSeen.push(false);
+    });
+
+    this.sampledDataIndices =
+        shuffle(d3.range(this.points.length)).slice(0, SAMPLE_SIZE);
+    this.traces = this.computeTraces(points, indicesSeen);
+
+    this.normalize();
+    this.dim = [this.points.length, this.points[0].vector.length];
+  }
+
+  private computeTraces(points: DataPoint[], indicesSeen: boolean[]) {
+    // Compute traces.
+    let indexToTrace: {[index: number]: scatter.DataTrace} = {};
+    let traces: scatter.DataTrace[] = [];
+    for (let i = 0; i < points.length; i++) {
+      if (indicesSeen[i]) {
+        continue;
+      }
+      indicesSeen[i] = true;
+
+      // Ignore points without a trace attribute.
+      let next = points[i].metadata[TRACE_METADATA_ATTR];
+      if (next == null || next === '') {
+        continue;
+      }
+      if (next in indexToTrace) {
+        let existingTrace = indexToTrace[+next];
+        // Pushing at the beginning of the array.
+        existingTrace.pointIndices.unshift(i);
+        indexToTrace[i] = existingTrace;
+        continue;
+      }
+      // The current point is pointing to a new/unseen trace.
+      let newTrace: scatter.DataTrace = {pointIndices: []};
+      indexToTrace[i] = newTrace;
+      traces.push(newTrace);
+      let currentIndex = i;
+      while (points[currentIndex]) {
+        newTrace.pointIndices.push(currentIndex);
+        let next = points[currentIndex].metadata[TRACE_METADATA_ATTR];
+        if (next != null && next !== '') {
+          indicesSeen[+next] = true;
+          currentIndex = +next;
+        } else {
+          currentIndex = -1;
+        }
+      }
+    }
+    return traces;
+  }
+
+
+  /**
+   * Computes the centroid, shifts all points to that centroid,
+   * then makes them all unit norm.
+   */
+  private normalize() {
+    // Compute the centroid of all data points.
+    let centroid =
+        vector.centroid(this.points, () => true, a => a.vector).centroid;
+    if (centroid == null) {
+      throw Error('centroid should not be null');
+    }
+    // Shift all points by the centroid and make them unit norm.
+    for (let id = 0; id < this.points.length; ++id) {
+      let dataPoint = this.points[id];
+      dataPoint.vector = vector.sub(dataPoint.vector, centroid);
+      vector.unit(dataPoint.vector);
+    }
+  }
+
+  /** Projects the dataset onto a given vector and caches the result. */
+  projectLinear(dir: vector.Vector, label: string) {
+    this.projections.add(label);
+    this.points.forEach(dataPoint => {
+      dataPoint.projections[label] = vector.dot(dataPoint.vector, dir);
+    });
+  }
+
+  /** Projects the dataset along the top 10 principal components. */
+  projectPCA(): Promise<void> {
+    if (this.projections.has('pca-0')) {
+      return Promise.resolve<void>();
+    }
+    return runAsyncTask('Computing PCA...', () => {
+      // Approximate pca vectors by sampling the dimensions.
+      let numDim = Math.min(this.points[0].vector.length, PCA_SAMPLE_DIM);
+      let reducedDimData =
+          vector.projectRandom(this.points.map(d => d.vector), numDim);
+      let sigma = numeric.div(
+          numeric.dot(numeric.transpose(reducedDimData), reducedDimData),
+          reducedDimData.length);
+      let U: any;
+      U = numeric.svd(sigma).U;
+      let pcaVectors = reducedDimData.map(vector => {
+        let newV: number[] = [];
+        for (let d = 0; d < NUM_PCA_COMPONENTS; d++) {
+          let dot = 0;
+          for (let i = 0; i < vector.length; i++) {
+            dot += vector[i] * U[i][d];
+          }
+          newV.push(dot);
+        }
+        return newV;
+      });
+      for (let j = 0; j < NUM_PCA_COMPONENTS; j++) {
+        let label = 'pca-' + j;
+        this.projections.add(label);
+        this.points.forEach(
+            (d, i) => { d.projections[label] = pcaVectors[i][j]; });
+      }
+    });
+  }
+
+  /** Runs tsne on the data. */
+  projectTSNE(
+      perplexity: number, learningRate: number, tsneDim: number,
+      stepCallback: (iter: number) => void) {
+    let k = Math.floor(3 * perplexity);
+    let opt = {epsilon: learningRate, perplexity: perplexity, dim: tsneDim};
+    this.tsne = new TSNE(opt);
+    this.tSNEShouldStop = false;
+    let iter = 0;
+
+    let step = () => {
+      if (this.tSNEShouldStop || iter > MAX_TSNE_ITERS) {
+        stepCallback(null);
+        return;
+      }
+      this.tsne.step();
+      let result = this.tsne.getSolution();
+      this.sampledDataIndices.forEach((index, i) => {
+        let dataPoint = this.points[index];
+
+        dataPoint.projections['tsne-0'] = result[i * tsneDim + 0];
+        dataPoint.projections['tsne-1'] = result[i * tsneDim + 1];
+        if (tsneDim === 3) {
+          dataPoint.projections['tsne-2'] = result[i * tsneDim + 2];
+        }
+      });
+      iter++;
+      stepCallback(iter);
+      requestAnimationFrame(step);
+    };
+
+    // Nearest neighbors calculations.
+    let knnComputation: Promise<knn.NearestEntry[][]>;
+
+    if (this.nearest != null && k === this.nearestK) {
+      // We found the nearest neighbors before and will reuse them.
+      knnComputation = Promise.resolve(this.nearest);
+    } else {
+      let sampledData = this.sampledDataIndices.map(i => this.points[i]);
+      this.nearestK = k;
+      knnComputation = WEBGL_SUPPORT ?
+          knn.findKNNGPUCosine(sampledData, k, (d => d.vector)) :
+          knn.findKNN(
+              sampledData, k, (d => d.vector),
+              (a, b, limit) => vector.cosDistNorm(a, b));
+    }
+    knnComputation.then(nearest => {
+      this.nearest = nearest;
+      runAsyncTask('Initializing T-SNE...', () => {
+        this.tsne.initDataDist(this.nearest);
+      }).then(step);
+
+
+    });
+  }
+
+  stopTSNE() { this.tSNEShouldStop = true; }
+}
+
+export interface DatasetMetadata {
+  /**
+   * Metadata for an associated image sprite. The sprite should be a matrix
+   * of smaller images, filled in a row-by-row order.
+   *
+   * E.g. the image for the first data point should be in the upper-left
+   * corner, and to the right of it should be the image of the second data
+   * point.
+   */
+  image?: {
+    /** The file path pointing to the sprite image. */
+    sprite_fpath: string;
+    /** The dimensions of the image for a single data point. */
+    single_image_dim: [number, number];
+  };
+}
diff --git a/tensorflow/tensorboard/components/vz-projector/data_test.ts b/tensorflow/tensorboard/components/vz-projector/data_test.ts
new file mode 100644
index 0000000000..07286f8bcc
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/data_test.ts
@@ -0,0 +1,66 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+import {DataPoint, DataSet} from './data';
+
+
+/**
+ * Helper method that makes a list of points given an array of
+ * trace indexes.
+ *
+ * @param traces The i-th entry holds the 'next' attribute for the i-th point.
+ */
+function makePointsWithTraces(traces: number[]) {
+  let nextAttr = '__next__';
+  let points: DataPoint[] = [];
+  traces.forEach((t, i) => {
+    let metadata: {[key: string]: any} = {};
+    metadata[nextAttr] = t >= 0 ? t : null;
+    points.push({
+      vector: [],
+      metadata: metadata,
+      projections: {},
+      projectedPoint: null,
+      dataSourceIndex: i
+    });
+  });
+  return points;
+}
+
+const assert = chai.assert;
+
+it('Simple forward pointing traces', () => {
+  // The input is: 0->2, 1->None, 2->3, 3->None. This should return
+  // one trace 0->2->3.
+  let points = makePointsWithTraces([2, -1, 3, -1]);
+  let dataset = new DataSet(points);
+  assert.equal(dataset.traces.length, 1);
+  assert.deepEqual(dataset.traces[0].pointIndices, [0, 2, 3]);
+});
+
+it('No traces', () => {
+  let points = makePointsWithTraces([-1, -1, -1, -1]);
+  let dataset = new DataSet(points);
+  assert.equal(dataset.traces.length, 0);
+});
+
+it('A trace that goes backwards and forward in the array', () => {
+  // The input is: 0->2, 1->0, 2->nothing, 3->1. This should return
+  // one trace 3->1->0->2.
+  let points = makePointsWithTraces([2, 0, -1, 1]);
+  let dataset = new DataSet(points);
+  assert.equal(dataset.traces.length, 1);
+  assert.deepEqual(dataset.traces[0].pointIndices, [3, 1, 0, 2]);
+});
diff --git a/tensorflow/tensorboard/components/vz-projector/demo/index.html b/tensorflow/tensorboard/components/vz-projector/demo/index.html
new file mode 100644
index 0000000000..17ca273a3f
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/demo/index.html
@@ -0,0 +1,73 @@
+<!DOCTYPE html>
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+<html>
+<head>
+  <link rel="icon" type="image/png" href="favicon.png">
+  <!-- Polyfill for non-Chrome browsers -->
+  <script src="../../webcomponentsjs/webcomponents-lite.js"></script>
+  <link rel="import" href="../vz-projector.html">
+  <link rel="import" href="../../paper-icon-button/paper-icon-button.html">
+  <!-- TODO(jart): Refactor js_binary rule into ../ rather than ../../ -->
+  <script src="../../bundle.js"></script>
+  <title>Embedding projector - visualization of high-dimensional data</title>
+  <style>
+    html {
+      width: 100%;
+      height: 100%;
+    }
+
+    body {
+      display: flex;
+      flex-direction: column;
+      font-family: "Roboto", "Helvetica", "Arial", sans-serif;
+      margin: 0;
+      width: 100%;
+      height: 100%;
+    }
+
+    #appbar {
+      display: flex;
+      align-items: center;
+      justify-content: space-between;
+      padding: 0 24px;
+      height: 60px;
+      color: white;
+      background: black;
+    }
+
+    #appbar .logo {
+      font-size: 18px;
+      font-weight: 300;
+    }
+
+    .icons {
+      display: flex;
+    }
+
+    .icons a {
+      color: white;
+    }
+  </style>
+</head>
+<body>
+  <div id="appbar">
+    <div>Embedding Projector</div>
+  </div>
+  <vz-projector></vz-projector>
+</body>
+</html>
diff --git a/tensorflow/tensorboard/components/vz-projector/external.d.ts b/tensorflow/tensorboard/components/vz-projector/external.d.ts
new file mode 100644
index 0000000000..673896557b
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/external.d.ts
@@ -0,0 +1,25 @@
+// TODO(smilkov): Split into weblas.d.ts and numeric.d.ts and write
+// typings for numeric.
+interface Tensor {
+  new(size: [number, number], data: Float32Array);
+  transfer(): Float32Array;
+  delete(): void;
+}
+
+interface Weblas {
+  sgemm(M: number, N: number, K: number, alpha: number,
+      A: Float32Array, B: Float32Array, beta: number, C: Float32Array):
+      Float32Array;
+  pipeline: {
+     Tensor: Tensor;
+     sgemm(alpha: number, A: Tensor, B: Tensor, beta: number,
+         C: Tensor): Tensor;
+  };
+  util: {
+    transpose(M: number, N: number, data: Float32Array): Tensor;
+  };
+
+}
+
+declare let numeric: any;
+declare let weblas: Weblas;
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/vz-projector/heap.ts b/tensorflow/tensorboard/components/vz-projector/heap.ts
new file mode 100644
index 0000000000..35f178e000
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/heap.ts
@@ -0,0 +1,146 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+/** Min key heap. */
+export type HeapItem<T> = {
+  key: number,
+  value: T
+};
+
+/**
+ * Min-heap data structure. Provides O(1) for peek, returning the smallest key.
+ */
+// TODO(jart): Rename to Heap and use Comparator.
+export class MinHeap<T> {
+  private arr: HeapItem<T>[] = [];
+
+  /** Push an element with the provided key. */
+  push(key: number, value: T): void {
+    this.arr.push({key, value});
+    this.bubbleUp(this.arr.length - 1);
+  }
+
+  /** Pop the element with the smallest key. */
+  pop(): HeapItem<T> {
+    if (this.arr.length === 0) {
+      throw new Error('pop() called on empty binary heap');
+    }
+    let item = this.arr[0];
+    let last = this.arr.length - 1;
+    this.arr[0] = this.arr[last];
+    this.arr.pop();
+    if (last > 0) {
+      this.bubbleDown(0);
+    }
+    return item;
+  };
+
+  /** Returns, but doesn't remove the element with the smallest key */
+  peek(): HeapItem<T> { return this.arr[0]; }
+
+  /**
+   * Pops the element with the smallest key and at the same time
+   * adds the newly provided element. This is faster than calling
+   * pop() and push() separately.
+   */
+  popPush(key: number, value: T): HeapItem<T> {
+    if (this.arr.length === 0) {
+      throw new Error('pop() called on empty binary heap');
+    }
+    let item = this.arr[0];
+    this.arr[0] = {key, value};
+    if (this.arr.length > 0) {
+      this.bubbleDown(0);
+    }
+    return item;
+  }
+
+  /** Returns the number of elements in the heap. */
+  size(): number { return this.arr.length; }
+
+  /** Returns all the items in the heap. */
+  items(): HeapItem<T>[] { return this.arr; }
+
+  private swap(a: number, b: number) {
+    let temp = this.arr[a];
+    this.arr[a] = this.arr[b];
+    this.arr[b] = temp;
+  }
+
+  private bubbleDown(pos: number) {
+    let left = (pos << 1) + 1;
+    let right = left + 1;
+    let largest = pos;
+    if (left < this.arr.length && this.arr[left].key < this.arr[largest].key) {
+      largest = left;
+    }
+    if (right < this.arr.length &&
+        this.arr[right].key < this.arr[largest].key) {
+      largest = right;
+    }
+    if (largest != pos) {
+      this.swap(largest, pos);
+      this.bubbleDown(largest);
+    }
+  }
+
+  private bubbleUp(pos: number) {
+    if (pos <= 0) {
+      return;
+    }
+    let parent = ((pos - 1) >> 1);
+    if (this.arr[pos].key < this.arr[parent].key) {
+      this.swap(pos, parent);
+      this.bubbleUp(parent);
+    }
+  }
+}
+
+/** List that keeps the K elements with the smallest keys. */
+export class KMin<T> {
+  private k: number;
+  private maxHeap = new MinHeap<T>();
+
+  /** Constructs a new k-min data structure with the provided k. */
+  constructor(k: number) { this.k = k; }
+
+  /** Adds an element to the list. */
+  add(key: number, value: T) {
+    if (this.maxHeap.size() < this.k) {
+      this.maxHeap.push(-key, value);
+      return;
+    }
+    let largest = this.maxHeap.peek();
+    // If the new element is smaller, replace the largest with the new element.
+    if (key < -largest.key) {
+      this.maxHeap.popPush(-key, value);
+    }
+  }
+
+  /** Returns the k items with the smallest keys. */
+  getMinKItems(): T[] {
+    let items = this.maxHeap.items();
+    items.sort((a, b) => b.key - a.key);
+    return items.map(a => a.value);
+  }
+
+  /** Returns the size of the list. */
+  getSize(): number { return this.maxHeap.size(); }
+
+  /** Returns the largest key in the list. */
+  getLargestKey(): number {
+    return this.maxHeap.size() == 0 ? null : -this.maxHeap.peek().key;
+  }
+}
diff --git a/tensorflow/tensorboard/components/vz-projector/knn.ts b/tensorflow/tensorboard/components/vz-projector/knn.ts
new file mode 100644
index 0000000000..4d64595a3f
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/knn.ts
@@ -0,0 +1,223 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+import {runAsyncTask} from './async';
+import {KMin} from './heap';
+import * as vector from './vector';
+
+export type NearestEntry = {
+  index: number,
+  dist: number
+};
+
+/**
+ * Optimal size for the height of the matrix when doing computation on the GPU
+ * using WebGL. This was found experimentally.
+ *
+ * This also guarantees that for computing pair-wise distance for up to 10K
+ * vectors, no more than 40MB will be allocated in the GPU. Without the
+ * allocation limit, we can freeze the graphics of the whole OS.
+ */
+const OPTIMAL_GPU_BLOCK_SIZE = 512;
+
+/**
+ * Returns the K nearest neighbors for each vector where the distance
+ * computation is done on the GPU (WebGL) using cosine distance.
+ *
+ * @param dataPoints List of data points, where each data point holds an
+ *   n-dimensional vector.
+ * @param k Number of nearest neighbors to find.
+ * @param accessor A method that returns the vector, given the data point.
+ */
+export function findKNNGPUCosine<T>(
+    dataPoints: T[], k: number,
+    accessor: (dataPoint: T) => number[]): Promise<NearestEntry[][]> {
+  let N = dataPoints.length;
+  let dim = accessor(dataPoints[0]).length;
+
+  // The goal is to compute a large matrix multiplication A*A.T where A is of
+  // size NxD and A.T is its transpose. This results in a NxN matrix which
+  // could be too big to store on the GPU memory. To avoid memory overflow, we
+  // compute multiple A*partial_A.T where partial_A is of size BxD (B is much
+  // smaller than N). This results in storing only NxB size matrices on the GPU
+  // at a given time.
+
+  // A*A.T will give us NxN matrix holding the cosine distance between every
+  // pair of points, which we sort using KMin data structure to obtain the
+  // K nearest neighbors for each point.
+  let typedArray = vector.toTypedArray(dataPoints, accessor);
+  let bigMatrix = new weblas.pipeline.Tensor([N, dim], typedArray);
+  let nearest: NearestEntry[][] = new Array(N);
+  let numPieces = Math.ceil(N / OPTIMAL_GPU_BLOCK_SIZE);
+  let M = Math.floor(N / numPieces);
+  let modulo = N % numPieces;
+  let offset = 0;
+  let progress = 0;
+  let progressDiff = 1 / (2 * numPieces);
+  let piece = 0;
+
+  function step(resolve: (result: NearestEntry[][]) => void) {
+    let progressMsg =
+        'Finding nearest neighbors: ' + (progress * 100).toFixed() + '%';
+    runAsyncTask(progressMsg, () => {
+      let B = piece < modulo ? M + 1 : M;
+      let typedB = new Float32Array(B * dim);
+      for (let i = 0; i < B; ++i) {
+        let vector = accessor(dataPoints[offset + i]);
+        for (let d = 0; d < dim; ++d) {
+          typedB[i * dim + d] = vector[d];
+        }
+      }
+      let partialMatrix = new weblas.pipeline.Tensor([B, dim], typedB);
+      // Result is N x B matrix.
+      let result =
+          weblas.pipeline.sgemm(1, bigMatrix, partialMatrix, null, null);
+      let partial = result.transfer();
+      partialMatrix.delete();
+      result.delete();
+      progress += progressDiff;
+      for (let i = 0; i < B; i++) {
+        let kMin = new KMin<NearestEntry>(k);
+        let iReal = offset + i;
+        for (let j = 0; j < N; j++) {
+          if (j === iReal) {
+            continue;
+          }
+          let cosDist = 1 - partial[j * B + i];  // [j, i];
+          kMin.add(cosDist, {index: j, dist: cosDist});
+        }
+        nearest[iReal] = kMin.getMinKItems();
+      }
+      progress += progressDiff;
+      offset += B;
+      piece++;
+    }).then(() => {
+      if (piece < numPieces) {
+        step(resolve);
+      } else {
+        bigMatrix.delete();
+        resolve(nearest);
+      }
+    });
+  }
+  return new Promise<NearestEntry[][]>(resolve => step(resolve));
+}
+
+/**
+ * Returns the K nearest neighbors for each vector where the distance
+ * computation is done on the CPU using a user-specified distance method.
+ *
+ * @param dataPoints List of data points, where each data point holds an
+ *   n-dimensional vector.
+ * @param k Number of nearest neighbors to find.
+ * @param accessor A method that returns the vector, given the data point.
+ * @param dist Method that takes two vectors and a limit, and computes the
+ *   distance between two vectors, with the ability to stop early if the
+ *   distance is above the limit.
+ */
+export function findKNN<T>(
+    dataPoints: T[], k: number, accessor: (dataPoint: T) => number[],
+    dist: (a: number[], b: number[], limit: number) =>
+        number): Promise<NearestEntry[][]> {
+  return runAsyncTask<NearestEntry[][]>('Finding nearest neighbors...', () => {
+    let N = dataPoints.length;
+    let nearest: NearestEntry[][] = new Array(N);
+    // Find the distances from node i.
+    let kMin: KMin<NearestEntry>[] = new Array(N);
+    for (let i = 0; i < N; i++) {
+      kMin[i] = new KMin<NearestEntry>(k);
+    }
+    for (let i = 0; i < N; i++) {
+      let a = accessor(dataPoints[i]);
+      let kMinA = kMin[i];
+      for (let j = i + 1; j < N; j++) {
+        let kMinB = kMin[j];
+        let limitI = kMinA.getSize() === k ?
+            kMinA.getLargestKey() || Number.MAX_VALUE :
+            Number.MAX_VALUE;
+        let limitJ = kMinB.getSize() === k ?
+            kMinB.getLargestKey() || Number.MAX_VALUE :
+            Number.MAX_VALUE;
+        let limit = Math.max(limitI, limitJ);
+        let dist2ItoJ = dist(a, accessor(dataPoints[j]), limit);
+        if (dist2ItoJ >= 0) {
+          kMinA.add(dist2ItoJ, {index: j, dist: dist2ItoJ});
+          kMinB.add(dist2ItoJ, {index: i, dist: dist2ItoJ});
+        }
+      }
+    }
+    for (let i = 0; i < N; i++) {
+      nearest[i] = kMin[i].getMinKItems();
+    }
+    return nearest;
+  });
+}
+
+/** Calculates the minimum distance between a search point and a rectangle. */
+function minDist(
+    point: [number, number], x1: number, y1: number, x2: number, y2: number) {
+  let x = point[0];
+  let y = point[1];
+  let dx1 = x - x1;
+  let dx2 = x - x2;
+  let dy1 = y - y1;
+  let dy2 = y - y2;
+
+  if (dx1 * dx2 <= 0) {    // x is between x1 and x2
+    if (dy1 * dy2 <= 0) {  // (x,y) is inside the rectangle
+      return 0;            // return 0 as point is in rect
+    }
+    return Math.min(Math.abs(dy1), Math.abs(dy2));
+  }
+  if (dy1 * dy2 <= 0) {  // y is between y1 and y2
+    // We know it is already inside the rectangle
+    return Math.min(Math.abs(dx1), Math.abs(dx2));
+  }
+  let corner: [number, number];
+  if (x > x2) {
+    // Upper-right vs lower-right.
+    corner = y > y2 ? [x2, y2] : [x2, y1];
+  } else {
+    // Upper-left vs lower-left.
+    corner = y > y2 ? [x1, y2] : [x1, y1];
+  }
+  return Math.sqrt(vector.dist22D([x, y], corner));
+}
+
+/**
+ * Returns the nearest neighbors of a particular point.
+ *
+ * @param dataPoints List of data points.
+ * @param pointIndex The index of the point we need the nearest neighbors of.
+ * @param k Number of nearest neighbors to search for.
+ * @param accessor Method that maps a data point => vector (array of numbers).
+ * @param distance Method that takes two vectors and returns their distance.
+ */
+export function findKNNofPoint<T>(
+    dataPoints: T[], pointIndex: number, k: number,
+    accessor: (dataPoint: T) => number[],
+    distance: (a: number[], b: number[]) => number) {
+  let kMin = new KMin<NearestEntry>(k);
+  let a = accessor(dataPoints[pointIndex]);
+  for (let i = 0; i < dataPoints.length; ++i) {
+    if (i == pointIndex) {
+      continue;
+    }
+    let b = accessor(dataPoints[i]);
+    let dist = distance(a, b);
+    kMin.add(dist, {index: i, dist: dist});
+  }
+  return kMin.getMinKItems();
+}
diff --git a/tensorflow/tensorboard/components/vz-projector/label.ts b/tensorflow/tensorboard/components/vz-projector/label.ts
new file mode 100644
index 0000000000..9689ef5869
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/label.ts
@@ -0,0 +1,151 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+export interface BoundingBox {
+  loX: number;
+  loY: number;
+  hiX: number;
+  hiY: number;
+}
+
+/**
+ * Accelerates label placement by dividing the view into a uniform grid.
+ * Labels only need to be tested for collision with other labels that overlap
+ * the same grid cells. This is a fork of {@code amoeba.CollisionGrid}.
+ */
+export class CollisionGrid {
+  private numHorizCells: number;
+  private numVertCells: number;
+  private grid: BoundingBox[][];
+  private bound: BoundingBox;
+  private cellWidth: number;
+  private cellHeight: number;
+
+  /**
+   * Constructs a new Collision grid.
+   *
+   * @param bound The bound of the grid. Labels out of bounds will be rejected.
+   * @param cellWidth Width of a cell in the grid.
+   * @param cellHeight Height of a cell in the grid.
+   */
+  constructor(bound: BoundingBox, cellWidth: number, cellHeight: number) {
+    /** The bound of the grid. Labels out of bounds will be rejected. */
+    this.bound = bound;
+
+    /** Width of a cell in the grid. */
+    this.cellWidth = cellWidth;
+
+    /** Height of a cell in the grid. */
+    this.cellHeight = cellHeight;
+
+    /** Number of grid cells along the x axis. */
+    this.numHorizCells = Math.ceil(this.boundWidth(bound) / cellWidth);
+
+    /** Number of grid cells along the y axis. */
+    this.numVertCells = Math.ceil(this.boundHeight(bound) / cellHeight);
+
+    /**
+     * The 2d grid (stored as a 1d array.) Each cell consists of an array of
+     * BoundingBoxes for objects that are in the cell.
+     */
+    this.grid = new Array(this.numHorizCells * this.numVertCells);
+  }
+
+  private boundWidth(bound: BoundingBox) { return bound.hiX - bound.loX; }
+
+  private boundHeight(bound: BoundingBox) { return bound.hiY - bound.loY; }
+
+  private boundsIntersect(a: BoundingBox, b: BoundingBox) {
+    return !(a.loX > b.hiX || a.loY > b.hiY || a.hiX < b.loX || a.hiY < b.loY);
+  }
+
+  /**
+   * Checks if a given bounding box has any conflicts in the grid and inserts it
+   * if none are found.
+   *
+   * @param bound The bound to insert.
+   * @param justTest If true, just test if it conflicts, without inserting.
+   * @return True if the bound was successfully inserted; false if it
+   *         could not be inserted due to a conflict.
+   */
+  insert(bound: BoundingBox, justTest = false): boolean {
+    // Reject if the label is out of bounds.
+    if (bound.loX < this.bound.loX || bound.hiX > this.bound.hiX ||
+        bound.loY < this.bound.loY || bound.hiY > this.bound.hiY) {
+      return false;
+    }
+
+    let minCellX = this.getCellX(bound.loX);
+    let maxCellX = this.getCellX(bound.hiX);
+    let minCellY = this.getCellY(bound.loY);
+    let maxCellY = this.getCellY(bound.hiY);
+
+    // Check all overlapped cells to verify that we can insert.
+    let baseIdx = minCellY * this.numHorizCells + minCellX;
+    let idx = baseIdx;
+    for (let j = minCellY; j <= maxCellY; j++) {
+      for (let i = minCellX; i <= maxCellX; i++) {
+        let cell = this.grid[idx++];
+        if (cell) {
+          for (let k = 0; k < cell.length; k++) {
+            if (this.boundsIntersect(bound, cell[k])) {
+              return false;
+            }
+          }
+        }
+      }
+      idx += this.numHorizCells - (maxCellX - minCellX + 1);
+    }
+
+    if (justTest) {
+      return true;
+    }
+
+    // Insert into the overlapped cells.
+    idx = baseIdx;
+    for (let j = minCellY; j <= maxCellY; j++) {
+      for (let i = minCellX; i <= maxCellX; i++) {
+        if (!this.grid[idx]) {
+          this.grid[idx] = [bound];
+        } else {
+          this.grid[idx].push(bound);
+        }
+        idx++;
+      }
+      idx += this.numHorizCells - (maxCellX - minCellX + 1);
+    }
+    return true;
+  }
+
+  /**
+   * Returns the x index of the grid cell where the given x coordinate falls.
+   *
+   * @param x the coordinate, in world space.
+   * @return the x index of the cell.
+   */
+  private getCellX(x: number) {
+    return Math.floor((x - this.bound.loX) / this.cellWidth);
+  };
+
+  /**
+   * Returns the y index of the grid cell where the given y coordinate falls.
+   *
+   * @param y the coordinate, in world space.
+   * @return the y index of the cell.
+   */
+  private getCellY(y: number) {
+    return Math.floor((y - this.bound.loY) / this.cellHeight);
+  };
+}
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/vz-projector/scatter.ts b/tensorflow/tensorboard/components/vz-projector/scatter.ts
new file mode 100644
index 0000000000..392d8085c6
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/scatter.ts
@@ -0,0 +1,135 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+export interface Point3D {
+  /** Original x coordinate. */
+  x: number;
+  /** Original y coordinate. */
+  y: number;
+  /** Original z coordinate. */
+  z: number;
+}
+;
+
+/** The spacial data of points and lines that will be shown in the projector. */
+export interface DataSet {
+  points: DataPoint[];
+  traces: DataTrace[];
+}
+
+/**
+ * Points in 3D space that will be used in the projector. If the projector is
+ * in 2D mode, the Z coordinate of the point will be 0.
+ */
+export interface DataPoint {
+  projectedPoint: Point3D;
+  /** index of the trace, used for highlighting on click */
+  traceIndex?: number;
+  /** index in the original data source */
+  dataSourceIndex: number;
+}
+
+/** A single collection of points which make up a trace through space. */
+export interface DataTrace {
+  /** Indices into the DataPoints array in the Data object. */
+  pointIndices: number[];
+}
+
+export type OnHoverListener = (index: number) => void;
+export type OnSelectionListener = (indexes: number[]) => void;
+
+/** Supported modes of interaction. */
+export enum Mode {
+  SELECT,
+  SEARCH,
+  HOVER
+}
+
+export interface Scatter {
+  /** Sets the data for the scatter plot. */
+  setDataSet(dataSet: DataSet, spriteImage?: HTMLImageElement): void;
+  /** Called with each data point in order to get its color. */
+  setColorAccessor(colorAccessor: ((index: number) => string)): void;
+  /** Called with each data point in order to get its label. */
+  setLabelAccessor(labelAccessor: ((index: number) => string)): void;
+  /** Called with each data point in order to get its x coordinate. */
+  setXAccessor(xAccessor: ((index: number) => number)): void;
+  /** Called with each data point in order to get its y coordinate. */
+  setYAccessor(yAccessor: ((index: number) => number)): void;
+  /** Called with each data point in order to get its z coordinate. */
+  setZAccessor(zAccessor: ((index: number) => number)): void;
+  /** Sets the interaction mode (search, select or hover). */
+  setMode(mode: Mode): void;
+  /** Returns the interaction mode. */
+  getMode(): Mode;
+  /** Resets the zoom level to 1.*/
+  resetZoom(): void;
+  /**
+   * Increases/decreases the zoom level.
+   *
+   * @param multiplier New zoom level = old zoom level * multiplier.
+   */
+  zoomStep(multiplier: number): void;
+  /**
+   * Highlights the provided points.
+   *
+   * @param pointIndexes List of point indexes to highlight. If null,
+   *   un-highlights all the points.
+   * @param stroke The stroke color used to highlight the point.
+   * @param favorLabels Whether to favor plotting the labels of the
+   *   highlighted point. Default is false for all points.
+   */
+  highlightPoints(
+      pointIndexes: number[], highlightStroke?: (index: number) => string,
+      favorLabels?: (index: number) => boolean): void;
+  /** Whether to show labels or not. */
+  showLabels(show: boolean): void;
+  /** Toggle between day and night modes. */
+  setDayNightMode(isNight: boolean): void;
+  /** Show/hide tick labels. */
+  showTickLabels(show: boolean): void;
+  /** Whether to show axes or not. */
+  showAxes(show: boolean): void;
+  /** Sets the axis labels. */
+  setAxisLabels(xLabel: string, yLabel: string): void;
+  /**
+   * Recreates the scene (demolishes all datastructures, etc.)
+   */
+  recreateScene(): void;
+  /**
+   * Redraws the data. Should be called anytime the accessor method
+   * for x and y coordinates changes, which means a new projection
+   * exists and the scatter plot should repaint the points.
+   */
+  update(): void;
+  /**
+   * Should be called to notify the scatter plot that the container
+   * was resized and it should resize and redraw itself.
+   */
+  resize(): void;
+  /** Registers a listener that will be called when selects some points. */
+  onSelection(listener: OnSelectionListener): void;
+  /**
+   * Registers a listener that will be called when the user hovers over
+   * a point.
+   */
+  onHover(listener: OnHoverListener): void;
+  /**
+   * Should emulate the same behavior as if the user clicked on the point.
+   * This is used to trigger a click from an external event, such as
+   * a search query.
+   */
+  clickOnPoint(pointIndex: number): void;
+}
diff --git a/tensorflow/tensorboard/components/vz-projector/scatterWebGL.ts b/tensorflow/tensorboard/components/vz-projector/scatterWebGL.ts
new file mode 100644
index 0000000000..79cee64c5b
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/scatterWebGL.ts
@@ -0,0 +1,1510 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+import {BoundingBox, CollisionGrid} from './label';
+import {DataSet, Mode, OnHoverListener, OnSelectionListener, Point3D, Scatter} from './scatter';
+import {shuffle} from './util';
+
+const FONT_SIZE = 10;
+
+// Colors (in various necessary formats).
+const BACKGROUND_COLOR_DAY = 0xffffff;
+const BACKGROUND_COLOR_NIGHT = 0x000000;
+const AXIS_COLOR = 0xb3b3b3;
+const LABEL_COLOR_DAY = 0x000000;
+const LABEL_COLOR_NIGHT = 0xffffff;
+const LABEL_STROKE_DAY = 0xffffff;
+const LABEL_STROKE_NIGHT = 0x000000;
+const POINT_COLOR = 0x7575D9;
+const POINT_COLOR_GRAYED = 0x888888;
+const BLENDING_DAY = THREE.MultiplyBlending;
+const BLENDING_NIGHT = THREE.AdditiveBlending;
+const TRACE_START_HUE = 60;
+const TRACE_END_HUE = 360;
+const TRACE_SATURATION = 1;
+const TRACE_LIGHTNESS = .3;
+const TRACE_DEFAULT_OPACITY = .2;
+const TRACE_DEFAULT_LINEWIDTH = 2;
+const TRACE_SELECTED_OPACITY = .9;
+const TRACE_SELECTED_LINEWIDTH = 3;
+const TRACE_DESELECTED_OPACITY = .05;
+
+// Various distance bounds.
+const MAX_ZOOM = 10;
+const MIN_ZOOM = .05;
+const NUM_POINTS_FOG_THRESHOLD = 5000;
+const MIN_POINT_SIZE = 5.0;
+const IMAGE_SIZE = 30;
+
+// Constants relating to the camera parameters.
+/** Camera frustum vertical field of view. */
+const FOV = 70;
+const NEAR = 0.01;
+const FAR = 100;
+
+// Constants relating to the indices of buffer arrays.
+/** Item size of a single point in a bufferArray representing colors */
+const RGB_NUM_BYTES = 3;
+/** Item size of a single point in a bufferArray representing indices */
+const INDEX_NUM_BYTES = 1;
+/** Item size of a single point in a bufferArray representing locations */
+const XYZ_NUM_BYTES = 3;
+
+// Key presses.
+const SHIFT_KEY = 16;
+const CTRL_KEY = 17;
+
+// Original positions of camera and camera target, in 2d and 3d
+const POS_3D = {
+  x: 1.5,
+  y: 1.5,
+  z: 1.5
+};
+
+// Target for the camera in 3D is the center of the 1, 1, 1 square, as all our
+// data is scaled to this.
+const TAR_3D = {
+  x: 0,
+  y: 0,
+  z: 0
+};
+
+const POS_2D = {
+  x: 0,
+  y: 0,
+  z: 2
+};
+
+// In 3D, the target is the center of the xy plane.
+const TAR_2D = {
+  x: 0,
+  y: 0,
+  z: 0
+};
+
+// The maximum number of labels to draw to keep the frame rate up.
+const SAMPLE_SIZE = 10000;
+
+// Shaders for images.
+const VERTEX_SHADER = `
+  // Index of the specific vertex (passed in as bufferAttribute), and the
+  // variable that will be used to pass it to the fragment shader.
+  attribute float vertexIndex;
+  varying vec2 xyIndex;
+
+  // Similar to above, but for colors.
+  attribute vec3 color;
+  varying vec3 vColor;
+
+  // If the point is highlighted, this will be 1.0 (else 0.0).
+  attribute float isHighlight;
+
+  // Uniform passed in as a property from THREE.ShaderMaterial.
+  uniform bool sizeAttenuation;
+  uniform float pointSize;
+  uniform float imageWidth;
+  uniform float imageHeight;
+
+  void main() {
+    // Pass index and color values to fragment shader.
+    vColor = color;
+    xyIndex = vec2(mod(vertexIndex, imageWidth),
+              floor(vertexIndex / imageWidth));
+
+    // Transform current vertex by modelViewMatrix (model world position and
+    // camera world position matrix).
+    vec4 mvPosition = modelViewMatrix * vec4(position, 1.0);
+
+    // Project vertex in camera-space to screen coordinates using the camera's
+    // projection matrix.
+    gl_Position = projectionMatrix * mvPosition;
+
+    // Create size attenuation (if we're in 3D mode) by making the size of
+    // each point inversly proportional to its distance to the camera.
+    float attenuatedSize = - pointSize / mvPosition.z;
+    gl_PointSize = sizeAttenuation ? attenuatedSize : pointSize;
+
+    // If the point is a highlight, make it slightly bigger than the other
+    // points, and also don't let it get smaller than some threshold.
+    if (isHighlight == 1.0) {
+      gl_PointSize = max(gl_PointSize * 1.2, ${MIN_POINT_SIZE.toFixed(1)});
+    };
+  }`;
+
+const FRAGMENT_SHADER = `
+  // Values passed in from the vertex shader.
+  varying vec2 xyIndex;
+  varying vec3 vColor;
+
+  // Adding in the THREEjs shader chunks for fog.
+  ${THREE.ShaderChunk['common']}
+  ${THREE.ShaderChunk['fog_pars_fragment']}
+
+  // Uniforms passed in as properties from THREE.ShaderMaterial.
+  uniform sampler2D texture;
+  uniform float imageWidth;
+  uniform float imageHeight;
+  uniform bool isImage;
+
+  void main() {
+    // A mystery variable that is required to make the THREE shaderchunk for fog
+    // work correctly.
+    vec3 outgoingLight = vec3(0.0);
+
+    if (isImage) {
+      // Coordinates of the vertex within the entire sprite image.
+      vec2 coords = (gl_PointCoord + xyIndex) / vec2(imageWidth, imageHeight);
+      // Determine the color of the spritesheet at the calculate spot.
+      vec4 fromTexture = texture2D(texture, coords);
+
+      // Finally, set the fragment color.
+      gl_FragColor = vec4(vColor, 1.0) * fromTexture;
+    } else {
+      // Discard pixels outside the radius so points are rendered as circles.
+      vec2 uv = gl_PointCoord.xy - 0.5;
+      if (length(uv) > 0.5) discard;
+
+      // If the point is not an image, just color it.
+      gl_FragColor = vec4(vColor, 1.0);
+    }
+    ${THREE.ShaderChunk['fog_fragment']}
+  }`;
+
+export class ScatterWebGL implements Scatter {
+  // MISC UNINITIALIZED VARIABLES.
+
+  // Colors and options that are changed between Day and Night modes.
+  private backgroundColor: number;
+  private labelColor: number;
+  private labelStroke: number;
+  private blending: THREE.Blending;
+  private isNight: boolean;
+
+  // THREE.js necessities.
+  private scene: THREE.Scene;
+  private perspCamera: THREE.PerspectiveCamera;
+  private renderer: THREE.WebGLRenderer;
+  private cameraControls: any;
+  private light: THREE.PointLight;
+  private fog: THREE.Fog;
+
+  // Data structures (and THREE.js objects) associated with points.
+  private geometry: THREE.BufferGeometry;
+  /** Texture for rendering offscreen in order to enable interactive hover. */
+  private pickingTexture: THREE.WebGLRenderTarget;
+  /** Array of unique colors for each point used in detecting hover. */
+  private uniqueColArr: Float32Array;
+  private materialOptions: THREE.ShaderMaterialParameters;
+  private points: THREE.Points;
+  private traces: THREE.Line[];
+  private dataSet: DataSet;
+  private shuffledData: number[];
+  /** Holds the indexes of the points to be labeled. */
+  private labeledPoints: number[] = [];
+  private highlightedPoints: number[] = [];
+  private nearestPoint: number;
+  private pointSize2D: number;
+  private pointSize3D: number;
+  /** The buffer attribute that holds the positions of the points. */
+  private positionBufferArray: THREE.BufferAttribute;
+
+  // Accessors for rendering and labeling the points.
+  private xAccessor: (index: number) => number;
+  private yAccessor: (index: number) => number;
+  private zAccessor: (index: number) => number;
+  private labelAccessor: (index: number) => string;
+  private colorAccessor: (index: number) => string;
+  private highlightStroke: (i: number) => string;
+  private favorLabels: (i: number) => boolean;
+
+  // Scaling functions for each axis.
+  private xScale: d3.scale.Linear<number, number>;
+  private yScale: d3.scale.Linear<number, number>;
+  private zScale: d3.scale.Linear<number, number>;
+
+  // Listeners
+  private onHoverListeners: OnHoverListener[] = [];
+  private onSelectionListeners: OnSelectionListener[] = [];
+  private lazySusanAnimation: number;
+
+  // Other variables associated with layout and interaction.
+  private height: number;
+  private width: number;
+  private mode: Mode;
+  /** Whether the user has turned labels on or off. */
+  private labelsAreOn = true;
+  /** Whether the label canvas has been already cleared. */
+  private labelCanvasIsCleared = true;
+
+  private animating: boolean;
+  private axis3D: THREE.AxisHelper;
+  private axis2D: THREE.LineSegments;
+  private dpr: number;        // The device pixelratio
+  private selecting = false;  // whether or not we are selecting points.
+  private mouseIsDown = false;
+  // Whether the current click sequence contains a drag, so we can determine
+  // whether to update the selection.
+  private isDragSequence = false;
+  private selectionSphere: THREE.Mesh;
+  private image: HTMLImageElement;
+  private animationID: number;
+  /** Color of any point not selected (or NN of selected) */
+  private defaultPointColor = POINT_COLOR;
+
+  // HTML elements.
+  private gc: CanvasRenderingContext2D;
+  private containerNode: HTMLElement;
+  private canvas: HTMLCanvasElement;
+
+  /** Get things started up! */
+  constructor(
+      container: d3.Selection<any>, labelAccessor: (index: number) => string) {
+    this.labelAccessor = labelAccessor;
+    this.xScale = d3.scale.linear();
+    this.yScale = d3.scale.linear();
+    this.zScale = d3.scale.linear();
+
+    // Set up non-THREEjs layout.
+    this.containerNode = container.node() as HTMLElement;
+    this.getLayoutValues();
+
+    // For now, labels are drawn on this transparent canvas with no touch events
+    // rather than being rendered in webGL.
+    this.canvas = container.append('canvas').node() as HTMLCanvasElement;
+    this.gc = this.canvas.getContext('2d');
+    d3.select(this.canvas).style({position: 'absolute', left: 0, top: 0});
+    this.canvas.style.pointerEvents = 'none';
+
+    // Set up THREE.js.
+    this.createSceneAndRenderer();
+    this.setDayNightMode(false);
+    this.createLight();
+    this.makeCamera();
+    this.resize(false);
+    // Render now so no black background appears during startup.
+    this.renderer.render(this.scene, this.perspCamera);
+    // Add interaction listeners.
+    this.addInteractionListeners();
+  }
+
+  // SET UP
+  private addInteractionListeners() {
+    this.containerNode.addEventListener(
+        'mousemove', this.onMouseMove.bind(this));
+    this.containerNode.addEventListener(
+        'mousedown', this.onMouseDown.bind(this));
+    this.containerNode.addEventListener('mouseup', this.onMouseUp.bind(this));
+    this.containerNode.addEventListener('click', this.onClick.bind(this));
+    window.addEventListener('keydown', this.onKeyDown.bind(this), false);
+    window.addEventListener('keyup', this.onKeyUp.bind(this), false);
+  }
+
+  /** Updates the positions buffer array to reflect the actual data. */
+  private updatePositionsArray() {
+    for (let i = 0; i < this.dataSet.points.length; i++) {
+      // Set position based on projected point.
+      let pp = this.dataSet.points[i].projectedPoint;
+      this.positionBufferArray.setXYZ(i, pp.x, pp.y, pp.z);
+    }
+    if (this.geometry) {
+      this.positionBufferArray.needsUpdate = true;
+    }
+  }
+
+  /**
+   * Returns an x, y, z value for each item of our data based on the accessor
+   * methods.
+   */
+  private getPointsCoordinates() {
+    // Determine max and min of each axis of our data.
+    let xExtent = d3.extent(this.dataSet.points, (p, i) => this.xAccessor(i));
+    let yExtent = d3.extent(this.dataSet.points, (p, i) => this.yAccessor(i));
+    this.xScale.domain(xExtent).range([-1, 1]);
+    this.yScale.domain(yExtent).range([-1, 1]);
+    if (this.zAccessor) {
+      let zExtent = d3.extent(this.dataSet.points, (p, i) => this.zAccessor(i));
+      this.zScale.domain(zExtent).range([-1, 1]);
+    }
+
+    // Determine 3d coordinates of each data point.
+    this.dataSet.points.forEach((d, i) => {
+      d.projectedPoint.x = this.xScale(this.xAccessor(i));
+      d.projectedPoint.y = this.yScale(this.yAccessor(i));
+      d.projectedPoint.z =
+          (this.zAccessor ? this.zScale(this.zAccessor(i)) : 0);
+    });
+  }
+
+  private createLight() {
+    this.light = new THREE.PointLight(0xFFECBF, 1, 0);
+    this.scene.add(this.light);
+  }
+
+  /** General setup of scene and renderer. */
+  private createSceneAndRenderer() {
+    this.scene = new THREE.Scene();
+    this.renderer = new THREE.WebGLRenderer();
+    // Accouting for retina displays.
+    this.renderer.setPixelRatio(window.devicePixelRatio || 1);
+    this.renderer.setSize(this.width, this.height);
+    this.containerNode.appendChild(this.renderer.domElement);
+    this.pickingTexture = new THREE.WebGLRenderTarget(this.width, this.height);
+    this.pickingTexture.texture.minFilter = THREE.LinearFilter;
+  }
+
+  /** Set up camera and camera's controller. */
+  private makeCamera() {
+    this.perspCamera =
+        new THREE.PerspectiveCamera(FOV, this.width / this.height, NEAR, FAR);
+    this.cameraControls =
+        new (THREE as any)
+            .OrbitControls(this.perspCamera, this.renderer.domElement);
+    this.cameraControls.mouseButtons.ORBIT = THREE.MOUSE.LEFT;
+    this.cameraControls.mouseButtons.PAN = THREE.MOUSE.RIGHT;
+    // Start is called when the user stars interacting with
+    // orbit controls.
+    this.cameraControls.addEventListener('start', () => {
+      this.cameraControls.autoRotate = false;
+      cancelAnimationFrame(this.lazySusanAnimation);
+    });
+    // Change is called everytime the user interacts with the
+    // orbit controls.
+    this.cameraControls.addEventListener('change', () => {
+      this.removeAllLabels();
+      this.render();
+    });
+    // End is called when the user stops interacting with the
+    // orbit controls (e.g. on mouse up, after dragging).
+    this.cameraControls.addEventListener('end', () => { this.makeLabels(); });
+  }
+
+  /** Sets up camera to work in 3D (called after makeCamera()). */
+  private makeCamera3D() {
+    // Set up the camera position at a skewed angle from the xy plane, looking
+    // toward the origin
+    this.cameraControls.position0.set(POS_3D.x, POS_3D.y, POS_3D.z);
+    this.cameraControls.target0.set(TAR_3D.x, TAR_3D.y, TAR_3D.z);
+    this.cameraControls.enableRotate = true;
+    let position = new THREE.Vector3(POS_3D.x, POS_3D.y, POS_3D.z);
+    let target = new THREE.Vector3(TAR_3D.x, TAR_3D.y, TAR_3D.z);
+    this.animate(position, target, () => {
+      // Start lazy susan after the animation is done.
+      this.startLazySusanAnimation();
+    });
+  }
+
+  /** Sets up camera to work in 2D (called after makeCamera()). */
+  private makeCamera2D(animate?: boolean) {
+    // Set the camera position in the middle of the screen, looking directly
+    // toward the middle of the xy plane
+    this.cameraControls.position0.set(POS_2D.x, POS_2D.y, POS_2D.z);
+    this.cameraControls.target0.set(TAR_2D.x, TAR_2D.y, TAR_2D.z);
+    let position = new THREE.Vector3(POS_2D.x, POS_2D.y, POS_2D.z);
+    let target = new THREE.Vector3(TAR_2D.x, TAR_2D.y, TAR_2D.z);
+    this.animate(position, target);
+    this.cameraControls.enableRotate = false;
+  }
+
+  /**
+   * Set up buffer attributes to be used for the points/images.
+   */
+  private createBufferAttributes() {
+    // Set up buffer attribute arrays.
+    let numPoints = this.dataSet.points.length;
+    let colArr = new Float32Array(numPoints * RGB_NUM_BYTES);
+    this.uniqueColArr = new Float32Array(numPoints * RGB_NUM_BYTES);
+    let colors = new THREE.BufferAttribute(this.uniqueColArr, RGB_NUM_BYTES);
+    // Assign each point a unique color in order to identify when the user
+    // hovers over a point.
+    for (let i = 0; i < numPoints; i++) {
+      let color = new THREE.Color(i);
+      colors.setXYZ(i, color.r, color.g, color.b);
+    }
+    colors.array = colArr;
+    let hiArr = new Float32Array(numPoints);
+
+    /** Indices cooresponding to highlighted points. */
+    let highlights = new THREE.BufferAttribute(hiArr, INDEX_NUM_BYTES);
+
+    // Note that we need two index arrays.
+
+    /**
+     * The actual indices of the points which we use for sizeAttenuation in
+     * the shader.
+     */
+    let indicesShader =
+        new THREE.BufferAttribute(new Float32Array(numPoints), 1);
+
+    for (let i = 0; i < numPoints; i++) {
+      // Create the array of indices.
+      indicesShader.setX(i, this.dataSet.points[i].dataSourceIndex);
+    }
+
+    // Finally, add all attributes to the geometry.
+    this.geometry.addAttribute('position', this.positionBufferArray);
+    this.positionBufferArray.needsUpdate = true;
+    this.geometry.addAttribute('color', colors);
+    this.geometry.addAttribute('vertexIndex', indicesShader);
+    this.geometry.addAttribute('isHighlight', highlights);
+
+    // For now, nothing is highlighted.
+    this.colorSprites(null);
+  }
+
+  /**
+   * Generate a texture for the points/images and sets some initial params
+   */
+  private createTexture(image: HTMLImageElement|
+                        HTMLCanvasElement): THREE.Texture {
+    let tex = new THREE.Texture(image);
+    tex.needsUpdate = true;
+    // Used if the texture isn't a power of 2.
+    tex.minFilter = THREE.LinearFilter;
+    tex.generateMipmaps = false;
+    tex.flipY = false;
+    return tex;
+  }
+
+  /**
+   * Create points, set their locations and actually instantiate the
+   * geometry.
+   */
+  private addSprites() {
+    // Create geometry.
+    this.geometry = new THREE.BufferGeometry();
+    this.createBufferAttributes();
+    let canvas = document.createElement('canvas');
+    let image = this.image || canvas;
+    // TODO(smilkov): Pass sprite dim to the renderer.
+    let spriteDim = 28.0;
+    let tex = this.createTexture(image);
+    let pointSize = (this.zAccessor ? this.pointSize3D : this.pointSize2D);
+    if (this.image) {
+      pointSize = IMAGE_SIZE;
+    }
+    let uniforms = {
+      texture: {type: 't', value: tex},
+      imageWidth: {type: 'f', value: image.width / spriteDim},
+      imageHeight: {type: 'f', value: image.height / spriteDim},
+      fogColor: {type: 'c', value: this.fog.color},
+      fogNear: {type: 'f', value: this.fog.near},
+      fogFar: {type: 'f', value: this.fog.far},
+      sizeAttenuation: {type: 'bool', value: !!this.zAccessor},
+      isImage: {type: 'bool', value: !!this.image},
+      pointSize: {type: 'f', value: pointSize}
+    };
+    this.materialOptions = {
+      uniforms: uniforms,
+      vertexShader: VERTEX_SHADER,
+      fragmentShader: FRAGMENT_SHADER,
+      transparent: (this.image ? false : true),
+      // When rendering points with blending, we want depthTest/Write
+      // turned off.
+      depthTest: (this.image ? true : false),
+      depthWrite: (this.image ? true : false),
+      fog: true,
+      blending: (this.image ? THREE.NormalBlending : this.blending),
+    };
+    // Give it some material.
+    let material = new THREE.ShaderMaterial(this.materialOptions);
+
+    // And finally initialize it and add it to the scene.
+    this.points = new THREE.Points(this.geometry, material);
+    this.scene.add(this.points);
+  }
+
+  /**
+   * Create line traces between connected points and instantiate the geometry.
+   */
+  private addTraces() {
+    if (!this.dataSet || !this.dataSet.traces) {
+      return;
+    }
+
+    this.traces = [];
+
+    for (let i = 0; i < this.dataSet.traces.length; i++) {
+      let dataTrace = this.dataSet.traces[i];
+
+      let geometry = new THREE.BufferGeometry();
+      let vertices: number[] = [];
+      let colors: number[] = [];
+
+      for (let j = 0; j < dataTrace.pointIndices.length - 1; j++) {
+        this.dataSet.points[dataTrace.pointIndices[j]].traceIndex = i;
+        this.dataSet.points[dataTrace.pointIndices[j + 1]].traceIndex = i;
+
+        let point1 = this.dataSet.points[dataTrace.pointIndices[j]];
+        let point2 = this.dataSet.points[dataTrace.pointIndices[j + 1]];
+
+        vertices.push(
+            point1.projectedPoint.x, point1.projectedPoint.y,
+            point1.projectedPoint.z);
+        vertices.push(
+            point2.projectedPoint.x, point2.projectedPoint.y,
+            point2.projectedPoint.z);
+
+        let color1 =
+            this.getPointInTraceColor(j, dataTrace.pointIndices.length);
+        let color2 =
+            this.getPointInTraceColor(j + 1, dataTrace.pointIndices.length);
+
+        colors.push(
+            color1.r / 255, color1.g / 255, color1.b / 255, color2.r / 255,
+            color2.g / 255, color2.b / 255);
+      }
+
+      geometry.addAttribute(
+          'position',
+          new THREE.BufferAttribute(new Float32Array(vertices), XYZ_NUM_BYTES));
+      geometry.addAttribute(
+          'color',
+          new THREE.BufferAttribute(new Float32Array(colors), RGB_NUM_BYTES));
+
+      // We use the same material for every line.
+      let material = new THREE.LineBasicMaterial({
+        linewidth: TRACE_DEFAULT_LINEWIDTH,
+        opacity: TRACE_DEFAULT_OPACITY,
+        transparent: true,
+        vertexColors: THREE.VertexColors
+      });
+
+      let trace = new THREE.LineSegments(geometry, material);
+      this.traces.push(trace);
+      this.scene.add(trace);
+    }
+  }
+
+  /**
+   * Returns the color of a point along a trace.
+   */
+  private getPointInTraceColor(index: number, totalPoints: number) {
+    let hue = TRACE_START_HUE +
+        (TRACE_END_HUE - TRACE_START_HUE) * index / totalPoints;
+
+    return d3.hsl(hue, TRACE_SATURATION, TRACE_LIGHTNESS).rgb();
+  }
+
+  /** Clean up any old axes that we may have made previously.  */
+  private removeOldAxes() {
+    if (this.axis3D) {
+      this.scene.remove(this.axis3D);
+    }
+    if (this.axis2D) {
+      this.scene.remove(this.axis2D);
+    }
+  }
+
+  /** Add axis. */
+  private addAxis3D() {
+    this.axis3D = new THREE.AxisHelper();
+    this.scene.add(this.axis3D);
+  }
+
+  /** Manually make axis if we're in 2d. */
+  private addAxis2D() {
+    let vertices = new Float32Array([
+      0,
+      0,
+      0,
+      this.xScale(1),
+      0,
+      0,
+      0,
+      0,
+      0,
+      0,
+      this.yScale(1),
+      0,
+    ]);
+
+    let axisColor = new THREE.Color(AXIS_COLOR);
+    let axisColors = new Float32Array([
+      axisColor.r,
+      axisColor.b,
+      axisColor.g,
+      axisColor.r,
+      axisColor.b,
+      axisColor.g,
+      axisColor.r,
+      axisColor.b,
+      axisColor.g,
+      axisColor.r,
+      axisColor.b,
+      axisColor.g,
+    ]);
+
+    // Create line geometry based on above position and color.
+    let lineGeometry = new THREE.BufferGeometry();
+    lineGeometry.addAttribute(
+        'position', new THREE.BufferAttribute(vertices, XYZ_NUM_BYTES));
+    lineGeometry.addAttribute(
+        'color', new THREE.BufferAttribute(axisColors, RGB_NUM_BYTES));
+
+    // And use it to create the actual object and add this new axis to the
+    // scene!
+    let axesMaterial =
+        new THREE.LineBasicMaterial({vertexColors: THREE.VertexColors});
+    this.axis2D = new THREE.LineSegments(lineGeometry, axesMaterial);
+    this.scene.add(this.axis2D);
+  }
+
+  // DYNAMIC (post-load) CHANGES
+
+  /** When we stop dragging/zooming, return to normal behavior. */
+  private onClick(e?: MouseEvent) {
+    if (e && this.selecting || !this.points) {
+      this.resetTraces();
+      return;
+    }
+    let selection = this.nearestPoint || null;
+    this.defaultPointColor = (selection ? POINT_COLOR_GRAYED : POINT_COLOR);
+    // Only call event handlers if the click originated from the scatter plot.
+    if (e && !this.isDragSequence) {
+      this.onSelectionListeners.forEach(l => l(selection ? [selection] : []));
+    }
+    this.isDragSequence = false;
+    this.labeledPoints =
+        this.highlightedPoints.filter((id, i) => this.favorLabels(i));
+
+    this.resetTraces();
+    if (selection && this.dataSet.points[selection].traceIndex) {
+      for (let i = 0; i < this.traces.length; i++) {
+        this.traces[i].material.opacity = TRACE_DESELECTED_OPACITY;
+        this.traces[i].material.needsUpdate = true;
+      }
+      this.traces[this.dataSet.points[selection].traceIndex].material.opacity =
+          TRACE_SELECTED_OPACITY;
+      (this.traces[this.dataSet.points[selection].traceIndex].material as
+           THREE.LineBasicMaterial)
+          .linewidth = TRACE_SELECTED_LINEWIDTH;
+      this.traces[this.dataSet.points[selection].traceIndex]
+          .material.needsUpdate = true;
+    }
+    this.render();
+    this.makeLabels();
+  }
+
+  private resetTraces() {
+    if (!this.traces) {
+      return;
+    }
+    for (let i = 0; i < this.traces.length; i++) {
+      this.traces[i].material.opacity = TRACE_DEFAULT_OPACITY;
+      (this.traces[i].material as THREE.LineBasicMaterial).linewidth =
+          TRACE_DEFAULT_LINEWIDTH;
+      this.traces[i].material.needsUpdate = true;
+    }
+  }
+
+  /** When dragging, do not redraw labels. */
+  private onMouseDown(e: MouseEvent) {
+    this.animating = false;
+    this.isDragSequence = false;
+    this.mouseIsDown = true;
+    // If we are in selection mode, and we have in fact clicked a valid point,
+    // create a sphere so we can select things
+    if (this.selecting) {
+      this.cameraControls.enabled = false;
+      this.setNearestPointToMouse(e);
+      if (this.nearestPoint) {
+        this.createSelectionSphere();
+      }
+    } else if (
+        !e.ctrlKey &&
+        this.cameraControls.mouseButtons.ORBIT == THREE.MOUSE.RIGHT) {
+      // The user happened to press the ctrl key when the tab was active,
+      // unpressed the ctrl when the tab was inactive, and now he/she
+      // is back to the projector tab.
+      this.cameraControls.mouseButtons.ORBIT = THREE.MOUSE.LEFT;
+      this.cameraControls.mouseButtons.PAN = THREE.MOUSE.RIGHT;
+    } else if (
+        e.ctrlKey &&
+        this.cameraControls.mouseButtons.ORBIT == THREE.MOUSE.LEFT) {
+      // Similarly to the situation above.
+      this.cameraControls.mouseButtons.ORBIT = THREE.MOUSE.RIGHT;
+      this.cameraControls.mouseButtons.PAN = THREE.MOUSE.LEFT;
+    }
+  }
+
+
+  /** When we stop dragging/zooming, return to normal behavior. */
+  private onMouseUp(e: any) {
+    if (this.selecting) {
+      this.cameraControls.enabled = true;
+      this.scene.remove(this.selectionSphere);
+      this.selectionSphere = null;
+      this.render();
+    }
+    this.mouseIsDown = false;
+  }
+
+  /**
+   * When the mouse moves, find the nearest point (if any) and send it to the
+   * hoverlisteners (usually called from embedding.ts)
+   */
+  private onMouseMove(e: MouseEvent) {
+    if (this.cameraControls.autoRotate) {
+      // Cancel the lazy susan.
+      this.cameraControls.autoRotate = false;
+      cancelAnimationFrame(this.lazySusanAnimation);
+      this.makeLabels();
+    }
+
+    // A quick check to make sure data has come in.
+    if (!this.points) {
+      return;
+    }
+    this.isDragSequence = this.mouseIsDown;
+    // Depending if we're selecting or just navigating, handle accordingly.
+    if (this.selecting && this.mouseIsDown) {
+      if (this.selectionSphere) {
+        this.adjustSelectionSphere(e);
+      }
+      this.render();
+    } else if (!this.mouseIsDown) {
+      let lastNearestPoint = this.nearestPoint;
+      this.setNearestPointToMouse(e);
+      if (lastNearestPoint != this.nearestPoint) {
+        this.onHoverListeners.forEach(l => l(this.nearestPoint));
+      }
+    }
+  }
+
+  /** For using ctrl + left click as right click, and for circle select */
+  private onKeyDown(e: any) {
+    // If ctrl is pressed, use left click to orbit
+    if (e.keyCode === CTRL_KEY) {
+      this.cameraControls.mouseButtons.ORBIT = THREE.MOUSE.RIGHT;
+      this.cameraControls.mouseButtons.PAN = THREE.MOUSE.LEFT;
+    }
+
+    // If shift is pressed, start selecting
+    if (e.keyCode === SHIFT_KEY) {
+      this.selecting = true;
+      this.containerNode.style.cursor = 'crosshair';
+    }
+  }
+
+  /** For using ctrl + left click as right click, and for circle select */
+  private onKeyUp(e: any) {
+    if (e.keyCode === CTRL_KEY) {
+      this.cameraControls.mouseButtons.ORBIT = THREE.MOUSE.LEFT;
+      this.cameraControls.mouseButtons.PAN = THREE.MOUSE.RIGHT;
+    }
+
+    // If shift is released, stop selecting
+    if (e.keyCode === SHIFT_KEY) {
+      this.selecting = (this.getMode() === Mode.SELECT);
+      if (!this.selecting) {
+        this.containerNode.style.cursor = 'default';
+      }
+      this.scene.remove(this.selectionSphere);
+      this.selectionSphere = null;
+      this.render();
+    }
+  }
+
+  private setNearestPointToMouse(e: MouseEvent) {
+    // Create buffer for reading a single pixel.
+    let pixelBuffer = new Uint8Array(4);
+    // No need to account for dpr (device pixel ratio) since the pickingTexture
+    // has the same coordinates as the mouse (flipped on y).
+    let x = e.offsetX;
+    let y = e.offsetY;
+
+    // Read the pixel under the mouse from the texture.
+    this.renderer.readRenderTargetPixels(
+        this.pickingTexture, x, this.pickingTexture.height - y, 1, 1,
+        pixelBuffer);
+
+    // Interpret the pixel as an ID.
+    let id = (pixelBuffer[0] << 16) | (pixelBuffer[1] << 8) | pixelBuffer[2];
+    this.nearestPoint =
+        id != 0xffffff && id < this.dataSet.points.length ? id : null;
+  }
+
+  /** Returns the squared distance to the mouse for the i-th point. */
+  private getDist2ToMouse(i: number, e: MouseEvent) {
+    let point = this.getProjectedPointFromIndex(i);
+    let screenCoords = this.vector3DToScreenCoords(point);
+    return this.dist2D(
+        [e.offsetX * this.dpr, e.offsetY * this.dpr],
+        [screenCoords.x, screenCoords.y]);
+  }
+
+  private adjustSelectionSphere(e: MouseEvent) {
+    let dist2 = this.getDist2ToMouse(this.nearestPoint, e) / 100;
+    this.selectionSphere.scale.set(dist2, dist2, dist2);
+    this.selectPoints(dist2);
+  }
+
+  private getProjectedPointFromIndex(i: number): THREE.Vector3 {
+    return new THREE.Vector3(
+        this.dataSet.points[i].projectedPoint.x,
+        this.dataSet.points[i].projectedPoint.y,
+        this.dataSet.points[i].projectedPoint.z);
+  }
+
+  private calibratePointSize() {
+    let numPts = this.dataSet.points.length;
+    let scaleConstant = 200;
+    let logBase = 8;
+    // Scale point size inverse-logarithmically to the number of points.
+    this.pointSize3D = scaleConstant / Math.log(numPts) / Math.log(logBase);
+    this.pointSize2D = this.pointSize3D / 1.5;
+  }
+
+  private setFogDistances() {
+    let dists = this.getNearFarPoints();
+    this.fog.near = dists.shortestDist;
+    // If there are fewer points we want less fog. We do this
+    // by making the "far" value (that is, the distance from the camera to the
+    // far edge of the fog) proportional to the number of points.
+    let multiplier = 2 -
+        Math.min(this.dataSet.points.length, NUM_POINTS_FOG_THRESHOLD) /
+            NUM_POINTS_FOG_THRESHOLD;
+    this.fog.far = dists.furthestDist * multiplier;
+  }
+
+  private getNearFarPoints() {
+    let shortestDist: number = Infinity;
+    let furthestDist: number = 0;
+    for (let i = 0; i < this.dataSet.points.length; i++) {
+      let point = this.getProjectedPointFromIndex(i);
+      if (!this.isPointWithinCameraView(point)) {
+        continue;
+      };
+      let distToCam = this.dist3D(point, this.perspCamera.position);
+      furthestDist = Math.max(furthestDist, distToCam);
+      shortestDist = Math.min(shortestDist, distToCam);
+    }
+    return {shortestDist, furthestDist};
+  }
+
+  /**
+   * Renders the scene and updates the label for the point, which is rendered
+   * as a div on top of WebGL.
+   */
+  private render() {
+    if (!this.dataSet) {
+      return;
+    }
+    let lightPos = new THREE.Vector3().copy(this.perspCamera.position);
+    lightPos.x += 1;
+    lightPos.y += 1;
+    this.light.position.set(lightPos.x, lightPos.y, lightPos.z);
+
+    // We want to determine which point the user is hovering over. So, rather
+    // than linearly iterating through each point to see if it is under the
+    // mouse, we render another set of the points offscreen, where each point is
+    // at full opacity and has its id encoded in its color. Then, we see the
+    // color of the pixel under the mouse, decode the color, and get the id of
+    // of the point.
+    let shaderMaterial = this.points.material as THREE.ShaderMaterial;
+    let colors = this.geometry.getAttribute('color') as THREE.BufferAttribute;
+    // Make shallow copy of the shader options and modify the necessary values.
+    let offscreenOptions =
+        Object.create(this.materialOptions) as THREE.ShaderMaterialParameters;
+    // Since THREE.js errors if we remove the fog, the workaround is to set the
+    // near value to very far, so no points have fog.
+    this.fog.near = 1000;
+    this.fog.far = 10000;
+    // Render offscreen as non transparent points (even when we have images).
+    offscreenOptions.uniforms.isImage.value = false;
+    offscreenOptions.transparent = false;
+    offscreenOptions.depthTest = true;
+    offscreenOptions.depthWrite = true;
+    shaderMaterial.setValues(offscreenOptions);
+    // Give each point a unique color.
+    let origColArr = colors.array;
+    colors.array = this.uniqueColArr;
+    colors.needsUpdate = true;
+    this.renderer.render(this.scene, this.perspCamera, this.pickingTexture);
+
+    // Now render onscreen.
+
+    // Change to original color array.
+    colors.array = origColArr;
+    colors.needsUpdate = true;
+    // Bring back the fog.
+    if (this.zAccessor && this.geometry) {
+      this.setFogDistances();
+    }
+    offscreenOptions.uniforms.isImage.value = !!this.image;
+    // Bring back the standard shader material options.
+    shaderMaterial.setValues(this.materialOptions);
+    // Render onscreen.
+    this.renderer.render(this.scene, this.perspCamera);
+  }
+
+  /**
+   * Make sure that the point is in view of the camera (as opposed to behind
+   * this is a problem because we are projecting to the camera)
+   */
+  private isPointWithinCameraView(point: THREE.Vector3): boolean {
+    let camToTarget = new THREE.Vector3()
+                          .copy(this.perspCamera.position)
+                          .sub(this.cameraControls.target);
+    let camToPoint =
+        new THREE.Vector3().copy(this.perspCamera.position).sub(point);
+    // If the angle between the camera-target and camera-point vectors is more
+    // than 90, the point is behind the camera
+    if (camToPoint.angleTo(camToTarget) > Math.PI / 2) {
+      return false;
+    };
+    return true;
+  }
+
+  private vector3DToScreenCoords(v: THREE.Vector3) {
+    let vector = new THREE.Vector3().copy(v).project(this.perspCamera);
+    let coords = {
+      // project() returns the point in perspCamera's coordinates, with the
+      // origin in the center and a positive upward y. To get it into screen
+      // coordinates, normalize by adding 1 and dividing by 2.
+      x: ((vector.x + 1) / 2 * this.width) * this.dpr,
+      y: -((vector.y - 1) / 2 * this.height) * this.dpr
+    };
+    return coords;
+  }
+
+  /** Checks if a given label will be within the screen's bounds. */
+  private isLabelInBounds(labelWidth: number, coords: {x: number, y: number}) {
+    let padding = 7;
+    if ((coords.x < 0) || (coords.y < 0) ||
+        (coords.x > this.width * this.dpr - labelWidth - padding) ||
+        (coords.y > this.height * this.dpr)) {
+      return false;
+    };
+    return true;
+  }
+
+  /** Add a specific label to the canvas. */
+  private formatLabel(
+      text: string, point: {x: number, y: number}, opacity: number) {
+    let ls = new THREE.Color(this.labelStroke);
+    let lc = new THREE.Color(this.labelColor);
+    this.gc.strokeStyle = 'rgba(' + ls.r * 255 + ',' + ls.g * 255 + ',' +
+        ls.b * 255 + ',' + opacity + ')';
+    this.gc.fillStyle = 'rgba(' + lc.r * 255 + ',' + lc.g * 255 + ',' +
+        lc.b * 255 + ',' + opacity + ')';
+    this.gc.strokeText(text, point.x + 4, point.y);
+    this.gc.fillText(text, point.x + 4, point.y);
+  }
+
+  /**
+   * Reset the positions of all labels, and check for overlapps using the
+   * collision grid.
+   */
+  private makeLabels() {
+    // Don't make labels if they are turned off.
+    if (!this.labelsAreOn || this.points == null) {
+      return;
+    }
+    // First, remove all old labels.
+    this.removeAllLabels();
+
+    this.labelCanvasIsCleared = false;
+    // If we are passed no points to label (that is, not mousing over any
+    // points) then want to label ALL the points that we can.
+    if (!this.labeledPoints.length) {
+      this.labeledPoints = this.shuffledData;
+    }
+
+    // We never render more than ~500 labels, so when we get much past that
+    // point, just break.
+    let numRenderedLabels: number = 0;
+    let labelHeight = parseInt(this.gc.font, 10);
+
+    // Bounding box for collision grid.
+    let boundingBox: BoundingBox = {
+      loX: 0,
+      hiX: this.width * this.dpr,
+      loY: 0,
+      hiY: this.height * this.dpr
+    };
+
+    // Make collision grid with cells proportional to window dimensions.
+    let grid =
+        new CollisionGrid(boundingBox, this.width / 25, this.height / 50);
+
+    let dists = this.getNearFarPoints();
+    let opacityRange = dists.furthestDist - dists.shortestDist;
+
+    // Setting styles for the labeled font.
+    this.gc.lineWidth = 6;
+    this.gc.textBaseline = 'middle';
+    this.gc.font = (FONT_SIZE * this.dpr).toString() + 'px roboto';
+
+    for (let i = 0;
+         (i < this.labeledPoints.length) && !(numRenderedLabels > SAMPLE_SIZE);
+         i++) {
+      let index = this.labeledPoints[i];
+      let point = this.getProjectedPointFromIndex(index);
+      if (!this.isPointWithinCameraView(point)) {
+        continue;
+      };
+      let screenCoords = this.vector3DToScreenCoords(point);
+      // Have extra space between neighboring labels. Don't pack too tightly.
+      let labelMargin = 2;
+      // Shift the label to the right of the point circle.
+      let xShift = 3;
+      let textBoundingBox = {
+        loX: screenCoords.x + xShift - labelMargin,
+        // Computing the width of the font is expensive,
+        // so we assume width of 1 at first. Then, if the label doesn't
+        // conflict with other labels, we measure the actual width.
+        hiX: screenCoords.x + xShift + /* labelWidth - 1 */ +1 + labelMargin,
+        loY: screenCoords.y - labelHeight / 2 - labelMargin,
+        hiY: screenCoords.y + labelHeight / 2 + labelMargin
+      };
+
+      if (grid.insert(textBoundingBox, true)) {
+        let dataSet = this.dataSet;
+        let text = this.labelAccessor(index);
+        let labelWidth = this.gc.measureText(text).width;
+
+        // Now, check with properly computed width.
+        textBoundingBox.hiX += labelWidth - 1;
+        if (grid.insert(textBoundingBox) &&
+            this.isLabelInBounds(labelWidth, screenCoords)) {
+          let lenToCamera = this.dist3D(point, this.perspCamera.position);
+          // Opacity is scaled between 0.2 and 1, based on how far a label is
+          // from the camera (Unless we are in 2d mode, in which case opacity is
+          // just 1!)
+          let opacity = this.zAccessor ?
+              1.2 - (lenToCamera - dists.shortestDist) / opacityRange :
+              1;
+          this.formatLabel(text, screenCoords, opacity);
+          numRenderedLabels++;
+        }
+      }
+    }
+
+    if (this.highlightedPoints.length > 0) {
+      // Force-draw the first favored point with increased font size.
+      let index = this.highlightedPoints[0];
+      let point = this.dataSet.points[index];
+      this.gc.font = (FONT_SIZE * this.dpr * 1.7).toString() + 'px roboto';
+      let coords = new THREE.Vector3(
+          point.projectedPoint.x, point.projectedPoint.y,
+          point.projectedPoint.z);
+      let screenCoords = this.vector3DToScreenCoords(coords);
+      let text = this.labelAccessor(index);
+      this.formatLabel(text, screenCoords, 255);
+    }
+  }
+
+  /** Returns the distance between two points in 3d space */
+  private dist3D(a: Point3D, b: Point3D): number {
+    let dx = a.x - b.x;
+    let dy = a.y - b.y;
+    let dz = a.z - b.z;
+    return Math.sqrt(dx * dx + dy * dy + dz * dz);
+  }
+
+  private dist2D(a: [number, number], b: [number, number]): number {
+    let dX = a[0] - b[0];
+    let dY = a[1] - b[1];
+    return Math.sqrt(dX * dX + dY * dY);
+  }
+
+  /** Cancels current animation */
+  private cancelAnimation() {
+    if (this.animationID) {
+      cancelAnimationFrame(this.animationID);
+    }
+  }
+
+  private startLazySusanAnimation() {
+    this.cameraControls.autoRotate = true;
+    this.cameraControls.update();
+    this.lazySusanAnimation =
+        requestAnimationFrame(() => this.startLazySusanAnimation());
+  }
+
+  /**
+   * Animates the camera between one location and another.
+   * If callback is specified, it gets called when the animation is done.
+   */
+  private animate(
+      pos: THREE.Vector3, target: THREE.Vector3, callback?: () => void) {
+    this.cameraControls.autoRotate = false;
+    cancelAnimationFrame(this.lazySusanAnimation);
+
+    let currPos = this.perspCamera.position;
+    let currTarget = this.cameraControls.target;
+    let speed = 3;
+    this.animating = true;
+    let interp = (a: THREE.Vector3, b: THREE.Vector3) => {
+      let x = (a.x - b.x) / speed + b.x;
+      let y = (a.y - b.y) / speed + b.y;
+      let z = (a.z - b.z) / speed + b.z;
+      return {x: x, y: y, z: z};
+    };
+    // If we're still relatively far away from the target, go closer
+    if (this.dist3D(currPos, pos) > .03) {
+      let newTar = interp(target, currTarget);
+      this.cameraControls.target.set(newTar.x, newTar.y, newTar.z);
+
+      let newPos = interp(pos, currPos);
+      this.perspCamera.position.set(newPos.x, newPos.y, newPos.z);
+      this.cameraControls.update();
+      this.render();
+      this.animationID =
+          requestAnimationFrame(() => this.animate(pos, target, callback));
+    } else {
+      // Once we get close enough, update flags and stop moving
+      this.animating = false;
+      this.cameraControls.target.set(target.x, target.y, target.z);
+      this.cameraControls.update();
+      this.makeLabels();
+      this.render();
+      if (callback) {
+        callback();
+      }
+    }
+  }
+
+  /** Removes all points geometry from the scene. */
+  private removeAll() {
+    this.scene.remove(this.points);
+    this.removeOldAxes();
+    this.removeAllLabels();
+    this.removeAllTraces();
+  }
+
+  /** Removes all traces from the scene. */
+  private removeAllTraces() {
+    if (!this.traces) {
+      return;
+    }
+
+    for (let i = 0; i < this.traces.length; i++) {
+      this.scene.remove(this.traces[i]);
+    }
+    this.traces = [];
+  }
+
+  /** Removes all the labels. */
+  private removeAllLabels() {
+    // If labels are already removed, do not spend compute power to clear the
+    // canvas.
+    if (!this.labelCanvasIsCleared) {
+      this.gc.clearRect(0, 0, this.width * this.dpr, this.height * this.dpr);
+      this.labelCanvasIsCleared = true;
+    }
+  }
+
+  private colorSprites(highlightStroke: ((index: number) => string)) {
+    // Update attributes to change colors
+    let colors = this.geometry.getAttribute('color') as THREE.BufferAttribute;
+    let highlights =
+        this.geometry.getAttribute('isHighlight') as THREE.BufferAttribute;
+    for (let i = 0; i < this.dataSet.points.length; i++) {
+      let unhighlightedColor = this.image ?
+          new THREE.Color() :
+          new THREE.Color(
+              this.colorAccessor ? this.colorAccessor(i) :
+                                   (this.defaultPointColor as any));
+      colors.setXYZ(
+          i, unhighlightedColor.r, unhighlightedColor.g, unhighlightedColor.b);
+      highlights.setX(i, 0.0);
+    }
+    if (highlightStroke) {
+      // Traverse in reverse so that the point we are hovering over
+      // (highlightedPoints[0]) is painted last.
+      for (let i = this.highlightedPoints.length - 1; i >= 0; i--) {
+        let assocPoint = this.highlightedPoints[i];
+        let color = new THREE.Color(highlightStroke(i));
+        // Fill colors array (single array of numPoints*3 elements,
+        // triples of which refer to the rgb values of a single vertex).
+        colors.setXYZ(assocPoint, color.r, color.g, color.b);
+        highlights.setX(assocPoint, 1.0);
+      }
+    }
+    colors.needsUpdate = true;
+    highlights.needsUpdate = true;
+  }
+
+  /**
+   * This is called when we update the data to make sure we don't have stale
+   * data lying around.
+   */
+  private cleanVariables() {
+    this.removeAll();
+    if (this.geometry) {
+      this.geometry.dispose();
+    }
+    this.geometry = null;
+    this.points = null;
+    this.labeledPoints = [];
+    this.highlightedPoints = [];
+  }
+
+  /** Select the points inside the sphere of radius dist */
+  private selectPoints(dist: number) {
+    let selectedPoints: Array<number> = new Array();
+    this.dataSet.points.forEach(point => {
+      let pt = point.projectedPoint;
+      let pointVect = new THREE.Vector3(pt.x, pt.y, pt.z);
+      let distPointToSphereOrigin = new THREE.Vector3()
+                                        .copy(this.selectionSphere.position)
+                                        .sub(pointVect)
+                                        .length();
+      if (distPointToSphereOrigin < dist) {
+        selectedPoints.push(this.dataSet.points.indexOf(point));
+      }
+    });
+    this.labeledPoints = selectedPoints;
+    // Whenever anything is selected, we want to set the corect point color.
+    this.defaultPointColor = POINT_COLOR_GRAYED;
+    this.onSelectionListeners.forEach(l => l(selectedPoints));
+  }
+
+  private createSelectionSphere() {
+    let geometry = new THREE.SphereGeometry(1, 300, 100);
+    let material = new THREE.MeshPhongMaterial({
+      color: 0x000000,
+      specular: (this.zAccessor && 0xffffff),  // In 2d, make sphere look flat.
+      emissive: 0x000000,
+      shininess: 10,
+      shading: THREE.SmoothShading,
+      opacity: 0.125,
+      transparent: true,
+    });
+    this.selectionSphere = new THREE.Mesh(geometry, material);
+    this.selectionSphere.scale.set(0, 0, 0);
+    let pos = this.dataSet.points[this.nearestPoint].projectedPoint;
+    this.scene.add(this.selectionSphere);
+    this.selectionSphere.position.set(pos.x, pos.y, pos.z);
+  }
+
+  private getLayoutValues() {
+    this.width = this.containerNode.offsetWidth;
+    this.height = Math.max(1, this.containerNode.offsetHeight);
+    this.dpr = window.devicePixelRatio;
+  }
+
+  // PUBLIC API
+
+  /** Sets the data for the scatter plot. */
+  setDataSet(dataSet: DataSet, spriteImage: HTMLImageElement) {
+    this.dataSet = dataSet;
+    this.calibratePointSize();
+    let positions =
+        new Float32Array(this.dataSet.points.length * XYZ_NUM_BYTES);
+    this.positionBufferArray =
+        new THREE.BufferAttribute(positions, XYZ_NUM_BYTES);
+    this.image = spriteImage;
+    this.shuffledData = new Array(this.dataSet.points.length);
+    for (let i = 0; i < this.dataSet.points.length; i++) {
+      this.shuffledData[i] = i;
+    }
+    shuffle(this.shuffledData);
+    this.cleanVariables();
+  }
+
+  setColorAccessor(colorAccessor: (index: number) => string) {
+    this.colorAccessor = colorAccessor;
+    // Render only if there is a geometry.
+    if (this.geometry) {
+      this.colorSprites(this.highlightStroke);
+      this.render();
+    }
+  }
+
+  setXAccessor(xAccessor: (index: number) => number) {
+    this.xAccessor = xAccessor;
+  }
+
+  setYAccessor(yAccessor: (index: number) => number) {
+    this.yAccessor = yAccessor;
+  }
+
+  setZAccessor(zAccessor: (index: number) => number) {
+    this.zAccessor = zAccessor;
+  }
+
+  setLabelAccessor(labelAccessor: (index: number) => string) {
+    this.labelAccessor = labelAccessor;
+    this.render();
+  }
+
+  setMode(mode: Mode) {
+    this.mode = mode;
+    if (mode === Mode.SELECT) {
+      this.selecting = true;
+      this.containerNode.style.cursor = 'crosshair';
+    } else {
+      this.selecting = false;
+      this.containerNode.style.cursor = 'default';
+    }
+  }
+
+  getMode(): Mode { return this.mode; }
+
+  resetZoom() {
+    if (this.animating) {
+      return;
+    }
+    let resetPos = this.cameraControls.position0;
+    let resetTarget = this.cameraControls.target0;
+    this.removeAllLabels();
+    this.animate(resetPos, resetTarget, () => {
+      // Start rotating when the animation is done, if we are in 3D mode.
+      if (this.zAccessor) {
+        this.startLazySusanAnimation();
+      }
+    });
+  }
+
+  /** Zoom by moving the camera toward the target. */
+  zoomStep(multiplier: number) {
+    let additiveZoom = Math.log(multiplier);
+    if (this.animating) {
+      return;
+    }
+
+    // Zoomvect is the vector along which we want to move the camera
+    // It is the (normalized) vector from the camera to its target
+    let zoomVect = new THREE.Vector3()
+                       .copy(this.cameraControls.target)
+                       .sub(this.perspCamera.position)
+                       .multiplyScalar(additiveZoom);
+    let position =
+        new THREE.Vector3().copy(this.perspCamera.position).add(zoomVect);
+
+    // Make sure that we're not too far zoomed in. If not, zoom!
+    if ((this.dist3D(position, this.cameraControls.target) > MIN_ZOOM) &&
+        (this.dist3D(position, this.cameraControls.target) < MAX_ZOOM)) {
+      this.removeAllLabels();
+      this.animate(position, this.cameraControls.target);
+    }
+  }
+
+  highlightPoints(
+      pointIndexes: number[], highlightStroke: (i: number) => string,
+      favorLabels: (i: number) => boolean): void {
+    this.favorLabels = favorLabels;
+    this.highlightedPoints = pointIndexes;
+    this.labeledPoints = pointIndexes;
+    this.highlightStroke = highlightStroke;
+    this.colorSprites(highlightStroke);
+    this.render();
+    this.makeLabels();
+  }
+
+  getHighlightedPoints(): number[] { return this.highlightedPoints; }
+
+  showLabels(show: boolean) {
+    this.labelsAreOn = show;
+    if (this.labelsAreOn) {
+      this.makeLabels();
+    } else {
+      this.removeAllLabels();
+    }
+  }
+
+  /**
+   * Toggles between day and night mode (resets corresponding variables for
+   * color, etc.)
+   */
+  setDayNightMode(isNight: boolean) {
+    this.isNight = isNight;
+    this.labelColor = (isNight ? LABEL_COLOR_NIGHT : LABEL_COLOR_DAY);
+    this.labelStroke = (isNight ? LABEL_STROKE_NIGHT : LABEL_STROKE_DAY);
+    this.backgroundColor =
+        (isNight ? BACKGROUND_COLOR_NIGHT : BACKGROUND_COLOR_DAY);
+    this.blending = (isNight ? BLENDING_NIGHT : BLENDING_DAY);
+    this.renderer.setClearColor(this.backgroundColor);
+  }
+
+  showAxes(show: boolean) {
+    // TODO(ereif): implement
+  }
+
+  setAxisLabels(xLabel: string, yLabel: string) {
+    // TODO(ereif): implement
+  }
+  /**
+   * Recreates the scene in its entirety, not only resetting the point
+   * locations but also demolishing and recreating the THREEjs structures.
+   */
+  recreateScene() {
+    this.removeAll();
+    this.cancelAnimation();
+    this.fog = this.zAccessor ?
+        new THREE.Fog(this.backgroundColor) :
+        new THREE.Fog(this.backgroundColor, Infinity, Infinity);
+    this.scene.fog = this.fog;
+    this.addSprites();
+    this.addTraces();
+    if (this.zAccessor) {
+      this.addAxis3D();
+      this.makeCamera3D();
+    } else {
+      this.addAxis2D();
+      this.makeCamera2D();
+    }
+    this.render();
+  }
+
+  /**
+   * Redraws the data. Should be called anytime the accessor method
+   * for x and y coordinates changes, which means a new projection
+   * exists and the scatter plot should repaint the points.
+   */
+  update() {
+    this.cancelAnimation();
+    this.getPointsCoordinates();
+    this.updatePositionsArray();
+    if (this.geometry) {
+      this.makeLabels();
+      this.render();
+    }
+  }
+
+  resize(render = true) {
+    this.getLayoutValues();
+    this.perspCamera.aspect = this.width / this.height;
+    this.perspCamera.updateProjectionMatrix();
+    d3.select(this.canvas)
+        .attr('width', this.width * this.dpr)
+        .attr('height', this.height * this.dpr)
+        .style({width: this.width + 'px', height: this.height + 'px'});
+    this.renderer.setSize(this.width, this.height);
+    this.pickingTexture = new THREE.WebGLRenderTarget(this.width, this.height);
+    this.pickingTexture.texture.minFilter = THREE.LinearFilter;
+    if (render) {
+      this.render();
+    };
+  }
+
+  showTickLabels(show: boolean) {
+    // TODO(ereif): implement
+  }
+
+  onSelection(listener: OnSelectionListener) {
+    this.onSelectionListeners.push(listener);
+  }
+
+  onHover(listener: OnHoverListener) { this.onHoverListeners.push(listener); }
+
+  clickOnPoint(pointIndex: number) {
+    this.nearestPoint = pointIndex;
+    this.onClick();
+  }
+}
diff --git a/tensorflow/tensorboard/components/vz-projector/sptree.ts b/tensorflow/tensorboard/components/vz-projector/sptree.ts
new file mode 100644
index 0000000000..f9310d2f1e
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/sptree.ts
@@ -0,0 +1,189 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+/** How many elements can be stored in each node of the tree. */
+const NODE_CAPACITY = 4;
+
+/** N-dimensional point. Usually 2D or 3D. */
+export type Point = number[];
+
+export interface BBox {
+  center: Point;
+  halfDim: number;
+}
+
+/** A node in a space-partitioning tree. */
+export interface SPNode {
+  /** The children of this node. */
+  children?: SPNode[];
+  /** The bounding box of the region this node occupies. */
+  box: BBox;
+  /** One or more points this node has. */
+  points?: Point[];
+}
+
+/**
+ * A Space-partitioning tree (https://en.wikipedia.org/wiki/Space_partitioning)
+ * that recursively divides the space into regions of equal sizes. This data
+ * structure can act both as a Quad tree and an Octree when the data is 2 or
+ * 3 dimensional respectively. One usage is in t-SNE in order to do Barnes-Hut
+ * approximation.
+ */
+export class SPTree {
+  root: SPNode;
+
+  private masks: number[];
+  private capacity: number;
+  private dim: number;
+
+  /**
+   * Constructs a new tree with the provided data.
+   *
+   * @param data List of n-dimensional data points.
+   * @param capacity Number of data points to store in a single node.
+   */
+  constructor(data: Point[], capacity = NODE_CAPACITY) {
+    if (data.length < 1) {
+      throw new Error('There should be at least 1 data point');
+    }
+    this.capacity = capacity;
+    // Make a bounding box based on the extent of the data.
+    this.dim = data[0].length;
+    // Each node has 2^d children, where d is the dimension of the space.
+    // Binary masks (e.g. 000, 001, ... 111 in 3D) are used to determine in
+    // which child (e.g. quadron in 2D) the new point is going to be assigned.
+    // For more details, see the insert() method and its comments.
+    this.masks = new Array(Math.pow(2, this.dim));
+    for (let d = 0; d < this.masks.length; ++d) {
+      this.masks[d] = (1 << d);
+    }
+    let min: Point = new Array(this.dim);
+    fillArray(min, Number.POSITIVE_INFINITY);
+    let max: Point = new Array(this.dim);
+    fillArray(max, Number.NEGATIVE_INFINITY);
+
+    for (let i = 0; i < data.length; ++i) {
+      // For each dim get the min and max.
+      // E.g. For 2-D, get the x_min, x_max, y_min, y_max.
+      for (let d = 0; d < this.dim; ++d) {
+        min[d] = Math.min(min[d], data[i][d]);
+        max[d] = Math.max(max[d], data[i][d]);
+      }
+    }
+    // Create a bounding box with the center of the largest span.
+    let center: Point = new Array(this.dim);
+    let halfDim = 0;
+    for (let d = 0; d < this.dim; ++d) {
+      let span = max[d] - min[d];
+      center[d] = min[d] + span / 2;
+      halfDim = Math.max(halfDim, span / 2);
+    }
+    this.root = {box: {center: center, halfDim: halfDim}};
+    for (let i = 0; i < data.length; ++i) {
+      this.insert(this.root, data[i]);
+    }
+  }
+
+  /**
+   * Visits every node in the tree. Each node can store 1 or more points,
+   * depending on the node capacity provided in the constructor.
+   *
+   * @param accessor Method that takes the currently visited node, and the
+   * low and high point of the region that this node occupies. E.g. in 2D,
+   * the low and high points will be the lower-left corner and the upper-right
+   * corner.
+   */
+  visit(
+      accessor: (node: SPNode, lowPoint: Point, highPoint: Point) => boolean,
+      noBox = false) {
+    this.visitNode(this.root, accessor, noBox);
+  }
+
+  private visitNode(
+      node: SPNode,
+      accessor: (node: SPNode, lowPoint?: Point, highPoint?: Point) => boolean,
+      noBox: boolean) {
+    let skipChildren: boolean;
+    if (noBox) {
+      skipChildren = accessor(node);
+    } else {
+      let lowPoint = new Array(this.dim);
+      let highPoint = new Array(this.dim);
+      for (let d = 0; d < this.dim; ++d) {
+        lowPoint[d] = node.box.center[d] - node.box.halfDim;
+        highPoint[d] = node.box.center[d] + node.box.halfDim;
+      }
+      skipChildren = accessor(node, lowPoint, highPoint);
+    }
+    if (!node.children || skipChildren) {
+      return;
+    }
+    for (let i = 0; i < node.children.length; ++i) {
+      let child = node.children[i];
+      if (child) {
+        this.visitNode(child, accessor, noBox);
+      }
+    }
+  }
+
+  private insert(node: SPNode, p: Point): boolean {
+    if (node.points == null) {
+      node.points = [];
+    }
+    // If there is space in this node, add the object here.
+    if (node.points.length < this.capacity) {
+      node.points.push(p);
+      return true;
+    }
+    // Otherwise, subdivide and then add the point to whichever node will
+    // accept it.
+    if (node.children == null) {
+      node.children = new Array(this.masks.length);
+    }
+
+    // Decide which child will get the new point by constructing a D-bits binary
+    // signature (D=3 for 3D) where the k-th bit is 1 if the point's k-th
+    // coordinate is greater than the node's k-th coordinate, 0 otherwise.
+    // Then the binary signature in decimal system gives us the index of the
+    // child where the new point should be.
+    let index = 0;
+    for (let d = 0; d < this.dim; ++d) {
+      if (p[d] > node.box.center[d]) {
+        index |= this.masks[d];
+      }
+    }
+    if (node.children[index] == null) {
+      this.makeChild(node, index);
+    }
+    this.insert(node.children[index], p);
+    return true;
+  }
+
+  private makeChild(node: SPNode, index: number): void {
+    let oldC = node.box.center;
+    let h = node.box.halfDim / 2;
+    let newC: Point = new Array(this.dim);
+    for (let d = 0; d < this.dim; ++d) {
+      newC[d] = (index & (1 << d)) ? oldC[d] + h : oldC[d] - h;
+    }
+    node.children[index] = {box: {center: newC, halfDim: h}};
+  }
+}
+
+function fillArray<T>(arr: T[], value: T): void {
+  for (let i = 0; i < arr.length; ++i) {
+    arr[i] = value;
+  }
+}
diff --git a/tensorflow/tensorboard/components/vz-projector/sptree_test.ts b/tensorflow/tensorboard/components/vz-projector/sptree_test.ts
new file mode 100644
index 0000000000..e48ae761e1
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/sptree_test.ts
@@ -0,0 +1,106 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+import {SPTree} from './sptree';
+
+const assert = chai.assert;
+
+it('simple 2D data', () => {
+  let data = [
+    [0, 1],
+    [1, 0],
+    [1, 1],
+    [0, 0],
+  ];
+  let tree = new SPTree(data, 1);
+  // Check that each point is within the bound.
+  tree.visit((node, low, high) => {
+    assert.equal(low.length, 2);
+    assert.equal(high.length, 2);
+    node.points.forEach(point => {
+      assert.equal(point.length, 2);
+      // Each point should be in the node's bounding box.
+      assert.equal(
+          point[0] >= low[0] && point[0] <= high[0] && point[1] >= low[1] &&
+              point[1] <= high[1],
+          true);
+    });
+    return false;
+  });
+});
+
+it('simple 3D data', () => {
+  let data = [
+    [0, 1, 0],
+    [1, 0.4, 2],
+    [1, 1, 3],
+    [0, 0, 5],
+  ];
+  let tree = new SPTree(data, 1);
+  // Check that each point is within the bound.
+  tree.visit((node, low, high) => {
+    assert.equal(low.length, 3);
+    assert.equal(high.length, 3);
+    node.points.forEach(point => {
+      assert.equal(point.length, 3);
+      // Each point should be in the node's bounding box.
+      assert.equal(
+          point[0] >= low[0] && point[0] <= high[0] && point[1] >= low[1] &&
+              point[1] <= high[1] && point[2] >= low[2] && point[2] <= high[2],
+          true);
+    });
+    return false;
+  });
+});
+
+it('Only visit root', () => {
+  let data = [
+    [0, 1, 0],
+    [1, 0.4, 2],
+    [1, 1, 3],
+    [0, 0, 5],
+  ];
+  let tree = new SPTree(data, 1);
+  let numVisits = 0;
+  tree.visit((node, low, high) => {
+    numVisits++;
+    return true;
+  });
+  assert.equal(numVisits, 1);
+});
+
+it('Search in random data', () => {
+  let N = 10000;
+  let data = new Array(N);
+  for (let i = 0; i < N; i++) {
+    data[i] = [Math.random(), Math.random()];
+  }
+  let tree = new SPTree(data, 1);
+  let numVisits = 0;
+  let query = data[Math.floor(Math.random() * N)];
+  let found = false;
+  tree.visit((node, low, high) => {
+    numVisits++;
+    if (node.points.length > 0 && node.points[0] === query) {
+      found = true;
+      return true;
+    }
+    let outOfBounds = query[0] < low[0] || query[0] > high[0] ||
+        query[1] < low[1] || query[1] > high[1];
+    return outOfBounds;
+  });
+  assert.equal(found, true);
+  assert.isBelow(numVisits, N / 4);
+});
diff --git a/tensorflow/tensorboard/components/vz-projector/util.ts b/tensorflow/tensorboard/components/vz-projector/util.ts
new file mode 100644
index 0000000000..975f7e122c
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/util.ts
@@ -0,0 +1,43 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+/** Shuffles the array in-place in O(n) time using Fisher-Yates algorithm. */
+export function shuffle<T>(array: T[]): T[] {
+  let m = array.length;
+  let t: T;
+  let i: number;
+
+  // While there remain elements to shuffle.
+  while (m) {
+    // Pick a remaining element
+    i = Math.floor(Math.random() * m--);
+    // And swap it with the current element.
+    t = array[m];
+    array[m] = array[i];
+    array[i] = t;
+  }
+  return array;
+}
+
+/**
+ * Assert that the condition is satisfied; if not, log user-specified message
+ * to the console.
+ */
+export function assert(condition: boolean, message?: string) {
+  if (!condition) {
+    message = message || 'Assertion failed';
+    throw new Error(message);
+  }
+}
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/vz-projector/vector.ts b/tensorflow/tensorboard/components/vz-projector/vector.ts
new file mode 100644
index 0000000000..edb6e9bdd0
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/vector.ts
@@ -0,0 +1,270 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+import {assert} from './util';
+
+
+/**
+ * @fileoverview Useful vector utilities.
+ */
+
+export type Vector = number[];
+export type Point2D = [number, number];
+
+/** Returns the dot product of two vectors. */
+export function dot(a: Vector, b: Vector): number {
+  assert(a.length == b.length, 'Vectors a and b must be of same length');
+  let result = 0;
+  for (let i = 0; i < a.length; ++i) {
+    result += a[i] * b[i];
+  }
+  return result;
+}
+
+/** Sums all the elements in the vector */
+export function sum(a: Vector): number {
+  let result = 0;
+  for (let i = 0; i < a.length; ++i) {
+    result += a[i];
+  }
+  return result;
+}
+
+/** Returns the sum of two vectors, i.e. a + b */
+export function add(a: Vector, b: Vector): Vector {
+  assert(a.length == b.length, 'Vectors a and b must be of same length');
+  let result = new Array(a.length);
+  for (let i = 0; i < a.length; ++i) {
+    result[i] = a[i] + b[i];
+  }
+  return result;
+}
+
+/** Subtracts vector b from vector a, i.e. returns a - b */
+export function sub(a: Vector, b: Vector): Vector {
+  assert(a.length == b.length, 'Vectors a and b must be of same length');
+  let result = new Array(a.length);
+  for (let i = 0; i < a.length; ++i) {
+    result[i] = a[i] - b[i];
+  }
+  return result;
+}
+
+/** Returns the square norm of the vector */
+export function norm2(a: Vector): number {
+  let result = 0;
+  for (let i = 0; i < a.length; ++i) {
+    result += a[i] * a[i];
+  }
+  return result;
+}
+
+/** Returns the euclidean distance between two vectors. */
+export function dist(a: Vector, b: Vector): number {
+  return Math.sqrt(dist2(a, b));
+}
+
+/** Returns the square euclidean distance between two vectors. */
+export function dist2(a: Vector, b: Vector): number {
+  assert(a.length == b.length, 'Vectors a and b must be of same length');
+  let result = 0;
+  for (let i = 0; i < a.length; ++i) {
+    let diff = a[i] - b[i];
+    result += diff * diff;
+  }
+  return result;
+}
+
+/** Returns the square euclidean distance between two 2D points. */
+export function dist2_2D(a: Vector, b: Vector): number {
+  let dX = a[0] - b[0];
+  let dY = a[1] - b[1];
+  return dX * dX + dY * dY;
+}
+
+/** Returns the square euclidean distance between two 3D points. */
+export function dist2_3D(a: Vector, b: Vector): number {
+  let dX = a[0] - b[0];
+  let dY = a[1] - b[1];
+  let dZ = a[2] - b[2];
+  return dX * dX + dY * dY + dZ * dZ;
+}
+
+/**
+ * Returns the square euclidean distance between two vectors, with an early
+ * exit (returns -1) if the distance is >= to the provided limit.
+ */
+export function dist2WithLimit(a: Vector, b: Vector, limit: number): number {
+  assert(a.length == b.length, 'Vectors a and b must be of same length');
+  let result = 0;
+  for (let i = 0; i < a.length; ++i) {
+    let diff = a[i] - b[i];
+    result += diff * diff;
+    if (result >= limit) {
+      return -1;
+    }
+  }
+  return result;
+}
+
+/** Returns the square euclidean distance between two 2D points. */
+export function dist22D(a: Point2D, b: Point2D): number {
+  let dX = a[0] - b[0];
+  let dY = a[1] - b[1];
+  return dX * dX + dY * dY;
+}
+
+/** Modifies the vector in-place to have unit norm. */
+export function unit(a: Vector): void {
+  let norm = Math.sqrt(norm2(a));
+  assert(norm >= 0, 'Norm of the vector must be > 0');
+  for (let i = 0; i < a.length; ++i) {
+    a[i] /= norm;
+  }
+}
+
+/**
+ *  Projects the vectors to a lower dimension
+ *
+ * @param vectors Array of vectors to be projected.
+ * @param newDim The resulting dimension of the vectors.
+ */
+export function projectRandom(vectors: number[][], newDim: number): number[][] {
+  let dim = vectors[0].length;
+  let N = vectors.length;
+  let newVectors: number[][] = new Array(N);
+  for (let i = 0; i < N; ++i) {
+    newVectors[i] = new Array(newDim);
+  }
+  // Make nDim projections.
+  for (let k = 0; k < newDim; ++k) {
+    let randomVector = rn(dim);
+    for (let i = 0; i < N; ++i) {
+      newVectors[i][k] = dot(vectors[i], randomVector);
+    }
+  }
+  return newVectors;
+}
+
+/**
+ * Projects a vector onto a 2D plane specified by the two direction vectors.
+ */
+export function project2d(a: Vector, dir1: Vector, dir2: Vector): Point2D {
+  return [dot(a, dir1), dot(a, dir2)];
+}
+
+/** Returns a vector filled with zeros */
+export function zeros(length: number): Vector {
+  let result = new Array(length);
+  for (let i = 0; i < length; ++i) {
+    result[i] = 0;
+  }
+  return result;
+}
+
+export type Predicate<T> = (a: T) => boolean;
+
+/**
+ * Computes the centroid of the data points that pass the specified predicate.
+ * If the provided data points are not vectors, an accessor function needs
+ * to be provided.
+ */
+export function centroid<T>(
+    dataPoints: T[], predicate: Predicate<T>,
+    accessor?: (a: T) => Vector): {centroid: Vector, numMatches: number} {
+  if (accessor == null) {
+    accessor = (a: T) => <any>a;
+  }
+  assert(dataPoints.length >= 0, '`vectors` must be of length >= 1');
+  let n = 0;
+  let centroid = zeros(accessor(dataPoints[0]).length);
+  for (let i = 0; i < dataPoints.length; ++i) {
+    let dataPoint = dataPoints[i];
+    if (!predicate(dataPoint)) {
+      continue;
+    }
+    ++n;
+    let vector = accessor(dataPoint);
+    for (let j = 0; j < centroid.length; ++j) {
+      centroid[j] += vector[j];
+    }
+  }
+  if (n == 0) {
+    return {centroid: null, numMatches: 0};
+  }
+  for (let j = 0; j < centroid.length; ++j) {
+    centroid[j] /= n;
+  }
+  return {centroid: centroid, numMatches: n};
+}
+
+/**
+ * Generates a vector of the specified size where each component is drawn from
+ * a random (0, 1) gaussian distribution.
+ */
+export function rn(size: number): Vector {
+  let normal = d3.random.normal();
+  let result = new Array(size);
+  for (let i = 0; i < size; ++i) {
+    result[i] = normal();
+  }
+  return result;
+}
+
+/**
+ * Returns the cosine distance ([0, 2]) between two vectors
+ * that have been normalized to unit norm.
+ */
+export function cosDistNorm(a: Vector, b: Vector): number {
+  return 1 - dot(a, b);
+}
+
+/** Returns the cosine similarity ([-1, 1]) between two vectors. */
+export function cosSim(a: Vector, b: Vector): number {
+  return dot(a, b) / Math.sqrt(norm2(a) * norm2(b));
+}
+
+/**
+ * Converts list of vectors (matrix) into a 1-dimensional
+ * typed array with row-first order.
+ */
+export function toTypedArray<T>(
+    dataPoints: T[], accessor: (dataPoint: T) => number[]): Float32Array {
+  let N = dataPoints.length;
+  let dim = accessor(dataPoints[0]).length;
+  let result = new Float32Array(N * dim);
+  for (let i = 0; i < N; ++i) {
+    let vector = accessor(dataPoints[i]);
+    for (let d = 0; d < dim; ++d) {
+      result[i * dim + d] = vector[d];
+    }
+  }
+  return result;
+}
+
+/**
+ * Transposes an RxC matrix represented as a flat typed array
+ * into a CxR matrix, again represented as a flat typed array.
+ */
+export function transposeTypedArray(
+    r: number, c: number, typedArray: Float32Array) {
+  let result = new Float32Array(r * c);
+  for (let i = 0; i < r; ++i) {
+    for (let j = 0; j < c; ++j) {
+      result[j * r + i] = typedArray[i * c + j];
+    }
+  }
+  return result;
+}
diff --git a/tensorflow/tensorboard/components/vz-projector/vz-projector-data-loader.html b/tensorflow/tensorboard/components/vz-projector/vz-projector-data-loader.html
new file mode 100644
index 0000000000..e6beaa837e
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/vz-projector-data-loader.html
@@ -0,0 +1,144 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+<link rel="import" href="../polymer/polymer.html">
+<link rel="import" href="../paper-dropdown-menu/paper-dropdown-menu.html">
+<link rel="import" href="../paper-listbox/paper-listbox.html">
+<link rel="import" href="../paper-item/paper-item.html">
+
+<dom-module id='vz-projector-data-loader'>
+<template>
+<style>
+:host {
+}
+
+input[type=file] {
+  display: none;
+}
+
+.file-name {
+  margin-right: 10px;
+}
+
+.dirs {
+  display: flex;
+  flex-direction: column;
+  margin-right: 10px;
+  line-height: 20px;
+}
+
+.dir {
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+}
+
+paper-item {
+  --paper-item-disabled: {
+    border-bottom: 1px solid black;
+    justify-content: center;
+    font-size: 12px;
+    line-height: normal;
+    min-height: 0px;
+  };
+}
+</style>
+
+<!-- Server-mode UI -->
+<div class="server-controls" style="display:none;">
+  <div class="dirs">
+    <div class="dir">Checkpoint: <span id="checkpoint-file"></span></div>
+    <div class="dir">Metadata: <span id="metadata-file"></span></div>
+  </div>
+  <!-- List of tensors in checkpoint -->
+  <paper-dropdown-menu noink no-animations label="[[getNumTensorsLabel(tensorNames)]] found">
+    <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{selectedTensor}}">
+      <template is="dom-repeat" items="[[tensorNames]]">
+        <paper-item style="justify-content: space-between;" value="[[item.name]]" label="[[item.name]]">
+          [[item.name]]
+          <span style="margin-left: 5px; color:gray; font-size: 12px;">[[item.shape.0]]x[[item.shape.1]]</span>
+        </paper-item>
+      </template>
+    </paper-listbox>
+  </paper-dropdown-menu>
+</div>
+
+<!-- Standalone-mode UI -->
+<div class="standalone-controls" style="display:none;">
+
+  <!-- Upload buttons -->
+  <div style="display: flex; justify-content: space-between;">
+    <!-- Upload data -->
+    <div>
+      <button id="upload" title="Upload a TSV file" class="ink-button">Upload data</button>
+      <span id="file-name" class="file-name dir"></span>
+      <input type="file" id="file" name="file"/>
+    </div>
+
+    <!-- Upload metadata -->
+    <div>
+      <button id="upload-metadata" title="Upload a TSV metadata file" class="ink-button">Upload Metadata</button>
+      <span id="file-metadata-name" class="file-name dir"></span>
+      <input type="file" id="file-metadata" name="file-metadata"/>
+    </div>
+  </div>
+
+  <!-- Demo datasets -->
+  <paper-dropdown-menu style="width: 100%" noink no-animations label="Select a demo dataset">
+    <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{selectedDemo}}">
+        <paper-item value="smartreply_full">SmartReply All</paper-item>
+        <paper-item value="smartreply_5k">SmartReply 5K</paper-item>
+        <paper-item value="wiki_5k">Glove Wiki 5K</paper-item>
+        <paper-item value="wiki_10k">Glove Wiki 10K</paper-item>
+        <paper-item value="wiki_40k">Glove Wiki 40K</paper-item>
+        <paper-item value="mnist_10k">MNIST 10K</paper-item>
+        <paper-item value="iris">Iris</paper-item>
+    </paper-listbox>
+  </paper-dropdown-menu>
+
+</div>
+
+<!-- Label by -->
+<template is="dom-if" if="[[labelOptions.length]]">
+  <paper-dropdown-menu style="width: 100%" noink no-animations label="Label by">
+    <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{labelOption}}">
+      <template is="dom-repeat" items="[[labelOptions]]">
+        <paper-item style="justify-content: space-between;" value="[[item]]" label="[[item]]">
+          [[item]]
+        </paper-item>
+      </template>
+    </paper-listbox>
+  </paper-dropdown-menu>
+</template>
+
+<!-- Color by -->
+<template is="dom-if" if="[[colorOptions.length]]">
+  <paper-dropdown-menu id="colorby" style="width: 100%" noink no-animations label="Color by">
+    <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{colorOption}}">
+      <template is="dom-repeat" items="[[colorOptions]]">
+        <paper-item style="justify-content: space-between;" class$="[[getSeparatorClass(item.isSeparator)]]" value="[[item]]" label="[[item.name]]" disabled="[[item.isSeparator]]">
+          [[item.name]]
+          <span style="margin-left: 5px; color:gray; font-size: 12px;">[[item.desc]]</span>
+        </paper-item>
+      </template>
+    </paper-listbox>
+  </paper-dropdown-menu>
+</template>
+
+<!-- Closing global template -->
+</template>
+</dom-module>
diff --git a/tensorflow/tensorboard/components/vz-projector/vz-projector-data-loader.ts b/tensorflow/tensorboard/components/vz-projector/vz-projector-data-loader.ts
new file mode 100644
index 0000000000..f44bb6bbe9
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/vz-projector-data-loader.ts
@@ -0,0 +1,500 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+import {runAsyncTask, updateMessage} from './async';
+import {DataPoint, DataSet, DatasetMetadata, DataSource} from './data';
+import {PolymerElement} from './vz-projector-util';
+
+
+/** Prefix added to the http requests when asking the server for data. */
+const DATA_URL = 'data';
+
+type DemoDataset = {
+  fpath: string; metadata_path?: string; metadata?: DatasetMetadata;
+};
+
+type Metadata = {
+  [key: string]: (number|string);
+};
+
+/** List of compiled demo datasets for showing the capabilities of the tool. */
+const DEMO_DATASETS: {[name: string]: DemoDataset} = {
+  'wiki_5k': {
+    fpath: 'wiki_5000_50d_tensors.ssv',
+    metadata_path: 'wiki_5000_50d_labels.ssv'
+  },
+  'wiki_10k': {
+    fpath: 'wiki_10000_100d_tensors.ssv',
+    metadata_path: 'wiki_10000_100d_labels.ssv'
+  },
+  'wiki_40k': {
+    fpath: 'wiki_40000_100d_tensors.ssv',
+    metadata_path: 'wiki_40000_100d_labels.ssv'
+  },
+  'smartreply_5k': {
+    fpath: 'smartreply_5000_256d_tensors.tsv',
+    metadata_path: 'smartreply_5000_256d_labels.tsv'
+  },
+  'smartreply_full': {
+    fpath: 'smartreply_full_256d_tensors.tsv',
+    metadata_path: 'smartreply_full_256d_labels.tsv'
+  },
+  'mnist_10k': {
+    fpath: 'mnist_10k_784d_tensors.tsv',
+    metadata_path: 'mnist_10k_784d_labels.tsv',
+    metadata: {
+      image:
+          {sprite_fpath: 'mnist_10k_sprite.png', single_image_dim: [28, 28]}
+    },
+  },
+  'iris': {fpath: 'iris_tensors.tsv', metadata_path: 'iris_labels.tsv'}
+};
+
+/** Maximum number of colors supported in the color map. */
+const NUM_COLORS_COLOR_MAP = 20;
+
+interface ServerInfo {
+  tensors: {[name: string]: [number, number]};
+  tensors_file: string;
+  checkpoint_file: string;
+  checkpoint_dir: string;
+  metadata_file: string;
+}
+
+let DataLoaderPolymer = PolymerElement({
+  is: 'vz-projector-data-loader',
+  properties: {
+    dataSource: {
+      type: Object,  // DataSource
+      notify: true
+    },
+    selectedDemo: {type: String, value: 'wiki_5k', notify: true},
+    selectedTensor: {type: String, notify: true},
+    labelOption: {type: String, notify: true},
+    colorOption: {type: Object, notify: true},
+    // Private.
+    tensorNames: Array
+  }
+});
+
+export type ColorOption = {
+  name: string; desc?: string; map?: (value: string | number) => string;
+  isSeparator?: boolean;
+};
+
+class DataLoader extends DataLoaderPolymer {
+  dataSource: DataSource;
+  selectedDemo: string;
+  labelOption: string;
+  labelOptions: string[];
+  colorOption: ColorOption;
+  colorOptions: ColorOption[];
+  selectedTensor: string;
+  tensorNames: {name: string, shape: number[]}[];
+
+  private dom: d3.Selection<any>;
+
+  ready() {
+    this.dom = d3.select(this);
+    if (this.dataSource) {
+      // There is data already.
+      return;
+    }
+    // Check to see if there is a server.
+    d3.json(`${DATA_URL}/info`, (err, serverInfo) => {
+      if (err) {
+        // No server was found, thus operate in standalone mode.
+        this.setupStandaloneMode();
+        return;
+      }
+      // Server was found, thus show the checkpoint dir and the tensors.
+      this.setupServerMode(serverInfo);
+    });
+  }
+
+  getSeparatorClass(isSeparator: boolean): string {
+    return isSeparator ? 'separator' : null;
+  }
+
+  private setupServerMode(info: ServerInfo) {
+    // Display the server-mode controls.
+    this.dom.select('.server-controls').style('display', null);
+    this.dom.select('#checkpoint-file')
+        .text(info.checkpoint_file)
+        .attr('title', info.checkpoint_file);
+    this.dom.select('#metadata-file')
+        .text(info.metadata_file)
+        .attr('title', info.metadata_file);
+
+    // Handle the list of checkpoint tensors.
+    this.dom.on('selected-tensor-changed', () => {
+      this.selectedTensorChanged(this.selectedTensor);
+    });
+    let names = Object.keys(info.tensors)
+                    .filter(name => {
+                      let shape = info.tensors[name];
+                      return shape.length == 2 && shape[0] > 1 && shape[1] > 1;
+                    })
+                    .sort((a, b) => info.tensors[b][0] - info.tensors[a][0]);
+    this.tensorNames =
+        names.map(name => { return {name, shape: info.tensors[name]}; });
+  }
+
+  private updateMetadataUI(columnStats: ColumnStats[]) {
+    // Label by options.
+    let labelIndex = -1;
+    this.labelOptions = columnStats.length > 1 ? columnStats.map((stats, i) => {
+      // Make the default label by the first non-numeric column.
+      if (!stats.isNumeric && labelIndex == -1) {
+        labelIndex = i;
+      }
+      return stats.name;
+    }) :
+                                                 ['label'];
+    this.labelOption = this.labelOptions[Math.max(0, labelIndex)];
+
+    // Color by options.
+    let standardColorOption: ColorOption[] = [
+      {name: 'No color map'},
+      // TODO(smilkov): Implement this.
+      //{name: 'Distance of neighbors',
+      //    desc: 'How far is each point from its neighbors'}
+    ];
+    let metadataColorOption: ColorOption[] =
+        columnStats
+            .filter(stats => {
+              return !stats.tooManyUniqueValues || stats.isNumeric;
+            })
+            .map(stats => {
+              let map: (v: string|number) => string;
+              if (!stats.tooManyUniqueValues) {
+                let scale = d3.scale.category20();
+                let range = scale.range();
+                // Re-order the range.
+                let newRange = range.map((color, i) => {
+                  let index = (i * 2) % (range.length - 1);
+                  if (index == 0) {
+                    index = range.length - 1;
+                  }
+                  return range[index];
+                });
+                scale.range(newRange).domain(stats.uniqueValues);
+                map = scale;
+              } else {
+                map = d3.scale.linear<string>()
+                          .domain([stats.min, stats.max])
+                          .range(['white', 'black']);
+              }
+              let desc = stats.tooManyUniqueValues ?
+                  'gradient' :
+                  stats.uniqueValues.length + ' colors';
+              return {name: stats.name, desc: desc, map: map};
+            });
+    if (metadataColorOption.length > 0) {
+      // Add a separator line between built-in color maps
+      // and those based on metadata columns.
+      standardColorOption.push({name: 'Metadata', isSeparator: true});
+    }
+    this.colorOptions = standardColorOption.concat(metadataColorOption);
+    this.colorOption = this.colorOptions[0];
+  }
+
+  private setupStandaloneMode() {
+    // Display the standalone UI controls.
+    this.dom.select('.standalone-controls').style('display', null);
+
+    // Demo dataset dropdown
+    let demoDatasetChanged = (demoDataSet: DemoDataset) => {
+      if (demoDataSet == null) {
+        return;
+      }
+
+      this.dom.selectAll('.file-name').style('display', 'none');
+      let separator = demoDataSet.fpath.substr(-3) == 'tsv' ? '\t' : ' ';
+      fetchDemoData(`${DATA_URL}/${demoDataSet.fpath}`, separator)
+          .then(points => {
+
+            let p1 = demoDataSet.metadata_path ?
+                new Promise<ColumnStats[]>((resolve, reject) => {
+                  updateMessage('Fetching metadata...');
+                  d3.text(
+                      `${DATA_URL}/${demoDataSet.metadata_path}`,
+                      (err: Error, rawMetadata: string) => {
+                        if (err) {
+                          console.error(err);
+                          reject(err);
+                          return;
+                        }
+                        resolve(parseAndMergeMetadata(rawMetadata, points));
+                      });
+                }) :
+                null;
+
+            let p2 = demoDataSet.metadata && demoDataSet.metadata.image ?
+                fetchImage(
+                    `${DATA_URL}/${demoDataSet.metadata.image.sprite_fpath}`) :
+                null;
+
+            Promise.all([p1, p2]).then(values => {
+              this.updateMetadataUI(values[0]);
+              let dataSource = new DataSource();
+              dataSource.originalDataSet = new DataSet(points);
+              dataSource.spriteImage = values[1];
+              dataSource.metadata = demoDataSet.metadata;
+              this.dataSource = dataSource;
+            });
+          });
+    };
+
+    this.dom.on('selected-demo-changed', () => {
+      demoDatasetChanged(DEMO_DATASETS[this.selectedDemo]);
+    });
+    demoDatasetChanged(DEMO_DATASETS[this.selectedDemo]);
+
+    // Show and setup the upload button.
+    let fileInput = this.dom.select('#file');
+    fileInput.on('change', () => {
+      let file: File = (<any>d3.event).target.files[0];
+      this.dom.select('#file-name')
+          .style('display', null)
+          .text(file.name)
+          .attr('title', file.name);
+      // Clear out the value of the file chooser. This ensures that if the user
+      // selects the same file, we'll re-read it.
+      (<any>d3.event).target.value = '';
+      // Clear the value of the datasets dropdown.
+      this.selectedDemo = null;
+      let fileReader = new FileReader();
+      fileReader.onload = evt => {
+        let str: string = (evt.target as any).result;
+        parseTensors(str).then(data => {
+          let dataSource = new DataSource();
+          dataSource.originalDataSet = new DataSet(data);
+          this.dataSource = dataSource;
+        });
+      };
+      fileReader.readAsText(file);
+    });
+
+    let uploadButton = this.dom.select('#upload');
+    uploadButton.on(
+        'click', () => { (<HTMLInputElement>fileInput.node()).click(); });
+
+    // Show and setup the upload metadata button.
+    let fileMetadataInput = this.dom.select('#file-metadata');
+    fileMetadataInput.on('change', () => {
+      let file: File = (<any>d3.event).target.files[0];
+      this.dom.select('#file-metadata-name')
+          .style('display', null)
+          .text(file.name)
+          .attr('title', file.name);
+      // Clear out the value of the file chooser. This ensures that if the user
+      // selects the same file, we'll re-read it.
+      (<any>d3.event).target.value = '';
+      // Clear the value of the datasets dropdown.
+      this.selectedDemo = null;
+      let fileReader = new FileReader();
+      fileReader.onload = evt => {
+        let str: string = (evt.target as any).result;
+        parseAndMergeMetadata(str, this.dataSource.originalDataSet.points)
+            .then(columnStats => {
+              this.updateMetadataUI(columnStats);
+              // Must make a shallow copy, otherwise polymer will not
+              // fire the 'data-changed' event, even if we explicitly
+              // call this.fire().
+              this.dataSource = this.dataSource.makeShallowCopy();
+            });
+      };
+      fileReader.readAsText(file);
+    });
+
+    let uploadMetadataButton = this.dom.select('#upload-metadata');
+    uploadMetadataButton.on('click', () => {
+      (<HTMLInputElement>fileMetadataInput.node()).click();
+    });
+  }
+
+  private selectedTensorChanged(name: string) {
+    // Get the tensor.
+    updateMessage('Fetching tensor values...');
+    d3.text(`${DATA_URL}/tensor?name=${name}`, (err: Error, tsv: string) => {
+      if (err) {
+        console.error(err);
+        return;
+      }
+      parseTensors(tsv).then(dataPoints => {
+        updateMessage('Fetching metadata...');
+        d3.text(`${DATA_URL}/metadata`, (err: Error, rawMetadata: string) => {
+          if (err) {
+            console.error(err);
+            return;
+          }
+          parseAndMergeMetadata(rawMetadata, dataPoints).then(columnStats => {
+            this.updateMetadataUI(columnStats);
+            let dataSource = new DataSource();
+            dataSource.originalDataSet = new DataSet(dataPoints);
+            this.dataSource = dataSource;
+          });
+        });
+      });
+    });
+  }
+
+  private getNumTensorsLabel(tensorNames: string[]) {
+    return tensorNames.length === 1 ? '1 tensor' :
+                                      tensorNames.length + ' tensors';
+  }
+}
+
+function fetchImage(url: string): Promise<HTMLImageElement> {
+  return new Promise<HTMLImageElement>((resolve, reject) => {
+    let image = new Image();
+    image.onload = () => resolve(image);
+    image.onerror = (err) => reject(err);
+    image.src = url;
+  });
+}
+
+/** Makes a network request for a delimited text file. */
+function fetchDemoData(url: string, separator: string): Promise<DataPoint[]> {
+  return new Promise<DataPoint[]>((resolve, reject) => {
+    updateMessage('Fetching tensors...');
+    d3.text(url, (error: Error, dataString: string) => {
+      if (error) {
+        console.error(error);
+        updateMessage('Error loading data.');
+        reject(error);
+      } else {
+        parseTensors(dataString, separator).then(data => resolve(data));
+      }
+    });
+  });
+}
+
+/** Parses a tsv text file. */
+function parseTensors(content: string, delim = '\t'): Promise<DataPoint[]> {
+  let data: DataPoint[] = [];
+  let numDim: number;
+  return runAsyncTask('Parsing tensors...', () => {
+    let lines = content.split('\n');
+    lines.forEach(line => {
+      line = line.trim();
+      if (line == '') {
+        return;
+      }
+      let row = line.split(delim);
+      let dataPoint: DataPoint = {
+        metadata: {},
+        vector: null,
+        dataSourceIndex: data.length,
+        projections: null,
+        projectedPoint: null
+      };
+      // If the first label is not a number, take it as the label.
+      if (isNaN(row[0] as any) || numDim == row.length - 1) {
+        dataPoint.metadata['label'] = row[0];
+        dataPoint.vector = row.slice(1).map(Number);
+      } else {
+        dataPoint.vector = row.map(Number);
+      }
+      data.push(dataPoint);
+      if (numDim == null) {
+        numDim = dataPoint.vector.length;
+      }
+      if (numDim != dataPoint.vector.length) {
+        updateMessage('Parsing failed. Vector dimensions do not match');
+        throw Error('Parsing failed');
+      }
+      if (numDim <= 1) {
+        updateMessage(
+            'Parsing failed. Found a vector with only one dimension?');
+        throw Error('Parsing failed');
+      }
+    });
+    return data;
+  });
+}
+
+/** Statistics for a metadata column. */
+type ColumnStats = {
+  name: string; isNumeric: boolean; tooManyUniqueValues: boolean;
+  uniqueValues?: string[];
+  min: number;
+  max: number;
+};
+
+function parseAndMergeMetadata(
+    content: string, data: DataPoint[]): Promise<ColumnStats[]> {
+  return runAsyncTask('Parsing metadata...', () => {
+    let lines = content.split('\n').filter(line => line.trim().length > 0);
+    let hasHeader = (lines.length - 1 == data.length);
+
+    // Dimension mismatch.
+    if (lines.length != data.length && !hasHeader) {
+      throw Error('Dimensions do not match');
+    }
+
+    // If the first row doesn't contain metadata keys, we assume that the values
+    // are labels.
+    let columnNames: string[] = ['label'];
+    if (hasHeader) {
+      columnNames = lines[0].split('\t');
+      lines = lines.slice(1);
+    }
+    let columnStats: ColumnStats[] = columnNames.map(name => {
+      return {
+        name: name,
+        isNumeric: true,
+        tooManyUniqueValues: false,
+        min: Number.POSITIVE_INFINITY,
+        max: Number.NEGATIVE_INFINITY
+      };
+    });
+    let setOfValues = columnNames.map(() => d3.set());
+    lines.forEach((line: string, i: number) => {
+      let rowValues = line.split('\t');
+      data[i].metadata = {};
+      columnNames.forEach((name: string, colIndex: number) => {
+        let value = rowValues[colIndex];
+        let set = setOfValues[colIndex];
+        let stats = columnStats[colIndex];
+        data[i].metadata[name] = value;
+
+        // Update stats.
+        if (!stats.tooManyUniqueValues) {
+          set.add(value);
+          if (set.size() > NUM_COLORS_COLOR_MAP) {
+            stats.tooManyUniqueValues = true;
+          }
+        }
+        if (isNaN(value as any)) {
+          stats.isNumeric = false;
+        } else {
+          stats.min = Math.min(stats.min, +value);
+          stats.max = Math.max(stats.max, +value);
+        }
+      });
+    });
+    columnStats.forEach((stats, colIndex) => {
+      let set = setOfValues[colIndex];
+      if (!stats.tooManyUniqueValues) {
+        stats.uniqueValues = set.values();
+      }
+    });
+    return columnStats;
+  });
+}
+
+document.registerElement(DataLoader.prototype.is, DataLoader);
diff --git a/tensorflow/tensorboard/components/vz-projector/vz-projector-util.ts b/tensorflow/tensorboard/components/vz-projector/vz-projector-util.ts
new file mode 100644
index 0000000000..cca1ea3988
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/vz-projector-util.ts
@@ -0,0 +1,33 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+export type Spec = {
+  is: string; properties: {
+    [key: string]:
+        (Function |
+         {
+           type: Function, value?: any;
+           readonly?: boolean;
+           notify?: boolean;
+           observer?: string;
+         })
+  };
+};
+
+export function PolymerElement(spec: Spec) {
+  return Polymer.Class(spec as any) as{new (): PolymerHTMLElement};
+}
+
+export interface PolymerHTMLElement extends HTMLElement, polymer.Base {}
diff --git a/tensorflow/tensorboard/components/vz-projector/vz-projector.html b/tensorflow/tensorboard/components/vz-projector/vz-projector.html
new file mode 100644
index 0000000000..25a90bb3d8
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/vz-projector.html
@@ -0,0 +1,628 @@
+<!--
+@license
+Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+<link rel="import" href="../polymer/polymer.html">
+<link rel="import" href="../paper-toggle-button/paper-toggle-button.html">
+<link rel="import" href="../paper-listbox/paper-listbox.html">
+<link rel="import" href="../paper-item/paper-item.html">
+<link rel="import" href="../paper-checkbox/paper-checkbox.html">
+<link rel="import" href="vz-projector-data-loader.html">
+
+<dom-module id='vz-projector'>
+<template>
+<style>
+
+:host {
+  display: flex;
+  width: 100%;
+  height: 100%;
+}
+
+#container {
+  display: flex;
+  width: 100%;
+  height: 100%;
+}
+
+.hidden {
+  display: none !important;
+}
+
+/* Main */
+
+#main {
+  position: relative;
+  flex-grow: 2;
+}
+
+#main .stage {
+  position: relative;
+  flex-grow: 2;
+}
+
+#scatter {
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+}
+
+#left-pane {
+  display: flex;
+  flex-direction: column;
+  min-width: 312px;
+  width: 312px;
+  border-right: 1px solid rgba(0, 0, 0, 0.1);
+  background: #fafafa;
+}
+
+#right-pane {
+  min-width: 300px;
+  width: 300px;
+  border-left: 1px solid rgba(0, 0, 0, 0.1);
+  background: #fafafa;
+}
+
+.file-name {
+  margin-right: 5px;
+}
+
+.control label {
+  font-size: 12px;
+  color: rgba(0, 0, 0, 0.7);
+  margin-top: 10px;
+  font-weight: 500;
+}
+
+.control .info {
+  display: block;
+  font-size: 12px;
+  color: rgba(0, 0, 0, 0.2);
+  margin-bottom: 18px;
+  white-space: nowrap;
+}
+
+.control input[type=text] {
+  font-weight: 300;
+  font-size: 16px;
+  display: block;
+  padding: 8px 0;
+  margin: 0 0 8px 0;
+  width: 100%;
+  box-sizing: border-box;
+  border: none;
+  border-bottom: 1px solid rgba(0, 0, 0, 0.2);
+  background: none;
+}
+
+.slider {
+  display: flex;
+  align-items: center;
+  margin-bottom: 10px;
+  justify-content: space-between;
+}
+
+.slider span {
+  width: 35px;
+  text-align: right;
+}
+
+.control input[type=text]:focus {
+  outline: none;
+  border-bottom: 1px solid rgba(0, 0, 0, 1);
+}
+
+.control {
+  display: inline-block;
+  width: 45%;
+  vertical-align: top;
+  margin-right: 10px;
+  overflow-x: hidden;
+}
+
+.control.last {
+  margin-right: 0;
+}
+
+#wrapper-notify-msg {
+  z-index: 1;
+  position: fixed;
+  top: 10px;
+  width: 100%;
+  display: flex;
+  justify-content: center;
+}
+
+#notify-msg {
+  display: none;
+  font-weight: 500;
+  color: black;
+  background-color: #FFF9C4;
+  padding: 5px;
+  border: 1px solid #FBC02D;
+}
+
+.brush .extent {
+  stroke: #fff;
+  fill-opacity: .125;
+  shape-rendering: crispEdges;
+}
+
+.ink-panel-content {
+  display: none;
+}
+
+.ink-panel-content.active {
+  display: block;
+}
+
+.nn-list .neighbor {
+  font-size: 12px;
+  margin-bottom: 6px;
+}
+
+.nn-list .neighbor .value {
+  float: right;
+  color: #666;
+  font-weight: 300;
+}
+
+.nn-list .neighbor .bar {
+  position: relative;
+  border-top: 1px solid rgba(0, 0, 0, 0.15);
+  margin: 2px 0;
+}
+
+.nn-list .neighbor .bar .fill {
+  position: absolute;
+  top: -1px;
+  border-top: 1px solid white;
+}
+
+.nn-list .neighbor .tick {
+  position: absolute;
+  top: 0px;
+  height: 3px;
+  border-left: 1px solid rgba(0, 0, 0, 0.15);
+}
+
+.nn-list .neighbor-link:hover {
+  cursor: pointer;
+}
+
+.origin text {
+  font-size: 12px;
+  font-weight: 500;
+}
+
+.origin line {
+  stroke: black;
+  stroke-opacity: 0.2;
+}
+
+/* Ink Framework */
+
+/* - Buttons */
+.ink-button, ::shadow .ink-button {
+  border: none;
+  border-radius: 2px;
+  font-size: 13px;
+  padding: 10px;
+  min-width: 100px;
+  flex-shrink: 0;
+  background: #e3e3e3;
+}
+
+/* - Tabs */
+
+.ink-tab-group {
+  display: flex;
+  justify-content: space-around;
+  box-sizing: border-box;
+  height: 100%;
+  margin: 0 12px;
+}
+
+.ink-tab-group .ink-tab {
+  font-weight: 300;
+  color: rgba(0, 0, 0, 0.5);
+  text-align: center;
+  text-transform: uppercase;
+  line-height: 60px;
+  cursor: pointer;
+  padding: 0 12px;
+}
+
+.ink-tab-group .ink-tab:hover {
+  color: black;
+}
+
+.ink-tab-group .ink-tab.active {
+  font-weight: 500;
+  color: black;
+  border-bottom: 2px solid black;
+
+}
+
+/* - Panel */
+
+.ink-panel {
+  display: flex;
+  flex-direction: column;
+  font-size: 14px;
+  line-height: 1.45em;
+}
+
+.ink-panel h4 {
+  font-size: 14px;
+  font-weight: 500;
+  margin: 0;
+  border-bottom: 1px solid #ddd;
+  padding-bottom: 5px;
+  margin-bottom: 10px;
+}
+
+.ink-panel-header {
+  height: 60px;
+  border-bottom: 1px solid rgba(0, 0, 0, 0.1);
+}
+
+.ink-panel-metadata-container span {
+  font-size: 16px;
+}
+
+.ink-panel-metadata {
+  border-bottom: 1px solid #ccc;
+  display: table;
+  padding: 10px 0;
+  width: 100%;
+}
+
+.ink-panel-metadata-row {
+  display: table-row;
+}
+
+.ink-panel-metadata-key {
+  font-weight: bold;
+}
+
+.ink-panel-metadata-key,
+.ink-panel-metadata-value {
+  display: table-cell;
+  padding-right: 10px;
+}
+
+.ink-panel-buttons {
+  margin-bottom: 10px;
+}
+
+.ink-panel-content {
+  padding: 24px;
+  overflow-y: auto;
+}
+
+.ink-panel-content .distance a {
+  text-decoration: none;
+  color: black;
+}
+
+.ink-panel-content .distance a.selected {
+  color: black;
+  border-bottom: 2px solid black;
+}
+
+.ink-panel-footer {
+  display: flex;
+  align-items: center;
+  border-top: solid 1px #eee;
+  height: 60px;
+  padding: 0 24px;
+  color: rgba(0, 0, 0, 0.5);
+}
+
+.ink-panel-content h3 {
+  font-weight: 500;
+  font-size: 14px;
+  text-transform: uppercase;
+  margin-top: 20px;
+  margin-bottom: 5px;
+}
+
+.ink-panel-header h3 {
+  margin: 0;
+  font-weight: 500;
+  font-size: 14px;
+  line-height: 60px;
+  text-transform: uppercase;
+  padding: 0 24px;
+}
+
+/* - Menubar */
+
+.ink-panel-menubar {
+  position: relative;
+  height: 60px;
+  border-bottom: solid 1px #eee;
+  padding: 0 24px;
+}
+
+.ink-panel-menubar .material-icons {
+  color: black;
+}
+
+.ink-panel-menubar .menu-button {
+  margin-right: 12px;
+  cursor: pointer;
+  line-height: 60px;
+  border: none;
+  background: none;
+  font-size: 13px;
+  font-weight: 200;
+  padding: 0;
+  margin: 0 20px 0 0;
+  outline: none;
+  color: #666;
+}
+
+.ink-panel-menubar button .material-icons {
+  position: relative;
+  top: 7px;
+  margin-right: 8px;
+  color: #999;
+}
+
+.ink-panel-menubar button.selected,
+.ink-panel-menubar button.selected .material-icons {
+  color: #880E4F;
+}
+
+.ink-panel-menubar .ink-fabs {
+  position: absolute;
+  right: 24px;
+  top: 60px;
+  z-index: 1;
+}
+
+.ink-panel-menubar .ink-fabs .ink-fab {
+  position: relative;
+  top: -20px;
+  width: 40px;
+  height: 40px;
+  border: 1px solid rgba(0, 0, 0, 0.02);
+  border-radius: 50%;
+  display: inline-block;
+  background: white;
+  margin-left: 8px;
+  box-shadow: 0 1px 3px rgba(0, 0, 0, 0.3);
+  cursor: pointer;
+}
+
+.ink-panel-menubar .ink-fabs .ink-fab .material-icons {
+  margin: 0;
+  display: block;
+  line-height: 24px;
+  position: absolute;
+  top: calc(50% - 12px);
+  left: calc(50% - 12px);
+}
+
+.ink-panel-menubar .search-box {
+  transition: width .2s;
+  margin-left: -65px;
+  width: 0;
+  margin-right: 65px;
+  background: white;
+}
+
+.two-way-toggle {
+  display: flex;
+  flex-direction: row;
+}
+
+.two-way-toggle span {
+  padding-right: 7px;
+}
+
+paper-listbox .pca-item {
+  cursor: pointer;
+  min-height: 17px;
+  font-size: 12px;
+  line-height: 17px;
+}
+
+.has-border {
+  border: 1px solid rgba(0, 0, 0, 0.1);
+}
+
+</style>
+<link href="https://fonts.googleapis.com/css?family=Roboto:300,400,500|Material+Icons" rel="stylesheet" type="text/css">
+<div id="wrapper-notify-msg">
+  <div id="notify-msg">Loading...</div>
+</div>
+<div id="container">
+  <div id="left-pane" class="ink-panel">
+    <div class="ink-panel-header">
+      <div class="ink-tab-group">
+        <div data-tab="tsne" class="ink-tab" title="t-distributed stochastic neighbor embedding">t-SNE</div>
+        <div data-tab="pca" class="ink-tab" title="Principal component analysis">PCA</div>
+        <div data-tab="custom" class="ink-tab" title="Linear projection of two custom vectors">Custom</div>
+      </div>
+    </div>
+    <!-- TSNE Controls -->
+    <div data-panel="tsne" class="ink-panel-content">
+      <p><a href="https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding">t-distributed stochastic neighbor embedding</a> is a dimensionality reduction technique</p>
+      <p style="color: #880E4F; font-weight: bold;">For fast results, your data will be sampled down to 10,000 points.</p>
+      <div class="slider"><label>Dimension</label><div class="two-way-toggle"><span>2D</span><paper-toggle-button id="tsne-toggle" noink checked>3D</paper-toggle-button></div></div>
+      <div class="slider tsne-perplexity">
+        <label>Perplexity</label>
+        <input type="range" min="2" max="100"></input>
+        <span></span>
+      </div>
+      <div class="slider tsne-learning-rate">
+        <label>Learning rate</label>
+        <input type="range" min="-3" max="2"></input>
+        <span></span>
+      </div>
+      <p>The most appropriate perplexity value depends on the density of your data. Loosely speaking, one could say that a larger / denser dataset requires a larger perplexity. Typical values for the perplexity range between 5 and 50.</p>
+      <p>The most appropriate learning rate depends on the size of your data, with smaller datasets requiring smaller learning rates.</p>
+      <p>
+        <button class="run-tsne ink-button">Run</button>
+        <button class="stop-tsne ink-button">Stop</button>
+      </p>
+      <p>Iteration: <span class="run-tsne-iter">0</span></p>
+    </div>
+    <!-- PCA Controls -->
+    <div data-panel="pca" class="ink-panel-content">
+      <p><a href="https://en.wikipedia.org/wiki/Principal_component_analysis">Principal component analysis</a> is a dimensionality reduction technique</p>
+      <label>X</label>
+      <paper-listbox class="has-border" selected="{{pcaX}}">
+        <template is="dom-repeat" items="[[pcaComponents]]">
+          <paper-item class="pca-item">Component #[[item]]</paper-item>
+        </template>
+      </paper-listbox>
+      <br/>
+      <label>Y</label>
+      <paper-listbox class="has-border" selected="{{pcaY}}">
+        <template is="dom-repeat" items="[[pcaComponents]]">
+          <paper-item class="pca-item">Component #[[item]]</paper-item>
+        </template>
+      </paper-listbox>
+      <br/>
+      <paper-checkbox noink id="z-checkbox" checked="{{hasPcaZ}}">Z</paper-checkbox>
+      <paper-listbox class="has-border" disabled="true" selected="{{pcaZ}}">
+        <template is="dom-repeat" items="[[pcaComponents]]">
+          <paper-item disabled="[[!hasPcaZ]]" class="pca-item">Component #[[item]]</paper-item>
+        </template>
+      </paper-listbox>
+    </div>
+    <!-- Custom Controls -->
+    <div data-panel="custom" class="ink-panel-content">
+      <p>Search for two vectors upon which to project all points. Use <code>/regex/</code> to signal a regular expression, otherwise does an exact match.<p>
+      <h3>Horizontal</h3>
+      <div class="control xLeft">
+        <label>Left</label>
+        <input type="text" value="/\./"></input>
+        <span class="info"></span>
+      </div>
+      <div class="control xRight last">
+        <label>Right</label>
+        <input type="text" value="/!/"></input>
+        <span class="info"> </span>
+      </div>
+      <h3>Vertical</h3>
+      <div class="control yUp">
+        <label>Up</label>
+        <input type="text" value="/./"></input>
+        <span class="info"> </span>
+      </div>
+      <div class="control yDown last">
+        <label>Down</label>
+        <input type="text" value="/\?/"></input>
+        <span class="info"> </span>
+      </div>
+    </div>
+  </div>
+  <div id="main" class="ink-panel">
+    <div class="ink-panel-menubar">
+      <button class="menu-button search" title="Search">
+        <i class="material-icons">search</i>
+        <span class="button-label">Search</span>
+      </button>
+      <div class="control search-box">
+          <input type="text" value="">
+          <span class="info"></span>
+      </div>
+      <button class="menu-button selectMode" title="Bounding box selection">
+        <i class="material-icons">photo_size_select_small</i>
+        Select
+      </button>
+      <button class="menu-button show-labels selected" title="Show/hide labels">
+        <i class="material-icons">text_fields</i>
+        Labels
+      </button>
+      <button class="menu-button nightDayMode" title="Toggle between night and day mode">
+        <i class="material-icons">brightness_2</i>
+        Night Mode
+      </button>
+      <div class="ink-fabs">
+        <div class="ink-fab reset-zoom" title="Zoom to fit all">
+          <i class="material-icons resetZoom">home</i>
+        </div>
+        <div class="ink-fab zoom-in" title="Zoom in">
+          <i class="material-icons">add</i>
+        </div>
+        <div class="ink-fab zoom-out" title="Zoom out">
+          <i class="material-icons">remove</i>
+        </div>
+      </div>
+    </div>
+    <div class="stage">
+      <div id="scatter"></div>
+    </div>
+    <div id="info-panel" class="ink-panel-footer">
+      <div>
+        Number of data points: <span class="numDataPoints"></span>, dimension of embedding: <span class="dim"></span>
+        | <span id="hoverInfo"></span>
+      </div>
+    </div>
+  </div>
+  <div id="right-pane" class="ink-panel">
+    <div class="ink-panel-header">
+      <div class="ink-tab-group">
+        <div data-tab="data" class="active ink-tab" title="Setup data">Data</div>
+        <div data-tab="inspector" class="ink-tab" title="Inspect data">Inspector</div>
+      </div>
+    </div>
+
+    <!-- Inspector UI controls -->
+    <div data-panel="inspector" class="ink-panel-content">
+      <div class="ink-panel-metadata-container" style="display: none">
+        <span>Metadata</span>
+        <div class="ink-panel-metadata"></div>
+      </div>
+      <div class="ink-panel-buttons">
+        <div style="margin-bottom: 10px">
+          <button style="display: none;" class="ink-button reset-filter">Show All Data</button>
+        </div>
+        <button style="display: none;" class="ink-button set-filter">Isolate selection</button>
+        <button class="ink-button clear-selection" style="display: none;">Clear selection</button>
+      </div>
+      <div class="slider num-nn">
+        <label>Number of neighbors</label>
+        <input type="range" min="5" max="1000"></input>
+        <span></span>
+      </div>
+      <div class="distance">
+        Distance:
+        <div style="float:right">
+          <a class="selected cosine" href="javascript:void(0);">cosine</a> |
+          <a class="euclidean" href="javascript:void(0);">euclidean</a>
+        </div>
+      </div>
+      <p>Nearest points to <b id="nn-title"></b></p>
+      <div class="nn-list"></div>
+    </div>
+
+    <!-- Data UI controls -->
+    <div data-panel="data" class="active ink-panel-content">
+      <vz-projector-data-loader data-source="{{dataSource}}" label-option="{{labelOption}}" color-option="{{colorOption}}"></vz-projector-data-loader>
+    </div>
+  </div>
+</div>
+</template>
+</dom-module>
diff --git a/tensorflow/tensorboard/components/vz-projector/vz-projector.ts b/tensorflow/tensorboard/components/vz-projector/vz-projector.ts
new file mode 100644
index 0000000000..e8b1a882ec
--- /dev/null
+++ b/tensorflow/tensorboard/components/vz-projector/vz-projector.ts
@@ -0,0 +1,762 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+import {DataPoint, DataSet, DataSource} from './data';
+import * as knn from './knn';
+import {Mode, Scatter} from './scatter';
+import {ScatterWebGL} from './scatterWebGL';
+import * as vector from './vector';
+import {ColorOption} from './vz-projector-data-loader';
+import {PolymerElement} from './vz-projector-util';
+
+
+/** T-SNE perplexity. Roughly how many neighbors each point influences. */
+let perplexity: number = 30;
+/** T-SNE learning rate. */
+let learningRate: number = 10;
+/** Number of dimensions for the scatter plot. */
+let dimension = 3;
+/** Number of nearest neighbors to highlight around the selected point. */
+let numNN = 100;
+
+/** Highlight stroke color for the nearest neighbors. */
+const NN_HIGHLIGHT_COLOR = '#6666FA';
+
+/** Highlight stroke color for the selected point */
+const POINT_HIGHLIGHT_COLOR_DAY = 'black';
+const POINT_HIGHLIGHT_COLOR_NIGHT = new THREE.Color(0xFFE11F).getStyle();
+
+/** Color scale for nearest neighbors. */
+const NN_COLOR_SCALE =
+    d3.scale.linear<string>()
+        .domain([1, 0.7, 0.4])
+        .range(['hsl(285, 80%, 40%)', 'hsl(0, 80%, 65%)', 'hsl(40, 70%, 60%)'])
+        .clamp(true);
+
+/** Text color used for error/important messages. */
+const CALLOUT_COLOR = '#880E4F';
+
+type Centroids = {
+  [key: string]: number[]; xLeft: number[]; xRight: number[]; yUp: number[];
+  yDown: number[];
+};
+
+let ProjectorPolymer = PolymerElement({
+  is: 'vz-projector',
+  properties: {
+    // A data source.
+    dataSource: {
+      type: Object,  // DataSource
+      observer: 'dataSourceChanged'
+    },
+
+    // Private.
+    pcaComponents: {type: Array, value: d3.range(1, 11)},
+    pcaX: {
+      type: Number,
+      value: 0,
+      notify: true,
+    },
+    pcaY: {
+      type: Number,
+      value: 1,
+      notify: true,
+    },
+    pcaZ: {
+      type: Number,
+      value: 2,
+      notify: true,
+    },
+    hasPcaZ: {type: Boolean, value: true, notify: true},
+    labelOption: {type: String, observer: 'labelOptionChanged'},
+    colorOption: {type: Object, observer: 'colorOptionChanged'},
+  }
+});
+
+class Projector extends ProjectorPolymer {
+  // Public API.
+  dataSource: DataSource;
+
+  private dom: d3.Selection<any>;
+  private pcaX: number;
+  private pcaY: number;
+  private pcaZ: number;
+  private hasPcaZ: boolean;
+  // The working subset of the data source's original data set.
+  private currentDataSet: DataSet;
+  private scatter: Scatter;
+  private dim: number;
+  private selectedDistance: (a: number[], b: number[]) => number;
+  private highlightedPoints: {index: number, color: string}[];
+  private selectedPoints: number[];
+  private centroidValues: any;
+  private centroids: Centroids;
+  /** The centroid across all points. */
+  private allCentroid: number[];
+  private labelOption: string;
+  private colorOption: ColorOption;
+
+  ready() {
+    this.hasPcaZ = true;
+    this.selectedDistance = vector.cosDistNorm;
+    this.highlightedPoints = [];
+    this.selectedPoints = [];
+    this.centroidValues = {xLeft: null, xRight: null, yUp: null, yDown: null};
+    this.centroids = {xLeft: null, xRight: null, yUp: null, yDown: null};
+    // Dynamically creating elements inside .nn-list.
+    this.scopeSubtree(this.$$('.nn-list'), true);
+    this.dom = d3.select(this);
+    // Sets up all the UI.
+    this.setupUIControls();
+    if (this.dataSource) {
+      this.dataSourceChanged();
+    }
+  }
+
+  labelOptionChanged() {
+    let labelAccessor = (i: number): string => {
+      return this.points[i].metadata[this.labelOption] as string;
+    };
+    this.scatter.setLabelAccessor(labelAccessor);
+  }
+
+  colorOptionChanged() {
+    let colorMap = this.colorOption.map;
+    if (colorMap == null) {
+      this.scatter.setColorAccessor(null);
+      return;
+    };
+    let colors = (i: number) => {
+      return colorMap(this.points[i].metadata[this.colorOption.name]);
+    };
+    this.scatter.setColorAccessor(colors);
+  }
+
+  dataSourceChanged() {
+    if (this.scatter == null || this.dataSource == null) {
+      // We are not ready yet.
+      return;
+    }
+    this.initFromSource(this.dataSource);
+    // Set the container to a fixed height, otherwise in Colab the
+    // height can grow indefinitely.
+    let container = this.dom.select('#container');
+    container.style('height', container.property('clientHeight') + 'px');
+  }
+
+  /**
+   * Normalizes the distance so it can be visually encoded with color.
+   * The normalization depends on the distance metric (cosine vs euclidean).
+   */
+  private normalizeDist(d: number, minDist: number): number {
+    return this.selectedDistance === vector.cosDistNorm ? 1 - d : minDist / d;
+  }
+
+  /** Normalizes and encodes the provided distance with color. */
+  private dist2color(d: number, minDist: number): string {
+    return NN_COLOR_SCALE(this.normalizeDist(d, minDist));
+  }
+
+  private initFromSource(source: DataSource) {
+    this.dataSource = source;
+    this.setDataSet(this.dataSource.getDataSet());
+    this.dom.select('.reset-filter').style('display', 'none');
+    // Regexp inputs.
+    this.setupInput('xLeft');
+    this.setupInput('xRight');
+    this.setupInput('yUp');
+    this.setupInput('yDown');
+  }
+
+  private setDataSet(ds: DataSet) {
+    this.currentDataSet = ds;
+    this.scatter.setDataSet(this.currentDataSet, this.dataSource.spriteImage);
+    this.updateMenuButtons();
+    this.dim = this.currentDataSet.dim[1];
+    this.dom.select('span.numDataPoints').text(this.currentDataSet.dim[0]);
+    this.dom.select('span.dim').text(this.currentDataSet.dim[1]);
+    this.showTab('pca');
+  }
+
+  private setupInput(name: string) {
+    let control = this.dom.select('.control.' + name);
+    let info = control.select('.info');
+
+    let updateInput = (value: string) => {
+      if (value.trim() === '') {
+        info.style('color', CALLOUT_COLOR).text('Enter a regex.');
+        return;
+      }
+      let result = this.getCentroid(value);
+      if (result.error) {
+        info.style('color', CALLOUT_COLOR)
+            .text('Invalid regex. Using a random vector.');
+        result.centroid = vector.rn(this.dim);
+      } else if (result.numMatches === 0) {
+        info.style('color', CALLOUT_COLOR)
+            .text('0 matches. Using a random vector.');
+        result.centroid = vector.rn(this.dim);
+      } else {
+        info.style('color', null).text(`${result.numMatches} matches.`);
+      }
+      this.centroids[name] = result.centroid;
+      this.centroidValues[name] = value;
+    };
+    let self = this;
+
+    let input = control.select('input').on('input', function() {
+      updateInput(this.value);
+      self.showCustom();
+    });
+    this.allCentroid = null;
+    // Init the control with the current input.
+    updateInput((input.node() as HTMLInputElement).value);
+  }
+
+  private setupUIControls() {
+    let self = this;
+    // Global tabs
+    d3.selectAll('.ink-tab').on('click', function() {
+      let id = this.getAttribute('data-tab');
+      self.showTab(id);
+    });
+
+    // Unknown why, but the polymer toggle button stops working
+    // as soon as you do d3.select() on it.
+    let tsneToggle = this.querySelector('#tsne-toggle') as HTMLInputElement;
+    let zCheckbox = this.querySelector('#z-checkbox') as HTMLInputElement;
+
+    // PCA controls.
+    zCheckbox.addEventListener('change', () => {
+      // Make sure tsne stays in the same dimension as PCA.
+      dimension = this.hasPcaZ ? 3 : 2;
+      tsneToggle.checked = this.hasPcaZ;
+      this.showPCA(() => { this.scatter.recreateScene(); });
+    });
+    this.dom.on('pca-x-changed', () => this.showPCA());
+    this.dom.on('pca-y-changed', () => this.showPCA());
+    this.dom.on('pca-z-changed', () => this.showPCA());
+
+    // TSNE controls.
+
+    tsneToggle.addEventListener('change', () => {
+      // Make sure PCA stays in the same dimension as tsne.
+      this.hasPcaZ = tsneToggle.checked;
+      dimension = tsneToggle.checked ? 3 : 2;
+      if (this.scatter) {
+        this.showTSNE();
+        this.scatter.recreateScene();
+      }
+    });
+
+    this.dom.select('.run-tsne').on('click', () => this.runTSNE());
+    this.dom.select('.stop-tsne').on('click', () => {
+      this.currentDataSet.stopTSNE();
+    });
+
+    let updatePerplexity = () => {
+      perplexity = +perplexityInput.property('value');
+      this.dom.select('.tsne-perplexity span').text(perplexity);
+    };
+    let perplexityInput = this.dom.select('.tsne-perplexity input')
+                              .property('value', perplexity)
+                              .on('input', updatePerplexity);
+    updatePerplexity();
+
+    let updateLearningRate = () => {
+      let val = +learningRateInput.property('value');
+      learningRate = Math.pow(10, val);
+      this.dom.select('.tsne-learning-rate span').text(learningRate);
+    };
+    let learningRateInput = this.dom.select('.tsne-learning-rate input')
+                                .property('value', 1)
+                                .on('input', updateLearningRate);
+    updateLearningRate();
+
+    // Nearest neighbors controls.
+    let updateNumNN = () => {
+      numNN = +numNNInput.property('value');
+      this.dom.select('.num-nn span').text(numNN);
+    };
+    let numNNInput = this.dom.select('.num-nn input')
+                         .property('value', numNN)
+                         .on('input', updateNumNN);
+    updateNumNN();
+
+    // View controls
+    this.dom.select('.reset-zoom').on('click', () => {
+      this.scatter.resetZoom();
+    });
+    this.dom.select('.zoom-in').on('click', () => {
+      this.scatter.zoomStep(2);
+    });
+    this.dom.select('.zoom-out').on('click', () => {
+      this.scatter.zoomStep(0.5);
+    });
+
+    // Toolbar controls
+    let searchBox = this.dom.select('.control.search-box');
+    let searchBoxInfo = searchBox.select('.info');
+
+    let searchByRegEx =
+        (pattern: string): {error?: Error, indices: number[]} => {
+          let regEx: RegExp;
+          try {
+            regEx = new RegExp(pattern, 'i');
+          } catch (e) {
+            return {error: e.message, indices: null};
+          }
+          let indices: number[] = [];
+          for (let id = 0; id < this.points.length; ++id) {
+            if (regEx.test('' + this.points[id].metadata['label'])) {
+              indices.push(id);
+            }
+          }
+          return {indices: indices};
+        };
+
+    // Called whenever the search text input changes.
+    let searchInputChanged = (value: string) => {
+      if (value.trim() === '') {
+        searchBoxInfo.style('color', CALLOUT_COLOR).text('Enter a regex.');
+        if (this.scatter != null) {
+          this.selectedPoints = [];
+          this.selectionWasUpdated();
+        }
+        return;
+      }
+      let result = searchByRegEx(value);
+      let indices = result.indices;
+      if (result.error) {
+        searchBoxInfo.style('color', CALLOUT_COLOR).text('Invalid regex.');
+      }
+      if (indices) {
+        if (indices.length === 0) {
+          searchBoxInfo.style('color', CALLOUT_COLOR).text(`0 matches.`);
+        } else {
+          searchBoxInfo.style('color', null).text(`${indices.length} matches.`);
+          this.showTab('inspector');
+          let neighbors = this.findNeighbors(indices[0]);
+          if (indices.length === 1) {
+            this.scatter.clickOnPoint(indices[0]);
+          }
+          this.selectedPoints = indices;
+          this.updateNNList(neighbors);
+        }
+        this.selectionWasUpdated();
+      }
+    };
+
+    searchBox.select('input').on(
+        'input', function() { searchInputChanged(this.value); });
+    let searchButton = this.dom.select('.search');
+
+    searchButton.on('click', () => {
+      let mode = this.scatter.getMode();
+      this.scatter.setMode(mode === Mode.SEARCH ? Mode.HOVER : Mode.SEARCH);
+      if (this.scatter.getMode() == Mode.HOVER) {
+        this.selectedPoints = [];
+        this.selectionWasUpdated();
+      } else {
+        searchInputChanged(searchBox.select('input').property('value'));
+      }
+      this.updateMenuButtons();
+    });
+    // Init the control with an empty input.
+    searchInputChanged('');
+
+    this.dom.select('.distance a.euclidean').on('click', function() {
+      d3.selectAll('.distance a').classed('selected', false);
+      d3.select(this).classed('selected', true);
+      self.selectedDistance = vector.dist;
+      if (self.selectedPoints.length > 0) {
+        let neighbors = self.findNeighbors(self.selectedPoints[0]);
+        self.updateNNList(neighbors);
+      }
+    });
+
+    this.dom.select('.distance a.cosine').on('click', function() {
+      d3.selectAll('.distance a').classed('selected', false);
+      d3.select(this).classed('selected', true);
+      self.selectedDistance = vector.cosDistNorm;
+      if (self.selectedPoints.length > 0) {
+        let neighbors = self.findNeighbors(self.selectedPoints[0]);
+        self.updateNNList(neighbors);
+      }
+    });
+
+    let selectModeButton = this.dom.select('.selectMode');
+
+    selectModeButton.on('click', () => {
+      let mode = this.scatter.getMode();
+      this.scatter.setMode(mode === Mode.SELECT ? Mode.HOVER : Mode.SELECT);
+      this.updateMenuButtons();
+    });
+
+    let showLabels = true;
+    let showLabelsButton = this.dom.select('.show-labels');
+    showLabelsButton.on('click', () => {
+      showLabels = !showLabels;
+      this.scatter.showLabels(showLabels);
+      showLabelsButton.classed('selected', showLabels);
+    });
+
+    let dayNightModeButton = this.dom.select('.nightDayMode');
+    let modeIsNight = dayNightModeButton.classed('selected');
+    dayNightModeButton.on('click', () => {
+      modeIsNight = !modeIsNight;
+      this.scatter.setDayNightMode(modeIsNight);
+      this.scatter.update();
+      dayNightModeButton.classed('selected', modeIsNight);
+    });
+
+    // Resize
+    window.addEventListener('resize', () => { this.scatter.resize(); });
+
+    // Canvas
+    this.scatter = new ScatterWebGL(
+        this.dom.select('#scatter'),
+        i => '' + this.points[i].metadata['label']);
+    this.scatter.onHover(hoveredIndex => {
+      if (hoveredIndex == null) {
+        this.highlightedPoints = [];
+      } else {
+        let point = this.points[hoveredIndex];
+        this.dom.select('#hoverInfo').text(point.metadata['label']);
+        let neighbors = this.findNeighbors(hoveredIndex);
+        let minDist = neighbors[0].dist;
+        let pointIndices = [hoveredIndex].concat(neighbors.map(d => d.index));
+        let pointHighlightColor = modeIsNight ? POINT_HIGHLIGHT_COLOR_NIGHT :
+                                                POINT_HIGHLIGHT_COLOR_DAY;
+        this.highlightedPoints = pointIndices.map((index, i) => {
+          let color = i == 0 ? pointHighlightColor :
+                               this.dist2color(neighbors[i - 1].dist, minDist);
+          return {index: index, color: color};
+        });
+      }
+      this.selectionWasUpdated();
+    });
+
+    this.scatter.onSelection(
+        selectedPoints => this.updateSelection(selectedPoints));
+
+    // Selection controls
+    this.dom.select('.set-filter').on('click', () => {
+      let highlighted = this.selectedPoints;
+      let highlightedOrig: number[] =
+          highlighted.map(d => { return this.points[d].dataSourceIndex; });
+      let subset = this.dataSource.getDataSet(highlightedOrig);
+      this.setDataSet(subset);
+      this.dom.select('.reset-filter').style('display', null);
+      this.selectedPoints = [];
+      this.scatter.recreateScene();
+      this.selectionWasUpdated();
+      this.updateIsolateButton();
+    });
+
+    this.dom.select('.reset-filter').on('click', () => {
+      let subset = this.dataSource.getDataSet();
+      this.setDataSet(subset);
+      this.dom.select('.reset-filter').style('display', 'none');
+    });
+
+    this.dom.select('.clear-selection').on('click', () => {
+      this.selectedPoints = [];
+      this.scatter.setMode(Mode.HOVER);
+      this.scatter.clickOnPoint(null);
+      this.updateMenuButtons();
+      this.selectionWasUpdated();
+    });
+  }
+
+  private updateSelection(selectedPoints: number[]) {
+    // If no points are selected, unselect everything.
+    if (!selectedPoints.length) {
+      this.selectedPoints = [];
+      this.updateNNList([]);
+    }
+    // If only one point is selected, we want to get its nearest neighbors
+    // and change the UI accordingly.
+    else if (selectedPoints.length === 1) {
+      let selectedPoint = selectedPoints[0];
+      this.showTab('inspector');
+      let neighbors = this.findNeighbors(selectedPoint);
+      this.selectedPoints = [selectedPoint].concat(neighbors.map(n => n.index));
+      this.updateNNList(neighbors);
+    }
+    // Otherwise, select all points and hide nearest neighbors list.
+    else {
+      this.selectedPoints = selectedPoints as number[];
+      this.highlightedPoints = [];
+      this.updateNNList([]);
+    }
+    this.updateMetadata();
+    this.selectionWasUpdated();
+  }
+
+  private showPCA(callback?: () => void) {
+    this.currentDataSet.projectPCA().then(() => {
+      this.scatter.showTickLabels(false);
+      let x = this.pcaX;
+      let y = this.pcaY;
+      let z = this.pcaZ;
+      let hasZ = dimension == 3;
+      this.scatter.setXAccessor(i => this.points[i].projections['pca-' + x]);
+      this.scatter.setYAccessor(i => this.points[i].projections['pca-' + y]);
+      this.scatter.setZAccessor(
+          hasZ ? (i => this.points[i].projections['pca-' + z]) : null);
+      this.scatter.setAxisLabels('pca-' + x, 'pca-' + y);
+      this.scatter.update();
+      if (callback) {
+        callback();
+      }
+    });
+  }
+
+  private showTab(id: string) {
+    let tab = this.dom.select('.ink-tab[data-tab="' + id + '"]');
+    let pane =
+        d3.select((tab.node() as HTMLElement).parentNode.parentNode.parentNode);
+    pane.selectAll('.ink-tab').classed('active', false);
+    tab.classed('active', true);
+    pane.selectAll('.ink-panel-content').classed('active', false);
+    pane.select('.ink-panel-content[data-panel="' + id + '"]')
+        .classed('active', true);
+    if (id === 'pca') {
+      this.showPCA(() => this.scatter.recreateScene());
+    } else if (id === 'tsne') {
+      this.showTSNE();
+    } else if (id === 'custom') {
+      this.showCustom();
+    }
+  }
+
+  private showCustom() {
+    this.scatter.showTickLabels(true);
+    let xDir = vector.sub(this.centroids.xRight, this.centroids.xLeft);
+    this.currentDataSet.projectLinear(xDir, 'linear-x');
+    this.scatter.setXAccessor(i => this.points[i].projections['linear-x']);
+
+    let yDir = vector.sub(this.centroids.yUp, this.centroids.yDown);
+    this.currentDataSet.projectLinear(yDir, 'linear-y');
+    this.scatter.setYAccessor(i => this.points[i].projections['linear-y']);
+
+    // Scatter is only in 2D in projection mode.
+    this.scatter.setZAccessor(null);
+
+    let xLabel = this.centroidValues.xLeft + ' → ' + this.centroidValues.xRight;
+    let yLabel = this.centroidValues.yUp + ' → ' + this.centroidValues.yDown;
+    this.scatter.setAxisLabels(xLabel, yLabel);
+    this.scatter.update();
+    this.scatter.recreateScene();
+  }
+
+  private get points() { return this.currentDataSet.points; }
+
+  private showTSNE() {
+    this.scatter.showTickLabels(false);
+    this.scatter.setXAccessor(i => this.points[i].projections['tsne-0']);
+    this.scatter.setYAccessor(i => this.points[i].projections['tsne-1']);
+    this.scatter.setZAccessor(
+        dimension === 3 ? (i => this.points[i].projections['tsne-2']) : null);
+    this.scatter.setAxisLabels('tsne-0', 'tsne-1');
+  }
+
+  private runTSNE() {
+    this.currentDataSet.projectTSNE(
+        perplexity, learningRate, dimension, (iteration: number) => {
+          if (iteration != null) {
+            this.dom.select('.run-tsne-iter').text(iteration);
+            this.scatter.update();
+          }
+        });
+  }
+
+  // Updates the displayed metadata for the selected point.
+  private updateMetadata() {
+    let metadataContainerElement = this.dom.select('.ink-panel-metadata');
+    metadataContainerElement.selectAll('*').remove();
+
+    let display = false;
+    if (this.selectedPoints.length >= 1) {
+      let selectedPoint = this.points[this.selectedPoints[0]];
+
+      for (let metadataKey in selectedPoint.metadata) {
+        let rowElement = document.createElement('div');
+        rowElement.className = 'ink-panel-metadata-row vz-projector';
+
+        let keyElement = document.createElement('div');
+        keyElement.className = 'ink-panel-metadata-key vz-projector';
+        keyElement.textContent = metadataKey;
+
+        let valueElement = document.createElement('div');
+        valueElement.className = 'ink-panel-metadata-value vz-projector';
+        valueElement.textContent = '' + selectedPoint.metadata[metadataKey];
+
+        rowElement.appendChild(keyElement);
+        rowElement.appendChild(valueElement);
+
+        metadataContainerElement.append(function() {
+          return this.appendChild(rowElement);
+        });
+      }
+
+      display = true;
+    }
+
+    this.dom.select('.ink-panel-metadata-container')
+        .style('display', display ? '' : 'none');
+  }
+
+  private selectionWasUpdated() {
+    this.dom.select('#hoverInfo')
+        .text(`Selected ${this.selectedPoints.length} points`);
+    let allPoints =
+        this.highlightedPoints.map(x => x.index).concat(this.selectedPoints);
+    let stroke = (i: number) => {
+      return i < this.highlightedPoints.length ?
+          this.highlightedPoints[i].color :
+          NN_HIGHLIGHT_COLOR;
+    };
+    let favor = (i: number) => {
+      return i == 0 || (i < this.highlightedPoints.length ? false : true);
+    };
+    this.scatter.highlightPoints(allPoints, stroke, favor);
+    this.updateIsolateButton();
+  }
+
+  private updateMenuButtons() {
+    let searchBox = this.dom.select('.control.search-box');
+    this.dom.select('.search').classed(
+        'selected', this.scatter.getMode() === Mode.SEARCH);
+    let searchMode = this.scatter.getMode() === Mode.SEARCH;
+    this.dom.select('.control.search-box')
+        .style('width', searchMode ? '110px' : null)
+        .style('margin-right', searchMode ? '10px' : null);
+    (searchBox.select('input').node() as HTMLInputElement).focus();
+    this.dom.select('.selectMode')
+        .classed('selected', this.scatter.getMode() === Mode.SELECT);
+  }
+
+  /**
+   * Finds the nearest neighbors of the currently selected point using the
+   * currently selected distance method.
+   */
+  private findNeighbors(pointIndex: number): knn.NearestEntry[] {
+    // Find the nearest neighbors of a particular point.
+    let neighbors = knn.findKNNofPoint(
+        this.points, pointIndex, numNN, (d => d.vector), this.selectedDistance);
+    let result = neighbors.slice(0, numNN);
+    return result;
+  }
+
+  /** Updates the nearest neighbors list in the inspector. */
+  private updateNNList(neighbors: knn.NearestEntry[]) {
+    let nnlist = this.dom.select('.nn-list');
+    nnlist.html('');
+
+    if (neighbors.length == 0) {
+      this.dom.select('#nn-title').text('');
+      return;
+    }
+
+    let selectedPoint = this.points[this.selectedPoints[0]];
+    this.dom.select('#nn-title')
+        .text(selectedPoint != null ? selectedPoint.metadata['label'] : '');
+
+    let minDist = neighbors.length > 0 ? neighbors[0].dist : 0;
+    let n = nnlist.selectAll('.neighbor')
+                .data(neighbors)
+                .enter()
+                .append('div')
+                .attr('class', 'neighbor')
+                .append('a')
+                .attr('class', 'neighbor-link');
+
+    n.append('span')
+        .attr('class', 'label')
+        .style('color', d => this.dist2color(d.dist, minDist))
+        .text(d => this.points[d.index].metadata['label']);
+
+    n.append('span').attr('class', 'value').text(d => d.dist.toFixed(2));
+
+    let bar = n.append('div').attr('class', 'bar');
+
+    bar.append('div')
+        .attr('class', 'fill')
+        .style('border-top-color', d => this.dist2color(d.dist, minDist))
+        .style('width', d => this.normalizeDist(d.dist, minDist) * 100 + '%');
+
+    bar.selectAll('.tick')
+        .data(d3.range(1, 4))
+        .enter()
+        .append('div')
+        .attr('class', 'tick')
+        .style('left', d => d * 100 / 4 + '%');
+
+    n.on('click', d => { this.updateSelection([d.index]); });
+  }
+
+  private updateIsolateButton() {
+    let numPoints = this.selectedPoints.length;
+    let isolateButton = this.dom.select('.set-filter');
+    let clearButton = this.dom.select('button.clear-selection');
+    if (numPoints > 1) {
+      isolateButton.text(`Isolate ${numPoints} points`).style('display', null);
+      clearButton.style('display', null);
+    } else {
+      isolateButton.style('display', 'none');
+      clearButton.style('display', 'none');
+    }
+  }
+
+  private getCentroid(pattern: string): CentroidResult {
+    let accessor = (a: DataPoint) => a.vector;
+    if (pattern == null) {
+      return {numMatches: 0};
+    }
+    if (pattern == '') {
+      if (this.allCentroid == null) {
+        this.allCentroid =
+            vector.centroid(this.points, () => true, accessor).centroid;
+      }
+      return {centroid: this.allCentroid, numMatches: this.points.length};
+    }
+
+    let regExp: RegExp;
+    let predicate: (a: DataPoint) => boolean;
+    // Check for a regex.
+    if (pattern.charAt(0) == '/' && pattern.charAt(pattern.length - 1) == '/') {
+      pattern = pattern.slice(1, pattern.length - 1);
+      try {
+        regExp = new RegExp(pattern, 'i');
+      } catch (e) {
+        return {error: e.message};
+      }
+      predicate =
+          (a: DataPoint) => { return regExp.test('' + a.metadata['label']); };
+      // else does an exact match
+    } else {
+      predicate = (a: DataPoint) => { return a.metadata['label'] == pattern; };
+    }
+    return vector.centroid(this.points, predicate, accessor);
+  }
+}
+
+type CentroidResult = {
+  centroid?: number[]; numMatches?: number; error?: string
+};
+
+document.registerElement(Projector.prototype.is, Projector);
diff --git a/tensorflow/tensorboard/gulp_tasks/compile.js b/tensorflow/tensorboard/gulp_tasks/compile.js
index 4877468ab3..93d3e50c3f 100644
--- a/tensorflow/tensorboard/gulp_tasks/compile.js
+++ b/tensorflow/tensorboard/gulp_tasks/compile.js
@@ -19,20 +19,67 @@ var typescript = require('typescript');
 var gutil = require('gulp-util');
 var filter = require('gulp-filter');
 var merge = require('merge2');
+var browserify = require('browserify');
+var tsify = require('tsify');
+var source = require('vinyl-source-stream');
+var glob = require('glob').sync;
+var concat = require('gulp-concat');
 
 var tsProject = ts.createProject('./tsconfig.json', {
   typescript: typescript,
   noExternalResolve: true, // opt-in for faster compilation!
 });
 
+/** List of components (and their external deps) that are using es6 modules. */
+var ES6_COMPONENTS = [{
+  name: 'vz-projector',
+  deps: [
+    'd3/d3.min.js', 'weblas/dist/weblas.js', 'three.js/build/three.min.js',
+    'three.js/examples/js/controls/OrbitControls.js',
+    'numericjs/lib/numeric-1.2.6.js'
+  ]
+}];
 
 module.exports = function() {
+  // Compile all components that are using ES6 modules into a bundle.js
+  // using browserify.
+  var entries = ['typings/index.d.ts'];
+  var deps = {};
+  ES6_COMPONENTS.forEach(function(component) {
+    // Collect all the typescript files across the components.
+    entries = entries.concat(glob(
+        'components/' + component.name + '/**/*.ts',
+        // Do not include tests.
+        {ignore: 'components/' + component.name + '/**/*_test.ts'}));
+    // Collect the unique external deps across all components using es6 modules.
+    component.deps.forEach(function(dep) { deps['components/' + dep] = true; });
+  });
+  deps = Object.keys(deps);
+
+  // Compile, bundle all the typescript files and prepend their deps.
+  browserify(entries)
+      .plugin(tsify)
+      .bundle()
+      .on('error', function(error) { console.error(error.toString()); })
+      .pipe(source('app.js'))
+      .pipe(gulp.dest('components'))
+      .on('end', function() {
+        // Typescript was compiled and bundled. Now we need to prepend
+        // the external dependencies.
+        gulp.src(deps.concat(['components/app.js']))
+            .pipe(concat('bundle.js'))
+            .pipe(gulp.dest('components'));
+      });
+
+  // Compile components that are using global namespaces producing 1 js file
+  // for each ts file.
   var isComponent = filter([
-    'components/tf-*/**/*.ts',
-    'components/vz-*/**/*.ts',
-    'typings/**/*.ts',
+    'components/tf-*/**/*.ts', 'components/vz-*/**/*.ts', 'typings/**/*.ts',
     'components/plottable/plottable.d.ts'
-  ]);
+    // Ignore components that use es6 modules.
+  ].concat(ES6_COMPONENTS.map(function(component) {
+    return '!components/' + component.name + '/**/*.ts';
+  })));
 
   return tsProject.src()
            .pipe(isComponent)
diff --git a/tensorflow/tensorboard/package.json b/tensorflow/tensorboard/package.json
index ce104cb2f8..8c8f4ac5dc 100644
--- a/tensorflow/tensorboard/package.json
+++ b/tensorflow/tensorboard/package.json
@@ -13,9 +13,14 @@
   "author": "Google",
   "license": "Apache-2.0",
   "devDependencies": {
+    "browserify": "^13.1.0",
     "gulp": "~3.9.0",
+    "gulp-bower": "0.0.13",
     "gulp-cli": "^1.1.0",
+    "gulp-concat": "^2.6.0",
     "gulp-filter": "~3.0.1",
+    "gulp-header": "~1.7.1",
+    "gulp-rename": "~1.2.2",
     "gulp-replace": "~0.5.4",
     "gulp-server-livereload": "~1.5.4",
     "gulp-tslint": "~4.2.2",
@@ -24,13 +29,12 @@
     "gulp-vulcanize": "~6.1.0",
     "merge2": "~0.3.6",
     "minimist": "~1.2.0",
+    "tsify": "^0.15.6",
     "tslint": "^3.2.1",
-    "typescript": "1.8.0",
+    "typescript": "^2.0.0",
+    "typings": "~1.0.4",
+    "vinyl-source-stream": "^1.1.0",
     "vulcanize": "^1.14.0",
-    "web-component-tester": "4.2.2",
-    "gulp-header": "~1.7.1",
-    "gulp-rename": "~1.2.2",
-    "gulp-bower": "0.0.13",
-    "typings": "~1.0.4"
+    "web-component-tester": "4.2.2"
   }
 }
diff --git a/tensorflow/tensorboard/tsconfig.json b/tensorflow/tensorboard/tsconfig.json
index 1ecec4f922..e51e70f848 100644
--- a/tensorflow/tensorboard/tsconfig.json
+++ b/tensorflow/tensorboard/tsconfig.json
@@ -2,7 +2,8 @@
   "compilerOptions": {
     "noImplicitAny": false,
     "noEmitOnError": true,
-    "target": "ES5"
+    "target": "ES5",
+    "module": "commonjs"
   },
   "compileOnSave": false,
   "exclude": [
diff --git a/tensorflow/tensorboard/typings.json b/tensorflow/tensorboard/typings.json
index 7e7e7f9e88..4679f7acfe 100644
--- a/tensorflow/tensorboard/typings.json
+++ b/tensorflow/tensorboard/typings.json
@@ -9,6 +9,7 @@
     "mocha": "registry:dt/mocha#2.2.5+20160317120654",
     "polymer": "registry:dt/polymer#1.1.6+20160317120654",
     "sinon": "registry:dt/sinon#1.16.0+20160517064723",
+    "three": "registry:dt/three#0.0.0+20160802154944",
     "webcomponents.js": "registry:dt/webcomponents.js#0.6.0+20160317120654"
   }
 }
-- 
GitLab


From 0f3111b99d4f456657e4d784674c4eab08d781ca Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Dan=20Man=C3=A9?= <danmane@google.com>
Date: Fri, 26 Aug 2016 10:42:17 -0800
Subject: [PATCH 211/223] Make the line chart no longer generate annoying
 ghostly blue outlines when you click on the whitespace (e.g. clicking on
 axis). Change: 131424176

---
 .../tensorboard/components/vz-line-chart/vz-line-chart.html      | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html
index c08e512ffe..3cffa4c65c 100644
--- a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html
+++ b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html
@@ -60,6 +60,7 @@ such as different X scales (linear and temporal), tooltips and smoothing.
         flex-grow: 1;
         flex-shrink: 1;
         position: relative;
+        outline: none;
       }
       svg {
         -webkit-user-select: none;
-- 
GitLab


From 91dc7c9372aa5f43a6a99e5f8cc24f508942478f Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 26 Aug 2016 10:43:34 -0800
Subject: [PATCH 212/223] Make sure the large sum in the duality gap
 computation happens on the PS and we don't send all values to the worker.
 Replace special values_reduce_sum() method with a generic export_sharded().
 Other minor cleanups. Change: 131424317

---
 .../python/kernel_tests/sdca_ops_test.py      | 22 ++++-
 .../linear_optimizer/python/ops/sdca_ops.py   | 83 +++++++++++--------
 2 files changed, 70 insertions(+), 35 deletions(-)

diff --git a/tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py b/tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py
index 865ae9299c..e92af51ba1 100644
--- a/tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py
+++ b/tensorflow/contrib/linear_optimizer/python/kernel_tests/sdca_ops_test.py
@@ -982,7 +982,27 @@ class ShardedMutableHashTableTest(SdcaModelTest):
         result = output.eval()
         self.assertAllEqual([0, 1, -1], result)
 
-        self.assertAllEqual(3, table.values_reduce_sum().eval())
+  def testExportSharded(self):
+    with self._single_threaded_test_session():
+      default_val = -1
+      num_shards = 2
+      keys = tf.constant(['a1', 'b1', 'c2'])
+      values = tf.constant([0, 1, 2], tf.int64)
+      table = _ShardedMutableHashTable(
+          tf.string, tf.int64, default_val, num_shards=num_shards)
+      self.assertAllEqual(0, table.size().eval())
+
+      table.insert(keys, values).run()
+      self.assertAllEqual(3, table.size().eval())
+
+      keys_list, values_list = table.export_sharded()
+      self.assertAllEqual(num_shards, len(keys_list))
+      self.assertAllEqual(num_shards, len(values_list))
+
+      self.assertAllEqual(set([b'b1', b'c2']), set(keys_list[0].eval()))
+      self.assertAllEqual([b'a1'], keys_list[1].eval())
+      self.assertAllEqual(set([1, 2]), set(values_list[0].eval()))
+      self.assertAllEqual([0], values_list[1].eval())
 
 
 if __name__ == '__main__':
diff --git a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py
index 6f35472f85..db8a2f91ee 100644
--- a/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py
+++ b/tensorflow/contrib/linear_optimizer/python/ops/sdca_ops.py
@@ -150,24 +150,20 @@ class _ShardedMutableHashTable(lookup_ops.LookupInterface):
 
     return control_flow_ops.group(*return_values)
 
-  def values_reduce_sum(self, dtype=dtypes.float64, name=None):
-    """Computes reduce_sum reducing dimension 0 across all values in all shards.
-
-    Args:
-      name: A name for the operation (optional).
+  def export_sharded(self, name=None):
+    """Returns lists of the keys and values tensors in the sharded table.
 
     Returns:
-      A tensor with the sum across all values in the same shape as the table's
-      value shape.
+      A pair of lists with the first list containing the key tensors and the
+        second list containing the value tensors from each shard.
     """
-    # TODO(andreasst): consider replacing with something like export_sharded
-    # and doing the sum in SdcaModel.
-    sums = []
+    keys_list = []
+    values_list = []
     for table_shard in self._table_shards:
-      _, exported_values = table_shard.export(name=name)
-      sums.append(math_ops.reduce_sum(
-          math_ops.cast(exported_values, dtype), 0))
-    return math_ops.add_n(sums)
+      exported_keys, exported_values = table_shard.export(name=name)
+      keys_list.append(exported_keys)
+      values_list.append(exported_values)
+    return keys_list, values_list
 
 
 class SparseFeatureColumn(object):
@@ -419,21 +415,29 @@ class SdcaModel(object):
 
   def _l1_loss(self):
     """Computes the (un-normalized) l1 loss of the model."""
-    with name_scope('l1_loss'):
-      sum = 0.0
+    with name_scope('sdca/l1_loss'):
+      sums = []
       for name in ['sparse_features_weights', 'dense_features_weights']:
         for weights in self._convert_n_to_tensor(self._variables[name]):
-          sum += math_ops.reduce_sum(math_ops.abs(weights))
+          with ops.device(weights.device):
+            sums.append(
+                math_ops.reduce_sum(
+                    math_ops.abs(math_ops.cast(weights, dtypes.float64))))
+      sum = math_ops.add_n(sums)
       # SDCA L1 regularization cost is: l1 * sum(|weights|)
       return self._options['symmetric_l1_regularization'] * sum
 
   def _l2_loss(self, l2):
     """Computes the (un-normalized) l2 loss of the model."""
-    with name_scope('l2_loss'):
-      sum = 0.0
+    with name_scope('sdca/l2_loss'):
+      sums = []
       for name in ['sparse_features_weights', 'dense_features_weights']:
         for weights in self._convert_n_to_tensor(self._variables[name]):
-          sum += math_ops.reduce_sum(math_ops.square(weights))
+          with ops.device(weights.device):
+            sums.append(
+                math_ops.reduce_sum(
+                    math_ops.square(math_ops.cast(weights, dtypes.float64))))
+      sum = math_ops.add_n(sums)
       # SDCA L2 regularization cost is: l2 * sum(weights^2) / 2
       return l2 * sum / 2.0
 
@@ -589,16 +593,23 @@ class SdcaModel(object):
       An Operation that computes the approximate duality gap over all
       examples.
     """
-    summed_values = self._hashtable.values_reduce_sum()
-    primal_loss = summed_values[1]
-    dual_loss = summed_values[2]
-    example_weights = summed_values[3]
-    # TODO(andreasst): what about handle examples_weights == 0?
-    return (
-        primal_loss + dual_loss + math_ops.to_double(self._l1_loss()) +
-        (2.0 *
-         math_ops.to_double(self._l2_loss(self._symmetric_l2_regularization())))
-    ) / example_weights
+    with name_scope('sdca/approximate_duality_gap'):
+      _, values_list = self._hashtable.export_sharded()
+      shard_sums = []
+      for values in values_list:
+        with ops.device(values.device):
+          shard_sums.append(
+              math_ops.reduce_sum(math_ops.cast(values, dtypes.float64), 0))
+      summed_values = math_ops.add_n(shard_sums)
+
+      primal_loss = summed_values[1]
+      dual_loss = summed_values[2]
+      example_weights = summed_values[3]
+      # Note: we return NaN if there are no weights or all weights are 0, e.g.
+      # if no examples have been processed
+      return (primal_loss + dual_loss + self._l1_loss() +
+              (2.0 * self._l2_loss(self._symmetric_l2_regularization()))
+             ) / example_weights
 
   def unregularized_loss(self, examples):
     """Add operations to compute the loss (without the regularization loss).
@@ -617,9 +628,12 @@ class SdcaModel(object):
                            'sparse_features', 'dense_features'], examples)
     self._assertList(['sparse_features', 'dense_features'], examples)
     with name_scope('sdca/unregularized_loss'):
-      predictions = self._linear_predictions(examples)
-      labels = convert_to_tensor(examples['example_labels'])
-      weights = convert_to_tensor(examples['example_weights'])
+      predictions = math_ops.cast(
+          self._linear_predictions(examples), dtypes.float64)
+      labels = math_ops.cast(
+          convert_to_tensor(examples['example_labels']), dtypes.float64)
+      weights = math_ops.cast(
+          convert_to_tensor(examples['example_weights']), dtypes.float64)
 
       if self._options['loss_type'] == 'logistic_loss':
         return math_ops.reduce_sum(math_ops.mul(
@@ -670,4 +684,5 @@ class SdcaModel(object):
           # (as specified by the user) and *not*
           # self._symmetric_l2_regularization().
           self._l2_loss(self._options['symmetric_l2_regularization'])) /
-              math_ops.reduce_sum(weights) + self.unregularized_loss(examples))
+              math_ops.reduce_sum(math_ops.cast(weights, dtypes.float64)) +
+              self.unregularized_loss(examples))
-- 
GitLab


From da7eb65ba523c0ca5a807b7bbb2331b217b01dae Mon Sep 17 00:00:00 2001
From: Ilya Edrenkin <ilya.edrenkin@gmail.com>
Date: Fri, 26 Aug 2016 21:02:02 +0200
Subject: [PATCH 213/223] Fix DFS in quantize_graph.py (#4010)

Mark nodes as visited before recursing into their children to avoid infinite loops (e.g. when quantizing graphs using tf.dynamic_rnn construction).
---
 tensorflow/contrib/quantization/tools/quantize_graph.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tensorflow/contrib/quantization/tools/quantize_graph.py b/tensorflow/contrib/quantization/tools/quantize_graph.py
index 60cbca9834..0a814dadae 100644
--- a/tensorflow/contrib/quantization/tools/quantize_graph.py
+++ b/tensorflow/contrib/quantization/tools/quantize_graph.py
@@ -66,12 +66,12 @@ flags.DEFINE_boolean("load_quantization_so", True,
 
 def print_input_nodes(current_node, nodes_map, indent, already_visited):
   print(" " * indent + current_node.op + ":" + current_node.name)
+  already_visited[current_node.name] = True
   for input_node_name in current_node.input:
     if input_node_name in already_visited:
       continue
     input_node = nodes_map[input_node_name]
     print_input_nodes(input_node, nodes_map, indent + 1, already_visited)
-  already_visited[current_node.name] = True
 
 
 def create_node(op, name, inputs):
@@ -350,13 +350,13 @@ class GraphRewriter(object):
 
   def round_nodes_recursively(self, current_node):
     """The entry point for simple rounding quantization."""
+    self.already_visited[current_node.name] = True
     for input_node_name in current_node.input:
       input_node_name = node_name_from_input(input_node_name)
       if input_node_name in self.already_visited:
         continue
       input_node = self.nodes_map[input_node_name]
       self.round_nodes_recursively(input_node)
-    self.already_visited[current_node.name] = True
     nodes_to_quantize = ["Conv2D", "BiasAdd", "MatMul"]
     if any(current_node.op in s for s in nodes_to_quantize):
       new_node = tf.NodeDef()
@@ -381,13 +381,13 @@ class GraphRewriter(object):
 
   def quantize_nodes_recursively(self, current_node):
     """The entry point for quantizing nodes to eight bit and back."""
+    self.already_visited[current_node.name] = True
     for input_node_name in current_node.input:
       input_node_name = node_name_from_input(input_node_name)
       if input_node_name in self.already_visited:
         continue
       input_node = self.nodes_map[input_node_name]
       self.quantize_nodes_recursively(input_node)
-    self.already_visited[current_node.name] = True
     nodes_to_quantize = ["Conv2D", "BiasAdd", "MatMul"]
     if any(current_node.op in s for s in nodes_to_quantize):
       for input_name in current_node.input:
@@ -448,13 +448,13 @@ class GraphRewriter(object):
 
   def eightbitize_nodes_recursively(self, current_node):
     """The entry point for transforming a graph into full eight bit."""
+    self.already_visited[current_node.name] = True
     for input_node_name in current_node.input:
       input_node_name = node_name_from_input(input_node_name)
       if input_node_name in self.already_visited:
         continue
       input_node = self.nodes_map[input_node_name]
       self.eightbitize_nodes_recursively(input_node)
-    self.already_visited[current_node.name] = True
     if current_node.op == "MatMul":
       self.eightbitize_mat_mul_node(current_node)
     elif current_node.op == "Conv2D":
-- 
GitLab


From 5e80240e3b7a3892d25749e6889b2e93f41ead80 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 26 Aug 2016 10:53:33 -0800
Subject: [PATCH 214/223] Fix type bug in Bernoulli.sample_n. Change: 131425456

---
 .../distributions/python/kernel_tests/bernoulli_test.py      | 5 +++++
 tensorflow/contrib/distributions/python/ops/bernoulli.py     | 2 +-
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bernoulli_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bernoulli_test.py
index 565cb7f76c..ad1402de77 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/bernoulli_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/bernoulli_test.py
@@ -197,6 +197,11 @@ class BernoulliTest(tf.test.TestCase):
       # as well as n.
       self.assertAllClose(p, np.mean(sample_values, axis=0), atol=1e-2)
       self.assertEqual(set([0, 1]), set(sample_values.flatten()))
+      # In this test we're just interested in verifying there isn't a crash
+      # owing to mismatched types. b/30940152
+      dist = tf.contrib.distributions.Bernoulli(np.log([.2, .4]))
+      self.assertAllEqual(
+          (1, 2), dist.sample_n(1, seed=42).get_shape().as_list())
 
   def testSampleActsLikeSampleN(self):
     with self.test_session() as sess:
diff --git a/tensorflow/contrib/distributions/python/ops/bernoulli.py b/tensorflow/contrib/distributions/python/ops/bernoulli.py
index 49a4e6f8fc..60c1f3fa4c 100644
--- a/tensorflow/contrib/distributions/python/ops/bernoulli.py
+++ b/tensorflow/contrib/distributions/python/ops/bernoulli.py
@@ -107,7 +107,7 @@ class Bernoulli(distribution.Distribution):
   def _sample_n(self, n, seed=None):
     new_shape = array_ops.concat(0, ([n], self.batch_shape()))
     uniform = random_ops.random_uniform(
-        new_shape, seed=seed, dtype=dtypes.float32)
+        new_shape, seed=seed, dtype=self.p.dtype)
     sample = math_ops.less(uniform, self.p)
     return math_ops.cast(sample, self.dtype)
 
-- 
GitLab


From e48667aaaff51b0d2db14f5e03908105d2a391e9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Dan=20Man=C3=A9?= <danmane@google.com>
Date: Fri, 26 Aug 2016 11:08:30 -0800
Subject: [PATCH 215/223] Remove the flicker on vz-line-chart's top and right
 border (in Chrome). This will allow our internal screendiff tests to stop
 flaking. Change: 131427035

---
 .../components/vz-line-chart/vz-line-chart.ts | 26 +++++++++++++++++--
 1 file changed, 24 insertions(+), 2 deletions(-)

diff --git a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts
index 6fde2706f1..a94152af13 100644
--- a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts
+++ b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts
@@ -46,6 +46,8 @@ module VZ {
     private tooltipSortingMethod: string;
     private tooltipPosition: string;
 
+    private targetSVG: d3.Selection<any>;
+
     constructor(
         xType: string, colorScale: Plottable.Scales.Color,
         tooltip: d3.Selection<any>) {
@@ -504,9 +506,29 @@ module VZ {
       this.tooltipPosition = position;
     }
 
-    public renderTo(target: d3.Selection<any>) { this.outer.renderTo(target); }
+    public renderTo(targetSVG: d3.Selection<any>) {
+      this.outer.renderTo(targetSVG);
+      this.targetSVG = targetSVG;
+      this.setViewBox();
+    }
+
 
-    public redraw() { this.outer.redraw(); }
+    /** There's an issue in Chrome where the svg overflow is a bit
+     * "flickery". There is a border on the gridlines on the extreme edge of the
+     * chart, which behaves inconsistently and causes the screendiffing tests to
+     * flake. We can solve this by creating 1px effective margin for the svg by
+     * setting the viewBox on the containing svg.
+     */
+    private setViewBox() {
+      let w = this.outer.width();
+      let h = this.outer.height();
+      this.targetSVG.attr('viewBox', `0 0 ${w+1} ${h+1}`);
+    }
+
+    public redraw() {
+      this.outer.redraw();
+      this.setViewBox();
+    }
 
     public destroy() { this.outer.destroy(); }
   }
-- 
GitLab


From ec1077aae8e6fc39bcd8cf3185deb9454aa3ebcd Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Dan=20Man=C3=A9?= <danmane@google.com>
Date: Fri, 26 Aug 2016 11:37:27 -0800
Subject: [PATCH 216/223] Properly re-add iron-icons as a dependency for
 TensorBoard. This should merge to fix the r0.10 release. Change: 131429807

---
 WORKSPACE                                      |  7 +++++++
 bower.BUILD                                    | 18 ++++++++++++++++++
 tensorflow/tensorboard/bower.json              |  1 +
 tensorflow/tensorboard/bower/BUILD             |  1 +
 .../tf-tensorboard/tf-tensorboard.html         |  1 +
 tensorflow/tensorboard/dist/index.html         |  1 +
 6 files changed, 29 insertions(+)

diff --git a/WORKSPACE b/WORKSPACE
index 7991061d1e..60e37a43af 100644
--- a/WORKSPACE
+++ b/WORKSPACE
@@ -143,6 +143,13 @@ new_git_repository(
   tag = "v1.0.8",
 )
 
+new_git_repository(
+  name = "iron_icons",
+  build_file = "bower.BUILD",
+  remote = "https://github.com/polymerelements/iron-icons.git",
+  tag = "v1.1.3",
+)
+
 new_git_repository(
   name = "iron_iconset_svg",
   build_file = "bower.BUILD",
diff --git a/bower.BUILD b/bower.BUILD
index e01fb6d7e9..3cb112ff41 100644
--- a/bower.BUILD
+++ b/bower.BUILD
@@ -142,6 +142,24 @@ filegroup(
     ],
 )
 
+filegroup(
+    name = "iron_icons",
+    srcs = [
+        "av-icons.html",
+        "communication-icons.html",
+        "device-icons.html",
+        "editor-icons.html",
+        "hardware-icons.html",
+        "image-icons.html",
+        "index.html",
+        "iron-icons.html",
+        "maps-icons.html",
+        "notification-icons.html",
+        "places-icons.html",
+        "social-icons.html",
+    ],
+)
+
 filegroup(
     name = "iron_iconset_svg",
     srcs = [
diff --git a/tensorflow/tensorboard/bower.json b/tensorflow/tensorboard/bower.json
index 368b0f8abd..10d8056b0a 100644
--- a/tensorflow/tensorboard/bower.json
+++ b/tensorflow/tensorboard/bower.json
@@ -48,6 +48,7 @@
     "iron-flex-layout": "PolymerElements/iron-flex-layout#1.3.0",
     "iron-form-element-behavior": "PolymerElements/iron-form-element-behavior#1.0.6",
     "iron-icon": "PolymerElements/iron-icon#1.0.8",
+    "iron-icons": "PolymerElements/iron-icons#1.1.3",
     "iron-iconset-svg": "PolymerElements/iron-iconset-svg#1.0.9",
     "iron-input": "PolymerElements/iron-input#1.0.10",
     "iron-list": "PolymerElements/iron-list#1.1.7",
diff --git a/tensorflow/tensorboard/bower/BUILD b/tensorflow/tensorboard/bower/BUILD
index 88b9314080..9fca4f0457 100644
--- a/tensorflow/tensorboard/bower/BUILD
+++ b/tensorflow/tensorboard/bower/BUILD
@@ -22,6 +22,7 @@ filegroup(
         "@iron_flex_layout//:iron_flex_layout",
         "@iron_form_element_behavior//:iron_form_element_behavior",
         "@iron_icon//:iron_icon",
+        "@iron_icons//:iron_icons",
         "@iron_iconset_svg//:iron_iconset_svg",
         "@iron_input//:iron_input",
         "@iron_list//:iron_list",
diff --git a/tensorflow/tensorboard/components/tf-tensorboard/tf-tensorboard.html b/tensorflow/tensorboard/components/tf-tensorboard/tf-tensorboard.html
index ec62c60633..4a19257145 100644
--- a/tensorflow/tensorboard/components/tf-tensorboard/tf-tensorboard.html
+++ b/tensorflow/tensorboard/components/tf-tensorboard/tf-tensorboard.html
@@ -16,6 +16,7 @@ limitations under the License.
 -->
 
 <link rel="import" href="../polymer/polymer.html">
+<link rel="import" href="../iron-icons/iron-icons.html">
 <link rel="import" href="../paper-tabs/paper-tabs.html">
 <link rel="import" href="../paper-dialog/paper-dialog.html">
 <link rel="import" href="../paper-checkbox/paper-checkbox.html">
diff --git a/tensorflow/tensorboard/dist/index.html b/tensorflow/tensorboard/dist/index.html
index fa3020e1cb..38f3ab1448 100644
--- a/tensorflow/tensorboard/dist/index.html
+++ b/tensorflow/tensorboard/dist/index.html
@@ -36,6 +36,7 @@ limitations under the License.
 
     <link rel="import" href="external/iron-ajax/iron-ajax.html">
     <link rel="import" href="external/iron-collapse/iron-collapse.html">
+    <link rel="import" href="external/iron-icons/iron-icons.html">
     <link rel="import" href="external/iron-list/iron-list.html">
     <link rel="import" href="external/paper-button/paper-button.html">
     <link rel="import" href="external/paper-checkbox/paper-checkbox.html">
-- 
GitLab


From 25666f6493e361c39a7c350cbcb577a393e49c8f Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Fri, 26 Aug 2016 13:28:31 -0700
Subject: [PATCH 217/223] Add some more info in ISSUE_TEMPLATE.md, some text
 cleanup (#4065)

Clarifies what kind of issues should be reported and what we will close, asks people to try searching on the web for similar issues first, asks for a minimal reproducible example.
---
 ISSUE_TEMPLATE.md | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/ISSUE_TEMPLATE.md b/ISSUE_TEMPLATE.md
index f323d23972..af76188c2f 100644
--- a/ISSUE_TEMPLATE.md
+++ b/ISSUE_TEMPLATE.md
@@ -1,4 +1,5 @@
-GitHub issues are for bugs / installation problems / feature requests.  
+NOTE: Only file GitHub issues for bugs and feature requests.  All other topics will be closed.
+
 For general support from the community, see [StackOverflow](https://stackoverflow.com/questions/tagged/tensorflow).
 To make bugs and feature requests more easy to find and organize, we close issues that are deemed
 out of scope for GitHub Issues and point people to StackOverflow.
@@ -7,6 +8,8 @@ For bugs or installation issues, please provide the following information.
 The more information you provide, the more easily we will be able to offer
 help and advice.
 
+### What related GitHub issues or StackOverflow threads have you found by searching the web for your problem?
+
 ### Environment info
 Operating System:
 
@@ -15,7 +18,7 @@ Installed version of CUDA and cuDNN:
 
 If installed from binary pip package, provide:
 
-1. Which pip package you installed.
+1. A link to the pip package you installed:
 2. The output from `python -c "import tensorflow; print(tensorflow.__version__)"`.
 
 If installed from source, provide 
@@ -23,13 +26,11 @@ If installed from source, provide
 1. The commit hash (`git rev-parse HEAD`)
 2. The output of `bazel version`
 
-### Steps to reproduce
-1.
-2.
-3.
+### If possible, provide a minimal reproducible example (We usually don't have time to read hundreds of lines of your code)
+
+
+### What other attempted solutions have you tried?
 
-### What have you tried?
-1.
 
 ### Logs or other output that would be helpful
-(If logs are large, please upload as attachment).
+(If logs are large, please upload as attachment or provide link).
-- 
GitLab


From 8c37911d8644e8d27d3ca6246b4468ec67b1abed Mon Sep 17 00:00:00 2001
From: Renato Utsch <renatoutsch@google.com>
Date: Fri, 26 Aug 2016 12:33:15 -0800
Subject: [PATCH 218/223] Fix the vz-line-chart resize on redraw

This fixes the expand button resizing wrongly. This was due to getting old
width and height when calling setViewBox(). Now it uses the bounding box to
get the width and height directly from the SVG.
Change: 131435410
---
 .../components/vz-line-chart/vz-line-chart.ts            | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts
index a94152af13..9643e51ac0 100644
--- a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts
+++ b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts
@@ -512,7 +512,6 @@ module VZ {
       this.setViewBox();
     }
 
-
     /** There's an issue in Chrome where the svg overflow is a bit
      * "flickery". There is a border on the gridlines on the extreme edge of the
      * chart, which behaves inconsistently and causes the screendiffing tests to
@@ -520,9 +519,11 @@ module VZ {
      * setting the viewBox on the containing svg.
      */
     private setViewBox() {
-      let w = this.outer.width();
-      let h = this.outer.height();
-      this.targetSVG.attr('viewBox', `0 0 ${w+1} ${h+1}`);
+      let svg = this.targetSVG.node() as HTMLElement;
+      let brect = svg.getBoundingClientRect();
+      let w = brect.width;
+      let h = brect.height;
+      this.targetSVG.attr('viewBox', `0 0 ${w + 1} ${h + 1}`);
     }
 
     public redraw() {
-- 
GitLab


From 0f867ebf83cd594aeb73b1577044c91a749ddac5 Mon Sep 17 00:00:00 2001
From: Dan Smilkov <smilkov@google.com>
Date: Fri, 26 Aug 2016 12:49:43 -0800
Subject: [PATCH 219/223] Automated rollback of change 131423744 Change:
 131437266

---
 WORKSPACE                                     |   30 +-
 bower.BUILD                                   |  334 ----
 tensorflow/BUILD                              |    1 -
 tensorflow/tensorboard/bower.json             |   16 +-
 tensorflow/tensorboard/bower/BUILD            |    4 -
 .../tensorboard/components/vz-projector/BUILD |   19 -
 .../components/vz-projector/async.ts          |   51 -
 .../components/vz-projector/bh_tsne.ts        |  472 ------
 .../components/vz-projector/data.ts           |  324 ----
 .../components/vz-projector/data_test.ts      |   66 -
 .../components/vz-projector/demo/index.html   |   73 -
 .../components/vz-projector/external.d.ts     |   25 -
 .../components/vz-projector/heap.ts           |  146 --
 .../components/vz-projector/knn.ts            |  223 ---
 .../components/vz-projector/label.ts          |  151 --
 .../components/vz-projector/scatter.ts        |  135 --
 .../components/vz-projector/scatterWebGL.ts   | 1510 -----------------
 .../components/vz-projector/sptree.ts         |  189 ---
 .../components/vz-projector/sptree_test.ts    |  106 --
 .../components/vz-projector/util.ts           |   43 -
 .../components/vz-projector/vector.ts         |  270 ---
 .../vz-projector-data-loader.html             |  144 --
 .../vz-projector/vz-projector-data-loader.ts  |  500 ------
 .../vz-projector/vz-projector-util.ts         |   33 -
 .../components/vz-projector/vz-projector.html |  628 -------
 .../components/vz-projector/vz-projector.ts   |  762 ---------
 tensorflow/tensorboard/gulp_tasks/compile.js  |   55 +-
 tensorflow/tensorboard/package.json           |   16 +-
 tensorflow/tensorboard/tsconfig.json          |    3 +-
 tensorflow/tensorboard/typings.json           |    1 -
 30 files changed, 16 insertions(+), 6314 deletions(-)
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/BUILD
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/async.ts
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/bh_tsne.ts
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/data.ts
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/data_test.ts
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/demo/index.html
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/external.d.ts
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/heap.ts
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/knn.ts
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/label.ts
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/scatter.ts
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/scatterWebGL.ts
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/sptree.ts
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/sptree_test.ts
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/util.ts
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/vector.ts
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/vz-projector-data-loader.html
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/vz-projector-data-loader.ts
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/vz-projector-util.ts
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/vz-projector.html
 delete mode 100644 tensorflow/tensorboard/components/vz-projector/vz-projector.ts

diff --git a/WORKSPACE b/WORKSPACE
index 60e37a43af..9ff57bf9eb 100644
--- a/WORKSPACE
+++ b/WORKSPACE
@@ -234,13 +234,6 @@ new_git_repository(
   tag = "v1.2.2",
 )
 
-new_git_repository(
-  name = "numericjs",
-  build_file = "bower.BUILD",
-  remote = "https://github.com/sloisel/numeric.git",
-  tag = "v1.2.6",
-)
-
 new_git_repository(
   name = "paper_behaviors",
   build_file = "bower.BUILD",
@@ -311,13 +304,6 @@ new_git_repository(
   tag = "v1.1.4",
 )
 
-new_git_repository(
-  name = "paper_listbox",
-  build_file = "bower.BUILD",
-  remote = "https://github.com/polymerelements/paper-listbox.git",
-  tag = "v1.1.2",
-)
-
 new_git_repository(
   name = "paper_material",
   build_file = "bower.BUILD",
@@ -420,7 +406,7 @@ new_git_repository(
   name = "polymer",
   build_file = "bower.BUILD",
   remote = "https://github.com/polymer/polymer.git",
-  tag = "v1.6.1",
+  tag = "v1.6.0",
 )
 
 new_git_repository(
@@ -430,13 +416,6 @@ new_git_repository(
   tag = "v1.0.0",
 )
 
-new_git_repository(
-  name = "three_js",
-  build_file = "bower.BUILD",
-  remote = "https://github.com/mrdoob/three.js.git",
-  tag = "r77",
-)
-
 new_git_repository(
   name = "web_animations_js",
   build_file = "bower.BUILD",
@@ -450,10 +429,3 @@ new_git_repository(
   remote = "https://github.com/webcomponents/webcomponentsjs.git",
   tag = "v0.7.22",
 )
-
-new_git_repository(
-  name = "weblas",
-  build_file = "bower.BUILD",
-  remote = "https://github.com/waylonflinn/weblas.git",
-  tag = "v0.9.0",
-)
diff --git a/bower.BUILD b/bower.BUILD
index 3cb112ff41..9f4994944f 100644
--- a/bower.BUILD
+++ b/bower.BUILD
@@ -320,41 +320,6 @@ filegroup(
     ],
 )
 
-filegroup(
-    name = "numericjs",
-    srcs = [
-        "benchmark.html",
-        "benchmark2.html",
-        "demo.html",
-        "documentation.html",
-        "myworker.js",
-        "resources/style.css",
-        "resources/style-ie.css",
-        "src/documentation.html",
-        "src/numeric.js",
-        "src/quadprog.js",
-        "src/seedrandom.js",
-        "src/sparse2.js",
-        "src/svd.js",
-        "tools/XMLHttpRequest.js",
-        "tools/closurelib.js",
-        "tools/excanvas.min.js",
-        "tools/goog-require.js",
-        "tools/jquery.flot.image.js",
-        "tools/jquery.flot.image.min.js",
-        "tools/jquery.flot.js",
-        "tools/jquery.flot.min.js",
-        "tools/jquery-1.7.1.js",
-        "tools/jquery-1.7.1.min.js",
-        "tools/json2.js",
-        "tools/megalib.js",
-        "tools/mytest.html",
-        "tools/sylvester.js",
-        "tools/unit2.js",
-        "tools/workshop.html",
-    ],
-)
-
 filegroup(
     name = "paper_behaviors",
     srcs = [
@@ -457,14 +422,6 @@ filegroup(
     ],
 )
 
-filegroup(
-    name = "paper_listbox",
-    srcs = [
-        "index.html",
-        "paper-listbox.html",
-    ],
-)
-
 filegroup(
     name = "paper_material",
     srcs = [
@@ -616,275 +573,6 @@ filegroup(
     ],
 )
 
-filegroup(
-    name = "three_js",
-    srcs = [
-        "build/three.js",
-        "build/three.min.js",
-        "examples/js/AnimationClipCreator.js",
-        "examples/js/BlendCharacter.js",
-        "examples/js/BlendCharacterGui.js",
-        "examples/js/BufferGeometryUtils.js",
-        "examples/js/Car.js",
-        "examples/js/Cloth.js",
-        "examples/js/CurveExtras.js",
-        "examples/js/Detector.js",
-        "examples/js/Encodings.js",
-        "examples/js/GPUParticleSystem.js",
-        "examples/js/Gyroscope.js",
-        "examples/js/Half.js",
-        "examples/js/ImprovedNoise.js",
-        "examples/js/MD2Character.js",
-        "examples/js/MD2CharacterComplex.js",
-        "examples/js/MarchingCubes.js",
-        "examples/js/Mirror.js",
-        "examples/js/MorphAnimMesh.js",
-        "examples/js/MorphAnimation.js",
-        "examples/js/Ocean.js",
-        "examples/js/Octree.js",
-        "examples/js/PRNG.js",
-        "examples/js/ParametricGeometries.js",
-        "examples/js/RollerCoaster.js",
-        "examples/js/ShaderGodRays.js",
-        "examples/js/ShaderSkin.js",
-        "examples/js/ShaderTerrain.js",
-        "examples/js/ShaderToon.js",
-        "examples/js/SimplexNoise.js",
-        "examples/js/SimulationRenderer.js",
-        "examples/js/SkyShader.js",
-        "examples/js/TimelinerController.js",
-        "examples/js/TypedArrayUtils.js",
-        "examples/js/UCSCharacter.js",
-        "examples/js/Volume.js",
-        "examples/js/VolumeSlice.js",
-        "examples/js/WaterShader.js",
-        "examples/js/WebVR.js",
-        "examples/js/animation/CCDIKSolver.js",
-        "examples/js/animation/MMDPhysics.js",
-        "examples/js/cameras/CinematicCamera.js",
-        "examples/js/cameras/CombinedCamera.js",
-        "examples/js/controls/DeviceOrientationControls.js",
-        "examples/js/controls/DragControls.js",
-        "examples/js/controls/EditorControls.js",
-        "examples/js/controls/FirstPersonControls.js",
-        "examples/js/controls/FlyControls.js",
-        "examples/js/controls/MouseControls.js",
-        "examples/js/controls/OrbitControls.js",
-        "examples/js/controls/OrthographicTrackballControls.js",
-        "examples/js/controls/PointerLockControls.js",
-        "examples/js/controls/TrackballControls.js",
-        "examples/js/controls/TransformControls.js",
-        "examples/js/controls/VRControls.js",
-        "examples/js/crossfade/gui.js",
-        "examples/js/crossfade/scenes.js",
-        "examples/js/crossfade/transition.js",
-        "examples/js/curves/NURBSCurve.js",
-        "examples/js/curves/NURBSSurface.js",
-        "examples/js/curves/NURBSUtils.js",
-        "examples/js/effects/AnaglyphEffect.js",
-        "examples/js/effects/AsciiEffect.js",
-        "examples/js/effects/ParallaxBarrierEffect.js",
-        "examples/js/effects/PeppersGhostEffect.js",
-        "examples/js/effects/StereoEffect.js",
-        "examples/js/effects/VREffect.js",
-        "examples/js/exporters/OBJExporter.js",
-        "examples/js/exporters/STLBinaryExporter.js",
-        "examples/js/exporters/STLExporter.js",
-        "examples/js/exporters/TypedGeometryExporter.js",
-        "examples/js/geometries/ConvexGeometry.js",
-        "examples/js/geometries/DecalGeometry.js",
-        "examples/js/geometries/TeapotBufferGeometry.js",
-        "examples/js/geometries/hilbert2D.js",
-        "examples/js/geometries/hilbert3D.js",
-        "examples/js/libs/ammo.js",
-        "examples/js/libs/charsetencoder.min.js",
-        "examples/js/libs/dat.gui.min.js",
-        "examples/js/libs/earcut.js",
-        "examples/js/libs/inflate.min.js",
-        "examples/js/libs/jszip.min.js",
-        "examples/js/libs/msgpack-js.js",
-        "examples/js/libs/pnltri.min.js",
-        "examples/js/libs/stats.min.js",
-        "examples/js/libs/system.min.js",
-        "examples/js/libs/timeliner_gui.min.js",
-        "examples/js/libs/tween.min.js",
-        "examples/js/libs/zlib_and_gzip.min.js",
-        "examples/js/loaders/3MFLoader.js",
-        "examples/js/loaders/AMFLoader.js",
-        "examples/js/loaders/AWDLoader.js",
-        "examples/js/loaders/AssimpJSONLoader.js",
-        "examples/js/loaders/BabylonLoader.js",
-        "examples/js/loaders/BinaryLoader.js",
-        "examples/js/loaders/ColladaLoader.js",
-        "examples/js/loaders/ColladaLoader2.js",
-        "examples/js/loaders/DDSLoader.js",
-        "examples/js/loaders/FBXLoader.js",
-        "examples/js/loaders/HDRCubeTextureLoader.js",
-        "examples/js/loaders/KMZLoader.js",
-        "examples/js/loaders/MD2Loader.js",
-        "examples/js/loaders/MMDLoader.js",
-        "examples/js/loaders/MTLLoader.js",
-        "examples/js/loaders/NRRDLoader.js",
-        "examples/js/loaders/OBJLoader.js",
-        "examples/js/loaders/PCDLoader.js",
-        "examples/js/loaders/PDBLoader.js",
-        "examples/js/loaders/PLYLoader.js",
-        "examples/js/loaders/PVRLoader.js",
-        "examples/js/loaders/PlayCanvasLoader.js",
-        "examples/js/loaders/RGBELoader.js",
-        "examples/js/loaders/STLLoader.js",
-        "examples/js/loaders/SVGLoader.js",
-        "examples/js/loaders/TGALoader.js",
-        "examples/js/loaders/UTF8Loader.js",
-        "examples/js/loaders/VRMLLoader.js",
-        "examples/js/loaders/VTKLoader.js",
-        "examples/js/loaders/collada/Animation.js",
-        "examples/js/loaders/collada/AnimationHandler.js",
-        "examples/js/loaders/collada/KeyFrameAnimation.js",
-        "examples/js/loaders/ctm/CTMLoader.js",
-        "examples/js/loaders/ctm/CTMWorker.js",
-        "examples/js/loaders/ctm/ctm.js",
-        "examples/js/loaders/ctm/lzma.js",
-        "examples/js/loaders/deprecated/SceneLoader.js",
-        "examples/js/loaders/gltf/glTF-parser.js",
-        "examples/js/loaders/gltf/glTFAnimation.js",
-        "examples/js/loaders/gltf/glTFLoader.js",
-        "examples/js/loaders/gltf/glTFLoaderUtils.js",
-        "examples/js/loaders/gltf/glTFShaders.js",
-        "examples/js/loaders/gltf/gltfUtilities.js",
-        "examples/js/loaders/sea3d/SEA3D.js",
-        "examples/js/loaders/sea3d/SEA3DDeflate.js",
-        "examples/js/loaders/sea3d/SEA3DLZMA.js",
-        "examples/js/loaders/sea3d/SEA3DLegacy.js",
-        "examples/js/loaders/sea3d/SEA3DLoader.js",
-        "examples/js/math/ColorConverter.js",
-        "examples/js/math/Lut.js",
-        "examples/js/modifiers/BufferSubdivisionModifier.js",
-        "examples/js/modifiers/ExplodeModifier.js",
-        "examples/js/modifiers/SubdivisionModifier.js",
-        "examples/js/modifiers/TessellateModifier.js",
-        "examples/js/nodes/BuilderNode.js",
-        "examples/js/nodes/ConstNode.js",
-        "examples/js/nodes/FunctionCallNode.js",
-        "examples/js/nodes/FunctionNode.js",
-        "examples/js/nodes/GLNode.js",
-        "examples/js/nodes/InputNode.js",
-        "examples/js/nodes/NodeLib.js",
-        "examples/js/nodes/NodeMaterial.js",
-        "examples/js/nodes/RawNode.js",
-        "examples/js/nodes/TempNode.js",
-        "examples/js/nodes/accessors/CameraNode.js",
-        "examples/js/nodes/accessors/ColorsNode.js",
-        "examples/js/nodes/accessors/LightNode.js",
-        "examples/js/nodes/accessors/NormalNode.js",
-        "examples/js/nodes/accessors/PositionNode.js",
-        "examples/js/nodes/accessors/ReflectNode.js",
-        "examples/js/nodes/accessors/ScreenUVNode.js",
-        "examples/js/nodes/accessors/UVNode.js",
-        "examples/js/nodes/inputs/ColorNode.js",
-        "examples/js/nodes/inputs/CubeTextureNode.js",
-        "examples/js/nodes/inputs/FloatNode.js",
-        "examples/js/nodes/inputs/IntNode.js",
-        "examples/js/nodes/inputs/Matrix4Node.js",
-        "examples/js/nodes/inputs/MirrorNode.js",
-        "examples/js/nodes/inputs/ScreenNode.js",
-        "examples/js/nodes/inputs/TextureNode.js",
-        "examples/js/nodes/inputs/Vector2Node.js",
-        "examples/js/nodes/inputs/Vector3Node.js",
-        "examples/js/nodes/inputs/Vector4Node.js",
-        "examples/js/nodes/materials/PhongNode.js",
-        "examples/js/nodes/materials/PhongNodeMaterial.js",
-        "examples/js/nodes/materials/StandardNode.js",
-        "examples/js/nodes/materials/StandardNodeMaterial.js",
-        "examples/js/nodes/math/Math1Node.js",
-        "examples/js/nodes/math/Math2Node.js",
-        "examples/js/nodes/math/Math3Node.js",
-        "examples/js/nodes/math/OperatorNode.js",
-        "examples/js/nodes/postprocessing/NodePass.js",
-        "examples/js/nodes/utils/ColorAdjustmentNode.js",
-        "examples/js/nodes/utils/JoinNode.js",
-        "examples/js/nodes/utils/LuminanceNode.js",
-        "examples/js/nodes/utils/NoiseNode.js",
-        "examples/js/nodes/utils/NormalMapNode.js",
-        "examples/js/nodes/utils/ResolutionNode.js",
-        "examples/js/nodes/utils/RoughnessToBlinnExponentNode.js",
-        "examples/js/nodes/utils/SwitchNode.js",
-        "examples/js/nodes/utils/TimerNode.js",
-        "examples/js/nodes/utils/VelocityNode.js",
-        "examples/js/objects/ShadowMesh.js",
-        "examples/js/pmrem/PMREMCubeUVPacker.js",
-        "examples/js/pmrem/PMREMGenerator.js",
-        "examples/js/postprocessing/AdaptiveToneMappingPass.js",
-        "examples/js/postprocessing/BloomPass.js",
-        "examples/js/postprocessing/BokehPass.js",
-        "examples/js/postprocessing/ClearPass.js",
-        "examples/js/postprocessing/DotScreenPass.js",
-        "examples/js/postprocessing/EffectComposer.js",
-        "examples/js/postprocessing/FilmPass.js",
-        "examples/js/postprocessing/GlitchPass.js",
-        "examples/js/postprocessing/ManualMSAARenderPass.js",
-        "examples/js/postprocessing/MaskPass.js",
-        "examples/js/postprocessing/RenderPass.js",
-        "examples/js/postprocessing/SMAAPass.js",
-        "examples/js/postprocessing/SavePass.js",
-        "examples/js/postprocessing/ShaderPass.js",
-        "examples/js/postprocessing/TAARenderPass.js",
-        "examples/js/postprocessing/TexturePass.js",
-        "examples/js/renderers/CSS2DRenderer.js",
-        "examples/js/renderers/CSS3DRenderer.js",
-        "examples/js/renderers/CanvasRenderer.js",
-        "examples/js/renderers/Projector.js",
-        "examples/js/renderers/RaytracingRenderer.js",
-        "examples/js/renderers/RaytracingWorker.js",
-        "examples/js/renderers/SVGRenderer.js",
-        "examples/js/renderers/SoftwareRenderer.js",
-        "examples/js/shaders/BasicShader.js",
-        "examples/js/shaders/BleachBypassShader.js",
-        "examples/js/shaders/BlendShader.js",
-        "examples/js/shaders/BokehShader.js",
-        "examples/js/shaders/BokehShader2.js",
-        "examples/js/shaders/BrightnessContrastShader.js",
-        "examples/js/shaders/ColorCorrectionShader.js",
-        "examples/js/shaders/ColorifyShader.js",
-        "examples/js/shaders/ConvolutionShader.js",
-        "examples/js/shaders/CopyShader.js",
-        "examples/js/shaders/DOFMipMapShader.js",
-        "examples/js/shaders/DigitalGlitch.js",
-        "examples/js/shaders/DotScreenShader.js",
-        "examples/js/shaders/EdgeShader.js",
-        "examples/js/shaders/EdgeShader2.js",
-        "examples/js/shaders/FXAAShader.js",
-        "examples/js/shaders/FilmShader.js",
-        "examples/js/shaders/FocusShader.js",
-        "examples/js/shaders/FresnelShader.js",
-        "examples/js/shaders/GammaCorrectionShader.js",
-        "examples/js/shaders/HorizontalBlurShader.js",
-        "examples/js/shaders/HorizontalTiltShiftShader.js",
-        "examples/js/shaders/HueSaturationShader.js",
-        "examples/js/shaders/KaleidoShader.js",
-        "examples/js/shaders/LuminosityShader.js",
-        "examples/js/shaders/MirrorShader.js",
-        "examples/js/shaders/NormalMapShader.js",
-        "examples/js/shaders/OceanShaders.js",
-        "examples/js/shaders/ParallaxShader.js",
-        "examples/js/shaders/RGBShiftShader.js",
-        "examples/js/shaders/SMAAShader.js",
-        "examples/js/shaders/SSAOShader.js",
-        "examples/js/shaders/SepiaShader.js",
-        "examples/js/shaders/TechnicolorShader.js",
-        "examples/js/shaders/ToneMapShader.js",
-        "examples/js/shaders/TriangleBlurShader.js",
-        "examples/js/shaders/UnpackDepthRGBAShader.js",
-        "examples/js/shaders/VerticalBlurShader.js",
-        "examples/js/shaders/VerticalTiltShiftShader.js",
-        "examples/js/shaders/VignetteShader.js",
-        "examples/js/utils/GeometryUtils.js",
-        "examples/js/utils/ImageUtils.js",
-        "examples/js/utils/ShadowMapViewer.js",
-        "examples/js/utils/UVsDebug.js",
-    ],
-)
-
 filegroup(
     name = "web_animations_js",
     srcs = [
@@ -912,25 +600,3 @@ filegroup(
         "webcomponents-lite.min.js",
     ],
 )
-
-filegroup(
-    name = "weblas",
-    srcs = [
-        "benchmark.html",
-        "benchmark/sgemm.js",
-        "dist/weblas.js",
-        "index.js",
-        "lib/globals.js",
-        "lib/pipeline.js",
-        "lib/saxpycalculator.js",
-        "lib/sclmpcalculator.js",
-        "lib/sdwnscalculator.js",
-        "lib/sgemmcalculator.js",
-        "lib/sscalcalculator.js",
-        "lib/tensor.js",
-        "lib/test.js",
-        "lib/webgl.js",
-        "test.html",
-        "test/data/generate.js",
-    ],
-)
diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 2221d26c37..add5d97169 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -148,7 +148,6 @@ filegroup(
         "//tensorflow/tensorboard/app:all_files",
         "//tensorflow/tensorboard/backend:all_files",
         "//tensorflow/tensorboard/components:all_files",
-        "//tensorflow/tensorboard/components/vz-projector:all_files",
         "//tensorflow/tensorboard/lib:all_files",
         "//tensorflow/tensorboard/lib/python:all_files",
         "//tensorflow/tensorboard/scripts:all_files",
diff --git a/tensorflow/tensorboard/bower.json b/tensorflow/tensorboard/bower.json
index 10d8056b0a..355fd9684b 100644
--- a/tensorflow/tensorboard/bower.json
+++ b/tensorflow/tensorboard/bower.json
@@ -61,7 +61,6 @@
     "iron-validatable-behavior": "PolymerElements/iron-validatable-behavior#1.1.1",
     "lodash": "3.8.0",
     "neon-animation": "PolymerElements/neon-animation#1.2.2",
-    "numericjs": "1.2.6",
     "paper-behaviors": "PolymerElements/paper-behaviors#1.0.11",
     "paper-button": "PolymerElements/paper-button#1.0.11",
     "paper-checkbox": "PolymerElements/paper-checkbox#1.1.3",
@@ -72,7 +71,6 @@
     "paper-icon-button": "PolymerElements/paper-icon-button#1.1.1",
     "paper-input": "PolymerElements/paper-input#1.1.14",
     "paper-item": "PolymerElements/paper-item#1.1.4",
-    "paper-listbox": "PolymerElements/paper-listbox#1.1.2",
     "paper-material": "PolymerElements/paper-material#1.0.6",
     "paper-menu": "PolymerElements/paper-menu#1.2.2",
     "paper-menu-button": "PolymerElements/paper-menu-button#1.5.0",
@@ -87,12 +85,10 @@
     "paper-toolbar": "PolymerElements/paper-toolbar#1.1.4",
     "paper-tooltip": "PolymerElements/paper-tooltip#1.1.2",
     "plottable": "1.16.1",
-    "polymer": "1.6.1",
+    "polymer": "1.6.0",
     "promise-polyfill": "polymerlabs/promise-polyfill#1.0.0",
-    "three.js": "threejs#r77",
     "web-animations-js": "web-animations/web-animations-js#2.2.1",
-    "webcomponentsjs": "webcomponents/webcomponentsjs#0.7.22",
-    "weblas": "0.9.0"
+    "webcomponentsjs": "webcomponents/webcomponentsjs#0.7.22"
   },
   "description": "TensorBoard: Visualizations for TensorFlow",
   "devDependencies": {
@@ -141,7 +137,6 @@
     "iron-validatable-behavior": "1.1.1",
     "lodash": "3.8.0",
     "neon-animation": "1.2.2",
-    "numericjs": "1.2.6",
     "paper-behaviors": "1.0.11",
     "paper-button": "1.0.11",
     "paper-checkbox": "1.1.3",
@@ -152,7 +147,6 @@
     "paper-icon-button": "1.1.1",
     "paper-input": "1.1.14",
     "paper-item": "1.1.4",
-    "paper-listbox": "1.1.2",
     "paper-material": "1.0.6",
     "paper-menu": "1.2.2",
     "paper-menu-button": "1.5.0",
@@ -167,12 +161,10 @@
     "paper-toolbar": "1.1.4",
     "paper-tooltip": "1.1.2",
     "plottable": "1.16.1",
-    "polymer": "1.6.1",
+    "polymer": "1.6.0",
     "promise-polyfill": "1.0.0",
-    "three.js": "threejs#r77",
     "web-animations-js": "2.2.1",
-    "webcomponentsjs": "0.7.22",
-    "weblas": "0.9.0"
+    "webcomponentsjs": "0.7.22"
   },
   "version": "0.0.0"
 }
diff --git a/tensorflow/tensorboard/bower/BUILD b/tensorflow/tensorboard/bower/BUILD
index 9fca4f0457..90d9910205 100644
--- a/tensorflow/tensorboard/bower/BUILD
+++ b/tensorflow/tensorboard/bower/BUILD
@@ -35,7 +35,6 @@ filegroup(
         "@iron_validatable_behavior//:iron_validatable_behavior",
         "@lodash//:lodash",
         "@neon_animation//:neon_animation",
-        "@numericjs//:numericjs",
         "@paper_behaviors//:paper_behaviors",
         "@paper_button//:paper_button",
         "@paper_checkbox//:paper_checkbox",
@@ -46,7 +45,6 @@ filegroup(
         "@paper_icon_button//:paper_icon_button",
         "@paper_input//:paper_input",
         "@paper_item//:paper_item",
-        "@paper_listbox//:paper_listbox",
         "@paper_material//:paper_material",
         "@paper_menu//:paper_menu",
         "@paper_menu_button//:paper_menu_button",
@@ -63,9 +61,7 @@ filegroup(
         "@plottable//:plottable",
         "@polymer//:polymer",
         "@promise_polyfill//:promise_polyfill",
-        "@three_js//:three_js",
         "@web_animations_js//:web_animations_js",
         "@webcomponentsjs//:webcomponentsjs",
-        "@weblas//:weblas",
     ],
 )
diff --git a/tensorflow/tensorboard/components/vz-projector/BUILD b/tensorflow/tensorboard/components/vz-projector/BUILD
deleted file mode 100644
index 8c222be10e..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/BUILD
+++ /dev/null
@@ -1,19 +0,0 @@
-# Description:
-# Package for the Embedding Projector component.
-package(default_visibility = ["//tensorflow:internal"])
-
-licenses(["notice"])  # Apache 2.0
-
-exports_files(["LICENSE"])
-
-filegroup(
-    name = "all_files",
-    srcs = glob(
-        ["**/*"],
-        exclude = [
-            "**/METADATA",
-            "**/OWNERS",
-        ],
-    ),
-    visibility = ["//tensorflow:__subpackages__"],
-)
diff --git a/tensorflow/tensorboard/components/vz-projector/async.ts b/tensorflow/tensorboard/components/vz-projector/async.ts
deleted file mode 100644
index 9ee6746b8d..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/async.ts
+++ /dev/null
@@ -1,51 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/** Delay for running async tasks, in milliseconds. */
-const ASYNC_DELAY = 15;
-
-/**
- * Runs an expensive task asynchronously with some delay
- * so that it doesn't block the UI thread immediately.
- */
-export function runAsyncTask<T>(message: string, task: () => T): Promise<T> {
-  updateMessage(message);
-  return new Promise<T>((resolve, reject) => {
-    d3.timer(() => {
-      try {
-        let result = task();
-        // Clearing the old message.
-        updateMessage();
-        resolve(result);
-      } catch (ex) {
-        updateMessage('Error: ' + ex.message);
-        reject(ex);
-      }
-      return true;
-    }, ASYNC_DELAY);
-  });
-}
-
-/**
- * Updates the user message at the top of the page. If the provided msg is
- * null, the message box is hidden from the user.
- */
-export function updateMessage(msg?: string): void {
-  if (msg == null) {
-    d3.select('#notify-msg').style('display', 'none');
-  } else {
-    d3.select('#notify-msg').style('display', 'block').text(msg);
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz-projector/bh_tsne.ts b/tensorflow/tensorboard/components/vz-projector/bh_tsne.ts
deleted file mode 100644
index 95c207ec6c..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/bh_tsne.ts
+++ /dev/null
@@ -1,472 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/**
- * This is a fork of the Karpathy's TSNE.js (original license below).
- * This fork implements Barnes-Hut approximation and runs in O(NlogN)
- * time, as opposed to the Karpathy's O(N^2) version.
- *
- * @author smilkov@google.com (Daniel Smilkov)
- */
-
-/**
- * The MIT License (MIT)
- * Copyright (c) 2015 Andrej Karpathy
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in
- * all copies or substantial portions of the Software.
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
- * THE SOFTWARE.
- */
-
-import {SPNode, SPTree} from './sptree';
-
-type AugmSPNode = SPNode&{numCells: number, yCell: number[], rCell: number};
-
-/**
- * Barnes-hut approximation level. Higher means more approximation and faster
- * results. Recommended value mentioned in the paper is 0.8.
- */
-const THETA = 0.8;
-
-// Variables used for memorizing the second random number since running
-// gaussRandom() generates two random numbers at the cost of 1 atomic
-// computation. This optimization results in 2X speed-up of the generator.
-let return_v = false;
-let v_val = 0.0;
-
-/** Returns a vector filled with zeros */
-function zerosArray(length: number): number[] {
-  let result = new Array(length);
-  for (let i = 0; i < length; ++i) {
-    result[i] = 0;
-  }
-  return result;
-}
-
-
-/** Returns the square euclidean distance between two vectors. */
-export function dist2(a: number[], b: number[]): number {
-  if (a.length != b.length) {
-    throw new Error('Vectors a and b must be of same length');
-  }
-
-  let result = 0;
-  for (let i = 0; i < a.length; ++i) {
-    let diff = a[i] - b[i];
-    result += diff * diff;
-  }
-  return result;
-}
-
-/** Returns the square euclidean distance between two 2D points. */
-export function dist2_2D(a: number[], b: number[]): number {
-  let dX = a[0] - b[0];
-  let dY = a[1] - b[1];
-  return dX * dX + dY * dY;
-}
-
-/** Returns the square euclidean distance between two 3D points. */
-export function dist2_3D(a: number[], b: number[]): number {
-  let dX = a[0] - b[0];
-  let dY = a[1] - b[1];
-  let dZ = a[2] - b[2];
-  return dX * dX + dY * dY + dZ * dZ;
-}
-
-function gaussRandom(rng: () => number): number {
-  if (return_v) {
-    return_v = false;
-    return v_val;
-  }
-  let u = 2 * rng() - 1;
-  let v = 2 * rng() - 1;
-  let r = u * u + v * v;
-  if (r == 0 || r > 1) {
-    return gaussRandom(rng);
-  }
-  let c = Math.sqrt(-2 * Math.log(r) / r);
-  v_val = v * c;  // cache this for next function call for efficiency
-  return_v = true;
-  return u * c;
-};
-
-// return random normal number
-function randn(rng: () => number, mu: number, std: number) {
-  return mu + gaussRandom(rng) * std;
-};
-
-// utilitity that creates contiguous vector of zeros of size n
-function zeros(n: number): Float64Array {
-  return new Float64Array(n);
-};
-
-// utility that returns a matrix filled with random numbers
-// generated by the provided generator.
-function randnMatrix(n: number, d: number, rng: () => number) {
-  let nd = n * d;
-  let x = zeros(nd);
-  for (let i = 0; i < nd; ++i) {
-    x[i] = randn(rng, 0.0, 1E-4);
-  }
-  return x;
-};
-
-// utility that returns a matrix filled with the provided value.
-function arrayofs(n: number, d: number, val: number) {
-  let x: number[][] = [];
-  for (let i = 0; i < n; ++i) {
-    let row = new Array(d);
-    for (let j = 0; j < d; ++j) {
-      row[j] = val;
-    }
-    x.push(row);
-  }
-  return x;
-};
-
-// compute (p_{i|j} + p_{j|i})/(2n)
-function nearest2P(
-    nearest: {index: number, dist: number}[][], perplexity: number,
-    tol: number) {
-  let N = nearest.length;
-  let Htarget = Math.log(perplexity);  // target entropy of distribution
-  let P = zeros(N * N);                // temporary probability matrix
-  let K = nearest[0].length;
-  let pRow: number[] = new Array(K);  // pij[].
-
-  for (let i = 0; i < N; ++i) {
-    let neighbors = nearest[i];
-    let betaMin = -Infinity;
-    let betaMax = Infinity;
-    let beta = 1;  // initial value of precision
-    let maxTries = 50;
-
-    // perform binary search to find a suitable precision beta
-    // so that the entropy of the distribution is appropriate
-    let numTries = 0;
-    while (true) {
-      // compute entropy and kernel row with beta precision
-      let psum = 0.0;
-      for (let k = 0; k < neighbors.length; ++k) {
-        let neighbor = neighbors[k];
-        let pij = (i == neighbor.index) ? 0 : Math.exp(-neighbor.dist * beta);
-        pRow[k] = pij;
-        psum += pij;
-      }
-      // normalize p and compute entropy
-      let Hhere = 0.0;
-      for (let k = 0; k < pRow.length; ++k) {
-        pRow[k] /= psum;
-        let pij = pRow[k];
-        if (pij > 1E-7) {
-          Hhere -= pij * Math.log(pij);
-        };
-      }
-
-      // adjust beta based on result
-      if (Hhere > Htarget) {
-        // entropy was too high (distribution too diffuse)
-        // so we need to increase the precision for more peaky distribution
-        betaMin = beta;  // move up the bounds
-        if (betaMax === Infinity) {
-          beta = beta * 2;
-        } else {
-          beta = (beta + betaMax) / 2;
-        }
-
-      } else {
-        // converse case. make distrubtion less peaky
-        betaMax = beta;
-        if (betaMin === -Infinity) {
-          beta = beta / 2;
-        } else {
-          beta = (beta + betaMin) / 2;
-        }
-      }
-      numTries++;
-      // stopping conditions: too many tries or got a good precision
-      if (numTries >= maxTries || Math.abs(Hhere - Htarget) < tol) {
-        break;
-      }
-    }
-
-    // copy over the final prow to P at row i
-    for (let k = 0; k < pRow.length; ++k) {
-      let pij = pRow[k];
-      let j = neighbors[k].index;
-      P[i * N + j] = pij;
-    }
-  }  // end loop over examples i
-
-  // symmetrize P and normalize it to sum to 1 over all ij
-  let N2 = N * 2;
-  for (let i = 0; i < N; ++i) {
-    for (let j = i + 1; j < N; ++j) {
-      let i_j = i * N + j;
-      let j_i = j * N + i;
-      let value = (P[i_j] + P[j_i]) / N2;
-      P[i_j] = value;
-      P[j_i] = value;
-    }
-  }
-  return P;
-};
-
-// helper function
-function sign(x: number) {
-  return x > 0 ? 1 : x < 0 ? -1 : 0;
-}
-
-export interface TSNEOptions {
-  /** How many dimensions. */
-  dim: number;
-  /** Roughly how many neighbors each point influences. */
-  perplexity?: number;
-  /** Learning rate. */
-  epsilon?: number;
-  /** A random number generator. */
-  rng?: () => number;
-}
-
-export class TSNE {
-  private perplexity: number;
-  private epsilon: number;
-  /** Random generator */
-  private rng: () => number;
-  private iter = 0;
-  private Y: Float64Array;
-  private N: number;
-  private P: Float64Array;
-  private gains: number[][];
-  private ystep: number[][];
-  private nearest: {index: number, dist: number}[][];
-  private dim: number;
-  private dist2: (a: number[], b: number[]) => number;
-
-  constructor(opt: TSNEOptions) {
-    opt = opt || {dim: 2};
-    this.perplexity = opt.perplexity || 30;
-    this.epsilon = opt.epsilon || 10;
-    this.rng = opt.rng || Math.random;
-    this.dim = opt.dim;
-    if (opt.dim == 2) {
-      this.dist2 = dist2_2D;
-    } else if (opt.dim == 3) {
-      this.dist2 = dist2_3D;
-    } else {
-      this.dist2 = dist2;
-    }
-  }
-
-  // this function takes a fattened distance matrix and creates
-  // matrix P from them.
-  // D is assumed to be provided as an array of size N^2.
-  initDataDist(nearest: {index: number, dist: number}[][]) {
-    let N = nearest.length;
-    this.nearest = nearest;
-    this.P = nearest2P(nearest, this.perplexity, 1E-4);
-    this.N = N;
-    this.initSolution();  // refresh this
-  }
-
-  // (re)initializes the solution to random
-  initSolution() {
-    // generate random solution to t-SNE
-    this.Y = randnMatrix(this.N, this.dim, this.rng);  // the solution
-    this.gains = arrayofs(this.N, this.dim, 1.0);      // step gains
-    // to accelerate progress in unchanging directions
-    this.ystep = arrayofs(this.N, this.dim, 0.0);  // momentum accumulator
-    this.iter = 0;
-  }
-
-  // return pointer to current solution
-  getSolution() { return this.Y; }
-
-  // perform a single step of optimization to improve the embedding
-  step() {
-    this.iter += 1;
-    let N = this.N;
-
-    let grad = this.costGrad(this.Y);  // evaluate gradient
-
-    // perform gradient step
-    let ymean = zerosArray(this.dim);
-    for (let i = 0; i < N; ++i) {
-      for (let d = 0; d < this.dim; ++d) {
-        let gid = grad[i][d];
-        let sid = this.ystep[i][d];
-        let gainid = this.gains[i][d];
-
-        // compute gain update
-        let newgain = sign(gid) === sign(sid) ? gainid * 0.8 : gainid + 0.2;
-        if (newgain < 0.01) {
-          newgain = 0.01;  // clamp
-        }
-        this.gains[i][d] = newgain;  // store for next turn
-
-        // compute momentum step direction
-        let momval = this.iter < 250 ? 0.5 : 0.8;
-        let newsid = momval * sid - this.epsilon * newgain * grad[i][d];
-        this.ystep[i][d] = newsid;  // remember the step we took
-
-        // step!
-        let i_d = i * this.dim + d;
-        this.Y[i_d] += newsid;
-        ymean[d] += this.Y[i_d];  // accumulate mean so that we
-                                  // can center later
-      }
-    }
-
-    // reproject Y to be zero mean
-    for (let i = 0; i < N; ++i) {
-      for (let d = 0; d < this.dim; ++d) {
-        this.Y[i * this.dim + d] -= ymean[d] / N;
-      }
-    }
-  }
-
-  // return cost and gradient, given an arrangement
-  costGrad(Y: Float64Array): number[][] {
-    let N = this.N;
-    let P = this.P;
-
-    // Trick that helps with local optima.
-    let alpha = this.iter < 100 ? 4 : 1;
-
-    // Make data for the SP tree.
-    let points: number[][] = new Array(N);  // (x, y)[]
-    for (let i = 0; i < N; ++i) {
-      let iTimesD = i * this.dim;
-      let row = new Array(this.dim);
-      for (let d = 0; d < this.dim; ++d) {
-        row[d] = Y[iTimesD + d];
-      }
-      points[i] = row;
-    }
-
-    // Make a tree.
-    let tree = new SPTree(points, 1);
-    let root = tree.root as AugmSPNode;
-    // Annotate the tree.
-
-    let annotateTree =
-        (node: AugmSPNode): {numCells: number, yCell: number[]} => {
-          let numCells = node.points ? node.points.length : 0;
-          if (node.children == null) {
-            // Update the current node and tell the parent.
-            node.numCells = numCells;
-            // TODO(smilkov): yCell should be average across all points.
-            node.yCell = node.points[0];
-            return {numCells, yCell: node.yCell};
-          }
-          // TODO(smilkov): yCell should be average across all points.
-          let yCell =
-              node.points ? node.points[0].slice() : zerosArray(this.dim);
-          for (let i = 0; i < node.children.length; ++i) {
-            let child = node.children[i];
-            if (child == null) {
-              continue;
-            }
-            let result = annotateTree(child as AugmSPNode);
-            numCells += result.numCells;
-            for (let d = 0; d < this.dim; ++d) {
-              yCell[d] += result.yCell[d];
-            }
-          }
-          // Update the node and tell the parent.
-          node.numCells = numCells;
-          node.yCell = yCell.map(v => v / numCells);
-          return {numCells, yCell};
-        };
-
-    // Augment the tree with more info.
-    annotateTree(root);
-    tree.visit((node: AugmSPNode, low: number[], high: number[]) => {
-      node.rCell = high[0] - low[0];
-      return false;
-    });
-    // compute current Q distribution, unnormalized first
-    let grad: number[][] = [];
-    let Z = 0;
-    let forces: [number[], number[]][] = new Array(N);
-    for (let i = 0; i < N; ++i) {
-      let pointI = points[i];
-      // Compute the positive forces for the i-th node.
-      let Fpos = zerosArray(this.dim);
-      let neighbors = this.nearest[i];
-      for (let k = 0; k < neighbors.length; ++k) {
-        let j = neighbors[k].index;
-        let pij = P[i * N + j];
-        let pointJ = points[j];
-        let squaredDistItoJ = this.dist2(pointI, pointJ);
-        let premult = pij / (1 + squaredDistItoJ);
-        for (let d = 0; d < this.dim; ++d) {
-          Fpos[d] += premult * (pointI[d] - pointJ[d]);
-        }
-      }
-      // Compute the negative forces for the i-th node.
-      let FnegZ = zerosArray(this.dim);
-      tree.visit((node: AugmSPNode) => {
-        let squaredDistToCell = this.dist2(pointI, node.yCell);
-        // Squared distance from point i to cell.
-        if (node.children == null ||
-            (node.rCell / Math.sqrt(squaredDistToCell) < THETA)) {
-          let qijZ = 1 / (1 + squaredDistToCell);
-          let dZ = node.numCells * qijZ;
-          Z += dZ;
-          dZ *= qijZ;
-          for (let d = 0; d < this.dim; ++d) {
-            FnegZ[d] += dZ * (pointI[d] - node.yCell[d]);
-          }
-          return true;
-        }
-        if (node.points != null) {
-          // TODO(smilkov): Iterate over all points.
-          let squaredDistToPoint = this.dist2(pointI, node.points[0]);
-          let qijZ = 1 / (1 + squaredDistToPoint);
-          Z += qijZ;
-          qijZ *= qijZ;
-          for (let d = 0; d < this.dim; ++d) {
-            FnegZ[d] += qijZ * (pointI[d] - node.points[0][d]);
-          }
-        }
-        return false;
-      }, true);
-      forces[i] = [Fpos, FnegZ];
-    }
-    // Normalize the negative forces and compute the gradient.
-    for (let i = 0; i < N; ++i) {
-      let [FPos, FNegZ] = forces[i];
-      let gsum = new Array(this.dim);
-      for (let d = 0; d < this.dim; ++d) {
-        gsum[d] = 4 * (alpha * FPos[d] - FNegZ[d] / Z);
-      }
-      grad.push(gsum);
-    }
-    return grad;
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz-projector/data.ts b/tensorflow/tensorboard/components/vz-projector/data.ts
deleted file mode 100644
index dc23defb4a..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/data.ts
+++ /dev/null
@@ -1,324 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {runAsyncTask} from './async';
-import {TSNE} from './bh_tsne';
-import * as knn from './knn';
-import * as scatter from './scatter';
-import {shuffle} from './util';
-import * as vector from './vector';
-
-
-/**
- * A DataSource is our ground truth data. The original parsed data should never
- * be modified, only copied out.
- */
-export class DataSource {
-  originalDataSet: DataSet;
-  spriteImage: HTMLImageElement;
-  metadata: DatasetMetadata;
-
-  /** A shallow-copy constructor. */
-  makeShallowCopy(): DataSource {
-    let copy = new DataSource();
-    copy.originalDataSet = this.originalDataSet;
-    copy.spriteImage = this.spriteImage;
-    copy.metadata = this.metadata;
-    return copy;
-  }
-
-  /** Returns a new dataset. */
-  getDataSet(subset?: number[]): DataSet {
-    let pointsSubset = subset ?
-        subset.map(i => this.originalDataSet.points[i]) :
-        this.originalDataSet.points;
-    return new DataSet(pointsSubset);
-  }
-}
-
-export interface DataPoint extends scatter.DataPoint {
-  /** The point in the original space. */
-  vector: number[];
-
-  /*
-   * Metadata for each point. Each metadata is a set of key/value pairs
-   * where the value can be a string or a number.
-   */
-  metadata: {[key: string]: number | string};
-
-  /** This is where the calculated projections space are cached */
-  projections: {[key: string]: number};
-}
-
-/** Checks to see if the browser supports webgl. */
-function hasWebGLSupport(): boolean {
-  try {
-    let c = document.createElement('canvas');
-    let gl = c.getContext('webgl') || c.getContext('experimental-webgl');
-    return gl != null && typeof weblas !== 'undefined';
-  } catch (e) {
-    return false;
-  }
-}
-
-const WEBGL_SUPPORT = hasWebGLSupport();
-const MAX_TSNE_ITERS = 500;
-/**
- * Sampling is used when computing expensive operations such as PCA, or T-SNE.
- */
-const SAMPLE_SIZE = 10000;
-/** Number of dimensions to sample when doing approximate PCA. */
-const PCA_SAMPLE_DIM = 100;
-/** Number of pca components to compute. */
-const NUM_PCA_COMPONENTS = 10;
-/** Reserved metadata attribute used for trace information. */
-const TRACE_METADATA_ATTR = '__next__';
-
-/**
- * Dataset contains a DataPoints array that should be treated as immutable. This
- * acts as a working subset of the original data, with cached properties
- * from computationally expensive operations. Because creating a subset
- * requires normalizing and shifting the vector space, we make a copy of the
- * data so we can still always create new subsets based on the original data.
- */
-export class DataSet implements scatter.DataSet {
-  points: DataPoint[];
-  traces: scatter.DataTrace[];
-
-  sampledDataIndices: number[] = [];
-
-  /**
-   * This keeps a list of all current projections so you can easily test to see
-   * if it's been calculated already.
-   */
-  projections = d3.set();
-  nearest: knn.NearestEntry[][];
-  nearestK: number;
-  tSNEShouldStop = true;
-  dim = [0, 0];
-  private tsne: TSNE;
-
-  /**
-   * Creates a new Dataset by copying out data from an array of datapoints.
-   * We make a copy because we have to modify the vectors by normalizing them.
-   */
-  constructor(points: DataPoint[]) {
-    // Keep a list of indices seen so we don't compute traces for a given
-    // point twice.
-    let indicesSeen: boolean[] = [];
-
-    this.points = [];
-    points.forEach(dp => {
-      this.points.push({
-        metadata: dp.metadata,
-        dataSourceIndex: dp.dataSourceIndex,
-        vector: dp.vector.slice(),
-        projectedPoint: {
-          x: 0,
-          y: 0,
-          z: 0,
-        },
-        projections: {}
-      });
-      indicesSeen.push(false);
-    });
-
-    this.sampledDataIndices =
-        shuffle(d3.range(this.points.length)).slice(0, SAMPLE_SIZE);
-    this.traces = this.computeTraces(points, indicesSeen);
-
-    this.normalize();
-    this.dim = [this.points.length, this.points[0].vector.length];
-  }
-
-  private computeTraces(points: DataPoint[], indicesSeen: boolean[]) {
-    // Compute traces.
-    let indexToTrace: {[index: number]: scatter.DataTrace} = {};
-    let traces: scatter.DataTrace[] = [];
-    for (let i = 0; i < points.length; i++) {
-      if (indicesSeen[i]) {
-        continue;
-      }
-      indicesSeen[i] = true;
-
-      // Ignore points without a trace attribute.
-      let next = points[i].metadata[TRACE_METADATA_ATTR];
-      if (next == null || next === '') {
-        continue;
-      }
-      if (next in indexToTrace) {
-        let existingTrace = indexToTrace[+next];
-        // Pushing at the beginning of the array.
-        existingTrace.pointIndices.unshift(i);
-        indexToTrace[i] = existingTrace;
-        continue;
-      }
-      // The current point is pointing to a new/unseen trace.
-      let newTrace: scatter.DataTrace = {pointIndices: []};
-      indexToTrace[i] = newTrace;
-      traces.push(newTrace);
-      let currentIndex = i;
-      while (points[currentIndex]) {
-        newTrace.pointIndices.push(currentIndex);
-        let next = points[currentIndex].metadata[TRACE_METADATA_ATTR];
-        if (next != null && next !== '') {
-          indicesSeen[+next] = true;
-          currentIndex = +next;
-        } else {
-          currentIndex = -1;
-        }
-      }
-    }
-    return traces;
-  }
-
-
-  /**
-   * Computes the centroid, shifts all points to that centroid,
-   * then makes them all unit norm.
-   */
-  private normalize() {
-    // Compute the centroid of all data points.
-    let centroid =
-        vector.centroid(this.points, () => true, a => a.vector).centroid;
-    if (centroid == null) {
-      throw Error('centroid should not be null');
-    }
-    // Shift all points by the centroid and make them unit norm.
-    for (let id = 0; id < this.points.length; ++id) {
-      let dataPoint = this.points[id];
-      dataPoint.vector = vector.sub(dataPoint.vector, centroid);
-      vector.unit(dataPoint.vector);
-    }
-  }
-
-  /** Projects the dataset onto a given vector and caches the result. */
-  projectLinear(dir: vector.Vector, label: string) {
-    this.projections.add(label);
-    this.points.forEach(dataPoint => {
-      dataPoint.projections[label] = vector.dot(dataPoint.vector, dir);
-    });
-  }
-
-  /** Projects the dataset along the top 10 principal components. */
-  projectPCA(): Promise<void> {
-    if (this.projections.has('pca-0')) {
-      return Promise.resolve<void>();
-    }
-    return runAsyncTask('Computing PCA...', () => {
-      // Approximate pca vectors by sampling the dimensions.
-      let numDim = Math.min(this.points[0].vector.length, PCA_SAMPLE_DIM);
-      let reducedDimData =
-          vector.projectRandom(this.points.map(d => d.vector), numDim);
-      let sigma = numeric.div(
-          numeric.dot(numeric.transpose(reducedDimData), reducedDimData),
-          reducedDimData.length);
-      let U: any;
-      U = numeric.svd(sigma).U;
-      let pcaVectors = reducedDimData.map(vector => {
-        let newV: number[] = [];
-        for (let d = 0; d < NUM_PCA_COMPONENTS; d++) {
-          let dot = 0;
-          for (let i = 0; i < vector.length; i++) {
-            dot += vector[i] * U[i][d];
-          }
-          newV.push(dot);
-        }
-        return newV;
-      });
-      for (let j = 0; j < NUM_PCA_COMPONENTS; j++) {
-        let label = 'pca-' + j;
-        this.projections.add(label);
-        this.points.forEach(
-            (d, i) => { d.projections[label] = pcaVectors[i][j]; });
-      }
-    });
-  }
-
-  /** Runs tsne on the data. */
-  projectTSNE(
-      perplexity: number, learningRate: number, tsneDim: number,
-      stepCallback: (iter: number) => void) {
-    let k = Math.floor(3 * perplexity);
-    let opt = {epsilon: learningRate, perplexity: perplexity, dim: tsneDim};
-    this.tsne = new TSNE(opt);
-    this.tSNEShouldStop = false;
-    let iter = 0;
-
-    let step = () => {
-      if (this.tSNEShouldStop || iter > MAX_TSNE_ITERS) {
-        stepCallback(null);
-        return;
-      }
-      this.tsne.step();
-      let result = this.tsne.getSolution();
-      this.sampledDataIndices.forEach((index, i) => {
-        let dataPoint = this.points[index];
-
-        dataPoint.projections['tsne-0'] = result[i * tsneDim + 0];
-        dataPoint.projections['tsne-1'] = result[i * tsneDim + 1];
-        if (tsneDim === 3) {
-          dataPoint.projections['tsne-2'] = result[i * tsneDim + 2];
-        }
-      });
-      iter++;
-      stepCallback(iter);
-      requestAnimationFrame(step);
-    };
-
-    // Nearest neighbors calculations.
-    let knnComputation: Promise<knn.NearestEntry[][]>;
-
-    if (this.nearest != null && k === this.nearestK) {
-      // We found the nearest neighbors before and will reuse them.
-      knnComputation = Promise.resolve(this.nearest);
-    } else {
-      let sampledData = this.sampledDataIndices.map(i => this.points[i]);
-      this.nearestK = k;
-      knnComputation = WEBGL_SUPPORT ?
-          knn.findKNNGPUCosine(sampledData, k, (d => d.vector)) :
-          knn.findKNN(
-              sampledData, k, (d => d.vector),
-              (a, b, limit) => vector.cosDistNorm(a, b));
-    }
-    knnComputation.then(nearest => {
-      this.nearest = nearest;
-      runAsyncTask('Initializing T-SNE...', () => {
-        this.tsne.initDataDist(this.nearest);
-      }).then(step);
-
-
-    });
-  }
-
-  stopTSNE() { this.tSNEShouldStop = true; }
-}
-
-export interface DatasetMetadata {
-  /**
-   * Metadata for an associated image sprite. The sprite should be a matrix
-   * of smaller images, filled in a row-by-row order.
-   *
-   * E.g. the image for the first data point should be in the upper-left
-   * corner, and to the right of it should be the image of the second data
-   * point.
-   */
-  image?: {
-    /** The file path pointing to the sprite image. */
-    sprite_fpath: string;
-    /** The dimensions of the image for a single data point. */
-    single_image_dim: [number, number];
-  };
-}
diff --git a/tensorflow/tensorboard/components/vz-projector/data_test.ts b/tensorflow/tensorboard/components/vz-projector/data_test.ts
deleted file mode 100644
index 07286f8bcc..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/data_test.ts
+++ /dev/null
@@ -1,66 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {DataPoint, DataSet} from './data';
-
-
-/**
- * Helper method that makes a list of points given an array of
- * trace indexes.
- *
- * @param traces The i-th entry holds the 'next' attribute for the i-th point.
- */
-function makePointsWithTraces(traces: number[]) {
-  let nextAttr = '__next__';
-  let points: DataPoint[] = [];
-  traces.forEach((t, i) => {
-    let metadata: {[key: string]: any} = {};
-    metadata[nextAttr] = t >= 0 ? t : null;
-    points.push({
-      vector: [],
-      metadata: metadata,
-      projections: {},
-      projectedPoint: null,
-      dataSourceIndex: i
-    });
-  });
-  return points;
-}
-
-const assert = chai.assert;
-
-it('Simple forward pointing traces', () => {
-  // The input is: 0->2, 1->None, 2->3, 3->None. This should return
-  // one trace 0->2->3.
-  let points = makePointsWithTraces([2, -1, 3, -1]);
-  let dataset = new DataSet(points);
-  assert.equal(dataset.traces.length, 1);
-  assert.deepEqual(dataset.traces[0].pointIndices, [0, 2, 3]);
-});
-
-it('No traces', () => {
-  let points = makePointsWithTraces([-1, -1, -1, -1]);
-  let dataset = new DataSet(points);
-  assert.equal(dataset.traces.length, 0);
-});
-
-it('A trace that goes backwards and forward in the array', () => {
-  // The input is: 0->2, 1->0, 2->nothing, 3->1. This should return
-  // one trace 3->1->0->2.
-  let points = makePointsWithTraces([2, 0, -1, 1]);
-  let dataset = new DataSet(points);
-  assert.equal(dataset.traces.length, 1);
-  assert.deepEqual(dataset.traces[0].pointIndices, [3, 1, 0, 2]);
-});
diff --git a/tensorflow/tensorboard/components/vz-projector/demo/index.html b/tensorflow/tensorboard/components/vz-projector/demo/index.html
deleted file mode 100644
index 17ca273a3f..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/demo/index.html
+++ /dev/null
@@ -1,73 +0,0 @@
-<!DOCTYPE html>
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-<html>
-<head>
-  <link rel="icon" type="image/png" href="favicon.png">
-  <!-- Polyfill for non-Chrome browsers -->
-  <script src="../../webcomponentsjs/webcomponents-lite.js"></script>
-  <link rel="import" href="../vz-projector.html">
-  <link rel="import" href="../../paper-icon-button/paper-icon-button.html">
-  <!-- TODO(jart): Refactor js_binary rule into ../ rather than ../../ -->
-  <script src="../../bundle.js"></script>
-  <title>Embedding projector - visualization of high-dimensional data</title>
-  <style>
-    html {
-      width: 100%;
-      height: 100%;
-    }
-
-    body {
-      display: flex;
-      flex-direction: column;
-      font-family: "Roboto", "Helvetica", "Arial", sans-serif;
-      margin: 0;
-      width: 100%;
-      height: 100%;
-    }
-
-    #appbar {
-      display: flex;
-      align-items: center;
-      justify-content: space-between;
-      padding: 0 24px;
-      height: 60px;
-      color: white;
-      background: black;
-    }
-
-    #appbar .logo {
-      font-size: 18px;
-      font-weight: 300;
-    }
-
-    .icons {
-      display: flex;
-    }
-
-    .icons a {
-      color: white;
-    }
-  </style>
-</head>
-<body>
-  <div id="appbar">
-    <div>Embedding Projector</div>
-  </div>
-  <vz-projector></vz-projector>
-</body>
-</html>
diff --git a/tensorflow/tensorboard/components/vz-projector/external.d.ts b/tensorflow/tensorboard/components/vz-projector/external.d.ts
deleted file mode 100644
index 673896557b..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/external.d.ts
+++ /dev/null
@@ -1,25 +0,0 @@
-// TODO(smilkov): Split into weblas.d.ts and numeric.d.ts and write
-// typings for numeric.
-interface Tensor {
-  new(size: [number, number], data: Float32Array);
-  transfer(): Float32Array;
-  delete(): void;
-}
-
-interface Weblas {
-  sgemm(M: number, N: number, K: number, alpha: number,
-      A: Float32Array, B: Float32Array, beta: number, C: Float32Array):
-      Float32Array;
-  pipeline: {
-     Tensor: Tensor;
-     sgemm(alpha: number, A: Tensor, B: Tensor, beta: number,
-         C: Tensor): Tensor;
-  };
-  util: {
-    transpose(M: number, N: number, data: Float32Array): Tensor;
-  };
-
-}
-
-declare let numeric: any;
-declare let weblas: Weblas;
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/vz-projector/heap.ts b/tensorflow/tensorboard/components/vz-projector/heap.ts
deleted file mode 100644
index 35f178e000..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/heap.ts
+++ /dev/null
@@ -1,146 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/** Min key heap. */
-export type HeapItem<T> = {
-  key: number,
-  value: T
-};
-
-/**
- * Min-heap data structure. Provides O(1) for peek, returning the smallest key.
- */
-// TODO(jart): Rename to Heap and use Comparator.
-export class MinHeap<T> {
-  private arr: HeapItem<T>[] = [];
-
-  /** Push an element with the provided key. */
-  push(key: number, value: T): void {
-    this.arr.push({key, value});
-    this.bubbleUp(this.arr.length - 1);
-  }
-
-  /** Pop the element with the smallest key. */
-  pop(): HeapItem<T> {
-    if (this.arr.length === 0) {
-      throw new Error('pop() called on empty binary heap');
-    }
-    let item = this.arr[0];
-    let last = this.arr.length - 1;
-    this.arr[0] = this.arr[last];
-    this.arr.pop();
-    if (last > 0) {
-      this.bubbleDown(0);
-    }
-    return item;
-  };
-
-  /** Returns, but doesn't remove the element with the smallest key */
-  peek(): HeapItem<T> { return this.arr[0]; }
-
-  /**
-   * Pops the element with the smallest key and at the same time
-   * adds the newly provided element. This is faster than calling
-   * pop() and push() separately.
-   */
-  popPush(key: number, value: T): HeapItem<T> {
-    if (this.arr.length === 0) {
-      throw new Error('pop() called on empty binary heap');
-    }
-    let item = this.arr[0];
-    this.arr[0] = {key, value};
-    if (this.arr.length > 0) {
-      this.bubbleDown(0);
-    }
-    return item;
-  }
-
-  /** Returns the number of elements in the heap. */
-  size(): number { return this.arr.length; }
-
-  /** Returns all the items in the heap. */
-  items(): HeapItem<T>[] { return this.arr; }
-
-  private swap(a: number, b: number) {
-    let temp = this.arr[a];
-    this.arr[a] = this.arr[b];
-    this.arr[b] = temp;
-  }
-
-  private bubbleDown(pos: number) {
-    let left = (pos << 1) + 1;
-    let right = left + 1;
-    let largest = pos;
-    if (left < this.arr.length && this.arr[left].key < this.arr[largest].key) {
-      largest = left;
-    }
-    if (right < this.arr.length &&
-        this.arr[right].key < this.arr[largest].key) {
-      largest = right;
-    }
-    if (largest != pos) {
-      this.swap(largest, pos);
-      this.bubbleDown(largest);
-    }
-  }
-
-  private bubbleUp(pos: number) {
-    if (pos <= 0) {
-      return;
-    }
-    let parent = ((pos - 1) >> 1);
-    if (this.arr[pos].key < this.arr[parent].key) {
-      this.swap(pos, parent);
-      this.bubbleUp(parent);
-    }
-  }
-}
-
-/** List that keeps the K elements with the smallest keys. */
-export class KMin<T> {
-  private k: number;
-  private maxHeap = new MinHeap<T>();
-
-  /** Constructs a new k-min data structure with the provided k. */
-  constructor(k: number) { this.k = k; }
-
-  /** Adds an element to the list. */
-  add(key: number, value: T) {
-    if (this.maxHeap.size() < this.k) {
-      this.maxHeap.push(-key, value);
-      return;
-    }
-    let largest = this.maxHeap.peek();
-    // If the new element is smaller, replace the largest with the new element.
-    if (key < -largest.key) {
-      this.maxHeap.popPush(-key, value);
-    }
-  }
-
-  /** Returns the k items with the smallest keys. */
-  getMinKItems(): T[] {
-    let items = this.maxHeap.items();
-    items.sort((a, b) => b.key - a.key);
-    return items.map(a => a.value);
-  }
-
-  /** Returns the size of the list. */
-  getSize(): number { return this.maxHeap.size(); }
-
-  /** Returns the largest key in the list. */
-  getLargestKey(): number {
-    return this.maxHeap.size() == 0 ? null : -this.maxHeap.peek().key;
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz-projector/knn.ts b/tensorflow/tensorboard/components/vz-projector/knn.ts
deleted file mode 100644
index 4d64595a3f..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/knn.ts
+++ /dev/null
@@ -1,223 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {runAsyncTask} from './async';
-import {KMin} from './heap';
-import * as vector from './vector';
-
-export type NearestEntry = {
-  index: number,
-  dist: number
-};
-
-/**
- * Optimal size for the height of the matrix when doing computation on the GPU
- * using WebGL. This was found experimentally.
- *
- * This also guarantees that for computing pair-wise distance for up to 10K
- * vectors, no more than 40MB will be allocated in the GPU. Without the
- * allocation limit, we can freeze the graphics of the whole OS.
- */
-const OPTIMAL_GPU_BLOCK_SIZE = 512;
-
-/**
- * Returns the K nearest neighbors for each vector where the distance
- * computation is done on the GPU (WebGL) using cosine distance.
- *
- * @param dataPoints List of data points, where each data point holds an
- *   n-dimensional vector.
- * @param k Number of nearest neighbors to find.
- * @param accessor A method that returns the vector, given the data point.
- */
-export function findKNNGPUCosine<T>(
-    dataPoints: T[], k: number,
-    accessor: (dataPoint: T) => number[]): Promise<NearestEntry[][]> {
-  let N = dataPoints.length;
-  let dim = accessor(dataPoints[0]).length;
-
-  // The goal is to compute a large matrix multiplication A*A.T where A is of
-  // size NxD and A.T is its transpose. This results in a NxN matrix which
-  // could be too big to store on the GPU memory. To avoid memory overflow, we
-  // compute multiple A*partial_A.T where partial_A is of size BxD (B is much
-  // smaller than N). This results in storing only NxB size matrices on the GPU
-  // at a given time.
-
-  // A*A.T will give us NxN matrix holding the cosine distance between every
-  // pair of points, which we sort using KMin data structure to obtain the
-  // K nearest neighbors for each point.
-  let typedArray = vector.toTypedArray(dataPoints, accessor);
-  let bigMatrix = new weblas.pipeline.Tensor([N, dim], typedArray);
-  let nearest: NearestEntry[][] = new Array(N);
-  let numPieces = Math.ceil(N / OPTIMAL_GPU_BLOCK_SIZE);
-  let M = Math.floor(N / numPieces);
-  let modulo = N % numPieces;
-  let offset = 0;
-  let progress = 0;
-  let progressDiff = 1 / (2 * numPieces);
-  let piece = 0;
-
-  function step(resolve: (result: NearestEntry[][]) => void) {
-    let progressMsg =
-        'Finding nearest neighbors: ' + (progress * 100).toFixed() + '%';
-    runAsyncTask(progressMsg, () => {
-      let B = piece < modulo ? M + 1 : M;
-      let typedB = new Float32Array(B * dim);
-      for (let i = 0; i < B; ++i) {
-        let vector = accessor(dataPoints[offset + i]);
-        for (let d = 0; d < dim; ++d) {
-          typedB[i * dim + d] = vector[d];
-        }
-      }
-      let partialMatrix = new weblas.pipeline.Tensor([B, dim], typedB);
-      // Result is N x B matrix.
-      let result =
-          weblas.pipeline.sgemm(1, bigMatrix, partialMatrix, null, null);
-      let partial = result.transfer();
-      partialMatrix.delete();
-      result.delete();
-      progress += progressDiff;
-      for (let i = 0; i < B; i++) {
-        let kMin = new KMin<NearestEntry>(k);
-        let iReal = offset + i;
-        for (let j = 0; j < N; j++) {
-          if (j === iReal) {
-            continue;
-          }
-          let cosDist = 1 - partial[j * B + i];  // [j, i];
-          kMin.add(cosDist, {index: j, dist: cosDist});
-        }
-        nearest[iReal] = kMin.getMinKItems();
-      }
-      progress += progressDiff;
-      offset += B;
-      piece++;
-    }).then(() => {
-      if (piece < numPieces) {
-        step(resolve);
-      } else {
-        bigMatrix.delete();
-        resolve(nearest);
-      }
-    });
-  }
-  return new Promise<NearestEntry[][]>(resolve => step(resolve));
-}
-
-/**
- * Returns the K nearest neighbors for each vector where the distance
- * computation is done on the CPU using a user-specified distance method.
- *
- * @param dataPoints List of data points, where each data point holds an
- *   n-dimensional vector.
- * @param k Number of nearest neighbors to find.
- * @param accessor A method that returns the vector, given the data point.
- * @param dist Method that takes two vectors and a limit, and computes the
- *   distance between two vectors, with the ability to stop early if the
- *   distance is above the limit.
- */
-export function findKNN<T>(
-    dataPoints: T[], k: number, accessor: (dataPoint: T) => number[],
-    dist: (a: number[], b: number[], limit: number) =>
-        number): Promise<NearestEntry[][]> {
-  return runAsyncTask<NearestEntry[][]>('Finding nearest neighbors...', () => {
-    let N = dataPoints.length;
-    let nearest: NearestEntry[][] = new Array(N);
-    // Find the distances from node i.
-    let kMin: KMin<NearestEntry>[] = new Array(N);
-    for (let i = 0; i < N; i++) {
-      kMin[i] = new KMin<NearestEntry>(k);
-    }
-    for (let i = 0; i < N; i++) {
-      let a = accessor(dataPoints[i]);
-      let kMinA = kMin[i];
-      for (let j = i + 1; j < N; j++) {
-        let kMinB = kMin[j];
-        let limitI = kMinA.getSize() === k ?
-            kMinA.getLargestKey() || Number.MAX_VALUE :
-            Number.MAX_VALUE;
-        let limitJ = kMinB.getSize() === k ?
-            kMinB.getLargestKey() || Number.MAX_VALUE :
-            Number.MAX_VALUE;
-        let limit = Math.max(limitI, limitJ);
-        let dist2ItoJ = dist(a, accessor(dataPoints[j]), limit);
-        if (dist2ItoJ >= 0) {
-          kMinA.add(dist2ItoJ, {index: j, dist: dist2ItoJ});
-          kMinB.add(dist2ItoJ, {index: i, dist: dist2ItoJ});
-        }
-      }
-    }
-    for (let i = 0; i < N; i++) {
-      nearest[i] = kMin[i].getMinKItems();
-    }
-    return nearest;
-  });
-}
-
-/** Calculates the minimum distance between a search point and a rectangle. */
-function minDist(
-    point: [number, number], x1: number, y1: number, x2: number, y2: number) {
-  let x = point[0];
-  let y = point[1];
-  let dx1 = x - x1;
-  let dx2 = x - x2;
-  let dy1 = y - y1;
-  let dy2 = y - y2;
-
-  if (dx1 * dx2 <= 0) {    // x is between x1 and x2
-    if (dy1 * dy2 <= 0) {  // (x,y) is inside the rectangle
-      return 0;            // return 0 as point is in rect
-    }
-    return Math.min(Math.abs(dy1), Math.abs(dy2));
-  }
-  if (dy1 * dy2 <= 0) {  // y is between y1 and y2
-    // We know it is already inside the rectangle
-    return Math.min(Math.abs(dx1), Math.abs(dx2));
-  }
-  let corner: [number, number];
-  if (x > x2) {
-    // Upper-right vs lower-right.
-    corner = y > y2 ? [x2, y2] : [x2, y1];
-  } else {
-    // Upper-left vs lower-left.
-    corner = y > y2 ? [x1, y2] : [x1, y1];
-  }
-  return Math.sqrt(vector.dist22D([x, y], corner));
-}
-
-/**
- * Returns the nearest neighbors of a particular point.
- *
- * @param dataPoints List of data points.
- * @param pointIndex The index of the point we need the nearest neighbors of.
- * @param k Number of nearest neighbors to search for.
- * @param accessor Method that maps a data point => vector (array of numbers).
- * @param distance Method that takes two vectors and returns their distance.
- */
-export function findKNNofPoint<T>(
-    dataPoints: T[], pointIndex: number, k: number,
-    accessor: (dataPoint: T) => number[],
-    distance: (a: number[], b: number[]) => number) {
-  let kMin = new KMin<NearestEntry>(k);
-  let a = accessor(dataPoints[pointIndex]);
-  for (let i = 0; i < dataPoints.length; ++i) {
-    if (i == pointIndex) {
-      continue;
-    }
-    let b = accessor(dataPoints[i]);
-    let dist = distance(a, b);
-    kMin.add(dist, {index: i, dist: dist});
-  }
-  return kMin.getMinKItems();
-}
diff --git a/tensorflow/tensorboard/components/vz-projector/label.ts b/tensorflow/tensorboard/components/vz-projector/label.ts
deleted file mode 100644
index 9689ef5869..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/label.ts
+++ /dev/null
@@ -1,151 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-export interface BoundingBox {
-  loX: number;
-  loY: number;
-  hiX: number;
-  hiY: number;
-}
-
-/**
- * Accelerates label placement by dividing the view into a uniform grid.
- * Labels only need to be tested for collision with other labels that overlap
- * the same grid cells. This is a fork of {@code amoeba.CollisionGrid}.
- */
-export class CollisionGrid {
-  private numHorizCells: number;
-  private numVertCells: number;
-  private grid: BoundingBox[][];
-  private bound: BoundingBox;
-  private cellWidth: number;
-  private cellHeight: number;
-
-  /**
-   * Constructs a new Collision grid.
-   *
-   * @param bound The bound of the grid. Labels out of bounds will be rejected.
-   * @param cellWidth Width of a cell in the grid.
-   * @param cellHeight Height of a cell in the grid.
-   */
-  constructor(bound: BoundingBox, cellWidth: number, cellHeight: number) {
-    /** The bound of the grid. Labels out of bounds will be rejected. */
-    this.bound = bound;
-
-    /** Width of a cell in the grid. */
-    this.cellWidth = cellWidth;
-
-    /** Height of a cell in the grid. */
-    this.cellHeight = cellHeight;
-
-    /** Number of grid cells along the x axis. */
-    this.numHorizCells = Math.ceil(this.boundWidth(bound) / cellWidth);
-
-    /** Number of grid cells along the y axis. */
-    this.numVertCells = Math.ceil(this.boundHeight(bound) / cellHeight);
-
-    /**
-     * The 2d grid (stored as a 1d array.) Each cell consists of an array of
-     * BoundingBoxes for objects that are in the cell.
-     */
-    this.grid = new Array(this.numHorizCells * this.numVertCells);
-  }
-
-  private boundWidth(bound: BoundingBox) { return bound.hiX - bound.loX; }
-
-  private boundHeight(bound: BoundingBox) { return bound.hiY - bound.loY; }
-
-  private boundsIntersect(a: BoundingBox, b: BoundingBox) {
-    return !(a.loX > b.hiX || a.loY > b.hiY || a.hiX < b.loX || a.hiY < b.loY);
-  }
-
-  /**
-   * Checks if a given bounding box has any conflicts in the grid and inserts it
-   * if none are found.
-   *
-   * @param bound The bound to insert.
-   * @param justTest If true, just test if it conflicts, without inserting.
-   * @return True if the bound was successfully inserted; false if it
-   *         could not be inserted due to a conflict.
-   */
-  insert(bound: BoundingBox, justTest = false): boolean {
-    // Reject if the label is out of bounds.
-    if (bound.loX < this.bound.loX || bound.hiX > this.bound.hiX ||
-        bound.loY < this.bound.loY || bound.hiY > this.bound.hiY) {
-      return false;
-    }
-
-    let minCellX = this.getCellX(bound.loX);
-    let maxCellX = this.getCellX(bound.hiX);
-    let minCellY = this.getCellY(bound.loY);
-    let maxCellY = this.getCellY(bound.hiY);
-
-    // Check all overlapped cells to verify that we can insert.
-    let baseIdx = minCellY * this.numHorizCells + minCellX;
-    let idx = baseIdx;
-    for (let j = minCellY; j <= maxCellY; j++) {
-      for (let i = minCellX; i <= maxCellX; i++) {
-        let cell = this.grid[idx++];
-        if (cell) {
-          for (let k = 0; k < cell.length; k++) {
-            if (this.boundsIntersect(bound, cell[k])) {
-              return false;
-            }
-          }
-        }
-      }
-      idx += this.numHorizCells - (maxCellX - minCellX + 1);
-    }
-
-    if (justTest) {
-      return true;
-    }
-
-    // Insert into the overlapped cells.
-    idx = baseIdx;
-    for (let j = minCellY; j <= maxCellY; j++) {
-      for (let i = minCellX; i <= maxCellX; i++) {
-        if (!this.grid[idx]) {
-          this.grid[idx] = [bound];
-        } else {
-          this.grid[idx].push(bound);
-        }
-        idx++;
-      }
-      idx += this.numHorizCells - (maxCellX - minCellX + 1);
-    }
-    return true;
-  }
-
-  /**
-   * Returns the x index of the grid cell where the given x coordinate falls.
-   *
-   * @param x the coordinate, in world space.
-   * @return the x index of the cell.
-   */
-  private getCellX(x: number) {
-    return Math.floor((x - this.bound.loX) / this.cellWidth);
-  };
-
-  /**
-   * Returns the y index of the grid cell where the given y coordinate falls.
-   *
-   * @param y the coordinate, in world space.
-   * @return the y index of the cell.
-   */
-  private getCellY(y: number) {
-    return Math.floor((y - this.bound.loY) / this.cellHeight);
-  };
-}
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/vz-projector/scatter.ts b/tensorflow/tensorboard/components/vz-projector/scatter.ts
deleted file mode 100644
index 392d8085c6..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/scatter.ts
+++ /dev/null
@@ -1,135 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-export interface Point3D {
-  /** Original x coordinate. */
-  x: number;
-  /** Original y coordinate. */
-  y: number;
-  /** Original z coordinate. */
-  z: number;
-}
-;
-
-/** The spacial data of points and lines that will be shown in the projector. */
-export interface DataSet {
-  points: DataPoint[];
-  traces: DataTrace[];
-}
-
-/**
- * Points in 3D space that will be used in the projector. If the projector is
- * in 2D mode, the Z coordinate of the point will be 0.
- */
-export interface DataPoint {
-  projectedPoint: Point3D;
-  /** index of the trace, used for highlighting on click */
-  traceIndex?: number;
-  /** index in the original data source */
-  dataSourceIndex: number;
-}
-
-/** A single collection of points which make up a trace through space. */
-export interface DataTrace {
-  /** Indices into the DataPoints array in the Data object. */
-  pointIndices: number[];
-}
-
-export type OnHoverListener = (index: number) => void;
-export type OnSelectionListener = (indexes: number[]) => void;
-
-/** Supported modes of interaction. */
-export enum Mode {
-  SELECT,
-  SEARCH,
-  HOVER
-}
-
-export interface Scatter {
-  /** Sets the data for the scatter plot. */
-  setDataSet(dataSet: DataSet, spriteImage?: HTMLImageElement): void;
-  /** Called with each data point in order to get its color. */
-  setColorAccessor(colorAccessor: ((index: number) => string)): void;
-  /** Called with each data point in order to get its label. */
-  setLabelAccessor(labelAccessor: ((index: number) => string)): void;
-  /** Called with each data point in order to get its x coordinate. */
-  setXAccessor(xAccessor: ((index: number) => number)): void;
-  /** Called with each data point in order to get its y coordinate. */
-  setYAccessor(yAccessor: ((index: number) => number)): void;
-  /** Called with each data point in order to get its z coordinate. */
-  setZAccessor(zAccessor: ((index: number) => number)): void;
-  /** Sets the interaction mode (search, select or hover). */
-  setMode(mode: Mode): void;
-  /** Returns the interaction mode. */
-  getMode(): Mode;
-  /** Resets the zoom level to 1.*/
-  resetZoom(): void;
-  /**
-   * Increases/decreases the zoom level.
-   *
-   * @param multiplier New zoom level = old zoom level * multiplier.
-   */
-  zoomStep(multiplier: number): void;
-  /**
-   * Highlights the provided points.
-   *
-   * @param pointIndexes List of point indexes to highlight. If null,
-   *   un-highlights all the points.
-   * @param stroke The stroke color used to highlight the point.
-   * @param favorLabels Whether to favor plotting the labels of the
-   *   highlighted point. Default is false for all points.
-   */
-  highlightPoints(
-      pointIndexes: number[], highlightStroke?: (index: number) => string,
-      favorLabels?: (index: number) => boolean): void;
-  /** Whether to show labels or not. */
-  showLabels(show: boolean): void;
-  /** Toggle between day and night modes. */
-  setDayNightMode(isNight: boolean): void;
-  /** Show/hide tick labels. */
-  showTickLabels(show: boolean): void;
-  /** Whether to show axes or not. */
-  showAxes(show: boolean): void;
-  /** Sets the axis labels. */
-  setAxisLabels(xLabel: string, yLabel: string): void;
-  /**
-   * Recreates the scene (demolishes all datastructures, etc.)
-   */
-  recreateScene(): void;
-  /**
-   * Redraws the data. Should be called anytime the accessor method
-   * for x and y coordinates changes, which means a new projection
-   * exists and the scatter plot should repaint the points.
-   */
-  update(): void;
-  /**
-   * Should be called to notify the scatter plot that the container
-   * was resized and it should resize and redraw itself.
-   */
-  resize(): void;
-  /** Registers a listener that will be called when selects some points. */
-  onSelection(listener: OnSelectionListener): void;
-  /**
-   * Registers a listener that will be called when the user hovers over
-   * a point.
-   */
-  onHover(listener: OnHoverListener): void;
-  /**
-   * Should emulate the same behavior as if the user clicked on the point.
-   * This is used to trigger a click from an external event, such as
-   * a search query.
-   */
-  clickOnPoint(pointIndex: number): void;
-}
diff --git a/tensorflow/tensorboard/components/vz-projector/scatterWebGL.ts b/tensorflow/tensorboard/components/vz-projector/scatterWebGL.ts
deleted file mode 100644
index 79cee64c5b..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/scatterWebGL.ts
+++ /dev/null
@@ -1,1510 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {BoundingBox, CollisionGrid} from './label';
-import {DataSet, Mode, OnHoverListener, OnSelectionListener, Point3D, Scatter} from './scatter';
-import {shuffle} from './util';
-
-const FONT_SIZE = 10;
-
-// Colors (in various necessary formats).
-const BACKGROUND_COLOR_DAY = 0xffffff;
-const BACKGROUND_COLOR_NIGHT = 0x000000;
-const AXIS_COLOR = 0xb3b3b3;
-const LABEL_COLOR_DAY = 0x000000;
-const LABEL_COLOR_NIGHT = 0xffffff;
-const LABEL_STROKE_DAY = 0xffffff;
-const LABEL_STROKE_NIGHT = 0x000000;
-const POINT_COLOR = 0x7575D9;
-const POINT_COLOR_GRAYED = 0x888888;
-const BLENDING_DAY = THREE.MultiplyBlending;
-const BLENDING_NIGHT = THREE.AdditiveBlending;
-const TRACE_START_HUE = 60;
-const TRACE_END_HUE = 360;
-const TRACE_SATURATION = 1;
-const TRACE_LIGHTNESS = .3;
-const TRACE_DEFAULT_OPACITY = .2;
-const TRACE_DEFAULT_LINEWIDTH = 2;
-const TRACE_SELECTED_OPACITY = .9;
-const TRACE_SELECTED_LINEWIDTH = 3;
-const TRACE_DESELECTED_OPACITY = .05;
-
-// Various distance bounds.
-const MAX_ZOOM = 10;
-const MIN_ZOOM = .05;
-const NUM_POINTS_FOG_THRESHOLD = 5000;
-const MIN_POINT_SIZE = 5.0;
-const IMAGE_SIZE = 30;
-
-// Constants relating to the camera parameters.
-/** Camera frustum vertical field of view. */
-const FOV = 70;
-const NEAR = 0.01;
-const FAR = 100;
-
-// Constants relating to the indices of buffer arrays.
-/** Item size of a single point in a bufferArray representing colors */
-const RGB_NUM_BYTES = 3;
-/** Item size of a single point in a bufferArray representing indices */
-const INDEX_NUM_BYTES = 1;
-/** Item size of a single point in a bufferArray representing locations */
-const XYZ_NUM_BYTES = 3;
-
-// Key presses.
-const SHIFT_KEY = 16;
-const CTRL_KEY = 17;
-
-// Original positions of camera and camera target, in 2d and 3d
-const POS_3D = {
-  x: 1.5,
-  y: 1.5,
-  z: 1.5
-};
-
-// Target for the camera in 3D is the center of the 1, 1, 1 square, as all our
-// data is scaled to this.
-const TAR_3D = {
-  x: 0,
-  y: 0,
-  z: 0
-};
-
-const POS_2D = {
-  x: 0,
-  y: 0,
-  z: 2
-};
-
-// In 3D, the target is the center of the xy plane.
-const TAR_2D = {
-  x: 0,
-  y: 0,
-  z: 0
-};
-
-// The maximum number of labels to draw to keep the frame rate up.
-const SAMPLE_SIZE = 10000;
-
-// Shaders for images.
-const VERTEX_SHADER = `
-  // Index of the specific vertex (passed in as bufferAttribute), and the
-  // variable that will be used to pass it to the fragment shader.
-  attribute float vertexIndex;
-  varying vec2 xyIndex;
-
-  // Similar to above, but for colors.
-  attribute vec3 color;
-  varying vec3 vColor;
-
-  // If the point is highlighted, this will be 1.0 (else 0.0).
-  attribute float isHighlight;
-
-  // Uniform passed in as a property from THREE.ShaderMaterial.
-  uniform bool sizeAttenuation;
-  uniform float pointSize;
-  uniform float imageWidth;
-  uniform float imageHeight;
-
-  void main() {
-    // Pass index and color values to fragment shader.
-    vColor = color;
-    xyIndex = vec2(mod(vertexIndex, imageWidth),
-              floor(vertexIndex / imageWidth));
-
-    // Transform current vertex by modelViewMatrix (model world position and
-    // camera world position matrix).
-    vec4 mvPosition = modelViewMatrix * vec4(position, 1.0);
-
-    // Project vertex in camera-space to screen coordinates using the camera's
-    // projection matrix.
-    gl_Position = projectionMatrix * mvPosition;
-
-    // Create size attenuation (if we're in 3D mode) by making the size of
-    // each point inversly proportional to its distance to the camera.
-    float attenuatedSize = - pointSize / mvPosition.z;
-    gl_PointSize = sizeAttenuation ? attenuatedSize : pointSize;
-
-    // If the point is a highlight, make it slightly bigger than the other
-    // points, and also don't let it get smaller than some threshold.
-    if (isHighlight == 1.0) {
-      gl_PointSize = max(gl_PointSize * 1.2, ${MIN_POINT_SIZE.toFixed(1)});
-    };
-  }`;
-
-const FRAGMENT_SHADER = `
-  // Values passed in from the vertex shader.
-  varying vec2 xyIndex;
-  varying vec3 vColor;
-
-  // Adding in the THREEjs shader chunks for fog.
-  ${THREE.ShaderChunk['common']}
-  ${THREE.ShaderChunk['fog_pars_fragment']}
-
-  // Uniforms passed in as properties from THREE.ShaderMaterial.
-  uniform sampler2D texture;
-  uniform float imageWidth;
-  uniform float imageHeight;
-  uniform bool isImage;
-
-  void main() {
-    // A mystery variable that is required to make the THREE shaderchunk for fog
-    // work correctly.
-    vec3 outgoingLight = vec3(0.0);
-
-    if (isImage) {
-      // Coordinates of the vertex within the entire sprite image.
-      vec2 coords = (gl_PointCoord + xyIndex) / vec2(imageWidth, imageHeight);
-      // Determine the color of the spritesheet at the calculate spot.
-      vec4 fromTexture = texture2D(texture, coords);
-
-      // Finally, set the fragment color.
-      gl_FragColor = vec4(vColor, 1.0) * fromTexture;
-    } else {
-      // Discard pixels outside the radius so points are rendered as circles.
-      vec2 uv = gl_PointCoord.xy - 0.5;
-      if (length(uv) > 0.5) discard;
-
-      // If the point is not an image, just color it.
-      gl_FragColor = vec4(vColor, 1.0);
-    }
-    ${THREE.ShaderChunk['fog_fragment']}
-  }`;
-
-export class ScatterWebGL implements Scatter {
-  // MISC UNINITIALIZED VARIABLES.
-
-  // Colors and options that are changed between Day and Night modes.
-  private backgroundColor: number;
-  private labelColor: number;
-  private labelStroke: number;
-  private blending: THREE.Blending;
-  private isNight: boolean;
-
-  // THREE.js necessities.
-  private scene: THREE.Scene;
-  private perspCamera: THREE.PerspectiveCamera;
-  private renderer: THREE.WebGLRenderer;
-  private cameraControls: any;
-  private light: THREE.PointLight;
-  private fog: THREE.Fog;
-
-  // Data structures (and THREE.js objects) associated with points.
-  private geometry: THREE.BufferGeometry;
-  /** Texture for rendering offscreen in order to enable interactive hover. */
-  private pickingTexture: THREE.WebGLRenderTarget;
-  /** Array of unique colors for each point used in detecting hover. */
-  private uniqueColArr: Float32Array;
-  private materialOptions: THREE.ShaderMaterialParameters;
-  private points: THREE.Points;
-  private traces: THREE.Line[];
-  private dataSet: DataSet;
-  private shuffledData: number[];
-  /** Holds the indexes of the points to be labeled. */
-  private labeledPoints: number[] = [];
-  private highlightedPoints: number[] = [];
-  private nearestPoint: number;
-  private pointSize2D: number;
-  private pointSize3D: number;
-  /** The buffer attribute that holds the positions of the points. */
-  private positionBufferArray: THREE.BufferAttribute;
-
-  // Accessors for rendering and labeling the points.
-  private xAccessor: (index: number) => number;
-  private yAccessor: (index: number) => number;
-  private zAccessor: (index: number) => number;
-  private labelAccessor: (index: number) => string;
-  private colorAccessor: (index: number) => string;
-  private highlightStroke: (i: number) => string;
-  private favorLabels: (i: number) => boolean;
-
-  // Scaling functions for each axis.
-  private xScale: d3.scale.Linear<number, number>;
-  private yScale: d3.scale.Linear<number, number>;
-  private zScale: d3.scale.Linear<number, number>;
-
-  // Listeners
-  private onHoverListeners: OnHoverListener[] = [];
-  private onSelectionListeners: OnSelectionListener[] = [];
-  private lazySusanAnimation: number;
-
-  // Other variables associated with layout and interaction.
-  private height: number;
-  private width: number;
-  private mode: Mode;
-  /** Whether the user has turned labels on or off. */
-  private labelsAreOn = true;
-  /** Whether the label canvas has been already cleared. */
-  private labelCanvasIsCleared = true;
-
-  private animating: boolean;
-  private axis3D: THREE.AxisHelper;
-  private axis2D: THREE.LineSegments;
-  private dpr: number;        // The device pixelratio
-  private selecting = false;  // whether or not we are selecting points.
-  private mouseIsDown = false;
-  // Whether the current click sequence contains a drag, so we can determine
-  // whether to update the selection.
-  private isDragSequence = false;
-  private selectionSphere: THREE.Mesh;
-  private image: HTMLImageElement;
-  private animationID: number;
-  /** Color of any point not selected (or NN of selected) */
-  private defaultPointColor = POINT_COLOR;
-
-  // HTML elements.
-  private gc: CanvasRenderingContext2D;
-  private containerNode: HTMLElement;
-  private canvas: HTMLCanvasElement;
-
-  /** Get things started up! */
-  constructor(
-      container: d3.Selection<any>, labelAccessor: (index: number) => string) {
-    this.labelAccessor = labelAccessor;
-    this.xScale = d3.scale.linear();
-    this.yScale = d3.scale.linear();
-    this.zScale = d3.scale.linear();
-
-    // Set up non-THREEjs layout.
-    this.containerNode = container.node() as HTMLElement;
-    this.getLayoutValues();
-
-    // For now, labels are drawn on this transparent canvas with no touch events
-    // rather than being rendered in webGL.
-    this.canvas = container.append('canvas').node() as HTMLCanvasElement;
-    this.gc = this.canvas.getContext('2d');
-    d3.select(this.canvas).style({position: 'absolute', left: 0, top: 0});
-    this.canvas.style.pointerEvents = 'none';
-
-    // Set up THREE.js.
-    this.createSceneAndRenderer();
-    this.setDayNightMode(false);
-    this.createLight();
-    this.makeCamera();
-    this.resize(false);
-    // Render now so no black background appears during startup.
-    this.renderer.render(this.scene, this.perspCamera);
-    // Add interaction listeners.
-    this.addInteractionListeners();
-  }
-
-  // SET UP
-  private addInteractionListeners() {
-    this.containerNode.addEventListener(
-        'mousemove', this.onMouseMove.bind(this));
-    this.containerNode.addEventListener(
-        'mousedown', this.onMouseDown.bind(this));
-    this.containerNode.addEventListener('mouseup', this.onMouseUp.bind(this));
-    this.containerNode.addEventListener('click', this.onClick.bind(this));
-    window.addEventListener('keydown', this.onKeyDown.bind(this), false);
-    window.addEventListener('keyup', this.onKeyUp.bind(this), false);
-  }
-
-  /** Updates the positions buffer array to reflect the actual data. */
-  private updatePositionsArray() {
-    for (let i = 0; i < this.dataSet.points.length; i++) {
-      // Set position based on projected point.
-      let pp = this.dataSet.points[i].projectedPoint;
-      this.positionBufferArray.setXYZ(i, pp.x, pp.y, pp.z);
-    }
-    if (this.geometry) {
-      this.positionBufferArray.needsUpdate = true;
-    }
-  }
-
-  /**
-   * Returns an x, y, z value for each item of our data based on the accessor
-   * methods.
-   */
-  private getPointsCoordinates() {
-    // Determine max and min of each axis of our data.
-    let xExtent = d3.extent(this.dataSet.points, (p, i) => this.xAccessor(i));
-    let yExtent = d3.extent(this.dataSet.points, (p, i) => this.yAccessor(i));
-    this.xScale.domain(xExtent).range([-1, 1]);
-    this.yScale.domain(yExtent).range([-1, 1]);
-    if (this.zAccessor) {
-      let zExtent = d3.extent(this.dataSet.points, (p, i) => this.zAccessor(i));
-      this.zScale.domain(zExtent).range([-1, 1]);
-    }
-
-    // Determine 3d coordinates of each data point.
-    this.dataSet.points.forEach((d, i) => {
-      d.projectedPoint.x = this.xScale(this.xAccessor(i));
-      d.projectedPoint.y = this.yScale(this.yAccessor(i));
-      d.projectedPoint.z =
-          (this.zAccessor ? this.zScale(this.zAccessor(i)) : 0);
-    });
-  }
-
-  private createLight() {
-    this.light = new THREE.PointLight(0xFFECBF, 1, 0);
-    this.scene.add(this.light);
-  }
-
-  /** General setup of scene and renderer. */
-  private createSceneAndRenderer() {
-    this.scene = new THREE.Scene();
-    this.renderer = new THREE.WebGLRenderer();
-    // Accouting for retina displays.
-    this.renderer.setPixelRatio(window.devicePixelRatio || 1);
-    this.renderer.setSize(this.width, this.height);
-    this.containerNode.appendChild(this.renderer.domElement);
-    this.pickingTexture = new THREE.WebGLRenderTarget(this.width, this.height);
-    this.pickingTexture.texture.minFilter = THREE.LinearFilter;
-  }
-
-  /** Set up camera and camera's controller. */
-  private makeCamera() {
-    this.perspCamera =
-        new THREE.PerspectiveCamera(FOV, this.width / this.height, NEAR, FAR);
-    this.cameraControls =
-        new (THREE as any)
-            .OrbitControls(this.perspCamera, this.renderer.domElement);
-    this.cameraControls.mouseButtons.ORBIT = THREE.MOUSE.LEFT;
-    this.cameraControls.mouseButtons.PAN = THREE.MOUSE.RIGHT;
-    // Start is called when the user stars interacting with
-    // orbit controls.
-    this.cameraControls.addEventListener('start', () => {
-      this.cameraControls.autoRotate = false;
-      cancelAnimationFrame(this.lazySusanAnimation);
-    });
-    // Change is called everytime the user interacts with the
-    // orbit controls.
-    this.cameraControls.addEventListener('change', () => {
-      this.removeAllLabels();
-      this.render();
-    });
-    // End is called when the user stops interacting with the
-    // orbit controls (e.g. on mouse up, after dragging).
-    this.cameraControls.addEventListener('end', () => { this.makeLabels(); });
-  }
-
-  /** Sets up camera to work in 3D (called after makeCamera()). */
-  private makeCamera3D() {
-    // Set up the camera position at a skewed angle from the xy plane, looking
-    // toward the origin
-    this.cameraControls.position0.set(POS_3D.x, POS_3D.y, POS_3D.z);
-    this.cameraControls.target0.set(TAR_3D.x, TAR_3D.y, TAR_3D.z);
-    this.cameraControls.enableRotate = true;
-    let position = new THREE.Vector3(POS_3D.x, POS_3D.y, POS_3D.z);
-    let target = new THREE.Vector3(TAR_3D.x, TAR_3D.y, TAR_3D.z);
-    this.animate(position, target, () => {
-      // Start lazy susan after the animation is done.
-      this.startLazySusanAnimation();
-    });
-  }
-
-  /** Sets up camera to work in 2D (called after makeCamera()). */
-  private makeCamera2D(animate?: boolean) {
-    // Set the camera position in the middle of the screen, looking directly
-    // toward the middle of the xy plane
-    this.cameraControls.position0.set(POS_2D.x, POS_2D.y, POS_2D.z);
-    this.cameraControls.target0.set(TAR_2D.x, TAR_2D.y, TAR_2D.z);
-    let position = new THREE.Vector3(POS_2D.x, POS_2D.y, POS_2D.z);
-    let target = new THREE.Vector3(TAR_2D.x, TAR_2D.y, TAR_2D.z);
-    this.animate(position, target);
-    this.cameraControls.enableRotate = false;
-  }
-
-  /**
-   * Set up buffer attributes to be used for the points/images.
-   */
-  private createBufferAttributes() {
-    // Set up buffer attribute arrays.
-    let numPoints = this.dataSet.points.length;
-    let colArr = new Float32Array(numPoints * RGB_NUM_BYTES);
-    this.uniqueColArr = new Float32Array(numPoints * RGB_NUM_BYTES);
-    let colors = new THREE.BufferAttribute(this.uniqueColArr, RGB_NUM_BYTES);
-    // Assign each point a unique color in order to identify when the user
-    // hovers over a point.
-    for (let i = 0; i < numPoints; i++) {
-      let color = new THREE.Color(i);
-      colors.setXYZ(i, color.r, color.g, color.b);
-    }
-    colors.array = colArr;
-    let hiArr = new Float32Array(numPoints);
-
-    /** Indices cooresponding to highlighted points. */
-    let highlights = new THREE.BufferAttribute(hiArr, INDEX_NUM_BYTES);
-
-    // Note that we need two index arrays.
-
-    /**
-     * The actual indices of the points which we use for sizeAttenuation in
-     * the shader.
-     */
-    let indicesShader =
-        new THREE.BufferAttribute(new Float32Array(numPoints), 1);
-
-    for (let i = 0; i < numPoints; i++) {
-      // Create the array of indices.
-      indicesShader.setX(i, this.dataSet.points[i].dataSourceIndex);
-    }
-
-    // Finally, add all attributes to the geometry.
-    this.geometry.addAttribute('position', this.positionBufferArray);
-    this.positionBufferArray.needsUpdate = true;
-    this.geometry.addAttribute('color', colors);
-    this.geometry.addAttribute('vertexIndex', indicesShader);
-    this.geometry.addAttribute('isHighlight', highlights);
-
-    // For now, nothing is highlighted.
-    this.colorSprites(null);
-  }
-
-  /**
-   * Generate a texture for the points/images and sets some initial params
-   */
-  private createTexture(image: HTMLImageElement|
-                        HTMLCanvasElement): THREE.Texture {
-    let tex = new THREE.Texture(image);
-    tex.needsUpdate = true;
-    // Used if the texture isn't a power of 2.
-    tex.minFilter = THREE.LinearFilter;
-    tex.generateMipmaps = false;
-    tex.flipY = false;
-    return tex;
-  }
-
-  /**
-   * Create points, set their locations and actually instantiate the
-   * geometry.
-   */
-  private addSprites() {
-    // Create geometry.
-    this.geometry = new THREE.BufferGeometry();
-    this.createBufferAttributes();
-    let canvas = document.createElement('canvas');
-    let image = this.image || canvas;
-    // TODO(smilkov): Pass sprite dim to the renderer.
-    let spriteDim = 28.0;
-    let tex = this.createTexture(image);
-    let pointSize = (this.zAccessor ? this.pointSize3D : this.pointSize2D);
-    if (this.image) {
-      pointSize = IMAGE_SIZE;
-    }
-    let uniforms = {
-      texture: {type: 't', value: tex},
-      imageWidth: {type: 'f', value: image.width / spriteDim},
-      imageHeight: {type: 'f', value: image.height / spriteDim},
-      fogColor: {type: 'c', value: this.fog.color},
-      fogNear: {type: 'f', value: this.fog.near},
-      fogFar: {type: 'f', value: this.fog.far},
-      sizeAttenuation: {type: 'bool', value: !!this.zAccessor},
-      isImage: {type: 'bool', value: !!this.image},
-      pointSize: {type: 'f', value: pointSize}
-    };
-    this.materialOptions = {
-      uniforms: uniforms,
-      vertexShader: VERTEX_SHADER,
-      fragmentShader: FRAGMENT_SHADER,
-      transparent: (this.image ? false : true),
-      // When rendering points with blending, we want depthTest/Write
-      // turned off.
-      depthTest: (this.image ? true : false),
-      depthWrite: (this.image ? true : false),
-      fog: true,
-      blending: (this.image ? THREE.NormalBlending : this.blending),
-    };
-    // Give it some material.
-    let material = new THREE.ShaderMaterial(this.materialOptions);
-
-    // And finally initialize it and add it to the scene.
-    this.points = new THREE.Points(this.geometry, material);
-    this.scene.add(this.points);
-  }
-
-  /**
-   * Create line traces between connected points and instantiate the geometry.
-   */
-  private addTraces() {
-    if (!this.dataSet || !this.dataSet.traces) {
-      return;
-    }
-
-    this.traces = [];
-
-    for (let i = 0; i < this.dataSet.traces.length; i++) {
-      let dataTrace = this.dataSet.traces[i];
-
-      let geometry = new THREE.BufferGeometry();
-      let vertices: number[] = [];
-      let colors: number[] = [];
-
-      for (let j = 0; j < dataTrace.pointIndices.length - 1; j++) {
-        this.dataSet.points[dataTrace.pointIndices[j]].traceIndex = i;
-        this.dataSet.points[dataTrace.pointIndices[j + 1]].traceIndex = i;
-
-        let point1 = this.dataSet.points[dataTrace.pointIndices[j]];
-        let point2 = this.dataSet.points[dataTrace.pointIndices[j + 1]];
-
-        vertices.push(
-            point1.projectedPoint.x, point1.projectedPoint.y,
-            point1.projectedPoint.z);
-        vertices.push(
-            point2.projectedPoint.x, point2.projectedPoint.y,
-            point2.projectedPoint.z);
-
-        let color1 =
-            this.getPointInTraceColor(j, dataTrace.pointIndices.length);
-        let color2 =
-            this.getPointInTraceColor(j + 1, dataTrace.pointIndices.length);
-
-        colors.push(
-            color1.r / 255, color1.g / 255, color1.b / 255, color2.r / 255,
-            color2.g / 255, color2.b / 255);
-      }
-
-      geometry.addAttribute(
-          'position',
-          new THREE.BufferAttribute(new Float32Array(vertices), XYZ_NUM_BYTES));
-      geometry.addAttribute(
-          'color',
-          new THREE.BufferAttribute(new Float32Array(colors), RGB_NUM_BYTES));
-
-      // We use the same material for every line.
-      let material = new THREE.LineBasicMaterial({
-        linewidth: TRACE_DEFAULT_LINEWIDTH,
-        opacity: TRACE_DEFAULT_OPACITY,
-        transparent: true,
-        vertexColors: THREE.VertexColors
-      });
-
-      let trace = new THREE.LineSegments(geometry, material);
-      this.traces.push(trace);
-      this.scene.add(trace);
-    }
-  }
-
-  /**
-   * Returns the color of a point along a trace.
-   */
-  private getPointInTraceColor(index: number, totalPoints: number) {
-    let hue = TRACE_START_HUE +
-        (TRACE_END_HUE - TRACE_START_HUE) * index / totalPoints;
-
-    return d3.hsl(hue, TRACE_SATURATION, TRACE_LIGHTNESS).rgb();
-  }
-
-  /** Clean up any old axes that we may have made previously.  */
-  private removeOldAxes() {
-    if (this.axis3D) {
-      this.scene.remove(this.axis3D);
-    }
-    if (this.axis2D) {
-      this.scene.remove(this.axis2D);
-    }
-  }
-
-  /** Add axis. */
-  private addAxis3D() {
-    this.axis3D = new THREE.AxisHelper();
-    this.scene.add(this.axis3D);
-  }
-
-  /** Manually make axis if we're in 2d. */
-  private addAxis2D() {
-    let vertices = new Float32Array([
-      0,
-      0,
-      0,
-      this.xScale(1),
-      0,
-      0,
-      0,
-      0,
-      0,
-      0,
-      this.yScale(1),
-      0,
-    ]);
-
-    let axisColor = new THREE.Color(AXIS_COLOR);
-    let axisColors = new Float32Array([
-      axisColor.r,
-      axisColor.b,
-      axisColor.g,
-      axisColor.r,
-      axisColor.b,
-      axisColor.g,
-      axisColor.r,
-      axisColor.b,
-      axisColor.g,
-      axisColor.r,
-      axisColor.b,
-      axisColor.g,
-    ]);
-
-    // Create line geometry based on above position and color.
-    let lineGeometry = new THREE.BufferGeometry();
-    lineGeometry.addAttribute(
-        'position', new THREE.BufferAttribute(vertices, XYZ_NUM_BYTES));
-    lineGeometry.addAttribute(
-        'color', new THREE.BufferAttribute(axisColors, RGB_NUM_BYTES));
-
-    // And use it to create the actual object and add this new axis to the
-    // scene!
-    let axesMaterial =
-        new THREE.LineBasicMaterial({vertexColors: THREE.VertexColors});
-    this.axis2D = new THREE.LineSegments(lineGeometry, axesMaterial);
-    this.scene.add(this.axis2D);
-  }
-
-  // DYNAMIC (post-load) CHANGES
-
-  /** When we stop dragging/zooming, return to normal behavior. */
-  private onClick(e?: MouseEvent) {
-    if (e && this.selecting || !this.points) {
-      this.resetTraces();
-      return;
-    }
-    let selection = this.nearestPoint || null;
-    this.defaultPointColor = (selection ? POINT_COLOR_GRAYED : POINT_COLOR);
-    // Only call event handlers if the click originated from the scatter plot.
-    if (e && !this.isDragSequence) {
-      this.onSelectionListeners.forEach(l => l(selection ? [selection] : []));
-    }
-    this.isDragSequence = false;
-    this.labeledPoints =
-        this.highlightedPoints.filter((id, i) => this.favorLabels(i));
-
-    this.resetTraces();
-    if (selection && this.dataSet.points[selection].traceIndex) {
-      for (let i = 0; i < this.traces.length; i++) {
-        this.traces[i].material.opacity = TRACE_DESELECTED_OPACITY;
-        this.traces[i].material.needsUpdate = true;
-      }
-      this.traces[this.dataSet.points[selection].traceIndex].material.opacity =
-          TRACE_SELECTED_OPACITY;
-      (this.traces[this.dataSet.points[selection].traceIndex].material as
-           THREE.LineBasicMaterial)
-          .linewidth = TRACE_SELECTED_LINEWIDTH;
-      this.traces[this.dataSet.points[selection].traceIndex]
-          .material.needsUpdate = true;
-    }
-    this.render();
-    this.makeLabels();
-  }
-
-  private resetTraces() {
-    if (!this.traces) {
-      return;
-    }
-    for (let i = 0; i < this.traces.length; i++) {
-      this.traces[i].material.opacity = TRACE_DEFAULT_OPACITY;
-      (this.traces[i].material as THREE.LineBasicMaterial).linewidth =
-          TRACE_DEFAULT_LINEWIDTH;
-      this.traces[i].material.needsUpdate = true;
-    }
-  }
-
-  /** When dragging, do not redraw labels. */
-  private onMouseDown(e: MouseEvent) {
-    this.animating = false;
-    this.isDragSequence = false;
-    this.mouseIsDown = true;
-    // If we are in selection mode, and we have in fact clicked a valid point,
-    // create a sphere so we can select things
-    if (this.selecting) {
-      this.cameraControls.enabled = false;
-      this.setNearestPointToMouse(e);
-      if (this.nearestPoint) {
-        this.createSelectionSphere();
-      }
-    } else if (
-        !e.ctrlKey &&
-        this.cameraControls.mouseButtons.ORBIT == THREE.MOUSE.RIGHT) {
-      // The user happened to press the ctrl key when the tab was active,
-      // unpressed the ctrl when the tab was inactive, and now he/she
-      // is back to the projector tab.
-      this.cameraControls.mouseButtons.ORBIT = THREE.MOUSE.LEFT;
-      this.cameraControls.mouseButtons.PAN = THREE.MOUSE.RIGHT;
-    } else if (
-        e.ctrlKey &&
-        this.cameraControls.mouseButtons.ORBIT == THREE.MOUSE.LEFT) {
-      // Similarly to the situation above.
-      this.cameraControls.mouseButtons.ORBIT = THREE.MOUSE.RIGHT;
-      this.cameraControls.mouseButtons.PAN = THREE.MOUSE.LEFT;
-    }
-  }
-
-
-  /** When we stop dragging/zooming, return to normal behavior. */
-  private onMouseUp(e: any) {
-    if (this.selecting) {
-      this.cameraControls.enabled = true;
-      this.scene.remove(this.selectionSphere);
-      this.selectionSphere = null;
-      this.render();
-    }
-    this.mouseIsDown = false;
-  }
-
-  /**
-   * When the mouse moves, find the nearest point (if any) and send it to the
-   * hoverlisteners (usually called from embedding.ts)
-   */
-  private onMouseMove(e: MouseEvent) {
-    if (this.cameraControls.autoRotate) {
-      // Cancel the lazy susan.
-      this.cameraControls.autoRotate = false;
-      cancelAnimationFrame(this.lazySusanAnimation);
-      this.makeLabels();
-    }
-
-    // A quick check to make sure data has come in.
-    if (!this.points) {
-      return;
-    }
-    this.isDragSequence = this.mouseIsDown;
-    // Depending if we're selecting or just navigating, handle accordingly.
-    if (this.selecting && this.mouseIsDown) {
-      if (this.selectionSphere) {
-        this.adjustSelectionSphere(e);
-      }
-      this.render();
-    } else if (!this.mouseIsDown) {
-      let lastNearestPoint = this.nearestPoint;
-      this.setNearestPointToMouse(e);
-      if (lastNearestPoint != this.nearestPoint) {
-        this.onHoverListeners.forEach(l => l(this.nearestPoint));
-      }
-    }
-  }
-
-  /** For using ctrl + left click as right click, and for circle select */
-  private onKeyDown(e: any) {
-    // If ctrl is pressed, use left click to orbit
-    if (e.keyCode === CTRL_KEY) {
-      this.cameraControls.mouseButtons.ORBIT = THREE.MOUSE.RIGHT;
-      this.cameraControls.mouseButtons.PAN = THREE.MOUSE.LEFT;
-    }
-
-    // If shift is pressed, start selecting
-    if (e.keyCode === SHIFT_KEY) {
-      this.selecting = true;
-      this.containerNode.style.cursor = 'crosshair';
-    }
-  }
-
-  /** For using ctrl + left click as right click, and for circle select */
-  private onKeyUp(e: any) {
-    if (e.keyCode === CTRL_KEY) {
-      this.cameraControls.mouseButtons.ORBIT = THREE.MOUSE.LEFT;
-      this.cameraControls.mouseButtons.PAN = THREE.MOUSE.RIGHT;
-    }
-
-    // If shift is released, stop selecting
-    if (e.keyCode === SHIFT_KEY) {
-      this.selecting = (this.getMode() === Mode.SELECT);
-      if (!this.selecting) {
-        this.containerNode.style.cursor = 'default';
-      }
-      this.scene.remove(this.selectionSphere);
-      this.selectionSphere = null;
-      this.render();
-    }
-  }
-
-  private setNearestPointToMouse(e: MouseEvent) {
-    // Create buffer for reading a single pixel.
-    let pixelBuffer = new Uint8Array(4);
-    // No need to account for dpr (device pixel ratio) since the pickingTexture
-    // has the same coordinates as the mouse (flipped on y).
-    let x = e.offsetX;
-    let y = e.offsetY;
-
-    // Read the pixel under the mouse from the texture.
-    this.renderer.readRenderTargetPixels(
-        this.pickingTexture, x, this.pickingTexture.height - y, 1, 1,
-        pixelBuffer);
-
-    // Interpret the pixel as an ID.
-    let id = (pixelBuffer[0] << 16) | (pixelBuffer[1] << 8) | pixelBuffer[2];
-    this.nearestPoint =
-        id != 0xffffff && id < this.dataSet.points.length ? id : null;
-  }
-
-  /** Returns the squared distance to the mouse for the i-th point. */
-  private getDist2ToMouse(i: number, e: MouseEvent) {
-    let point = this.getProjectedPointFromIndex(i);
-    let screenCoords = this.vector3DToScreenCoords(point);
-    return this.dist2D(
-        [e.offsetX * this.dpr, e.offsetY * this.dpr],
-        [screenCoords.x, screenCoords.y]);
-  }
-
-  private adjustSelectionSphere(e: MouseEvent) {
-    let dist2 = this.getDist2ToMouse(this.nearestPoint, e) / 100;
-    this.selectionSphere.scale.set(dist2, dist2, dist2);
-    this.selectPoints(dist2);
-  }
-
-  private getProjectedPointFromIndex(i: number): THREE.Vector3 {
-    return new THREE.Vector3(
-        this.dataSet.points[i].projectedPoint.x,
-        this.dataSet.points[i].projectedPoint.y,
-        this.dataSet.points[i].projectedPoint.z);
-  }
-
-  private calibratePointSize() {
-    let numPts = this.dataSet.points.length;
-    let scaleConstant = 200;
-    let logBase = 8;
-    // Scale point size inverse-logarithmically to the number of points.
-    this.pointSize3D = scaleConstant / Math.log(numPts) / Math.log(logBase);
-    this.pointSize2D = this.pointSize3D / 1.5;
-  }
-
-  private setFogDistances() {
-    let dists = this.getNearFarPoints();
-    this.fog.near = dists.shortestDist;
-    // If there are fewer points we want less fog. We do this
-    // by making the "far" value (that is, the distance from the camera to the
-    // far edge of the fog) proportional to the number of points.
-    let multiplier = 2 -
-        Math.min(this.dataSet.points.length, NUM_POINTS_FOG_THRESHOLD) /
-            NUM_POINTS_FOG_THRESHOLD;
-    this.fog.far = dists.furthestDist * multiplier;
-  }
-
-  private getNearFarPoints() {
-    let shortestDist: number = Infinity;
-    let furthestDist: number = 0;
-    for (let i = 0; i < this.dataSet.points.length; i++) {
-      let point = this.getProjectedPointFromIndex(i);
-      if (!this.isPointWithinCameraView(point)) {
-        continue;
-      };
-      let distToCam = this.dist3D(point, this.perspCamera.position);
-      furthestDist = Math.max(furthestDist, distToCam);
-      shortestDist = Math.min(shortestDist, distToCam);
-    }
-    return {shortestDist, furthestDist};
-  }
-
-  /**
-   * Renders the scene and updates the label for the point, which is rendered
-   * as a div on top of WebGL.
-   */
-  private render() {
-    if (!this.dataSet) {
-      return;
-    }
-    let lightPos = new THREE.Vector3().copy(this.perspCamera.position);
-    lightPos.x += 1;
-    lightPos.y += 1;
-    this.light.position.set(lightPos.x, lightPos.y, lightPos.z);
-
-    // We want to determine which point the user is hovering over. So, rather
-    // than linearly iterating through each point to see if it is under the
-    // mouse, we render another set of the points offscreen, where each point is
-    // at full opacity and has its id encoded in its color. Then, we see the
-    // color of the pixel under the mouse, decode the color, and get the id of
-    // of the point.
-    let shaderMaterial = this.points.material as THREE.ShaderMaterial;
-    let colors = this.geometry.getAttribute('color') as THREE.BufferAttribute;
-    // Make shallow copy of the shader options and modify the necessary values.
-    let offscreenOptions =
-        Object.create(this.materialOptions) as THREE.ShaderMaterialParameters;
-    // Since THREE.js errors if we remove the fog, the workaround is to set the
-    // near value to very far, so no points have fog.
-    this.fog.near = 1000;
-    this.fog.far = 10000;
-    // Render offscreen as non transparent points (even when we have images).
-    offscreenOptions.uniforms.isImage.value = false;
-    offscreenOptions.transparent = false;
-    offscreenOptions.depthTest = true;
-    offscreenOptions.depthWrite = true;
-    shaderMaterial.setValues(offscreenOptions);
-    // Give each point a unique color.
-    let origColArr = colors.array;
-    colors.array = this.uniqueColArr;
-    colors.needsUpdate = true;
-    this.renderer.render(this.scene, this.perspCamera, this.pickingTexture);
-
-    // Now render onscreen.
-
-    // Change to original color array.
-    colors.array = origColArr;
-    colors.needsUpdate = true;
-    // Bring back the fog.
-    if (this.zAccessor && this.geometry) {
-      this.setFogDistances();
-    }
-    offscreenOptions.uniforms.isImage.value = !!this.image;
-    // Bring back the standard shader material options.
-    shaderMaterial.setValues(this.materialOptions);
-    // Render onscreen.
-    this.renderer.render(this.scene, this.perspCamera);
-  }
-
-  /**
-   * Make sure that the point is in view of the camera (as opposed to behind
-   * this is a problem because we are projecting to the camera)
-   */
-  private isPointWithinCameraView(point: THREE.Vector3): boolean {
-    let camToTarget = new THREE.Vector3()
-                          .copy(this.perspCamera.position)
-                          .sub(this.cameraControls.target);
-    let camToPoint =
-        new THREE.Vector3().copy(this.perspCamera.position).sub(point);
-    // If the angle between the camera-target and camera-point vectors is more
-    // than 90, the point is behind the camera
-    if (camToPoint.angleTo(camToTarget) > Math.PI / 2) {
-      return false;
-    };
-    return true;
-  }
-
-  private vector3DToScreenCoords(v: THREE.Vector3) {
-    let vector = new THREE.Vector3().copy(v).project(this.perspCamera);
-    let coords = {
-      // project() returns the point in perspCamera's coordinates, with the
-      // origin in the center and a positive upward y. To get it into screen
-      // coordinates, normalize by adding 1 and dividing by 2.
-      x: ((vector.x + 1) / 2 * this.width) * this.dpr,
-      y: -((vector.y - 1) / 2 * this.height) * this.dpr
-    };
-    return coords;
-  }
-
-  /** Checks if a given label will be within the screen's bounds. */
-  private isLabelInBounds(labelWidth: number, coords: {x: number, y: number}) {
-    let padding = 7;
-    if ((coords.x < 0) || (coords.y < 0) ||
-        (coords.x > this.width * this.dpr - labelWidth - padding) ||
-        (coords.y > this.height * this.dpr)) {
-      return false;
-    };
-    return true;
-  }
-
-  /** Add a specific label to the canvas. */
-  private formatLabel(
-      text: string, point: {x: number, y: number}, opacity: number) {
-    let ls = new THREE.Color(this.labelStroke);
-    let lc = new THREE.Color(this.labelColor);
-    this.gc.strokeStyle = 'rgba(' + ls.r * 255 + ',' + ls.g * 255 + ',' +
-        ls.b * 255 + ',' + opacity + ')';
-    this.gc.fillStyle = 'rgba(' + lc.r * 255 + ',' + lc.g * 255 + ',' +
-        lc.b * 255 + ',' + opacity + ')';
-    this.gc.strokeText(text, point.x + 4, point.y);
-    this.gc.fillText(text, point.x + 4, point.y);
-  }
-
-  /**
-   * Reset the positions of all labels, and check for overlapps using the
-   * collision grid.
-   */
-  private makeLabels() {
-    // Don't make labels if they are turned off.
-    if (!this.labelsAreOn || this.points == null) {
-      return;
-    }
-    // First, remove all old labels.
-    this.removeAllLabels();
-
-    this.labelCanvasIsCleared = false;
-    // If we are passed no points to label (that is, not mousing over any
-    // points) then want to label ALL the points that we can.
-    if (!this.labeledPoints.length) {
-      this.labeledPoints = this.shuffledData;
-    }
-
-    // We never render more than ~500 labels, so when we get much past that
-    // point, just break.
-    let numRenderedLabels: number = 0;
-    let labelHeight = parseInt(this.gc.font, 10);
-
-    // Bounding box for collision grid.
-    let boundingBox: BoundingBox = {
-      loX: 0,
-      hiX: this.width * this.dpr,
-      loY: 0,
-      hiY: this.height * this.dpr
-    };
-
-    // Make collision grid with cells proportional to window dimensions.
-    let grid =
-        new CollisionGrid(boundingBox, this.width / 25, this.height / 50);
-
-    let dists = this.getNearFarPoints();
-    let opacityRange = dists.furthestDist - dists.shortestDist;
-
-    // Setting styles for the labeled font.
-    this.gc.lineWidth = 6;
-    this.gc.textBaseline = 'middle';
-    this.gc.font = (FONT_SIZE * this.dpr).toString() + 'px roboto';
-
-    for (let i = 0;
-         (i < this.labeledPoints.length) && !(numRenderedLabels > SAMPLE_SIZE);
-         i++) {
-      let index = this.labeledPoints[i];
-      let point = this.getProjectedPointFromIndex(index);
-      if (!this.isPointWithinCameraView(point)) {
-        continue;
-      };
-      let screenCoords = this.vector3DToScreenCoords(point);
-      // Have extra space between neighboring labels. Don't pack too tightly.
-      let labelMargin = 2;
-      // Shift the label to the right of the point circle.
-      let xShift = 3;
-      let textBoundingBox = {
-        loX: screenCoords.x + xShift - labelMargin,
-        // Computing the width of the font is expensive,
-        // so we assume width of 1 at first. Then, if the label doesn't
-        // conflict with other labels, we measure the actual width.
-        hiX: screenCoords.x + xShift + /* labelWidth - 1 */ +1 + labelMargin,
-        loY: screenCoords.y - labelHeight / 2 - labelMargin,
-        hiY: screenCoords.y + labelHeight / 2 + labelMargin
-      };
-
-      if (grid.insert(textBoundingBox, true)) {
-        let dataSet = this.dataSet;
-        let text = this.labelAccessor(index);
-        let labelWidth = this.gc.measureText(text).width;
-
-        // Now, check with properly computed width.
-        textBoundingBox.hiX += labelWidth - 1;
-        if (grid.insert(textBoundingBox) &&
-            this.isLabelInBounds(labelWidth, screenCoords)) {
-          let lenToCamera = this.dist3D(point, this.perspCamera.position);
-          // Opacity is scaled between 0.2 and 1, based on how far a label is
-          // from the camera (Unless we are in 2d mode, in which case opacity is
-          // just 1!)
-          let opacity = this.zAccessor ?
-              1.2 - (lenToCamera - dists.shortestDist) / opacityRange :
-              1;
-          this.formatLabel(text, screenCoords, opacity);
-          numRenderedLabels++;
-        }
-      }
-    }
-
-    if (this.highlightedPoints.length > 0) {
-      // Force-draw the first favored point with increased font size.
-      let index = this.highlightedPoints[0];
-      let point = this.dataSet.points[index];
-      this.gc.font = (FONT_SIZE * this.dpr * 1.7).toString() + 'px roboto';
-      let coords = new THREE.Vector3(
-          point.projectedPoint.x, point.projectedPoint.y,
-          point.projectedPoint.z);
-      let screenCoords = this.vector3DToScreenCoords(coords);
-      let text = this.labelAccessor(index);
-      this.formatLabel(text, screenCoords, 255);
-    }
-  }
-
-  /** Returns the distance between two points in 3d space */
-  private dist3D(a: Point3D, b: Point3D): number {
-    let dx = a.x - b.x;
-    let dy = a.y - b.y;
-    let dz = a.z - b.z;
-    return Math.sqrt(dx * dx + dy * dy + dz * dz);
-  }
-
-  private dist2D(a: [number, number], b: [number, number]): number {
-    let dX = a[0] - b[0];
-    let dY = a[1] - b[1];
-    return Math.sqrt(dX * dX + dY * dY);
-  }
-
-  /** Cancels current animation */
-  private cancelAnimation() {
-    if (this.animationID) {
-      cancelAnimationFrame(this.animationID);
-    }
-  }
-
-  private startLazySusanAnimation() {
-    this.cameraControls.autoRotate = true;
-    this.cameraControls.update();
-    this.lazySusanAnimation =
-        requestAnimationFrame(() => this.startLazySusanAnimation());
-  }
-
-  /**
-   * Animates the camera between one location and another.
-   * If callback is specified, it gets called when the animation is done.
-   */
-  private animate(
-      pos: THREE.Vector3, target: THREE.Vector3, callback?: () => void) {
-    this.cameraControls.autoRotate = false;
-    cancelAnimationFrame(this.lazySusanAnimation);
-
-    let currPos = this.perspCamera.position;
-    let currTarget = this.cameraControls.target;
-    let speed = 3;
-    this.animating = true;
-    let interp = (a: THREE.Vector3, b: THREE.Vector3) => {
-      let x = (a.x - b.x) / speed + b.x;
-      let y = (a.y - b.y) / speed + b.y;
-      let z = (a.z - b.z) / speed + b.z;
-      return {x: x, y: y, z: z};
-    };
-    // If we're still relatively far away from the target, go closer
-    if (this.dist3D(currPos, pos) > .03) {
-      let newTar = interp(target, currTarget);
-      this.cameraControls.target.set(newTar.x, newTar.y, newTar.z);
-
-      let newPos = interp(pos, currPos);
-      this.perspCamera.position.set(newPos.x, newPos.y, newPos.z);
-      this.cameraControls.update();
-      this.render();
-      this.animationID =
-          requestAnimationFrame(() => this.animate(pos, target, callback));
-    } else {
-      // Once we get close enough, update flags and stop moving
-      this.animating = false;
-      this.cameraControls.target.set(target.x, target.y, target.z);
-      this.cameraControls.update();
-      this.makeLabels();
-      this.render();
-      if (callback) {
-        callback();
-      }
-    }
-  }
-
-  /** Removes all points geometry from the scene. */
-  private removeAll() {
-    this.scene.remove(this.points);
-    this.removeOldAxes();
-    this.removeAllLabels();
-    this.removeAllTraces();
-  }
-
-  /** Removes all traces from the scene. */
-  private removeAllTraces() {
-    if (!this.traces) {
-      return;
-    }
-
-    for (let i = 0; i < this.traces.length; i++) {
-      this.scene.remove(this.traces[i]);
-    }
-    this.traces = [];
-  }
-
-  /** Removes all the labels. */
-  private removeAllLabels() {
-    // If labels are already removed, do not spend compute power to clear the
-    // canvas.
-    if (!this.labelCanvasIsCleared) {
-      this.gc.clearRect(0, 0, this.width * this.dpr, this.height * this.dpr);
-      this.labelCanvasIsCleared = true;
-    }
-  }
-
-  private colorSprites(highlightStroke: ((index: number) => string)) {
-    // Update attributes to change colors
-    let colors = this.geometry.getAttribute('color') as THREE.BufferAttribute;
-    let highlights =
-        this.geometry.getAttribute('isHighlight') as THREE.BufferAttribute;
-    for (let i = 0; i < this.dataSet.points.length; i++) {
-      let unhighlightedColor = this.image ?
-          new THREE.Color() :
-          new THREE.Color(
-              this.colorAccessor ? this.colorAccessor(i) :
-                                   (this.defaultPointColor as any));
-      colors.setXYZ(
-          i, unhighlightedColor.r, unhighlightedColor.g, unhighlightedColor.b);
-      highlights.setX(i, 0.0);
-    }
-    if (highlightStroke) {
-      // Traverse in reverse so that the point we are hovering over
-      // (highlightedPoints[0]) is painted last.
-      for (let i = this.highlightedPoints.length - 1; i >= 0; i--) {
-        let assocPoint = this.highlightedPoints[i];
-        let color = new THREE.Color(highlightStroke(i));
-        // Fill colors array (single array of numPoints*3 elements,
-        // triples of which refer to the rgb values of a single vertex).
-        colors.setXYZ(assocPoint, color.r, color.g, color.b);
-        highlights.setX(assocPoint, 1.0);
-      }
-    }
-    colors.needsUpdate = true;
-    highlights.needsUpdate = true;
-  }
-
-  /**
-   * This is called when we update the data to make sure we don't have stale
-   * data lying around.
-   */
-  private cleanVariables() {
-    this.removeAll();
-    if (this.geometry) {
-      this.geometry.dispose();
-    }
-    this.geometry = null;
-    this.points = null;
-    this.labeledPoints = [];
-    this.highlightedPoints = [];
-  }
-
-  /** Select the points inside the sphere of radius dist */
-  private selectPoints(dist: number) {
-    let selectedPoints: Array<number> = new Array();
-    this.dataSet.points.forEach(point => {
-      let pt = point.projectedPoint;
-      let pointVect = new THREE.Vector3(pt.x, pt.y, pt.z);
-      let distPointToSphereOrigin = new THREE.Vector3()
-                                        .copy(this.selectionSphere.position)
-                                        .sub(pointVect)
-                                        .length();
-      if (distPointToSphereOrigin < dist) {
-        selectedPoints.push(this.dataSet.points.indexOf(point));
-      }
-    });
-    this.labeledPoints = selectedPoints;
-    // Whenever anything is selected, we want to set the corect point color.
-    this.defaultPointColor = POINT_COLOR_GRAYED;
-    this.onSelectionListeners.forEach(l => l(selectedPoints));
-  }
-
-  private createSelectionSphere() {
-    let geometry = new THREE.SphereGeometry(1, 300, 100);
-    let material = new THREE.MeshPhongMaterial({
-      color: 0x000000,
-      specular: (this.zAccessor && 0xffffff),  // In 2d, make sphere look flat.
-      emissive: 0x000000,
-      shininess: 10,
-      shading: THREE.SmoothShading,
-      opacity: 0.125,
-      transparent: true,
-    });
-    this.selectionSphere = new THREE.Mesh(geometry, material);
-    this.selectionSphere.scale.set(0, 0, 0);
-    let pos = this.dataSet.points[this.nearestPoint].projectedPoint;
-    this.scene.add(this.selectionSphere);
-    this.selectionSphere.position.set(pos.x, pos.y, pos.z);
-  }
-
-  private getLayoutValues() {
-    this.width = this.containerNode.offsetWidth;
-    this.height = Math.max(1, this.containerNode.offsetHeight);
-    this.dpr = window.devicePixelRatio;
-  }
-
-  // PUBLIC API
-
-  /** Sets the data for the scatter plot. */
-  setDataSet(dataSet: DataSet, spriteImage: HTMLImageElement) {
-    this.dataSet = dataSet;
-    this.calibratePointSize();
-    let positions =
-        new Float32Array(this.dataSet.points.length * XYZ_NUM_BYTES);
-    this.positionBufferArray =
-        new THREE.BufferAttribute(positions, XYZ_NUM_BYTES);
-    this.image = spriteImage;
-    this.shuffledData = new Array(this.dataSet.points.length);
-    for (let i = 0; i < this.dataSet.points.length; i++) {
-      this.shuffledData[i] = i;
-    }
-    shuffle(this.shuffledData);
-    this.cleanVariables();
-  }
-
-  setColorAccessor(colorAccessor: (index: number) => string) {
-    this.colorAccessor = colorAccessor;
-    // Render only if there is a geometry.
-    if (this.geometry) {
-      this.colorSprites(this.highlightStroke);
-      this.render();
-    }
-  }
-
-  setXAccessor(xAccessor: (index: number) => number) {
-    this.xAccessor = xAccessor;
-  }
-
-  setYAccessor(yAccessor: (index: number) => number) {
-    this.yAccessor = yAccessor;
-  }
-
-  setZAccessor(zAccessor: (index: number) => number) {
-    this.zAccessor = zAccessor;
-  }
-
-  setLabelAccessor(labelAccessor: (index: number) => string) {
-    this.labelAccessor = labelAccessor;
-    this.render();
-  }
-
-  setMode(mode: Mode) {
-    this.mode = mode;
-    if (mode === Mode.SELECT) {
-      this.selecting = true;
-      this.containerNode.style.cursor = 'crosshair';
-    } else {
-      this.selecting = false;
-      this.containerNode.style.cursor = 'default';
-    }
-  }
-
-  getMode(): Mode { return this.mode; }
-
-  resetZoom() {
-    if (this.animating) {
-      return;
-    }
-    let resetPos = this.cameraControls.position0;
-    let resetTarget = this.cameraControls.target0;
-    this.removeAllLabels();
-    this.animate(resetPos, resetTarget, () => {
-      // Start rotating when the animation is done, if we are in 3D mode.
-      if (this.zAccessor) {
-        this.startLazySusanAnimation();
-      }
-    });
-  }
-
-  /** Zoom by moving the camera toward the target. */
-  zoomStep(multiplier: number) {
-    let additiveZoom = Math.log(multiplier);
-    if (this.animating) {
-      return;
-    }
-
-    // Zoomvect is the vector along which we want to move the camera
-    // It is the (normalized) vector from the camera to its target
-    let zoomVect = new THREE.Vector3()
-                       .copy(this.cameraControls.target)
-                       .sub(this.perspCamera.position)
-                       .multiplyScalar(additiveZoom);
-    let position =
-        new THREE.Vector3().copy(this.perspCamera.position).add(zoomVect);
-
-    // Make sure that we're not too far zoomed in. If not, zoom!
-    if ((this.dist3D(position, this.cameraControls.target) > MIN_ZOOM) &&
-        (this.dist3D(position, this.cameraControls.target) < MAX_ZOOM)) {
-      this.removeAllLabels();
-      this.animate(position, this.cameraControls.target);
-    }
-  }
-
-  highlightPoints(
-      pointIndexes: number[], highlightStroke: (i: number) => string,
-      favorLabels: (i: number) => boolean): void {
-    this.favorLabels = favorLabels;
-    this.highlightedPoints = pointIndexes;
-    this.labeledPoints = pointIndexes;
-    this.highlightStroke = highlightStroke;
-    this.colorSprites(highlightStroke);
-    this.render();
-    this.makeLabels();
-  }
-
-  getHighlightedPoints(): number[] { return this.highlightedPoints; }
-
-  showLabels(show: boolean) {
-    this.labelsAreOn = show;
-    if (this.labelsAreOn) {
-      this.makeLabels();
-    } else {
-      this.removeAllLabels();
-    }
-  }
-
-  /**
-   * Toggles between day and night mode (resets corresponding variables for
-   * color, etc.)
-   */
-  setDayNightMode(isNight: boolean) {
-    this.isNight = isNight;
-    this.labelColor = (isNight ? LABEL_COLOR_NIGHT : LABEL_COLOR_DAY);
-    this.labelStroke = (isNight ? LABEL_STROKE_NIGHT : LABEL_STROKE_DAY);
-    this.backgroundColor =
-        (isNight ? BACKGROUND_COLOR_NIGHT : BACKGROUND_COLOR_DAY);
-    this.blending = (isNight ? BLENDING_NIGHT : BLENDING_DAY);
-    this.renderer.setClearColor(this.backgroundColor);
-  }
-
-  showAxes(show: boolean) {
-    // TODO(ereif): implement
-  }
-
-  setAxisLabels(xLabel: string, yLabel: string) {
-    // TODO(ereif): implement
-  }
-  /**
-   * Recreates the scene in its entirety, not only resetting the point
-   * locations but also demolishing and recreating the THREEjs structures.
-   */
-  recreateScene() {
-    this.removeAll();
-    this.cancelAnimation();
-    this.fog = this.zAccessor ?
-        new THREE.Fog(this.backgroundColor) :
-        new THREE.Fog(this.backgroundColor, Infinity, Infinity);
-    this.scene.fog = this.fog;
-    this.addSprites();
-    this.addTraces();
-    if (this.zAccessor) {
-      this.addAxis3D();
-      this.makeCamera3D();
-    } else {
-      this.addAxis2D();
-      this.makeCamera2D();
-    }
-    this.render();
-  }
-
-  /**
-   * Redraws the data. Should be called anytime the accessor method
-   * for x and y coordinates changes, which means a new projection
-   * exists and the scatter plot should repaint the points.
-   */
-  update() {
-    this.cancelAnimation();
-    this.getPointsCoordinates();
-    this.updatePositionsArray();
-    if (this.geometry) {
-      this.makeLabels();
-      this.render();
-    }
-  }
-
-  resize(render = true) {
-    this.getLayoutValues();
-    this.perspCamera.aspect = this.width / this.height;
-    this.perspCamera.updateProjectionMatrix();
-    d3.select(this.canvas)
-        .attr('width', this.width * this.dpr)
-        .attr('height', this.height * this.dpr)
-        .style({width: this.width + 'px', height: this.height + 'px'});
-    this.renderer.setSize(this.width, this.height);
-    this.pickingTexture = new THREE.WebGLRenderTarget(this.width, this.height);
-    this.pickingTexture.texture.minFilter = THREE.LinearFilter;
-    if (render) {
-      this.render();
-    };
-  }
-
-  showTickLabels(show: boolean) {
-    // TODO(ereif): implement
-  }
-
-  onSelection(listener: OnSelectionListener) {
-    this.onSelectionListeners.push(listener);
-  }
-
-  onHover(listener: OnHoverListener) { this.onHoverListeners.push(listener); }
-
-  clickOnPoint(pointIndex: number) {
-    this.nearestPoint = pointIndex;
-    this.onClick();
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz-projector/sptree.ts b/tensorflow/tensorboard/components/vz-projector/sptree.ts
deleted file mode 100644
index f9310d2f1e..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/sptree.ts
+++ /dev/null
@@ -1,189 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/** How many elements can be stored in each node of the tree. */
-const NODE_CAPACITY = 4;
-
-/** N-dimensional point. Usually 2D or 3D. */
-export type Point = number[];
-
-export interface BBox {
-  center: Point;
-  halfDim: number;
-}
-
-/** A node in a space-partitioning tree. */
-export interface SPNode {
-  /** The children of this node. */
-  children?: SPNode[];
-  /** The bounding box of the region this node occupies. */
-  box: BBox;
-  /** One or more points this node has. */
-  points?: Point[];
-}
-
-/**
- * A Space-partitioning tree (https://en.wikipedia.org/wiki/Space_partitioning)
- * that recursively divides the space into regions of equal sizes. This data
- * structure can act both as a Quad tree and an Octree when the data is 2 or
- * 3 dimensional respectively. One usage is in t-SNE in order to do Barnes-Hut
- * approximation.
- */
-export class SPTree {
-  root: SPNode;
-
-  private masks: number[];
-  private capacity: number;
-  private dim: number;
-
-  /**
-   * Constructs a new tree with the provided data.
-   *
-   * @param data List of n-dimensional data points.
-   * @param capacity Number of data points to store in a single node.
-   */
-  constructor(data: Point[], capacity = NODE_CAPACITY) {
-    if (data.length < 1) {
-      throw new Error('There should be at least 1 data point');
-    }
-    this.capacity = capacity;
-    // Make a bounding box based on the extent of the data.
-    this.dim = data[0].length;
-    // Each node has 2^d children, where d is the dimension of the space.
-    // Binary masks (e.g. 000, 001, ... 111 in 3D) are used to determine in
-    // which child (e.g. quadron in 2D) the new point is going to be assigned.
-    // For more details, see the insert() method and its comments.
-    this.masks = new Array(Math.pow(2, this.dim));
-    for (let d = 0; d < this.masks.length; ++d) {
-      this.masks[d] = (1 << d);
-    }
-    let min: Point = new Array(this.dim);
-    fillArray(min, Number.POSITIVE_INFINITY);
-    let max: Point = new Array(this.dim);
-    fillArray(max, Number.NEGATIVE_INFINITY);
-
-    for (let i = 0; i < data.length; ++i) {
-      // For each dim get the min and max.
-      // E.g. For 2-D, get the x_min, x_max, y_min, y_max.
-      for (let d = 0; d < this.dim; ++d) {
-        min[d] = Math.min(min[d], data[i][d]);
-        max[d] = Math.max(max[d], data[i][d]);
-      }
-    }
-    // Create a bounding box with the center of the largest span.
-    let center: Point = new Array(this.dim);
-    let halfDim = 0;
-    for (let d = 0; d < this.dim; ++d) {
-      let span = max[d] - min[d];
-      center[d] = min[d] + span / 2;
-      halfDim = Math.max(halfDim, span / 2);
-    }
-    this.root = {box: {center: center, halfDim: halfDim}};
-    for (let i = 0; i < data.length; ++i) {
-      this.insert(this.root, data[i]);
-    }
-  }
-
-  /**
-   * Visits every node in the tree. Each node can store 1 or more points,
-   * depending on the node capacity provided in the constructor.
-   *
-   * @param accessor Method that takes the currently visited node, and the
-   * low and high point of the region that this node occupies. E.g. in 2D,
-   * the low and high points will be the lower-left corner and the upper-right
-   * corner.
-   */
-  visit(
-      accessor: (node: SPNode, lowPoint: Point, highPoint: Point) => boolean,
-      noBox = false) {
-    this.visitNode(this.root, accessor, noBox);
-  }
-
-  private visitNode(
-      node: SPNode,
-      accessor: (node: SPNode, lowPoint?: Point, highPoint?: Point) => boolean,
-      noBox: boolean) {
-    let skipChildren: boolean;
-    if (noBox) {
-      skipChildren = accessor(node);
-    } else {
-      let lowPoint = new Array(this.dim);
-      let highPoint = new Array(this.dim);
-      for (let d = 0; d < this.dim; ++d) {
-        lowPoint[d] = node.box.center[d] - node.box.halfDim;
-        highPoint[d] = node.box.center[d] + node.box.halfDim;
-      }
-      skipChildren = accessor(node, lowPoint, highPoint);
-    }
-    if (!node.children || skipChildren) {
-      return;
-    }
-    for (let i = 0; i < node.children.length; ++i) {
-      let child = node.children[i];
-      if (child) {
-        this.visitNode(child, accessor, noBox);
-      }
-    }
-  }
-
-  private insert(node: SPNode, p: Point): boolean {
-    if (node.points == null) {
-      node.points = [];
-    }
-    // If there is space in this node, add the object here.
-    if (node.points.length < this.capacity) {
-      node.points.push(p);
-      return true;
-    }
-    // Otherwise, subdivide and then add the point to whichever node will
-    // accept it.
-    if (node.children == null) {
-      node.children = new Array(this.masks.length);
-    }
-
-    // Decide which child will get the new point by constructing a D-bits binary
-    // signature (D=3 for 3D) where the k-th bit is 1 if the point's k-th
-    // coordinate is greater than the node's k-th coordinate, 0 otherwise.
-    // Then the binary signature in decimal system gives us the index of the
-    // child where the new point should be.
-    let index = 0;
-    for (let d = 0; d < this.dim; ++d) {
-      if (p[d] > node.box.center[d]) {
-        index |= this.masks[d];
-      }
-    }
-    if (node.children[index] == null) {
-      this.makeChild(node, index);
-    }
-    this.insert(node.children[index], p);
-    return true;
-  }
-
-  private makeChild(node: SPNode, index: number): void {
-    let oldC = node.box.center;
-    let h = node.box.halfDim / 2;
-    let newC: Point = new Array(this.dim);
-    for (let d = 0; d < this.dim; ++d) {
-      newC[d] = (index & (1 << d)) ? oldC[d] + h : oldC[d] - h;
-    }
-    node.children[index] = {box: {center: newC, halfDim: h}};
-  }
-}
-
-function fillArray<T>(arr: T[], value: T): void {
-  for (let i = 0; i < arr.length; ++i) {
-    arr[i] = value;
-  }
-}
diff --git a/tensorflow/tensorboard/components/vz-projector/sptree_test.ts b/tensorflow/tensorboard/components/vz-projector/sptree_test.ts
deleted file mode 100644
index e48ae761e1..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/sptree_test.ts
+++ /dev/null
@@ -1,106 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {SPTree} from './sptree';
-
-const assert = chai.assert;
-
-it('simple 2D data', () => {
-  let data = [
-    [0, 1],
-    [1, 0],
-    [1, 1],
-    [0, 0],
-  ];
-  let tree = new SPTree(data, 1);
-  // Check that each point is within the bound.
-  tree.visit((node, low, high) => {
-    assert.equal(low.length, 2);
-    assert.equal(high.length, 2);
-    node.points.forEach(point => {
-      assert.equal(point.length, 2);
-      // Each point should be in the node's bounding box.
-      assert.equal(
-          point[0] >= low[0] && point[0] <= high[0] && point[1] >= low[1] &&
-              point[1] <= high[1],
-          true);
-    });
-    return false;
-  });
-});
-
-it('simple 3D data', () => {
-  let data = [
-    [0, 1, 0],
-    [1, 0.4, 2],
-    [1, 1, 3],
-    [0, 0, 5],
-  ];
-  let tree = new SPTree(data, 1);
-  // Check that each point is within the bound.
-  tree.visit((node, low, high) => {
-    assert.equal(low.length, 3);
-    assert.equal(high.length, 3);
-    node.points.forEach(point => {
-      assert.equal(point.length, 3);
-      // Each point should be in the node's bounding box.
-      assert.equal(
-          point[0] >= low[0] && point[0] <= high[0] && point[1] >= low[1] &&
-              point[1] <= high[1] && point[2] >= low[2] && point[2] <= high[2],
-          true);
-    });
-    return false;
-  });
-});
-
-it('Only visit root', () => {
-  let data = [
-    [0, 1, 0],
-    [1, 0.4, 2],
-    [1, 1, 3],
-    [0, 0, 5],
-  ];
-  let tree = new SPTree(data, 1);
-  let numVisits = 0;
-  tree.visit((node, low, high) => {
-    numVisits++;
-    return true;
-  });
-  assert.equal(numVisits, 1);
-});
-
-it('Search in random data', () => {
-  let N = 10000;
-  let data = new Array(N);
-  for (let i = 0; i < N; i++) {
-    data[i] = [Math.random(), Math.random()];
-  }
-  let tree = new SPTree(data, 1);
-  let numVisits = 0;
-  let query = data[Math.floor(Math.random() * N)];
-  let found = false;
-  tree.visit((node, low, high) => {
-    numVisits++;
-    if (node.points.length > 0 && node.points[0] === query) {
-      found = true;
-      return true;
-    }
-    let outOfBounds = query[0] < low[0] || query[0] > high[0] ||
-        query[1] < low[1] || query[1] > high[1];
-    return outOfBounds;
-  });
-  assert.equal(found, true);
-  assert.isBelow(numVisits, N / 4);
-});
diff --git a/tensorflow/tensorboard/components/vz-projector/util.ts b/tensorflow/tensorboard/components/vz-projector/util.ts
deleted file mode 100644
index 975f7e122c..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/util.ts
+++ /dev/null
@@ -1,43 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-/** Shuffles the array in-place in O(n) time using Fisher-Yates algorithm. */
-export function shuffle<T>(array: T[]): T[] {
-  let m = array.length;
-  let t: T;
-  let i: number;
-
-  // While there remain elements to shuffle.
-  while (m) {
-    // Pick a remaining element
-    i = Math.floor(Math.random() * m--);
-    // And swap it with the current element.
-    t = array[m];
-    array[m] = array[i];
-    array[i] = t;
-  }
-  return array;
-}
-
-/**
- * Assert that the condition is satisfied; if not, log user-specified message
- * to the console.
- */
-export function assert(condition: boolean, message?: string) {
-  if (!condition) {
-    message = message || 'Assertion failed';
-    throw new Error(message);
-  }
-}
\ No newline at end of file
diff --git a/tensorflow/tensorboard/components/vz-projector/vector.ts b/tensorflow/tensorboard/components/vz-projector/vector.ts
deleted file mode 100644
index edb6e9bdd0..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/vector.ts
+++ /dev/null
@@ -1,270 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {assert} from './util';
-
-
-/**
- * @fileoverview Useful vector utilities.
- */
-
-export type Vector = number[];
-export type Point2D = [number, number];
-
-/** Returns the dot product of two vectors. */
-export function dot(a: Vector, b: Vector): number {
-  assert(a.length == b.length, 'Vectors a and b must be of same length');
-  let result = 0;
-  for (let i = 0; i < a.length; ++i) {
-    result += a[i] * b[i];
-  }
-  return result;
-}
-
-/** Sums all the elements in the vector */
-export function sum(a: Vector): number {
-  let result = 0;
-  for (let i = 0; i < a.length; ++i) {
-    result += a[i];
-  }
-  return result;
-}
-
-/** Returns the sum of two vectors, i.e. a + b */
-export function add(a: Vector, b: Vector): Vector {
-  assert(a.length == b.length, 'Vectors a and b must be of same length');
-  let result = new Array(a.length);
-  for (let i = 0; i < a.length; ++i) {
-    result[i] = a[i] + b[i];
-  }
-  return result;
-}
-
-/** Subtracts vector b from vector a, i.e. returns a - b */
-export function sub(a: Vector, b: Vector): Vector {
-  assert(a.length == b.length, 'Vectors a and b must be of same length');
-  let result = new Array(a.length);
-  for (let i = 0; i < a.length; ++i) {
-    result[i] = a[i] - b[i];
-  }
-  return result;
-}
-
-/** Returns the square norm of the vector */
-export function norm2(a: Vector): number {
-  let result = 0;
-  for (let i = 0; i < a.length; ++i) {
-    result += a[i] * a[i];
-  }
-  return result;
-}
-
-/** Returns the euclidean distance between two vectors. */
-export function dist(a: Vector, b: Vector): number {
-  return Math.sqrt(dist2(a, b));
-}
-
-/** Returns the square euclidean distance between two vectors. */
-export function dist2(a: Vector, b: Vector): number {
-  assert(a.length == b.length, 'Vectors a and b must be of same length');
-  let result = 0;
-  for (let i = 0; i < a.length; ++i) {
-    let diff = a[i] - b[i];
-    result += diff * diff;
-  }
-  return result;
-}
-
-/** Returns the square euclidean distance between two 2D points. */
-export function dist2_2D(a: Vector, b: Vector): number {
-  let dX = a[0] - b[0];
-  let dY = a[1] - b[1];
-  return dX * dX + dY * dY;
-}
-
-/** Returns the square euclidean distance between two 3D points. */
-export function dist2_3D(a: Vector, b: Vector): number {
-  let dX = a[0] - b[0];
-  let dY = a[1] - b[1];
-  let dZ = a[2] - b[2];
-  return dX * dX + dY * dY + dZ * dZ;
-}
-
-/**
- * Returns the square euclidean distance between two vectors, with an early
- * exit (returns -1) if the distance is >= to the provided limit.
- */
-export function dist2WithLimit(a: Vector, b: Vector, limit: number): number {
-  assert(a.length == b.length, 'Vectors a and b must be of same length');
-  let result = 0;
-  for (let i = 0; i < a.length; ++i) {
-    let diff = a[i] - b[i];
-    result += diff * diff;
-    if (result >= limit) {
-      return -1;
-    }
-  }
-  return result;
-}
-
-/** Returns the square euclidean distance between two 2D points. */
-export function dist22D(a: Point2D, b: Point2D): number {
-  let dX = a[0] - b[0];
-  let dY = a[1] - b[1];
-  return dX * dX + dY * dY;
-}
-
-/** Modifies the vector in-place to have unit norm. */
-export function unit(a: Vector): void {
-  let norm = Math.sqrt(norm2(a));
-  assert(norm >= 0, 'Norm of the vector must be > 0');
-  for (let i = 0; i < a.length; ++i) {
-    a[i] /= norm;
-  }
-}
-
-/**
- *  Projects the vectors to a lower dimension
- *
- * @param vectors Array of vectors to be projected.
- * @param newDim The resulting dimension of the vectors.
- */
-export function projectRandom(vectors: number[][], newDim: number): number[][] {
-  let dim = vectors[0].length;
-  let N = vectors.length;
-  let newVectors: number[][] = new Array(N);
-  for (let i = 0; i < N; ++i) {
-    newVectors[i] = new Array(newDim);
-  }
-  // Make nDim projections.
-  for (let k = 0; k < newDim; ++k) {
-    let randomVector = rn(dim);
-    for (let i = 0; i < N; ++i) {
-      newVectors[i][k] = dot(vectors[i], randomVector);
-    }
-  }
-  return newVectors;
-}
-
-/**
- * Projects a vector onto a 2D plane specified by the two direction vectors.
- */
-export function project2d(a: Vector, dir1: Vector, dir2: Vector): Point2D {
-  return [dot(a, dir1), dot(a, dir2)];
-}
-
-/** Returns a vector filled with zeros */
-export function zeros(length: number): Vector {
-  let result = new Array(length);
-  for (let i = 0; i < length; ++i) {
-    result[i] = 0;
-  }
-  return result;
-}
-
-export type Predicate<T> = (a: T) => boolean;
-
-/**
- * Computes the centroid of the data points that pass the specified predicate.
- * If the provided data points are not vectors, an accessor function needs
- * to be provided.
- */
-export function centroid<T>(
-    dataPoints: T[], predicate: Predicate<T>,
-    accessor?: (a: T) => Vector): {centroid: Vector, numMatches: number} {
-  if (accessor == null) {
-    accessor = (a: T) => <any>a;
-  }
-  assert(dataPoints.length >= 0, '`vectors` must be of length >= 1');
-  let n = 0;
-  let centroid = zeros(accessor(dataPoints[0]).length);
-  for (let i = 0; i < dataPoints.length; ++i) {
-    let dataPoint = dataPoints[i];
-    if (!predicate(dataPoint)) {
-      continue;
-    }
-    ++n;
-    let vector = accessor(dataPoint);
-    for (let j = 0; j < centroid.length; ++j) {
-      centroid[j] += vector[j];
-    }
-  }
-  if (n == 0) {
-    return {centroid: null, numMatches: 0};
-  }
-  for (let j = 0; j < centroid.length; ++j) {
-    centroid[j] /= n;
-  }
-  return {centroid: centroid, numMatches: n};
-}
-
-/**
- * Generates a vector of the specified size where each component is drawn from
- * a random (0, 1) gaussian distribution.
- */
-export function rn(size: number): Vector {
-  let normal = d3.random.normal();
-  let result = new Array(size);
-  for (let i = 0; i < size; ++i) {
-    result[i] = normal();
-  }
-  return result;
-}
-
-/**
- * Returns the cosine distance ([0, 2]) between two vectors
- * that have been normalized to unit norm.
- */
-export function cosDistNorm(a: Vector, b: Vector): number {
-  return 1 - dot(a, b);
-}
-
-/** Returns the cosine similarity ([-1, 1]) between two vectors. */
-export function cosSim(a: Vector, b: Vector): number {
-  return dot(a, b) / Math.sqrt(norm2(a) * norm2(b));
-}
-
-/**
- * Converts list of vectors (matrix) into a 1-dimensional
- * typed array with row-first order.
- */
-export function toTypedArray<T>(
-    dataPoints: T[], accessor: (dataPoint: T) => number[]): Float32Array {
-  let N = dataPoints.length;
-  let dim = accessor(dataPoints[0]).length;
-  let result = new Float32Array(N * dim);
-  for (let i = 0; i < N; ++i) {
-    let vector = accessor(dataPoints[i]);
-    for (let d = 0; d < dim; ++d) {
-      result[i * dim + d] = vector[d];
-    }
-  }
-  return result;
-}
-
-/**
- * Transposes an RxC matrix represented as a flat typed array
- * into a CxR matrix, again represented as a flat typed array.
- */
-export function transposeTypedArray(
-    r: number, c: number, typedArray: Float32Array) {
-  let result = new Float32Array(r * c);
-  for (let i = 0; i < r; ++i) {
-    for (let j = 0; j < c; ++j) {
-      result[j * r + i] = typedArray[i * c + j];
-    }
-  }
-  return result;
-}
diff --git a/tensorflow/tensorboard/components/vz-projector/vz-projector-data-loader.html b/tensorflow/tensorboard/components/vz-projector/vz-projector-data-loader.html
deleted file mode 100644
index e6beaa837e..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/vz-projector-data-loader.html
+++ /dev/null
@@ -1,144 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-dropdown-menu/paper-dropdown-menu.html">
-<link rel="import" href="../paper-listbox/paper-listbox.html">
-<link rel="import" href="../paper-item/paper-item.html">
-
-<dom-module id='vz-projector-data-loader'>
-<template>
-<style>
-:host {
-}
-
-input[type=file] {
-  display: none;
-}
-
-.file-name {
-  margin-right: 10px;
-}
-
-.dirs {
-  display: flex;
-  flex-direction: column;
-  margin-right: 10px;
-  line-height: 20px;
-}
-
-.dir {
-  white-space: nowrap;
-  overflow: hidden;
-  text-overflow: ellipsis;
-}
-
-paper-item {
-  --paper-item-disabled: {
-    border-bottom: 1px solid black;
-    justify-content: center;
-    font-size: 12px;
-    line-height: normal;
-    min-height: 0px;
-  };
-}
-</style>
-
-<!-- Server-mode UI -->
-<div class="server-controls" style="display:none;">
-  <div class="dirs">
-    <div class="dir">Checkpoint: <span id="checkpoint-file"></span></div>
-    <div class="dir">Metadata: <span id="metadata-file"></span></div>
-  </div>
-  <!-- List of tensors in checkpoint -->
-  <paper-dropdown-menu noink no-animations label="[[getNumTensorsLabel(tensorNames)]] found">
-    <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{selectedTensor}}">
-      <template is="dom-repeat" items="[[tensorNames]]">
-        <paper-item style="justify-content: space-between;" value="[[item.name]]" label="[[item.name]]">
-          [[item.name]]
-          <span style="margin-left: 5px; color:gray; font-size: 12px;">[[item.shape.0]]x[[item.shape.1]]</span>
-        </paper-item>
-      </template>
-    </paper-listbox>
-  </paper-dropdown-menu>
-</div>
-
-<!-- Standalone-mode UI -->
-<div class="standalone-controls" style="display:none;">
-
-  <!-- Upload buttons -->
-  <div style="display: flex; justify-content: space-between;">
-    <!-- Upload data -->
-    <div>
-      <button id="upload" title="Upload a TSV file" class="ink-button">Upload data</button>
-      <span id="file-name" class="file-name dir"></span>
-      <input type="file" id="file" name="file"/>
-    </div>
-
-    <!-- Upload metadata -->
-    <div>
-      <button id="upload-metadata" title="Upload a TSV metadata file" class="ink-button">Upload Metadata</button>
-      <span id="file-metadata-name" class="file-name dir"></span>
-      <input type="file" id="file-metadata" name="file-metadata"/>
-    </div>
-  </div>
-
-  <!-- Demo datasets -->
-  <paper-dropdown-menu style="width: 100%" noink no-animations label="Select a demo dataset">
-    <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{selectedDemo}}">
-        <paper-item value="smartreply_full">SmartReply All</paper-item>
-        <paper-item value="smartreply_5k">SmartReply 5K</paper-item>
-        <paper-item value="wiki_5k">Glove Wiki 5K</paper-item>
-        <paper-item value="wiki_10k">Glove Wiki 10K</paper-item>
-        <paper-item value="wiki_40k">Glove Wiki 40K</paper-item>
-        <paper-item value="mnist_10k">MNIST 10K</paper-item>
-        <paper-item value="iris">Iris</paper-item>
-    </paper-listbox>
-  </paper-dropdown-menu>
-
-</div>
-
-<!-- Label by -->
-<template is="dom-if" if="[[labelOptions.length]]">
-  <paper-dropdown-menu style="width: 100%" noink no-animations label="Label by">
-    <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{labelOption}}">
-      <template is="dom-repeat" items="[[labelOptions]]">
-        <paper-item style="justify-content: space-between;" value="[[item]]" label="[[item]]">
-          [[item]]
-        </paper-item>
-      </template>
-    </paper-listbox>
-  </paper-dropdown-menu>
-</template>
-
-<!-- Color by -->
-<template is="dom-if" if="[[colorOptions.length]]">
-  <paper-dropdown-menu id="colorby" style="width: 100%" noink no-animations label="Color by">
-    <paper-listbox attr-for-selected="value" class="dropdown-content" selected="{{colorOption}}">
-      <template is="dom-repeat" items="[[colorOptions]]">
-        <paper-item style="justify-content: space-between;" class$="[[getSeparatorClass(item.isSeparator)]]" value="[[item]]" label="[[item.name]]" disabled="[[item.isSeparator]]">
-          [[item.name]]
-          <span style="margin-left: 5px; color:gray; font-size: 12px;">[[item.desc]]</span>
-        </paper-item>
-      </template>
-    </paper-listbox>
-  </paper-dropdown-menu>
-</template>
-
-<!-- Closing global template -->
-</template>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/vz-projector/vz-projector-data-loader.ts b/tensorflow/tensorboard/components/vz-projector/vz-projector-data-loader.ts
deleted file mode 100644
index f44bb6bbe9..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/vz-projector-data-loader.ts
+++ /dev/null
@@ -1,500 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {runAsyncTask, updateMessage} from './async';
-import {DataPoint, DataSet, DatasetMetadata, DataSource} from './data';
-import {PolymerElement} from './vz-projector-util';
-
-
-/** Prefix added to the http requests when asking the server for data. */
-const DATA_URL = 'data';
-
-type DemoDataset = {
-  fpath: string; metadata_path?: string; metadata?: DatasetMetadata;
-};
-
-type Metadata = {
-  [key: string]: (number|string);
-};
-
-/** List of compiled demo datasets for showing the capabilities of the tool. */
-const DEMO_DATASETS: {[name: string]: DemoDataset} = {
-  'wiki_5k': {
-    fpath: 'wiki_5000_50d_tensors.ssv',
-    metadata_path: 'wiki_5000_50d_labels.ssv'
-  },
-  'wiki_10k': {
-    fpath: 'wiki_10000_100d_tensors.ssv',
-    metadata_path: 'wiki_10000_100d_labels.ssv'
-  },
-  'wiki_40k': {
-    fpath: 'wiki_40000_100d_tensors.ssv',
-    metadata_path: 'wiki_40000_100d_labels.ssv'
-  },
-  'smartreply_5k': {
-    fpath: 'smartreply_5000_256d_tensors.tsv',
-    metadata_path: 'smartreply_5000_256d_labels.tsv'
-  },
-  'smartreply_full': {
-    fpath: 'smartreply_full_256d_tensors.tsv',
-    metadata_path: 'smartreply_full_256d_labels.tsv'
-  },
-  'mnist_10k': {
-    fpath: 'mnist_10k_784d_tensors.tsv',
-    metadata_path: 'mnist_10k_784d_labels.tsv',
-    metadata: {
-      image:
-          {sprite_fpath: 'mnist_10k_sprite.png', single_image_dim: [28, 28]}
-    },
-  },
-  'iris': {fpath: 'iris_tensors.tsv', metadata_path: 'iris_labels.tsv'}
-};
-
-/** Maximum number of colors supported in the color map. */
-const NUM_COLORS_COLOR_MAP = 20;
-
-interface ServerInfo {
-  tensors: {[name: string]: [number, number]};
-  tensors_file: string;
-  checkpoint_file: string;
-  checkpoint_dir: string;
-  metadata_file: string;
-}
-
-let DataLoaderPolymer = PolymerElement({
-  is: 'vz-projector-data-loader',
-  properties: {
-    dataSource: {
-      type: Object,  // DataSource
-      notify: true
-    },
-    selectedDemo: {type: String, value: 'wiki_5k', notify: true},
-    selectedTensor: {type: String, notify: true},
-    labelOption: {type: String, notify: true},
-    colorOption: {type: Object, notify: true},
-    // Private.
-    tensorNames: Array
-  }
-});
-
-export type ColorOption = {
-  name: string; desc?: string; map?: (value: string | number) => string;
-  isSeparator?: boolean;
-};
-
-class DataLoader extends DataLoaderPolymer {
-  dataSource: DataSource;
-  selectedDemo: string;
-  labelOption: string;
-  labelOptions: string[];
-  colorOption: ColorOption;
-  colorOptions: ColorOption[];
-  selectedTensor: string;
-  tensorNames: {name: string, shape: number[]}[];
-
-  private dom: d3.Selection<any>;
-
-  ready() {
-    this.dom = d3.select(this);
-    if (this.dataSource) {
-      // There is data already.
-      return;
-    }
-    // Check to see if there is a server.
-    d3.json(`${DATA_URL}/info`, (err, serverInfo) => {
-      if (err) {
-        // No server was found, thus operate in standalone mode.
-        this.setupStandaloneMode();
-        return;
-      }
-      // Server was found, thus show the checkpoint dir and the tensors.
-      this.setupServerMode(serverInfo);
-    });
-  }
-
-  getSeparatorClass(isSeparator: boolean): string {
-    return isSeparator ? 'separator' : null;
-  }
-
-  private setupServerMode(info: ServerInfo) {
-    // Display the server-mode controls.
-    this.dom.select('.server-controls').style('display', null);
-    this.dom.select('#checkpoint-file')
-        .text(info.checkpoint_file)
-        .attr('title', info.checkpoint_file);
-    this.dom.select('#metadata-file')
-        .text(info.metadata_file)
-        .attr('title', info.metadata_file);
-
-    // Handle the list of checkpoint tensors.
-    this.dom.on('selected-tensor-changed', () => {
-      this.selectedTensorChanged(this.selectedTensor);
-    });
-    let names = Object.keys(info.tensors)
-                    .filter(name => {
-                      let shape = info.tensors[name];
-                      return shape.length == 2 && shape[0] > 1 && shape[1] > 1;
-                    })
-                    .sort((a, b) => info.tensors[b][0] - info.tensors[a][0]);
-    this.tensorNames =
-        names.map(name => { return {name, shape: info.tensors[name]}; });
-  }
-
-  private updateMetadataUI(columnStats: ColumnStats[]) {
-    // Label by options.
-    let labelIndex = -1;
-    this.labelOptions = columnStats.length > 1 ? columnStats.map((stats, i) => {
-      // Make the default label by the first non-numeric column.
-      if (!stats.isNumeric && labelIndex == -1) {
-        labelIndex = i;
-      }
-      return stats.name;
-    }) :
-                                                 ['label'];
-    this.labelOption = this.labelOptions[Math.max(0, labelIndex)];
-
-    // Color by options.
-    let standardColorOption: ColorOption[] = [
-      {name: 'No color map'},
-      // TODO(smilkov): Implement this.
-      //{name: 'Distance of neighbors',
-      //    desc: 'How far is each point from its neighbors'}
-    ];
-    let metadataColorOption: ColorOption[] =
-        columnStats
-            .filter(stats => {
-              return !stats.tooManyUniqueValues || stats.isNumeric;
-            })
-            .map(stats => {
-              let map: (v: string|number) => string;
-              if (!stats.tooManyUniqueValues) {
-                let scale = d3.scale.category20();
-                let range = scale.range();
-                // Re-order the range.
-                let newRange = range.map((color, i) => {
-                  let index = (i * 2) % (range.length - 1);
-                  if (index == 0) {
-                    index = range.length - 1;
-                  }
-                  return range[index];
-                });
-                scale.range(newRange).domain(stats.uniqueValues);
-                map = scale;
-              } else {
-                map = d3.scale.linear<string>()
-                          .domain([stats.min, stats.max])
-                          .range(['white', 'black']);
-              }
-              let desc = stats.tooManyUniqueValues ?
-                  'gradient' :
-                  stats.uniqueValues.length + ' colors';
-              return {name: stats.name, desc: desc, map: map};
-            });
-    if (metadataColorOption.length > 0) {
-      // Add a separator line between built-in color maps
-      // and those based on metadata columns.
-      standardColorOption.push({name: 'Metadata', isSeparator: true});
-    }
-    this.colorOptions = standardColorOption.concat(metadataColorOption);
-    this.colorOption = this.colorOptions[0];
-  }
-
-  private setupStandaloneMode() {
-    // Display the standalone UI controls.
-    this.dom.select('.standalone-controls').style('display', null);
-
-    // Demo dataset dropdown
-    let demoDatasetChanged = (demoDataSet: DemoDataset) => {
-      if (demoDataSet == null) {
-        return;
-      }
-
-      this.dom.selectAll('.file-name').style('display', 'none');
-      let separator = demoDataSet.fpath.substr(-3) == 'tsv' ? '\t' : ' ';
-      fetchDemoData(`${DATA_URL}/${demoDataSet.fpath}`, separator)
-          .then(points => {
-
-            let p1 = demoDataSet.metadata_path ?
-                new Promise<ColumnStats[]>((resolve, reject) => {
-                  updateMessage('Fetching metadata...');
-                  d3.text(
-                      `${DATA_URL}/${demoDataSet.metadata_path}`,
-                      (err: Error, rawMetadata: string) => {
-                        if (err) {
-                          console.error(err);
-                          reject(err);
-                          return;
-                        }
-                        resolve(parseAndMergeMetadata(rawMetadata, points));
-                      });
-                }) :
-                null;
-
-            let p2 = demoDataSet.metadata && demoDataSet.metadata.image ?
-                fetchImage(
-                    `${DATA_URL}/${demoDataSet.metadata.image.sprite_fpath}`) :
-                null;
-
-            Promise.all([p1, p2]).then(values => {
-              this.updateMetadataUI(values[0]);
-              let dataSource = new DataSource();
-              dataSource.originalDataSet = new DataSet(points);
-              dataSource.spriteImage = values[1];
-              dataSource.metadata = demoDataSet.metadata;
-              this.dataSource = dataSource;
-            });
-          });
-    };
-
-    this.dom.on('selected-demo-changed', () => {
-      demoDatasetChanged(DEMO_DATASETS[this.selectedDemo]);
-    });
-    demoDatasetChanged(DEMO_DATASETS[this.selectedDemo]);
-
-    // Show and setup the upload button.
-    let fileInput = this.dom.select('#file');
-    fileInput.on('change', () => {
-      let file: File = (<any>d3.event).target.files[0];
-      this.dom.select('#file-name')
-          .style('display', null)
-          .text(file.name)
-          .attr('title', file.name);
-      // Clear out the value of the file chooser. This ensures that if the user
-      // selects the same file, we'll re-read it.
-      (<any>d3.event).target.value = '';
-      // Clear the value of the datasets dropdown.
-      this.selectedDemo = null;
-      let fileReader = new FileReader();
-      fileReader.onload = evt => {
-        let str: string = (evt.target as any).result;
-        parseTensors(str).then(data => {
-          let dataSource = new DataSource();
-          dataSource.originalDataSet = new DataSet(data);
-          this.dataSource = dataSource;
-        });
-      };
-      fileReader.readAsText(file);
-    });
-
-    let uploadButton = this.dom.select('#upload');
-    uploadButton.on(
-        'click', () => { (<HTMLInputElement>fileInput.node()).click(); });
-
-    // Show and setup the upload metadata button.
-    let fileMetadataInput = this.dom.select('#file-metadata');
-    fileMetadataInput.on('change', () => {
-      let file: File = (<any>d3.event).target.files[0];
-      this.dom.select('#file-metadata-name')
-          .style('display', null)
-          .text(file.name)
-          .attr('title', file.name);
-      // Clear out the value of the file chooser. This ensures that if the user
-      // selects the same file, we'll re-read it.
-      (<any>d3.event).target.value = '';
-      // Clear the value of the datasets dropdown.
-      this.selectedDemo = null;
-      let fileReader = new FileReader();
-      fileReader.onload = evt => {
-        let str: string = (evt.target as any).result;
-        parseAndMergeMetadata(str, this.dataSource.originalDataSet.points)
-            .then(columnStats => {
-              this.updateMetadataUI(columnStats);
-              // Must make a shallow copy, otherwise polymer will not
-              // fire the 'data-changed' event, even if we explicitly
-              // call this.fire().
-              this.dataSource = this.dataSource.makeShallowCopy();
-            });
-      };
-      fileReader.readAsText(file);
-    });
-
-    let uploadMetadataButton = this.dom.select('#upload-metadata');
-    uploadMetadataButton.on('click', () => {
-      (<HTMLInputElement>fileMetadataInput.node()).click();
-    });
-  }
-
-  private selectedTensorChanged(name: string) {
-    // Get the tensor.
-    updateMessage('Fetching tensor values...');
-    d3.text(`${DATA_URL}/tensor?name=${name}`, (err: Error, tsv: string) => {
-      if (err) {
-        console.error(err);
-        return;
-      }
-      parseTensors(tsv).then(dataPoints => {
-        updateMessage('Fetching metadata...');
-        d3.text(`${DATA_URL}/metadata`, (err: Error, rawMetadata: string) => {
-          if (err) {
-            console.error(err);
-            return;
-          }
-          parseAndMergeMetadata(rawMetadata, dataPoints).then(columnStats => {
-            this.updateMetadataUI(columnStats);
-            let dataSource = new DataSource();
-            dataSource.originalDataSet = new DataSet(dataPoints);
-            this.dataSource = dataSource;
-          });
-        });
-      });
-    });
-  }
-
-  private getNumTensorsLabel(tensorNames: string[]) {
-    return tensorNames.length === 1 ? '1 tensor' :
-                                      tensorNames.length + ' tensors';
-  }
-}
-
-function fetchImage(url: string): Promise<HTMLImageElement> {
-  return new Promise<HTMLImageElement>((resolve, reject) => {
-    let image = new Image();
-    image.onload = () => resolve(image);
-    image.onerror = (err) => reject(err);
-    image.src = url;
-  });
-}
-
-/** Makes a network request for a delimited text file. */
-function fetchDemoData(url: string, separator: string): Promise<DataPoint[]> {
-  return new Promise<DataPoint[]>((resolve, reject) => {
-    updateMessage('Fetching tensors...');
-    d3.text(url, (error: Error, dataString: string) => {
-      if (error) {
-        console.error(error);
-        updateMessage('Error loading data.');
-        reject(error);
-      } else {
-        parseTensors(dataString, separator).then(data => resolve(data));
-      }
-    });
-  });
-}
-
-/** Parses a tsv text file. */
-function parseTensors(content: string, delim = '\t'): Promise<DataPoint[]> {
-  let data: DataPoint[] = [];
-  let numDim: number;
-  return runAsyncTask('Parsing tensors...', () => {
-    let lines = content.split('\n');
-    lines.forEach(line => {
-      line = line.trim();
-      if (line == '') {
-        return;
-      }
-      let row = line.split(delim);
-      let dataPoint: DataPoint = {
-        metadata: {},
-        vector: null,
-        dataSourceIndex: data.length,
-        projections: null,
-        projectedPoint: null
-      };
-      // If the first label is not a number, take it as the label.
-      if (isNaN(row[0] as any) || numDim == row.length - 1) {
-        dataPoint.metadata['label'] = row[0];
-        dataPoint.vector = row.slice(1).map(Number);
-      } else {
-        dataPoint.vector = row.map(Number);
-      }
-      data.push(dataPoint);
-      if (numDim == null) {
-        numDim = dataPoint.vector.length;
-      }
-      if (numDim != dataPoint.vector.length) {
-        updateMessage('Parsing failed. Vector dimensions do not match');
-        throw Error('Parsing failed');
-      }
-      if (numDim <= 1) {
-        updateMessage(
-            'Parsing failed. Found a vector with only one dimension?');
-        throw Error('Parsing failed');
-      }
-    });
-    return data;
-  });
-}
-
-/** Statistics for a metadata column. */
-type ColumnStats = {
-  name: string; isNumeric: boolean; tooManyUniqueValues: boolean;
-  uniqueValues?: string[];
-  min: number;
-  max: number;
-};
-
-function parseAndMergeMetadata(
-    content: string, data: DataPoint[]): Promise<ColumnStats[]> {
-  return runAsyncTask('Parsing metadata...', () => {
-    let lines = content.split('\n').filter(line => line.trim().length > 0);
-    let hasHeader = (lines.length - 1 == data.length);
-
-    // Dimension mismatch.
-    if (lines.length != data.length && !hasHeader) {
-      throw Error('Dimensions do not match');
-    }
-
-    // If the first row doesn't contain metadata keys, we assume that the values
-    // are labels.
-    let columnNames: string[] = ['label'];
-    if (hasHeader) {
-      columnNames = lines[0].split('\t');
-      lines = lines.slice(1);
-    }
-    let columnStats: ColumnStats[] = columnNames.map(name => {
-      return {
-        name: name,
-        isNumeric: true,
-        tooManyUniqueValues: false,
-        min: Number.POSITIVE_INFINITY,
-        max: Number.NEGATIVE_INFINITY
-      };
-    });
-    let setOfValues = columnNames.map(() => d3.set());
-    lines.forEach((line: string, i: number) => {
-      let rowValues = line.split('\t');
-      data[i].metadata = {};
-      columnNames.forEach((name: string, colIndex: number) => {
-        let value = rowValues[colIndex];
-        let set = setOfValues[colIndex];
-        let stats = columnStats[colIndex];
-        data[i].metadata[name] = value;
-
-        // Update stats.
-        if (!stats.tooManyUniqueValues) {
-          set.add(value);
-          if (set.size() > NUM_COLORS_COLOR_MAP) {
-            stats.tooManyUniqueValues = true;
-          }
-        }
-        if (isNaN(value as any)) {
-          stats.isNumeric = false;
-        } else {
-          stats.min = Math.min(stats.min, +value);
-          stats.max = Math.max(stats.max, +value);
-        }
-      });
-    });
-    columnStats.forEach((stats, colIndex) => {
-      let set = setOfValues[colIndex];
-      if (!stats.tooManyUniqueValues) {
-        stats.uniqueValues = set.values();
-      }
-    });
-    return columnStats;
-  });
-}
-
-document.registerElement(DataLoader.prototype.is, DataLoader);
diff --git a/tensorflow/tensorboard/components/vz-projector/vz-projector-util.ts b/tensorflow/tensorboard/components/vz-projector/vz-projector-util.ts
deleted file mode 100644
index cca1ea3988..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/vz-projector-util.ts
+++ /dev/null
@@ -1,33 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-export type Spec = {
-  is: string; properties: {
-    [key: string]:
-        (Function |
-         {
-           type: Function, value?: any;
-           readonly?: boolean;
-           notify?: boolean;
-           observer?: string;
-         })
-  };
-};
-
-export function PolymerElement(spec: Spec) {
-  return Polymer.Class(spec as any) as{new (): PolymerHTMLElement};
-}
-
-export interface PolymerHTMLElement extends HTMLElement, polymer.Base {}
diff --git a/tensorflow/tensorboard/components/vz-projector/vz-projector.html b/tensorflow/tensorboard/components/vz-projector/vz-projector.html
deleted file mode 100644
index 25a90bb3d8..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/vz-projector.html
+++ /dev/null
@@ -1,628 +0,0 @@
-<!--
-@license
-Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<link rel="import" href="../polymer/polymer.html">
-<link rel="import" href="../paper-toggle-button/paper-toggle-button.html">
-<link rel="import" href="../paper-listbox/paper-listbox.html">
-<link rel="import" href="../paper-item/paper-item.html">
-<link rel="import" href="../paper-checkbox/paper-checkbox.html">
-<link rel="import" href="vz-projector-data-loader.html">
-
-<dom-module id='vz-projector'>
-<template>
-<style>
-
-:host {
-  display: flex;
-  width: 100%;
-  height: 100%;
-}
-
-#container {
-  display: flex;
-  width: 100%;
-  height: 100%;
-}
-
-.hidden {
-  display: none !important;
-}
-
-/* Main */
-
-#main {
-  position: relative;
-  flex-grow: 2;
-}
-
-#main .stage {
-  position: relative;
-  flex-grow: 2;
-}
-
-#scatter {
-  position: absolute;
-  top: 0;
-  left: 0;
-  right: 0;
-  bottom: 0;
-}
-
-#left-pane {
-  display: flex;
-  flex-direction: column;
-  min-width: 312px;
-  width: 312px;
-  border-right: 1px solid rgba(0, 0, 0, 0.1);
-  background: #fafafa;
-}
-
-#right-pane {
-  min-width: 300px;
-  width: 300px;
-  border-left: 1px solid rgba(0, 0, 0, 0.1);
-  background: #fafafa;
-}
-
-.file-name {
-  margin-right: 5px;
-}
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-.control label {
-  font-size: 12px;
-  color: rgba(0, 0, 0, 0.7);
-  margin-top: 10px;
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-}
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-.control .info {
-  display: block;
-  font-size: 12px;
-  color: rgba(0, 0, 0, 0.2);
-  margin-bottom: 18px;
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-}
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-  font-weight: 300;
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-  background: none;
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-.slider {
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-.brush .extent {
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-.ink-panel-content {
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-.ink-panel-content.active {
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-.nn-list .neighbor {
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-.nn-list .neighbor .bar {
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-  margin: 2px 0;
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-.nn-list .neighbor .bar .fill {
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-.nn-list .neighbor .tick {
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-.ink-button, ::shadow .ink-button {
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-.ink-tab-group .ink-tab.active {
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-.ink-panel {
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-.ink-panel h4 {
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-.ink-panel-header {
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-.ink-panel-metadata {
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-.ink-panel-metadata-key {
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-.ink-panel-metadata-key,
-.ink-panel-metadata-value {
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-.ink-panel-buttons {
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-.ink-panel-content {
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-.ink-panel-content .distance a {
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-.ink-panel-content .distance a.selected {
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-}
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-.ink-panel-footer {
-  display: flex;
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-  height: 60px;
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-}
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-.ink-panel-content h3 {
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-  margin-bottom: 5px;
-}
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-.ink-panel-header h3 {
-  margin: 0;
-  font-weight: 500;
-  font-size: 14px;
-  line-height: 60px;
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-  padding: 0 24px;
-}
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-/* - Menubar */
-
-.ink-panel-menubar {
-  position: relative;
-  height: 60px;
-  border-bottom: solid 1px #eee;
-  padding: 0 24px;
-}
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-.ink-panel-menubar .material-icons {
-  color: black;
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-.ink-panel-menubar .menu-button {
-  margin-right: 12px;
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-  font-weight: 200;
-  padding: 0;
-  margin: 0 20px 0 0;
-  outline: none;
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-}
-
-.ink-panel-menubar button .material-icons {
-  position: relative;
-  top: 7px;
-  margin-right: 8px;
-  color: #999;
-}
-
-.ink-panel-menubar button.selected,
-.ink-panel-menubar button.selected .material-icons {
-  color: #880E4F;
-}
-
-.ink-panel-menubar .ink-fabs {
-  position: absolute;
-  right: 24px;
-  top: 60px;
-  z-index: 1;
-}
-
-.ink-panel-menubar .ink-fabs .ink-fab {
-  position: relative;
-  top: -20px;
-  width: 40px;
-  height: 40px;
-  border: 1px solid rgba(0, 0, 0, 0.02);
-  border-radius: 50%;
-  display: inline-block;
-  background: white;
-  margin-left: 8px;
-  box-shadow: 0 1px 3px rgba(0, 0, 0, 0.3);
-  cursor: pointer;
-}
-
-.ink-panel-menubar .ink-fabs .ink-fab .material-icons {
-  margin: 0;
-  display: block;
-  line-height: 24px;
-  position: absolute;
-  top: calc(50% - 12px);
-  left: calc(50% - 12px);
-}
-
-.ink-panel-menubar .search-box {
-  transition: width .2s;
-  margin-left: -65px;
-  width: 0;
-  margin-right: 65px;
-  background: white;
-}
-
-.two-way-toggle {
-  display: flex;
-  flex-direction: row;
-}
-
-.two-way-toggle span {
-  padding-right: 7px;
-}
-
-paper-listbox .pca-item {
-  cursor: pointer;
-  min-height: 17px;
-  font-size: 12px;
-  line-height: 17px;
-}
-
-.has-border {
-  border: 1px solid rgba(0, 0, 0, 0.1);
-}
-
-</style>
-<link href="https://fonts.googleapis.com/css?family=Roboto:300,400,500|Material+Icons" rel="stylesheet" type="text/css">
-<div id="wrapper-notify-msg">
-  <div id="notify-msg">Loading...</div>
-</div>
-<div id="container">
-  <div id="left-pane" class="ink-panel">
-    <div class="ink-panel-header">
-      <div class="ink-tab-group">
-        <div data-tab="tsne" class="ink-tab" title="t-distributed stochastic neighbor embedding">t-SNE</div>
-        <div data-tab="pca" class="ink-tab" title="Principal component analysis">PCA</div>
-        <div data-tab="custom" class="ink-tab" title="Linear projection of two custom vectors">Custom</div>
-      </div>
-    </div>
-    <!-- TSNE Controls -->
-    <div data-panel="tsne" class="ink-panel-content">
-      <p><a href="https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding">t-distributed stochastic neighbor embedding</a> is a dimensionality reduction technique</p>
-      <p style="color: #880E4F; font-weight: bold;">For fast results, your data will be sampled down to 10,000 points.</p>
-      <div class="slider"><label>Dimension</label><div class="two-way-toggle"><span>2D</span><paper-toggle-button id="tsne-toggle" noink checked>3D</paper-toggle-button></div></div>
-      <div class="slider tsne-perplexity">
-        <label>Perplexity</label>
-        <input type="range" min="2" max="100"></input>
-        <span></span>
-      </div>
-      <div class="slider tsne-learning-rate">
-        <label>Learning rate</label>
-        <input type="range" min="-3" max="2"></input>
-        <span></span>
-      </div>
-      <p>The most appropriate perplexity value depends on the density of your data. Loosely speaking, one could say that a larger / denser dataset requires a larger perplexity. Typical values for the perplexity range between 5 and 50.</p>
-      <p>The most appropriate learning rate depends on the size of your data, with smaller datasets requiring smaller learning rates.</p>
-      <p>
-        <button class="run-tsne ink-button">Run</button>
-        <button class="stop-tsne ink-button">Stop</button>
-      </p>
-      <p>Iteration: <span class="run-tsne-iter">0</span></p>
-    </div>
-    <!-- PCA Controls -->
-    <div data-panel="pca" class="ink-panel-content">
-      <p><a href="https://en.wikipedia.org/wiki/Principal_component_analysis">Principal component analysis</a> is a dimensionality reduction technique</p>
-      <label>X</label>
-      <paper-listbox class="has-border" selected="{{pcaX}}">
-        <template is="dom-repeat" items="[[pcaComponents]]">
-          <paper-item class="pca-item">Component #[[item]]</paper-item>
-        </template>
-      </paper-listbox>
-      <br/>
-      <label>Y</label>
-      <paper-listbox class="has-border" selected="{{pcaY}}">
-        <template is="dom-repeat" items="[[pcaComponents]]">
-          <paper-item class="pca-item">Component #[[item]]</paper-item>
-        </template>
-      </paper-listbox>
-      <br/>
-      <paper-checkbox noink id="z-checkbox" checked="{{hasPcaZ}}">Z</paper-checkbox>
-      <paper-listbox class="has-border" disabled="true" selected="{{pcaZ}}">
-        <template is="dom-repeat" items="[[pcaComponents]]">
-          <paper-item disabled="[[!hasPcaZ]]" class="pca-item">Component #[[item]]</paper-item>
-        </template>
-      </paper-listbox>
-    </div>
-    <!-- Custom Controls -->
-    <div data-panel="custom" class="ink-panel-content">
-      <p>Search for two vectors upon which to project all points. Use <code>/regex/</code> to signal a regular expression, otherwise does an exact match.<p>
-      <h3>Horizontal</h3>
-      <div class="control xLeft">
-        <label>Left</label>
-        <input type="text" value="/\./"></input>
-        <span class="info"></span>
-      </div>
-      <div class="control xRight last">
-        <label>Right</label>
-        <input type="text" value="/!/"></input>
-        <span class="info"> </span>
-      </div>
-      <h3>Vertical</h3>
-      <div class="control yUp">
-        <label>Up</label>
-        <input type="text" value="/./"></input>
-        <span class="info"> </span>
-      </div>
-      <div class="control yDown last">
-        <label>Down</label>
-        <input type="text" value="/\?/"></input>
-        <span class="info"> </span>
-      </div>
-    </div>
-  </div>
-  <div id="main" class="ink-panel">
-    <div class="ink-panel-menubar">
-      <button class="menu-button search" title="Search">
-        <i class="material-icons">search</i>
-        <span class="button-label">Search</span>
-      </button>
-      <div class="control search-box">
-          <input type="text" value="">
-          <span class="info"></span>
-      </div>
-      <button class="menu-button selectMode" title="Bounding box selection">
-        <i class="material-icons">photo_size_select_small</i>
-        Select
-      </button>
-      <button class="menu-button show-labels selected" title="Show/hide labels">
-        <i class="material-icons">text_fields</i>
-        Labels
-      </button>
-      <button class="menu-button nightDayMode" title="Toggle between night and day mode">
-        <i class="material-icons">brightness_2</i>
-        Night Mode
-      </button>
-      <div class="ink-fabs">
-        <div class="ink-fab reset-zoom" title="Zoom to fit all">
-          <i class="material-icons resetZoom">home</i>
-        </div>
-        <div class="ink-fab zoom-in" title="Zoom in">
-          <i class="material-icons">add</i>
-        </div>
-        <div class="ink-fab zoom-out" title="Zoom out">
-          <i class="material-icons">remove</i>
-        </div>
-      </div>
-    </div>
-    <div class="stage">
-      <div id="scatter"></div>
-    </div>
-    <div id="info-panel" class="ink-panel-footer">
-      <div>
-        Number of data points: <span class="numDataPoints"></span>, dimension of embedding: <span class="dim"></span>
-        | <span id="hoverInfo"></span>
-      </div>
-    </div>
-  </div>
-  <div id="right-pane" class="ink-panel">
-    <div class="ink-panel-header">
-      <div class="ink-tab-group">
-        <div data-tab="data" class="active ink-tab" title="Setup data">Data</div>
-        <div data-tab="inspector" class="ink-tab" title="Inspect data">Inspector</div>
-      </div>
-    </div>
-
-    <!-- Inspector UI controls -->
-    <div data-panel="inspector" class="ink-panel-content">
-      <div class="ink-panel-metadata-container" style="display: none">
-        <span>Metadata</span>
-        <div class="ink-panel-metadata"></div>
-      </div>
-      <div class="ink-panel-buttons">
-        <div style="margin-bottom: 10px">
-          <button style="display: none;" class="ink-button reset-filter">Show All Data</button>
-        </div>
-        <button style="display: none;" class="ink-button set-filter">Isolate selection</button>
-        <button class="ink-button clear-selection" style="display: none;">Clear selection</button>
-      </div>
-      <div class="slider num-nn">
-        <label>Number of neighbors</label>
-        <input type="range" min="5" max="1000"></input>
-        <span></span>
-      </div>
-      <div class="distance">
-        Distance:
-        <div style="float:right">
-          <a class="selected cosine" href="javascript:void(0);">cosine</a> |
-          <a class="euclidean" href="javascript:void(0);">euclidean</a>
-        </div>
-      </div>
-      <p>Nearest points to <b id="nn-title"></b></p>
-      <div class="nn-list"></div>
-    </div>
-
-    <!-- Data UI controls -->
-    <div data-panel="data" class="active ink-panel-content">
-      <vz-projector-data-loader data-source="{{dataSource}}" label-option="{{labelOption}}" color-option="{{colorOption}}"></vz-projector-data-loader>
-    </div>
-  </div>
-</div>
-</template>
-</dom-module>
diff --git a/tensorflow/tensorboard/components/vz-projector/vz-projector.ts b/tensorflow/tensorboard/components/vz-projector/vz-projector.ts
deleted file mode 100644
index e8b1a882ec..0000000000
--- a/tensorflow/tensorboard/components/vz-projector/vz-projector.ts
+++ /dev/null
@@ -1,762 +0,0 @@
-/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-
-import {DataPoint, DataSet, DataSource} from './data';
-import * as knn from './knn';
-import {Mode, Scatter} from './scatter';
-import {ScatterWebGL} from './scatterWebGL';
-import * as vector from './vector';
-import {ColorOption} from './vz-projector-data-loader';
-import {PolymerElement} from './vz-projector-util';
-
-
-/** T-SNE perplexity. Roughly how many neighbors each point influences. */
-let perplexity: number = 30;
-/** T-SNE learning rate. */
-let learningRate: number = 10;
-/** Number of dimensions for the scatter plot. */
-let dimension = 3;
-/** Number of nearest neighbors to highlight around the selected point. */
-let numNN = 100;
-
-/** Highlight stroke color for the nearest neighbors. */
-const NN_HIGHLIGHT_COLOR = '#6666FA';
-
-/** Highlight stroke color for the selected point */
-const POINT_HIGHLIGHT_COLOR_DAY = 'black';
-const POINT_HIGHLIGHT_COLOR_NIGHT = new THREE.Color(0xFFE11F).getStyle();
-
-/** Color scale for nearest neighbors. */
-const NN_COLOR_SCALE =
-    d3.scale.linear<string>()
-        .domain([1, 0.7, 0.4])
-        .range(['hsl(285, 80%, 40%)', 'hsl(0, 80%, 65%)', 'hsl(40, 70%, 60%)'])
-        .clamp(true);
-
-/** Text color used for error/important messages. */
-const CALLOUT_COLOR = '#880E4F';
-
-type Centroids = {
-  [key: string]: number[]; xLeft: number[]; xRight: number[]; yUp: number[];
-  yDown: number[];
-};
-
-let ProjectorPolymer = PolymerElement({
-  is: 'vz-projector',
-  properties: {
-    // A data source.
-    dataSource: {
-      type: Object,  // DataSource
-      observer: 'dataSourceChanged'
-    },
-
-    // Private.
-    pcaComponents: {type: Array, value: d3.range(1, 11)},
-    pcaX: {
-      type: Number,
-      value: 0,
-      notify: true,
-    },
-    pcaY: {
-      type: Number,
-      value: 1,
-      notify: true,
-    },
-    pcaZ: {
-      type: Number,
-      value: 2,
-      notify: true,
-    },
-    hasPcaZ: {type: Boolean, value: true, notify: true},
-    labelOption: {type: String, observer: 'labelOptionChanged'},
-    colorOption: {type: Object, observer: 'colorOptionChanged'},
-  }
-});
-
-class Projector extends ProjectorPolymer {
-  // Public API.
-  dataSource: DataSource;
-
-  private dom: d3.Selection<any>;
-  private pcaX: number;
-  private pcaY: number;
-  private pcaZ: number;
-  private hasPcaZ: boolean;
-  // The working subset of the data source's original data set.
-  private currentDataSet: DataSet;
-  private scatter: Scatter;
-  private dim: number;
-  private selectedDistance: (a: number[], b: number[]) => number;
-  private highlightedPoints: {index: number, color: string}[];
-  private selectedPoints: number[];
-  private centroidValues: any;
-  private centroids: Centroids;
-  /** The centroid across all points. */
-  private allCentroid: number[];
-  private labelOption: string;
-  private colorOption: ColorOption;
-
-  ready() {
-    this.hasPcaZ = true;
-    this.selectedDistance = vector.cosDistNorm;
-    this.highlightedPoints = [];
-    this.selectedPoints = [];
-    this.centroidValues = {xLeft: null, xRight: null, yUp: null, yDown: null};
-    this.centroids = {xLeft: null, xRight: null, yUp: null, yDown: null};
-    // Dynamically creating elements inside .nn-list.
-    this.scopeSubtree(this.$$('.nn-list'), true);
-    this.dom = d3.select(this);
-    // Sets up all the UI.
-    this.setupUIControls();
-    if (this.dataSource) {
-      this.dataSourceChanged();
-    }
-  }
-
-  labelOptionChanged() {
-    let labelAccessor = (i: number): string => {
-      return this.points[i].metadata[this.labelOption] as string;
-    };
-    this.scatter.setLabelAccessor(labelAccessor);
-  }
-
-  colorOptionChanged() {
-    let colorMap = this.colorOption.map;
-    if (colorMap == null) {
-      this.scatter.setColorAccessor(null);
-      return;
-    };
-    let colors = (i: number) => {
-      return colorMap(this.points[i].metadata[this.colorOption.name]);
-    };
-    this.scatter.setColorAccessor(colors);
-  }
-
-  dataSourceChanged() {
-    if (this.scatter == null || this.dataSource == null) {
-      // We are not ready yet.
-      return;
-    }
-    this.initFromSource(this.dataSource);
-    // Set the container to a fixed height, otherwise in Colab the
-    // height can grow indefinitely.
-    let container = this.dom.select('#container');
-    container.style('height', container.property('clientHeight') + 'px');
-  }
-
-  /**
-   * Normalizes the distance so it can be visually encoded with color.
-   * The normalization depends on the distance metric (cosine vs euclidean).
-   */
-  private normalizeDist(d: number, minDist: number): number {
-    return this.selectedDistance === vector.cosDistNorm ? 1 - d : minDist / d;
-  }
-
-  /** Normalizes and encodes the provided distance with color. */
-  private dist2color(d: number, minDist: number): string {
-    return NN_COLOR_SCALE(this.normalizeDist(d, minDist));
-  }
-
-  private initFromSource(source: DataSource) {
-    this.dataSource = source;
-    this.setDataSet(this.dataSource.getDataSet());
-    this.dom.select('.reset-filter').style('display', 'none');
-    // Regexp inputs.
-    this.setupInput('xLeft');
-    this.setupInput('xRight');
-    this.setupInput('yUp');
-    this.setupInput('yDown');
-  }
-
-  private setDataSet(ds: DataSet) {
-    this.currentDataSet = ds;
-    this.scatter.setDataSet(this.currentDataSet, this.dataSource.spriteImage);
-    this.updateMenuButtons();
-    this.dim = this.currentDataSet.dim[1];
-    this.dom.select('span.numDataPoints').text(this.currentDataSet.dim[0]);
-    this.dom.select('span.dim').text(this.currentDataSet.dim[1]);
-    this.showTab('pca');
-  }
-
-  private setupInput(name: string) {
-    let control = this.dom.select('.control.' + name);
-    let info = control.select('.info');
-
-    let updateInput = (value: string) => {
-      if (value.trim() === '') {
-        info.style('color', CALLOUT_COLOR).text('Enter a regex.');
-        return;
-      }
-      let result = this.getCentroid(value);
-      if (result.error) {
-        info.style('color', CALLOUT_COLOR)
-            .text('Invalid regex. Using a random vector.');
-        result.centroid = vector.rn(this.dim);
-      } else if (result.numMatches === 0) {
-        info.style('color', CALLOUT_COLOR)
-            .text('0 matches. Using a random vector.');
-        result.centroid = vector.rn(this.dim);
-      } else {
-        info.style('color', null).text(`${result.numMatches} matches.`);
-      }
-      this.centroids[name] = result.centroid;
-      this.centroidValues[name] = value;
-    };
-    let self = this;
-
-    let input = control.select('input').on('input', function() {
-      updateInput(this.value);
-      self.showCustom();
-    });
-    this.allCentroid = null;
-    // Init the control with the current input.
-    updateInput((input.node() as HTMLInputElement).value);
-  }
-
-  private setupUIControls() {
-    let self = this;
-    // Global tabs
-    d3.selectAll('.ink-tab').on('click', function() {
-      let id = this.getAttribute('data-tab');
-      self.showTab(id);
-    });
-
-    // Unknown why, but the polymer toggle button stops working
-    // as soon as you do d3.select() on it.
-    let tsneToggle = this.querySelector('#tsne-toggle') as HTMLInputElement;
-    let zCheckbox = this.querySelector('#z-checkbox') as HTMLInputElement;
-
-    // PCA controls.
-    zCheckbox.addEventListener('change', () => {
-      // Make sure tsne stays in the same dimension as PCA.
-      dimension = this.hasPcaZ ? 3 : 2;
-      tsneToggle.checked = this.hasPcaZ;
-      this.showPCA(() => { this.scatter.recreateScene(); });
-    });
-    this.dom.on('pca-x-changed', () => this.showPCA());
-    this.dom.on('pca-y-changed', () => this.showPCA());
-    this.dom.on('pca-z-changed', () => this.showPCA());
-
-    // TSNE controls.
-
-    tsneToggle.addEventListener('change', () => {
-      // Make sure PCA stays in the same dimension as tsne.
-      this.hasPcaZ = tsneToggle.checked;
-      dimension = tsneToggle.checked ? 3 : 2;
-      if (this.scatter) {
-        this.showTSNE();
-        this.scatter.recreateScene();
-      }
-    });
-
-    this.dom.select('.run-tsne').on('click', () => this.runTSNE());
-    this.dom.select('.stop-tsne').on('click', () => {
-      this.currentDataSet.stopTSNE();
-    });
-
-    let updatePerplexity = () => {
-      perplexity = +perplexityInput.property('value');
-      this.dom.select('.tsne-perplexity span').text(perplexity);
-    };
-    let perplexityInput = this.dom.select('.tsne-perplexity input')
-                              .property('value', perplexity)
-                              .on('input', updatePerplexity);
-    updatePerplexity();
-
-    let updateLearningRate = () => {
-      let val = +learningRateInput.property('value');
-      learningRate = Math.pow(10, val);
-      this.dom.select('.tsne-learning-rate span').text(learningRate);
-    };
-    let learningRateInput = this.dom.select('.tsne-learning-rate input')
-                                .property('value', 1)
-                                .on('input', updateLearningRate);
-    updateLearningRate();
-
-    // Nearest neighbors controls.
-    let updateNumNN = () => {
-      numNN = +numNNInput.property('value');
-      this.dom.select('.num-nn span').text(numNN);
-    };
-    let numNNInput = this.dom.select('.num-nn input')
-                         .property('value', numNN)
-                         .on('input', updateNumNN);
-    updateNumNN();
-
-    // View controls
-    this.dom.select('.reset-zoom').on('click', () => {
-      this.scatter.resetZoom();
-    });
-    this.dom.select('.zoom-in').on('click', () => {
-      this.scatter.zoomStep(2);
-    });
-    this.dom.select('.zoom-out').on('click', () => {
-      this.scatter.zoomStep(0.5);
-    });
-
-    // Toolbar controls
-    let searchBox = this.dom.select('.control.search-box');
-    let searchBoxInfo = searchBox.select('.info');
-
-    let searchByRegEx =
-        (pattern: string): {error?: Error, indices: number[]} => {
-          let regEx: RegExp;
-          try {
-            regEx = new RegExp(pattern, 'i');
-          } catch (e) {
-            return {error: e.message, indices: null};
-          }
-          let indices: number[] = [];
-          for (let id = 0; id < this.points.length; ++id) {
-            if (regEx.test('' + this.points[id].metadata['label'])) {
-              indices.push(id);
-            }
-          }
-          return {indices: indices};
-        };
-
-    // Called whenever the search text input changes.
-    let searchInputChanged = (value: string) => {
-      if (value.trim() === '') {
-        searchBoxInfo.style('color', CALLOUT_COLOR).text('Enter a regex.');
-        if (this.scatter != null) {
-          this.selectedPoints = [];
-          this.selectionWasUpdated();
-        }
-        return;
-      }
-      let result = searchByRegEx(value);
-      let indices = result.indices;
-      if (result.error) {
-        searchBoxInfo.style('color', CALLOUT_COLOR).text('Invalid regex.');
-      }
-      if (indices) {
-        if (indices.length === 0) {
-          searchBoxInfo.style('color', CALLOUT_COLOR).text(`0 matches.`);
-        } else {
-          searchBoxInfo.style('color', null).text(`${indices.length} matches.`);
-          this.showTab('inspector');
-          let neighbors = this.findNeighbors(indices[0]);
-          if (indices.length === 1) {
-            this.scatter.clickOnPoint(indices[0]);
-          }
-          this.selectedPoints = indices;
-          this.updateNNList(neighbors);
-        }
-        this.selectionWasUpdated();
-      }
-    };
-
-    searchBox.select('input').on(
-        'input', function() { searchInputChanged(this.value); });
-    let searchButton = this.dom.select('.search');
-
-    searchButton.on('click', () => {
-      let mode = this.scatter.getMode();
-      this.scatter.setMode(mode === Mode.SEARCH ? Mode.HOVER : Mode.SEARCH);
-      if (this.scatter.getMode() == Mode.HOVER) {
-        this.selectedPoints = [];
-        this.selectionWasUpdated();
-      } else {
-        searchInputChanged(searchBox.select('input').property('value'));
-      }
-      this.updateMenuButtons();
-    });
-    // Init the control with an empty input.
-    searchInputChanged('');
-
-    this.dom.select('.distance a.euclidean').on('click', function() {
-      d3.selectAll('.distance a').classed('selected', false);
-      d3.select(this).classed('selected', true);
-      self.selectedDistance = vector.dist;
-      if (self.selectedPoints.length > 0) {
-        let neighbors = self.findNeighbors(self.selectedPoints[0]);
-        self.updateNNList(neighbors);
-      }
-    });
-
-    this.dom.select('.distance a.cosine').on('click', function() {
-      d3.selectAll('.distance a').classed('selected', false);
-      d3.select(this).classed('selected', true);
-      self.selectedDistance = vector.cosDistNorm;
-      if (self.selectedPoints.length > 0) {
-        let neighbors = self.findNeighbors(self.selectedPoints[0]);
-        self.updateNNList(neighbors);
-      }
-    });
-
-    let selectModeButton = this.dom.select('.selectMode');
-
-    selectModeButton.on('click', () => {
-      let mode = this.scatter.getMode();
-      this.scatter.setMode(mode === Mode.SELECT ? Mode.HOVER : Mode.SELECT);
-      this.updateMenuButtons();
-    });
-
-    let showLabels = true;
-    let showLabelsButton = this.dom.select('.show-labels');
-    showLabelsButton.on('click', () => {
-      showLabels = !showLabels;
-      this.scatter.showLabels(showLabels);
-      showLabelsButton.classed('selected', showLabels);
-    });
-
-    let dayNightModeButton = this.dom.select('.nightDayMode');
-    let modeIsNight = dayNightModeButton.classed('selected');
-    dayNightModeButton.on('click', () => {
-      modeIsNight = !modeIsNight;
-      this.scatter.setDayNightMode(modeIsNight);
-      this.scatter.update();
-      dayNightModeButton.classed('selected', modeIsNight);
-    });
-
-    // Resize
-    window.addEventListener('resize', () => { this.scatter.resize(); });
-
-    // Canvas
-    this.scatter = new ScatterWebGL(
-        this.dom.select('#scatter'),
-        i => '' + this.points[i].metadata['label']);
-    this.scatter.onHover(hoveredIndex => {
-      if (hoveredIndex == null) {
-        this.highlightedPoints = [];
-      } else {
-        let point = this.points[hoveredIndex];
-        this.dom.select('#hoverInfo').text(point.metadata['label']);
-        let neighbors = this.findNeighbors(hoveredIndex);
-        let minDist = neighbors[0].dist;
-        let pointIndices = [hoveredIndex].concat(neighbors.map(d => d.index));
-        let pointHighlightColor = modeIsNight ? POINT_HIGHLIGHT_COLOR_NIGHT :
-                                                POINT_HIGHLIGHT_COLOR_DAY;
-        this.highlightedPoints = pointIndices.map((index, i) => {
-          let color = i == 0 ? pointHighlightColor :
-                               this.dist2color(neighbors[i - 1].dist, minDist);
-          return {index: index, color: color};
-        });
-      }
-      this.selectionWasUpdated();
-    });
-
-    this.scatter.onSelection(
-        selectedPoints => this.updateSelection(selectedPoints));
-
-    // Selection controls
-    this.dom.select('.set-filter').on('click', () => {
-      let highlighted = this.selectedPoints;
-      let highlightedOrig: number[] =
-          highlighted.map(d => { return this.points[d].dataSourceIndex; });
-      let subset = this.dataSource.getDataSet(highlightedOrig);
-      this.setDataSet(subset);
-      this.dom.select('.reset-filter').style('display', null);
-      this.selectedPoints = [];
-      this.scatter.recreateScene();
-      this.selectionWasUpdated();
-      this.updateIsolateButton();
-    });
-
-    this.dom.select('.reset-filter').on('click', () => {
-      let subset = this.dataSource.getDataSet();
-      this.setDataSet(subset);
-      this.dom.select('.reset-filter').style('display', 'none');
-    });
-
-    this.dom.select('.clear-selection').on('click', () => {
-      this.selectedPoints = [];
-      this.scatter.setMode(Mode.HOVER);
-      this.scatter.clickOnPoint(null);
-      this.updateMenuButtons();
-      this.selectionWasUpdated();
-    });
-  }
-
-  private updateSelection(selectedPoints: number[]) {
-    // If no points are selected, unselect everything.
-    if (!selectedPoints.length) {
-      this.selectedPoints = [];
-      this.updateNNList([]);
-    }
-    // If only one point is selected, we want to get its nearest neighbors
-    // and change the UI accordingly.
-    else if (selectedPoints.length === 1) {
-      let selectedPoint = selectedPoints[0];
-      this.showTab('inspector');
-      let neighbors = this.findNeighbors(selectedPoint);
-      this.selectedPoints = [selectedPoint].concat(neighbors.map(n => n.index));
-      this.updateNNList(neighbors);
-    }
-    // Otherwise, select all points and hide nearest neighbors list.
-    else {
-      this.selectedPoints = selectedPoints as number[];
-      this.highlightedPoints = [];
-      this.updateNNList([]);
-    }
-    this.updateMetadata();
-    this.selectionWasUpdated();
-  }
-
-  private showPCA(callback?: () => void) {
-    this.currentDataSet.projectPCA().then(() => {
-      this.scatter.showTickLabels(false);
-      let x = this.pcaX;
-      let y = this.pcaY;
-      let z = this.pcaZ;
-      let hasZ = dimension == 3;
-      this.scatter.setXAccessor(i => this.points[i].projections['pca-' + x]);
-      this.scatter.setYAccessor(i => this.points[i].projections['pca-' + y]);
-      this.scatter.setZAccessor(
-          hasZ ? (i => this.points[i].projections['pca-' + z]) : null);
-      this.scatter.setAxisLabels('pca-' + x, 'pca-' + y);
-      this.scatter.update();
-      if (callback) {
-        callback();
-      }
-    });
-  }
-
-  private showTab(id: string) {
-    let tab = this.dom.select('.ink-tab[data-tab="' + id + '"]');
-    let pane =
-        d3.select((tab.node() as HTMLElement).parentNode.parentNode.parentNode);
-    pane.selectAll('.ink-tab').classed('active', false);
-    tab.classed('active', true);
-    pane.selectAll('.ink-panel-content').classed('active', false);
-    pane.select('.ink-panel-content[data-panel="' + id + '"]')
-        .classed('active', true);
-    if (id === 'pca') {
-      this.showPCA(() => this.scatter.recreateScene());
-    } else if (id === 'tsne') {
-      this.showTSNE();
-    } else if (id === 'custom') {
-      this.showCustom();
-    }
-  }
-
-  private showCustom() {
-    this.scatter.showTickLabels(true);
-    let xDir = vector.sub(this.centroids.xRight, this.centroids.xLeft);
-    this.currentDataSet.projectLinear(xDir, 'linear-x');
-    this.scatter.setXAccessor(i => this.points[i].projections['linear-x']);
-
-    let yDir = vector.sub(this.centroids.yUp, this.centroids.yDown);
-    this.currentDataSet.projectLinear(yDir, 'linear-y');
-    this.scatter.setYAccessor(i => this.points[i].projections['linear-y']);
-
-    // Scatter is only in 2D in projection mode.
-    this.scatter.setZAccessor(null);
-
-    let xLabel = this.centroidValues.xLeft + ' → ' + this.centroidValues.xRight;
-    let yLabel = this.centroidValues.yUp + ' → ' + this.centroidValues.yDown;
-    this.scatter.setAxisLabels(xLabel, yLabel);
-    this.scatter.update();
-    this.scatter.recreateScene();
-  }
-
-  private get points() { return this.currentDataSet.points; }
-
-  private showTSNE() {
-    this.scatter.showTickLabels(false);
-    this.scatter.setXAccessor(i => this.points[i].projections['tsne-0']);
-    this.scatter.setYAccessor(i => this.points[i].projections['tsne-1']);
-    this.scatter.setZAccessor(
-        dimension === 3 ? (i => this.points[i].projections['tsne-2']) : null);
-    this.scatter.setAxisLabels('tsne-0', 'tsne-1');
-  }
-
-  private runTSNE() {
-    this.currentDataSet.projectTSNE(
-        perplexity, learningRate, dimension, (iteration: number) => {
-          if (iteration != null) {
-            this.dom.select('.run-tsne-iter').text(iteration);
-            this.scatter.update();
-          }
-        });
-  }
-
-  // Updates the displayed metadata for the selected point.
-  private updateMetadata() {
-    let metadataContainerElement = this.dom.select('.ink-panel-metadata');
-    metadataContainerElement.selectAll('*').remove();
-
-    let display = false;
-    if (this.selectedPoints.length >= 1) {
-      let selectedPoint = this.points[this.selectedPoints[0]];
-
-      for (let metadataKey in selectedPoint.metadata) {
-        let rowElement = document.createElement('div');
-        rowElement.className = 'ink-panel-metadata-row vz-projector';
-
-        let keyElement = document.createElement('div');
-        keyElement.className = 'ink-panel-metadata-key vz-projector';
-        keyElement.textContent = metadataKey;
-
-        let valueElement = document.createElement('div');
-        valueElement.className = 'ink-panel-metadata-value vz-projector';
-        valueElement.textContent = '' + selectedPoint.metadata[metadataKey];
-
-        rowElement.appendChild(keyElement);
-        rowElement.appendChild(valueElement);
-
-        metadataContainerElement.append(function() {
-          return this.appendChild(rowElement);
-        });
-      }
-
-      display = true;
-    }
-
-    this.dom.select('.ink-panel-metadata-container')
-        .style('display', display ? '' : 'none');
-  }
-
-  private selectionWasUpdated() {
-    this.dom.select('#hoverInfo')
-        .text(`Selected ${this.selectedPoints.length} points`);
-    let allPoints =
-        this.highlightedPoints.map(x => x.index).concat(this.selectedPoints);
-    let stroke = (i: number) => {
-      return i < this.highlightedPoints.length ?
-          this.highlightedPoints[i].color :
-          NN_HIGHLIGHT_COLOR;
-    };
-    let favor = (i: number) => {
-      return i == 0 || (i < this.highlightedPoints.length ? false : true);
-    };
-    this.scatter.highlightPoints(allPoints, stroke, favor);
-    this.updateIsolateButton();
-  }
-
-  private updateMenuButtons() {
-    let searchBox = this.dom.select('.control.search-box');
-    this.dom.select('.search').classed(
-        'selected', this.scatter.getMode() === Mode.SEARCH);
-    let searchMode = this.scatter.getMode() === Mode.SEARCH;
-    this.dom.select('.control.search-box')
-        .style('width', searchMode ? '110px' : null)
-        .style('margin-right', searchMode ? '10px' : null);
-    (searchBox.select('input').node() as HTMLInputElement).focus();
-    this.dom.select('.selectMode')
-        .classed('selected', this.scatter.getMode() === Mode.SELECT);
-  }
-
-  /**
-   * Finds the nearest neighbors of the currently selected point using the
-   * currently selected distance method.
-   */
-  private findNeighbors(pointIndex: number): knn.NearestEntry[] {
-    // Find the nearest neighbors of a particular point.
-    let neighbors = knn.findKNNofPoint(
-        this.points, pointIndex, numNN, (d => d.vector), this.selectedDistance);
-    let result = neighbors.slice(0, numNN);
-    return result;
-  }
-
-  /** Updates the nearest neighbors list in the inspector. */
-  private updateNNList(neighbors: knn.NearestEntry[]) {
-    let nnlist = this.dom.select('.nn-list');
-    nnlist.html('');
-
-    if (neighbors.length == 0) {
-      this.dom.select('#nn-title').text('');
-      return;
-    }
-
-    let selectedPoint = this.points[this.selectedPoints[0]];
-    this.dom.select('#nn-title')
-        .text(selectedPoint != null ? selectedPoint.metadata['label'] : '');
-
-    let minDist = neighbors.length > 0 ? neighbors[0].dist : 0;
-    let n = nnlist.selectAll('.neighbor')
-                .data(neighbors)
-                .enter()
-                .append('div')
-                .attr('class', 'neighbor')
-                .append('a')
-                .attr('class', 'neighbor-link');
-
-    n.append('span')
-        .attr('class', 'label')
-        .style('color', d => this.dist2color(d.dist, minDist))
-        .text(d => this.points[d.index].metadata['label']);
-
-    n.append('span').attr('class', 'value').text(d => d.dist.toFixed(2));
-
-    let bar = n.append('div').attr('class', 'bar');
-
-    bar.append('div')
-        .attr('class', 'fill')
-        .style('border-top-color', d => this.dist2color(d.dist, minDist))
-        .style('width', d => this.normalizeDist(d.dist, minDist) * 100 + '%');
-
-    bar.selectAll('.tick')
-        .data(d3.range(1, 4))
-        .enter()
-        .append('div')
-        .attr('class', 'tick')
-        .style('left', d => d * 100 / 4 + '%');
-
-    n.on('click', d => { this.updateSelection([d.index]); });
-  }
-
-  private updateIsolateButton() {
-    let numPoints = this.selectedPoints.length;
-    let isolateButton = this.dom.select('.set-filter');
-    let clearButton = this.dom.select('button.clear-selection');
-    if (numPoints > 1) {
-      isolateButton.text(`Isolate ${numPoints} points`).style('display', null);
-      clearButton.style('display', null);
-    } else {
-      isolateButton.style('display', 'none');
-      clearButton.style('display', 'none');
-    }
-  }
-
-  private getCentroid(pattern: string): CentroidResult {
-    let accessor = (a: DataPoint) => a.vector;
-    if (pattern == null) {
-      return {numMatches: 0};
-    }
-    if (pattern == '') {
-      if (this.allCentroid == null) {
-        this.allCentroid =
-            vector.centroid(this.points, () => true, accessor).centroid;
-      }
-      return {centroid: this.allCentroid, numMatches: this.points.length};
-    }
-
-    let regExp: RegExp;
-    let predicate: (a: DataPoint) => boolean;
-    // Check for a regex.
-    if (pattern.charAt(0) == '/' && pattern.charAt(pattern.length - 1) == '/') {
-      pattern = pattern.slice(1, pattern.length - 1);
-      try {
-        regExp = new RegExp(pattern, 'i');
-      } catch (e) {
-        return {error: e.message};
-      }
-      predicate =
-          (a: DataPoint) => { return regExp.test('' + a.metadata['label']); };
-      // else does an exact match
-    } else {
-      predicate = (a: DataPoint) => { return a.metadata['label'] == pattern; };
-    }
-    return vector.centroid(this.points, predicate, accessor);
-  }
-}
-
-type CentroidResult = {
-  centroid?: number[]; numMatches?: number; error?: string
-};
-
-document.registerElement(Projector.prototype.is, Projector);
diff --git a/tensorflow/tensorboard/gulp_tasks/compile.js b/tensorflow/tensorboard/gulp_tasks/compile.js
index 93d3e50c3f..4877468ab3 100644
--- a/tensorflow/tensorboard/gulp_tasks/compile.js
+++ b/tensorflow/tensorboard/gulp_tasks/compile.js
@@ -19,67 +19,20 @@ var typescript = require('typescript');
 var gutil = require('gulp-util');
 var filter = require('gulp-filter');
 var merge = require('merge2');
-var browserify = require('browserify');
-var tsify = require('tsify');
-var source = require('vinyl-source-stream');
-var glob = require('glob').sync;
-var concat = require('gulp-concat');
 
 var tsProject = ts.createProject('./tsconfig.json', {
   typescript: typescript,
   noExternalResolve: true, // opt-in for faster compilation!
 });
 
-/** List of components (and their external deps) that are using es6 modules. */
-var ES6_COMPONENTS = [{
-  name: 'vz-projector',
-  deps: [
-    'd3/d3.min.js', 'weblas/dist/weblas.js', 'three.js/build/three.min.js',
-    'three.js/examples/js/controls/OrbitControls.js',
-    'numericjs/lib/numeric-1.2.6.js'
-  ]
-}];
 
 module.exports = function() {
-  // Compile all components that are using ES6 modules into a bundle.js
-  // using browserify.
-  var entries = ['typings/index.d.ts'];
-  var deps = {};
-  ES6_COMPONENTS.forEach(function(component) {
-    // Collect all the typescript files across the components.
-    entries = entries.concat(glob(
-        'components/' + component.name + '/**/*.ts',
-        // Do not include tests.
-        {ignore: 'components/' + component.name + '/**/*_test.ts'}));
-    // Collect the unique external deps across all components using es6 modules.
-    component.deps.forEach(function(dep) { deps['components/' + dep] = true; });
-  });
-  deps = Object.keys(deps);
-
-  // Compile, bundle all the typescript files and prepend their deps.
-  browserify(entries)
-      .plugin(tsify)
-      .bundle()
-      .on('error', function(error) { console.error(error.toString()); })
-      .pipe(source('app.js'))
-      .pipe(gulp.dest('components'))
-      .on('end', function() {
-        // Typescript was compiled and bundled. Now we need to prepend
-        // the external dependencies.
-        gulp.src(deps.concat(['components/app.js']))
-            .pipe(concat('bundle.js'))
-            .pipe(gulp.dest('components'));
-      });
-
-  // Compile components that are using global namespaces producing 1 js file
-  // for each ts file.
   var isComponent = filter([
-    'components/tf-*/**/*.ts', 'components/vz-*/**/*.ts', 'typings/**/*.ts',
+    'components/tf-*/**/*.ts',
+    'components/vz-*/**/*.ts',
+    'typings/**/*.ts',
     'components/plottable/plottable.d.ts'
-    // Ignore components that use es6 modules.
-  ].concat(ES6_COMPONENTS.map(function(component) {
-    return '!components/' + component.name + '/**/*.ts';
-  })));
+  ]);
 
   return tsProject.src()
            .pipe(isComponent)
diff --git a/tensorflow/tensorboard/package.json b/tensorflow/tensorboard/package.json
index 8c8f4ac5dc..ce104cb2f8 100644
--- a/tensorflow/tensorboard/package.json
+++ b/tensorflow/tensorboard/package.json
@@ -13,14 +13,9 @@
   "author": "Google",
   "license": "Apache-2.0",
   "devDependencies": {
-    "browserify": "^13.1.0",
     "gulp": "~3.9.0",
-    "gulp-bower": "0.0.13",
     "gulp-cli": "^1.1.0",
-    "gulp-concat": "^2.6.0",
     "gulp-filter": "~3.0.1",
-    "gulp-header": "~1.7.1",
-    "gulp-rename": "~1.2.2",
     "gulp-replace": "~0.5.4",
     "gulp-server-livereload": "~1.5.4",
     "gulp-tslint": "~4.2.2",
@@ -29,12 +24,13 @@
     "gulp-vulcanize": "~6.1.0",
     "merge2": "~0.3.6",
     "minimist": "~1.2.0",
-    "tsify": "^0.15.6",
     "tslint": "^3.2.1",
-    "typescript": "^2.0.0",
-    "typings": "~1.0.4",
-    "vinyl-source-stream": "^1.1.0",
+    "typescript": "1.8.0",
     "vulcanize": "^1.14.0",
-    "web-component-tester": "4.2.2"
+    "web-component-tester": "4.2.2",
+    "gulp-header": "~1.7.1",
+    "gulp-rename": "~1.2.2",
+    "gulp-bower": "0.0.13",
+    "typings": "~1.0.4"
   }
 }
diff --git a/tensorflow/tensorboard/tsconfig.json b/tensorflow/tensorboard/tsconfig.json
index e51e70f848..1ecec4f922 100644
--- a/tensorflow/tensorboard/tsconfig.json
+++ b/tensorflow/tensorboard/tsconfig.json
@@ -2,8 +2,7 @@
   "compilerOptions": {
     "noImplicitAny": false,
     "noEmitOnError": true,
-    "target": "ES5",
-    "module": "commonjs"
+    "target": "ES5"
   },
   "compileOnSave": false,
   "exclude": [
diff --git a/tensorflow/tensorboard/typings.json b/tensorflow/tensorboard/typings.json
index 4679f7acfe..7e7e7f9e88 100644
--- a/tensorflow/tensorboard/typings.json
+++ b/tensorflow/tensorboard/typings.json
@@ -9,7 +9,6 @@
     "mocha": "registry:dt/mocha#2.2.5+20160317120654",
     "polymer": "registry:dt/polymer#1.1.6+20160317120654",
     "sinon": "registry:dt/sinon#1.16.0+20160517064723",
-    "three": "registry:dt/three#0.0.0+20160802154944",
     "webcomponents.js": "registry:dt/webcomponents.js#0.6.0+20160317120654"
   }
 }
-- 
GitLab


From b0bdff4827f867a67f572ed99d85f9a847788326 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 26 Aug 2016 12:50:48 -0800
Subject: [PATCH 220/223] Merge changes from github. Change: 131437429

---
 README.md                                     |   4 +-
 configure                                     |  10 +-
 tensorflow/BUILD                              |   9 +
 tensorflow/cc/framework/cc_op_gen.cc          |   2 +-
 tensorflow/contrib/cmake/CMakeLists.txt       |   3 +-
 .../contrib/cmake/tf_core_kernels.cmake       |   2 +
 tensorflow/contrib/ios_examples/README.md     |   3 +
 .../benchmark.xcodeproj/project.pbxproj       |   4 +
 .../camera_example.xcodeproj/project.pbxproj  |   4 +
 .../project.pbxproj                           |   4 +
 tensorflow/contrib/layers/__init__.py         |   1 +
 .../contrib/layers/python/layers/layers.py    | 109 ++-
 .../layers/python/layers/layers_test.py       | 104 +++
 tensorflow/contrib/learn/BUILD                |   3 +
 tensorflow/contrib/makefile/Makefile          |  10 +
 tensorflow/contrib/makefile/README.md         |   4 +-
 tensorflow/contrib/makefile/tf_op_files.txt   |   1 +
 .../python/ops/confusion_matrix_ops.py        |   2 +-
 .../core/common_runtime/gpu/gpu_device.cc     |   3 +-
 tensorflow/core/debug/debug_graph_utils.cc    |   2 +-
 tensorflow/core/framework/log_memory.cc       |   2 +-
 tensorflow/core/framework/op.cc               |   4 +-
 tensorflow/core/framework/shape_inference.h   |   2 +-
 tensorflow/core/framework/tensor.cc           |   2 +-
 tensorflow/core/kernels/conv_ops.cc           |   4 +
 .../core/kernels/conv_ops_using_gemm.cc       | 622 ++++++++++++++++++
 tensorflow/core/kernels/reduce_join_op.cc     |   2 +-
 .../core/kernels/reverse_sequence_op.cc       |  88 ++-
 tensorflow/core/kernels/reverse_sequence_op.h |  15 +-
 .../kernels/reverse_sequence_op_gpu.cu.cc     |  18 +-
 .../kernels/sparse_dense_binary_op_shared.cc  |   2 +-
 tensorflow/core/ops/array_ops.cc              |   3 +-
 tensorflow/core/ops/ops.pbtxt                 |  36 +
 tensorflow/core/util/example_proto_helper.cc  |  12 +-
 tensorflow/core/util/sparse/sparse_tensor.h   |   2 +-
 .../examples/image_retraining/retrain.py      |   2 +-
 .../tutorials/mnist/mnist_with_summaries.py   |   2 +-
 .../shard5/tf.train.Optimizer.md              |  10 +-
 .../tf.contrib.metrics.confusion_matrix.md    |   2 +-
 tensorflow/g3doc/api_docs/python/train.md     |  10 +-
 tensorflow/g3doc/get_started/os_setup.md      |  13 +-
 .../summaries_and_tensorboard/index.md        |   2 +-
 tensorflow/python/BUILD                       |  28 +
 .../python/kernel_tests/clip_ops_test.py      |  40 ++
 .../kernel_tests/reverse_sequence_op_test.py  |   7 +-
 .../python/kernel_tests/sparse_ops_test.py    |  23 +
 tensorflow/python/ops/clip_ops.py             |   4 +-
 tensorflow/python/ops/io_ops.py               |   1 +
 tensorflow/python/ops/nn_ops.py               |   4 +-
 tensorflow/python/ops/sparse_ops.py           |  47 ++
 tensorflow/python/platform/app.py             |   4 +-
 tensorflow/python/platform/app_test.py        |  45 ++
 tensorflow/python/platform/flags.py           |   3 +-
 tensorflow/python/training/device_setter.py   |   2 +-
 .../python/training/device_setter_test.py     |  17 +
 .../tf-dashboard-common/dashboard-style.html  |  10 +-
 .../tf-event-dashboard.html                   |  20 +
 .../vz-line-chart/vz-line-chart.html          |  16 +-
 .../components/vz-line-chart/vz-line-chart.ts |  18 +-
 tensorflow/tensorboard/tensorboard.py         |   7 +-
 tensorflow/tools/ci_build/Dockerfile.gpu      |   2 +-
 .../docker/parameterized_docker_build.sh      |  16 +-
 tensorflow/workspace.bzl                      |  40 +-
 third_party/gpus/cuda/BUILD.tpl               |   2 +-
 third_party/gpus/cuda_configure.bzl           |  40 +-
 65 files changed, 1375 insertions(+), 160 deletions(-)
 create mode 100644 tensorflow/core/kernels/conv_ops_using_gemm.cc
 create mode 100644 tensorflow/python/platform/app_test.py

diff --git a/README.md b/README.md
index 3de86a2165..e356ff931c 100644
--- a/README.md
+++ b/README.md
@@ -34,9 +34,9 @@ and discussion.**
 People who are a little more adventurous can also try our nightly binaries:
 
 * Linux CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-python35-linux-cpu/))
-* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/140/artifact/pip_test/whl/tensorflow-0.8.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
+* Linux GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp27-none-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-linux/)) / [Python 3.4](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-cp34-cp34m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-linux/)) / [Python 3.5](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/140/artifact/pip_test/whl/tensorflow-0.8.0-cp35-cp35m-linux_x86_64.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-linux-gpu/TF_BUILD_CONTAINER_TYPE=GPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.5,label=gpu-linux/))
 * Mac CPU-only: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac1-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=mac1-slave/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac1-slave/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-cpu/TF_BUILD_CONTAINER_TYPE=CPU,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=mac1-slave/))
-* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nigntly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
+* Mac GPU: [Python 2](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py2-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=gpu-mac/)) / [Python 3](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/lastSuccessfulBuild/artifact/pip_test/whl/tensorflow-0.10.0rc0-py3-none-any.whl) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-mac-gpu/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=gpu-mac/))
 * [Android](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-android/TF_BUILD_CONTAINER_TYPE=ANDROID,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=NO_PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=android-slave/lastSuccessfulBuild/artifact/bazel-out/local_linux/bin/tensorflow/examples/android/tensorflow_demo.apk) ([build history](https://ci.tensorflow.org/view/Nightly/job/nightly-matrix-android/TF_BUILD_CONTAINER_TYPE=ANDROID,TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=NO_PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=android-slave/))
 
 #### *Try your first TensorFlow program*
diff --git a/configure b/configure
index bcef37bd26..f4b772c55e 100755
--- a/configure
+++ b/configure
@@ -98,7 +98,7 @@ while true; do
     fi
   fi
   if [ -e "$GCC_HOST_COMPILER_PATH" ]; then
-    export CC=$GCC_HOST_COMPILER_PATH
+    export GCC_HOST_COMPILER_PATH
     break
   fi
   echo "Invalid gcc path. ${GCC_HOST_COMPILER_PATH} cannot be found" 1>&2
@@ -142,7 +142,7 @@ while true; do
 
   if [ -e "${CUDA_TOOLKIT_PATH}/${CUDA_RT_LIB_PATH}" ]; then
     export CUDA_TOOLKIT_PATH
-    export CUDA_VERSION=$TF_CUDA_VERSION
+    export TF_CUDA_VERSION
     break
   fi
   echo "Invalid path to CUDA $TF_CUDA_VERSION toolkit. ${CUDA_TOOLKIT_PATH}/${CUDA_RT_LIB_PATH} cannot be found"
@@ -203,7 +203,7 @@ while true; do
   fi
 
   if [ -e "$CUDNN_INSTALL_PATH/${CUDA_DNN_LIB_ALT_PATH}" -o -e "$CUDNN_INSTALL_PATH/${CUDA_DNN_LIB_PATH}" ]; then
-    export CUDNN_VERSION=$TF_CUDNN_VERSION
+    export TF_CUDNN_VERSION
     export CUDNN_INSTALL_PATH
     break
   fi
@@ -211,7 +211,7 @@ while true; do
   if [ "$OSNAME" == "Linux" ]; then
     CUDNN_PATH_FROM_LDCONFIG="$(ldconfig -p | sed -n 's/.*libcudnn.so .* => \(.*\)/\1/p')"
     if [ -e "${CUDNN_PATH_FROM_LDCONFIG}${TF_CUDNN_EXT}" ]; then
-      export CUDNN_VERSION=$TF_CUDNN_VERSION
+      export TF_CUDNN_VERSION
       export CUDNN_INSTALL_PATH="$(dirname ${CUDNN_PATH_FROM_LDCONFIG})"
       break
     fi
@@ -263,7 +263,7 @@ EOF
       exit 1
     fi
   else
-    export CUDA_COMPUTE_CAPABILITIES=$TF_CUDA_COMPUTE_CAPABILITIES
+    export TF_CUDA_COMPUTE_CAPABILITIES
     break
   fi
   TF_CUDA_COMPUTE_CAPABILITIES=""
diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index add5d97169..0a73364471 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -174,6 +174,15 @@ cc_binary(
     ],
 )
 
+cc_binary(
+    name = "libtensorflow_c.so",
+    linkshared = 1,
+    deps = [
+        "//tensorflow/c:c_api",
+        "//tensorflow/core:tensorflow",
+    ],
+)
+
 cc_binary(
     name = "libtensorflow_cc.so",
     linkshared = 1,
diff --git a/tensorflow/cc/framework/cc_op_gen.cc b/tensorflow/cc/framework/cc_op_gen.cc
index 659fdfb153..8ed53404f1 100644
--- a/tensorflow/cc/framework/cc_op_gen.cc
+++ b/tensorflow/cc/framework/cc_op_gen.cc
@@ -232,7 +232,7 @@ string PrintAttrValue(string op, const AttrValue& attr_value) {
 string ToCamelCase(const string& str) {
   string result;
   const char joiner = '_';
-  int i = 0;
+  size_t i = 0;
   bool cap = true;
   while (i < str.size()) {
     const char c = str[i++];
diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt
index e5a0790ff4..4fe5960d3b 100644
--- a/tensorflow/contrib/cmake/CMakeLists.txt
+++ b/tensorflow/contrib/cmake/CMakeLists.txt
@@ -51,7 +51,8 @@ include(highwayhash)
 
 # Let's get to work!
 include(tf_core_framework.cmake)
-include(tf_stream_executor.cmake)
+# NOTE: Disabled until issue #3996 is fixed.
+# include(tf_stream_executor.cmake)
 include(tf_core_cpu.cmake)
 include(tf_models.cmake)
 include(tf_core_ops.cmake)
diff --git a/tensorflow/contrib/cmake/tf_core_kernels.cmake b/tensorflow/contrib/cmake/tf_core_kernels.cmake
index 2fff5c2dd3..8f911e3cb1 100644
--- a/tensorflow/contrib/cmake/tf_core_kernels.cmake
+++ b/tensorflow/contrib/cmake/tf_core_kernels.cmake
@@ -13,6 +13,8 @@ file(GLOB_RECURSE tf_core_kernels_exclude_srcs
    "${tensorflow_source_dir}/tensorflow/core/kernels/*testutil.cc"
    "${tensorflow_source_dir}/tensorflow/core/kernels/*main.cc"
    "${tensorflow_source_dir}/tensorflow/core/kernels/*.cu.cc"
+   "${tensorflow_source_dir}/tensorflow/core/kernels/debug_ops.h"
+   "${tensorflow_source_dir}/tensorflow/core/kernels/debug_ops.cc"
 )
 
 list(REMOVE_ITEM tf_core_kernels_srcs ${tf_core_kernels_exclude_srcs}) 
diff --git a/tensorflow/contrib/ios_examples/README.md b/tensorflow/contrib/ios_examples/README.md
index 1c29c74e51..83b1d7868a 100644
--- a/tensorflow/contrib/ios_examples/README.md
+++ b/tensorflow/contrib/ios_examples/README.md
@@ -71,6 +71,9 @@ rundown:
    inside the library are not stripped out. To the linker, they can appear
    unused because no other code references the variables, but in fact their
    constructors have the important side effect of registering the class.
+
+ - You'll need to include the Accelerate framework in the "Link Binary with
+   Libraries" build phase of your project.
  
  - C++11 support (or later) should be enabled by setting `C++ Language Dialect` to
    `GNU++11` (or `GNU++14`), and `C++ Standard Library` to `libc++`.
diff --git a/tensorflow/contrib/ios_examples/benchmark/benchmark.xcodeproj/project.pbxproj b/tensorflow/contrib/ios_examples/benchmark/benchmark.xcodeproj/project.pbxproj
index 934bafdeb9..a726698747 100644
--- a/tensorflow/contrib/ios_examples/benchmark/benchmark.xcodeproj/project.pbxproj
+++ b/tensorflow/contrib/ios_examples/benchmark/benchmark.xcodeproj/project.pbxproj
@@ -9,6 +9,7 @@
 /* Begin PBXBuildFile section */
 		590E7D881D02091F00DF5523 /* libprotobuf-lite.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 590E7D861D02091F00DF5523 /* libprotobuf-lite.a */; };
 		590E7D8A1D0209DD00DF5523 /* libprotobuf.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 590E7D871D02091F00DF5523 /* libprotobuf.a */; };
+		5993C7701D5D4E7F0048CE6A /* Accelerate.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 5993C76F1D5D4E7F0048CE6A /* Accelerate.framework */; };
 		59A3D0011CF4E68100C4259F /* AppDelegate.mm in Sources */ = {isa = PBXBuildFile; fileRef = 59A3CFF21CF4E68100C4259F /* AppDelegate.mm */; };
 		59A3D0031CF4E68100C4259F /* grace_hopper.jpg in Resources */ = {isa = PBXBuildFile; fileRef = 59A3CFF51CF4E68100C4259F /* grace_hopper.jpg */; };
 		59A3D0051CF4E68100C4259F /* imagenet_comp_graph_label_strings.txt in Resources */ = {isa = PBXBuildFile; fileRef = 59A3CFF71CF4E68100C4259F /* imagenet_comp_graph_label_strings.txt */; };
@@ -25,6 +26,7 @@
 		590E7D861D02091F00DF5523 /* libprotobuf-lite.a */ = {isa = PBXFileReference; lastKnownFileType = archive.ar; name = "libprotobuf-lite.a"; path = "../../makefile/gen/protobuf_ios/lib/libprotobuf-lite.a"; sourceTree = "<group>"; };
 		590E7D871D02091F00DF5523 /* libprotobuf.a */ = {isa = PBXFileReference; lastKnownFileType = archive.ar; name = libprotobuf.a; path = ../../makefile/gen/protobuf_ios/lib/libprotobuf.a; sourceTree = "<group>"; };
 		5911579B1CF4011C00C31E3A /* benchmark.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = benchmark.app; sourceTree = BUILT_PRODUCTS_DIR; };
+		5993C76F1D5D4E7F0048CE6A /* Accelerate.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Accelerate.framework; path = System/Library/Frameworks/Accelerate.framework; sourceTree = SDKROOT; };
 		59A3CFF11CF4E68100C4259F /* AppDelegate.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = AppDelegate.h; sourceTree = "<group>"; };
 		59A3CFF21CF4E68100C4259F /* AppDelegate.mm */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.objcpp; path = AppDelegate.mm; sourceTree = "<group>"; };
 		59A3CFF41CF4E68100C4259F /* cropped_panda.jpg */ = {isa = PBXFileReference; lastKnownFileType = image.jpeg; path = cropped_panda.jpg; sourceTree = "<group>"; };
@@ -50,6 +52,7 @@
 			isa = PBXFrameworksBuildPhase;
 			buildActionMask = 2147483647;
 			files = (
+				5993C7701D5D4E7F0048CE6A /* Accelerate.framework in Frameworks */,
 				590E7D8A1D0209DD00DF5523 /* libprotobuf.a in Frameworks */,
 				590E7D881D02091F00DF5523 /* libprotobuf-lite.a in Frameworks */,
 				59A3D0181CF4E86100C4259F /* UIKit.framework in Frameworks */,
@@ -63,6 +66,7 @@
 		591157921CF4011C00C31E3A = {
 			isa = PBXGroup;
 			children = (
+				5993C76F1D5D4E7F0048CE6A /* Accelerate.framework */,
 				590E7D861D02091F00DF5523 /* libprotobuf-lite.a */,
 				590E7D871D02091F00DF5523 /* libprotobuf.a */,
 				59A3D0171CF4E86100C4259F /* UIKit.framework */,
diff --git a/tensorflow/contrib/ios_examples/camera/camera_example.xcodeproj/project.pbxproj b/tensorflow/contrib/ios_examples/camera/camera_example.xcodeproj/project.pbxproj
index 14785cc708..451f536a7c 100644
--- a/tensorflow/contrib/ios_examples/camera/camera_example.xcodeproj/project.pbxproj
+++ b/tensorflow/contrib/ios_examples/camera/camera_example.xcodeproj/project.pbxproj
@@ -24,6 +24,7 @@
 		592FF90D18EDD0DA00C164F8 /* MainStoryboard_iPhone.storyboard in Resources */ = {isa = PBXBuildFile; fileRef = 592FF90A18EDD0DA00C164F8 /* MainStoryboard_iPhone.storyboard */; };
 		592FF92518EE240200C164F8 /* CameraExampleAppDelegate.m in Sources */ = {isa = PBXBuildFile; fileRef = 592FF92218EE240200C164F8 /* CameraExampleAppDelegate.m */; };
 		592FF92618EE240200C164F8 /* CameraExampleViewController.mm in Sources */ = {isa = PBXBuildFile; fileRef = 592FF92418EE240200C164F8 /* CameraExampleViewController.mm */; };
+		5993C7721D5D4E980048CE6A /* Accelerate.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 5993C7711D5D4E980048CE6A /* Accelerate.framework */; };
 /* End PBXBuildFile section */
 
 /* Begin PBXFileReference section */
@@ -52,6 +53,7 @@
 		592FF92218EE240200C164F8 /* CameraExampleAppDelegate.m */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.objc; path = CameraExampleAppDelegate.m; sourceTree = SOURCE_ROOT; };
 		592FF92318EE240200C164F8 /* CameraExampleViewController.h */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.c.h; path = CameraExampleViewController.h; sourceTree = SOURCE_ROOT; };
 		592FF92418EE240200C164F8 /* CameraExampleViewController.mm */ = {isa = PBXFileReference; fileEncoding = 4; lastKnownFileType = sourcecode.cpp.objcpp; path = CameraExampleViewController.mm; sourceTree = SOURCE_ROOT; };
+		5993C7711D5D4E980048CE6A /* Accelerate.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Accelerate.framework; path = Platforms/iPhoneOS.platform/Developer/SDKs/iPhoneOS9.3.sdk/System/Library/Frameworks/Accelerate.framework; sourceTree = DEVELOPER_DIR; };
 /* End PBXFileReference section */
 
 /* Begin PBXFrameworksBuildPhase section */
@@ -59,6 +61,7 @@
 			isa = PBXFrameworksBuildPhase;
 			buildActionMask = 2147483647;
 			files = (
+				5993C7721D5D4E980048CE6A /* Accelerate.framework in Frameworks */,
 				591D3EDF1CFFAD230059011C /* libprotobuf-lite.a in Frameworks */,
 				591D3EE01CFFAD230059011C /* libprotobuf.a in Frameworks */,
 				591D3ECF1CFF7FCE0059011C /* ImageIO.framework in Frameworks */,
@@ -103,6 +106,7 @@
 		592FF8B718ECBD7600C164F8 /* Frameworks */ = {
 			isa = PBXGroup;
 			children = (
+				5993C7711D5D4E980048CE6A /* Accelerate.framework */,
 				591D3EDD1CFFAD230059011C /* libprotobuf-lite.a */,
 				591D3EDE1CFFAD230059011C /* libprotobuf.a */,
 				591D3ECE1CFF7FCE0059011C /* ImageIO.framework */,
diff --git a/tensorflow/contrib/ios_examples/simple/tf_ios_makefile_example.xcodeproj/project.pbxproj b/tensorflow/contrib/ios_examples/simple/tf_ios_makefile_example.xcodeproj/project.pbxproj
index a81286a5b1..9e058be281 100644
--- a/tensorflow/contrib/ios_examples/simple/tf_ios_makefile_example.xcodeproj/project.pbxproj
+++ b/tensorflow/contrib/ios_examples/simple/tf_ios_makefile_example.xcodeproj/project.pbxproj
@@ -9,6 +9,7 @@
 /* Begin PBXBuildFile section */
 		590E7D881D02091F00DF5523 /* libprotobuf-lite.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 590E7D861D02091F00DF5523 /* libprotobuf-lite.a */; };
 		590E7D8A1D0209DD00DF5523 /* libprotobuf.a in Frameworks */ = {isa = PBXBuildFile; fileRef = 590E7D871D02091F00DF5523 /* libprotobuf.a */; };
+		5993C7741D5D4EAF0048CE6A /* Accelerate.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 5993C7731D5D4EAF0048CE6A /* Accelerate.framework */; };
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@@ -25,6 +26,7 @@
 		590E7D861D02091F00DF5523 /* libprotobuf-lite.a */ = {isa = PBXFileReference; lastKnownFileType = archive.ar; name = "libprotobuf-lite.a"; path = "../../makefile/gen/protobuf_ios/lib/libprotobuf-lite.a"; sourceTree = "<group>"; };
 		590E7D871D02091F00DF5523 /* libprotobuf.a */ = {isa = PBXFileReference; lastKnownFileType = archive.ar; name = libprotobuf.a; path = ../../makefile/gen/protobuf_ios/lib/libprotobuf.a; sourceTree = "<group>"; };
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+		5993C7731D5D4EAF0048CE6A /* Accelerate.framework */ = {isa = PBXFileReference; lastKnownFileType = wrapper.framework; name = Accelerate.framework; path = System/Library/Frameworks/Accelerate.framework; sourceTree = SDKROOT; };
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@@ -50,6 +52,7 @@
 			isa = PBXFrameworksBuildPhase;
 			buildActionMask = 2147483647;
 			files = (
+				5993C7741D5D4EAF0048CE6A /* Accelerate.framework in Frameworks */,
 				590E7D8A1D0209DD00DF5523 /* libprotobuf.a in Frameworks */,
 				590E7D881D02091F00DF5523 /* libprotobuf-lite.a in Frameworks */,
 				59A3D0181CF4E86100C4259F /* UIKit.framework in Frameworks */,
@@ -63,6 +66,7 @@
 		591157921CF4011C00C31E3A = {
 			isa = PBXGroup;
 			children = (
+				5993C7731D5D4EAF0048CE6A /* Accelerate.framework */,
 				590E7D861D02091F00DF5523 /* libprotobuf-lite.a */,
 				590E7D871D02091F00DF5523 /* libprotobuf.a */,
 				59A3D0171CF4E86100C4259F /* UIKit.framework */,
diff --git a/tensorflow/contrib/layers/__init__.py b/tensorflow/contrib/layers/__init__.py
index 20a82a235e..4bec7c2661 100644
--- a/tensorflow/contrib/layers/__init__.py
+++ b/tensorflow/contrib/layers/__init__.py
@@ -27,6 +27,7 @@ common machine learning algorithms.
 @@convolution2d_transpose
 @@flatten
 @@fully_connected
+@@layer_norm
 @@max_pool2d
 @@one_hot_encoding
 @@repeat
diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py
index 3290d92551..0947480d7c 100644
--- a/tensorflow/contrib/layers/python/layers/layers.py
+++ b/tensorflow/contrib/layers/python/layers/layers.py
@@ -52,6 +52,7 @@ __all__ = ['avg_pool2d',
            'dropout',
            'flatten',
            'fully_connected',
+           'layer_norm',
            'linear',
            'max_pool2d',
            'one_hot_encoding',
@@ -276,7 +277,8 @@ def batch_norm(inputs,
     outputs.set_shape(inputs_shape)
     if activation_fn:
       outputs = activation_fn(outputs)
-    return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope, outputs)
 
 
 @add_arg_scope
@@ -328,7 +330,8 @@ def bias_add(inputs,
     outputs = nn.bias_add(inputs, biases)
     if activation_fn:
       outputs = activation_fn(outputs)
-    return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope, outputs)
 
 
 @add_arg_scope
@@ -441,7 +444,8 @@ def convolution2d(inputs,
         outputs = nn.bias_add(outputs, biases)
     if activation_fn:
       outputs = activation_fn(outputs)
-    return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope, outputs)
 
 
 @add_arg_scope
@@ -541,7 +545,8 @@ def convolution2d_in_plane(
 
     if activation_fn:
       outputs = activation_fn(outputs)
-    return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope, outputs)
 
 
 @add_arg_scope
@@ -668,7 +673,8 @@ def convolution2d_transpose(
 
     if activation_fn:
       outputs = activation_fn(outputs)
-    return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope, outputs)
 
 
 @add_arg_scope
@@ -845,7 +851,95 @@ def fully_connected(inputs,
       # Reshape back outputs
       outputs = array_ops.reshape(outputs, array_ops.pack(out_shape))
       outputs.set_shape(static_shape)
-    return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope, outputs)
+
+
+@add_arg_scope
+def layer_norm(inputs,
+               center=True,
+               scale=True,
+               activation_fn=None,
+               reuse=None,
+               variables_collections=None,
+               outputs_collections=None,
+               trainable=True,
+               scope=None):
+  """Adds a Layer Normalization layer from https://arxiv.org/abs/1607.06450.
+
+    "Layer Normalization"
+
+    Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton
+
+  Can be used as a normalizer function for conv2d and fully_connected.
+
+  Args:
+    inputs: a tensor with 2 or more dimensions. The normalization
+            occurs over all but the first dimension.
+    center: If True, subtract `beta`. If False, `beta` is ignored.
+    scale: If True, multiply by `gamma`. If False, `gamma` is
+      not used. When the next layer is linear (also e.g. `nn.relu`), this can be
+      disabled since the scaling can be done by the next layer.
+    activation_fn: Optional activation function.
+    reuse: whether or not the layer and its variables should be reused. To be
+      able to reuse the layer scope must be given.
+    variables_collections: optional collections for the variables.
+    outputs_collections: collections to add the outputs.
+    trainable: If `True` also add variables to the graph collection
+      `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
+    scope: Optional scope for `variable_op_scope`.
+
+  Returns:
+    A `Tensor` representing the output of the operation.
+
+  Raises:
+    ValueError: if rank or last dimension of `inputs` is undefined.
+  """
+  with variable_scope.variable_scope(scope, 'LayerNorm', [inputs],
+                                     reuse=reuse) as sc:
+    inputs = ops.convert_to_tensor(inputs)
+    inputs_shape = inputs.get_shape()
+    inputs_rank = inputs_shape.ndims
+    if inputs_rank is None:
+      raise ValueError('Inputs %s has undefined rank.' % inputs.name)
+    dtype = inputs.dtype.base_dtype
+    axis = list(range(1, inputs_rank))
+    params_shape = inputs_shape[-1:]
+    if not params_shape.is_fully_defined():
+      raise ValueError('Inputs %s has undefined last dimension %s.' % (
+          inputs.name, params_shape))
+    # Allocate parameters for the beta and gamma of the normalization.
+    beta, gamma = None, None
+    if center:
+      beta_collections = utils.get_variable_collections(variables_collections,
+                                                        'beta')
+      beta = variables.model_variable('beta',
+                                      shape=params_shape,
+                                      dtype=dtype,
+                                      initializer=init_ops.zeros_initializer,
+                                      collections=beta_collections,
+                                      trainable=trainable)
+    if scale:
+      gamma_collections = utils.get_variable_collections(variables_collections,
+                                                         'gamma')
+      gamma = variables.model_variable('gamma',
+                                       shape=params_shape,
+                                       dtype=dtype,
+                                       initializer=init_ops.ones_initializer,
+                                       collections=gamma_collections,
+                                       trainable=trainable)
+    # Calculate the moments on the last axis (layer activations).
+    mean, variance = nn.moments(inputs, axis, keep_dims=True)
+    # Compute layer normalization using the batch_normalization function.
+    variance_epsilon = 1E-12
+    outputs = nn.batch_normalization(
+        inputs, mean, variance, beta, gamma, variance_epsilon)
+    outputs.set_shape(inputs_shape)
+    if activation_fn:
+      outputs = activation_fn(outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope,
+                                       outputs)
 
 
 @add_arg_scope
@@ -1094,7 +1188,8 @@ def separable_convolution2d(
 
     if activation_fn:
       outputs = activation_fn(outputs)
-    return utils.collect_named_outputs(outputs_collections, sc.name, outputs)
+    return utils.collect_named_outputs(outputs_collections,
+                                       sc.original_name_scope, outputs)
 
 
 @add_arg_scope
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index 711d17cb29..4fc2e07b9b 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -203,6 +203,16 @@ class Convolution2dTest(tf.test.TestCase):
                                                scope='conv1')
       self.assertEquals(output.op.name, 'conv1/Relu')
 
+  def testCreateConvWithCollection(self):
+    height, width = 3, 3
+    images = tf.random_uniform((5, height, width, 3), seed=1)
+    with tf.name_scope('fe'):
+      conv = tf.contrib.layers.convolution2d(
+        images, 32, [3, 3], outputs_collections='outputs',
+        scope='Conv')
+    namedOutputs = tf.get_collection('outputs')[0]
+    self.assertEquals(namedOutputs.name, 'fe/Conv')
+
   def testCreateConvWithoutActivation(self):
     height, width = 3, 3
     with self.test_session():
@@ -989,6 +999,16 @@ class FCTest(tf.test.TestCase):
       output = tf.contrib.layers.fully_connected(inputs, 32, scope='fc1')
       self.assertEquals(output.op.name, 'fc1/Relu')
 
+  def testCreateFCWithCollection(self):
+    height, width = 3, 3
+    inputs = tf.random_uniform((5, height * width * 3), seed=1)
+    with tf.name_scope('fe'):
+      fc = tf.contrib.layers.fully_connected(
+        inputs, 7, outputs_collections='outputs',
+        scope='fc')
+    namedOutputs = tf.get_collection('outputs')[0]
+    self.assertEquals(namedOutputs.name, 'fe/fc')
+
   def testCreateFcCreatesWeightsAndBiasesVars(self):
     height, width = 3, 3
     inputs = tf.random_uniform((5, height * width * 3), seed=1)
@@ -1542,6 +1562,90 @@ class BatchNormTest(tf.test.TestCase):
       self.assertAllClose(moving_variance.eval(), expected_var)
 
 
+class LayerNormTest(tf.test.TestCase):
+
+  def testUnknownShape(self):
+    with tf.Graph().as_default() as g, self.test_session(g):
+      inputs = tf.placeholder(dtype=tf.float32)
+      with self.assertRaisesRegexp(ValueError, 'undefined rank'):
+        tf.contrib.layers.layer_norm(inputs)
+
+  def testUnknownLastDim(self):
+    with tf.Graph().as_default() as g, self.test_session(g):
+      inputs = tf.placeholder(dtype=tf.float32)
+      inputs.set_shape(tf.TensorShape((5, 3, 3, None)))
+      with self.assertRaisesRegexp(ValueError, 'undefined last dimension'):
+        tf.contrib.layers.layer_norm(inputs)
+
+  def testCreateOp(self):
+    height, width = 3, 3
+    with self.test_session():
+      images = np.random.uniform(size=(5, height, width, 3))
+      output = tf.contrib.layers.layer_norm(images)
+      self.assertTrue(output.op.name.startswith('LayerNorm/batchnorm'))
+      self.assertListEqual(output.get_shape().as_list(), [5, height, width, 3])
+
+  def testCreateVariables(self):
+    height, width = 3, 3
+    with self.test_session():
+      images = tf.random_uniform((5, height, width, 3), seed=1)
+      tf.contrib.layers.layer_norm(images)
+      beta = tf.contrib.framework.get_variables_by_name('beta')[0]
+      gamma = tf.contrib.framework.get_variables_by_name('gamma')[0]
+      self.assertEquals(beta.op.name, 'LayerNorm/beta')
+      self.assertEquals(gamma.op.name, 'LayerNorm/gamma')
+
+  def testReuseVariables(self):
+    height, width = 3, 3
+    with self.test_session():
+      images = tf.random_uniform((5, height, width, 3), seed=1)
+      tf.contrib.layers.layer_norm(images, scope='ln')
+      tf.contrib.layers.layer_norm(images, scope='ln', reuse=True)
+      beta = tf.contrib.framework.get_variables_by_name('beta')
+      gamma = tf.contrib.framework.get_variables_by_name('gamma')
+      self.assertEquals(len(beta), 1)
+      self.assertEquals(len(gamma), 1)
+
+  def testReuseVars(self):
+    height, width = 3, 3
+    with self.test_session() as sess:
+      image_shape = (10, height, width, 3)
+      image_values = np.random.rand(*image_shape)
+      images = tf.constant(image_values, shape=image_shape, dtype=tf.float32)
+      output_train = tf.contrib.layers.layer_norm(images, scope='LN')
+      output_eval = tf.contrib.layers.layer_norm(images,
+                                                 scope='LN',
+                                                 reuse=True)
+      # Initialize all variables
+      sess.run(tf.initialize_all_variables())
+      # output_train and output_eval should be the same.
+      self.assertAllClose(sess.run([output_train]), sess.run([output_eval]))
+
+  def doOutputTest(self, input_shape):
+    with self.test_session() as sess:
+      input_values = np.random.rand(*input_shape)
+      inputs = tf.constant(input_values, shape=input_shape, dtype=tf.float32)
+      output_op = tf.contrib.layers.layer_norm(inputs, scope='LN')
+      # Initialize all variables
+      sess.run(tf.initialize_all_variables())
+      # The mean and variance of the output should be close to 0 and 1
+      # respectively.
+      moments_axis = tuple([i for i in range(1, len(input_shape))])
+      outputs = sess.run(output_op)
+      expected_mean = np.zeros(input_shape[0])
+      expected_var = np.ones(input_shape[0])
+      mean = np.mean(outputs, axis=moments_axis)
+      var = np.var(outputs, axis=moments_axis)
+      tol = 1e-5
+      self.assertAllClose(mean, expected_mean, rtol=tol, atol=tol)
+      self.assertAllClose(var, expected_var, rtol=tol, atol=tol)
+
+  def testOutput2DInput(self):
+    self.doOutputTest((10, 300))
+
+  def testOutput4DInput(self):
+    self.doOutputTest((100, 10, 10, 3))
+
 class MaxPool2DTest(tf.test.TestCase):
 
   def testCreateMaxPool(self):
diff --git a/tensorflow/contrib/learn/BUILD b/tensorflow/contrib/learn/BUILD
index 8278ab89a4..ad1706a6f9 100644
--- a/tensorflow/contrib/learn/BUILD
+++ b/tensorflow/contrib/learn/BUILD
@@ -684,6 +684,9 @@ py_test(
     size = "small",
     srcs = ["python/learn/utils/export_test.py"],
     srcs_version = "PY2AND3",
+    tags = [
+        "manual",  # http://b/31032996
+    ],
     deps = [
         ":learn",
         "//tensorflow:tensorflow_py",
diff --git a/tensorflow/contrib/makefile/Makefile b/tensorflow/contrib/makefile/Makefile
index e68e8dc1bb..69cb84c2bb 100644
--- a/tensorflow/contrib/makefile/Makefile
+++ b/tensorflow/contrib/makefile/Makefile
@@ -285,6 +285,7 @@ ifeq ($(TARGET),IOS)
 		CXXFLAGS += -miphoneos-version-min=$(MIN_SDK_VERSION) \
 		-arch armv7 \
 		-D__thread= \
+		-DUSE_GEMM_FOR_CONV \
 		-Wno-c++11-narrowing \
 		-mno-thumb \
 		-DTF_LEAN_BINARY \
@@ -295,6 +296,7 @@ ifeq ($(TARGET),IOS)
 		${IPHONEOS_SYSROOT}
 		LDFLAGS := -arch armv7 \
 		-miphoneos-version-min=${MIN_SDK_VERSION} \
+		-framework Accelerate \
 		-Xlinker -S \
 		-Xlinker -x \
 		-Xlinker -dead_strip \
@@ -306,6 +308,7 @@ ifeq ($(TARGET),IOS)
 		CXXFLAGS += -miphoneos-version-min=$(MIN_SDK_VERSION) \
 		-arch armv7s \
 		-D__thread= \
+		-DUSE_GEMM_FOR_CONV \
 		-Wno-c++11-narrowing \
 		-mno-thumb \
 		-DTF_LEAN_BINARY \
@@ -316,6 +319,7 @@ ifeq ($(TARGET),IOS)
 		${IPHONEOS_SYSROOT}
 		LDFLAGS := -arch armv7s \
 		-miphoneos-version-min=${MIN_SDK_VERSION} \
+		-framework Accelerate \
 		-Xlinker -S \
 		-Xlinker -x \
 		-Xlinker -dead_strip \
@@ -327,6 +331,7 @@ ifeq ($(TARGET),IOS)
 		CXXFLAGS += -miphoneos-version-min=$(MIN_SDK_VERSION) \
 		-arch arm64 \
 		-D__thread= \
+		-DUSE_GEMM_FOR_CONV \
 		-Wno-c++11-narrowing \
 		-DTF_LEAN_BINARY \
 		-D__ANDROID_TYPES_SLIM__ \
@@ -336,6 +341,7 @@ ifeq ($(TARGET),IOS)
 		${IPHONEOS_SYSROOT}
 		LDFLAGS := -arch arm64 \
 		-miphoneos-version-min=${MIN_SDK_VERSION} \
+		-framework Accelerate \
 		-Xlinker -S \
 		-Xlinker -x \
 		-Xlinker -dead_strip \
@@ -347,6 +353,7 @@ ifeq ($(TARGET),IOS)
 		CXXFLAGS += -mios-simulator-version-min=$(MIN_SDK_VERSION) \
 		-arch i386 \
 		-D__thread= \
+		-DUSE_GEMM_FOR_CONV \
 		-Wno-c++11-narrowing \
 		-DTF_LEAN_BINARY \
 		-D__ANDROID_TYPES_SLIM__ \
@@ -356,6 +363,7 @@ ifeq ($(TARGET),IOS)
 		${IPHONESIMULATOR_SYSROOT}
 		LDFLAGS := -arch i386 \
 		-mios-simulator-version-min=${MIN_SDK_VERSION} \
+		-framework Accelerate \
 		-Xlinker -S \
 		-Xlinker -x \
 		-Xlinker -dead_strip \
@@ -367,6 +375,7 @@ ifeq ($(TARGET),IOS)
 		CXXFLAGS += -mios-simulator-version-min=$(MIN_SDK_VERSION) \
 		-arch x86_64 \
 		-D__thread= \
+		-DUSE_GEMM_FOR_CONV \
 		-Wno-c++11-narrowing \
 		-DTF_LEAN_BINARY \
 		-D__ANDROID_TYPES_SLIM__ \
@@ -376,6 +385,7 @@ ifeq ($(TARGET),IOS)
 		${IPHONESIMULATOR_SYSROOT}
 		LDFLAGS := -arch x86_64 \
 		-mios-simulator-version-min=${MIN_SDK_VERSION} \
+		-framework Accelerate \
 		-Xlinker -S \
 		-Xlinker -x \
 		-Xlinker -dead_strip \
diff --git a/tensorflow/contrib/makefile/README.md b/tensorflow/contrib/makefile/README.md
index acfcf8f220..c0a68470ba 100644
--- a/tensorflow/contrib/makefile/README.md
+++ b/tensorflow/contrib/makefile/README.md
@@ -260,15 +260,17 @@ For other variations of valid optimization flags, see [clang optimization levels
 ## Raspberry Pi
 
 Building on the Raspberry Pi is similar to a normal Linux system. First
-download the dependencies and build protobuf:
+download the dependencies, install the required packages and build protobuf:
 
 ```bash
 tensorflow/contrib/makefile/download_dependencies.sh
+sudo apt-get install autoconf automake libtool
 cd tensorflow/contrib/makefile/downloads/protobuf/
 ./autogen.sh
 ./configure
 make
 sudo make install
+sudo ldconfig  # refresh shared library cache
 cd ../../../../..
 ```
 
diff --git a/tensorflow/contrib/makefile/tf_op_files.txt b/tensorflow/contrib/makefile/tf_op_files.txt
index 5c0128db78..4f5c3176de 100644
--- a/tensorflow/contrib/makefile/tf_op_files.txt
+++ b/tensorflow/contrib/makefile/tf_op_files.txt
@@ -99,6 +99,7 @@ tensorflow/core/kernels/cwise_op_equal_to.cc
 tensorflow/core/kernels/cwise_op_div.cc
 tensorflow/core/kernels/cwise_op_add.cc
 tensorflow/core/kernels/ctc_decoder_ops.cc
+tensorflow/core/kernels/conv_ops_using_gemm.cc
 tensorflow/core/kernels/conv_ops.cc
 tensorflow/core/kernels/conv_grad_ops.cc
 tensorflow/core/kernels/control_flow_ops.cc
diff --git a/tensorflow/contrib/metrics/python/ops/confusion_matrix_ops.py b/tensorflow/contrib/metrics/python/ops/confusion_matrix_ops.py
index cf000f06d3..5620001f1a 100644
--- a/tensorflow/contrib/metrics/python/ops/confusion_matrix_ops.py
+++ b/tensorflow/contrib/metrics/python/ops/confusion_matrix_ops.py
@@ -59,7 +59,7 @@ def confusion_matrix(predictions, labels, num_classes=None,
     name: Scope name.
 
   Returns:
-    A l X l matrix represeting the confusion matrix, where l in the number of
+    A k X k matrix represeting the confusion matrix, where k is the number of
     possible labels in the classification task.
 
   Raises:
diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc
index 38a759dbb7..033d67772c 100644
--- a/tensorflow/core/common_runtime/gpu/gpu_device.cc
+++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc
@@ -787,7 +787,8 @@ struct CudaVersion {
 };
 
 std::vector<CudaVersion> supported_cuda_compute_capabilities = {
-    TF_CUDA_CAPABILITIES,};
+  TF_CUDA_CAPABILITIES,
+};
 
 std::vector<CudaVersion> GetSupportedCudaComputeCapabilities() {
   auto cuda_caps = supported_cuda_compute_capabilities;
diff --git a/tensorflow/core/debug/debug_graph_utils.cc b/tensorflow/core/debug/debug_graph_utils.cc
index 118847686d..c2e50f4d1b 100644
--- a/tensorflow/core/debug/debug_graph_utils.cc
+++ b/tensorflow/core/debug/debug_graph_utils.cc
@@ -154,7 +154,7 @@ Status DebugNodeInserter::InsertNodes(
 
       // Create all requested debug nodes and their edges to the Copy node.
       std::vector<Node*> node_added_debug_nodes;
-      for (int i = 0; i < tensor_watches[tensor_name].size(); ++i) {
+      for (size_t i = 0; i < tensor_watches[tensor_name].size(); ++i) {
         const string& debug_op_name = tensor_watches[tensor_name][i];
 
         Node* debug_node;
diff --git a/tensorflow/core/framework/log_memory.cc b/tensorflow/core/framework/log_memory.cc
index ba221179ce..dc82504d43 100644
--- a/tensorflow/core/framework/log_memory.cc
+++ b/tensorflow/core/framework/log_memory.cc
@@ -30,7 +30,7 @@ namespace {
 template <typename T>
 void OutputToLog(const T& proto) {
   string type_name = proto.GetTypeName();
-  const int index = type_name.find_last_of(".");
+  const size_t index = type_name.find_last_of(".");
   if (index != string::npos) type_name = type_name.substr(index + 1);
   LOG(INFO) << LogMemory::kLogMemoryLabel << " " << type_name << " { "
             << ProtoShortDebugString(proto) << " }";
diff --git a/tensorflow/core/framework/op.cc b/tensorflow/core/framework/op.cc
index 65bfd47433..6bff192b1e 100644
--- a/tensorflow/core/framework/op.cc
+++ b/tensorflow/core/framework/op.cc
@@ -156,7 +156,7 @@ string OpRegistry::DebugString(bool include_internal) const {
 bool OpRegistry::MustCallDeferred() const {
   if (initialized_) return false;
   initialized_ = true;
-  for (int i = 0; i < deferred_.size(); ++i) {
+  for (size_t i = 0; i < deferred_.size(); ++i) {
     TF_QCHECK_OK(RegisterAlreadyLocked(deferred_[i]));
   }
   deferred_.clear();
@@ -166,7 +166,7 @@ bool OpRegistry::MustCallDeferred() const {
 Status OpRegistry::CallDeferred() const {
   if (initialized_) return Status::OK();
   initialized_ = true;
-  for (int i = 0; i < deferred_.size(); ++i) {
+  for (size_t i = 0; i < deferred_.size(); ++i) {
     Status s = RegisterAlreadyLocked(deferred_[i]);
     if (!s.ok()) {
       return s;
diff --git a/tensorflow/core/framework/shape_inference.h b/tensorflow/core/framework/shape_inference.h
index f65dac1686..74cfbba72e 100644
--- a/tensorflow/core/framework/shape_inference.h
+++ b/tensorflow/core/framework/shape_inference.h
@@ -57,7 +57,7 @@ class DimensionHandle {
 
   const Dimension* ptr_ = nullptr;
 
-  friend class DimensionOrConstant;
+  friend struct DimensionOrConstant;
   friend class InferenceContext;
   friend class ShapeInferenceTest;
   friend class ShapeInferenceTestutil;
diff --git a/tensorflow/core/framework/tensor.cc b/tensorflow/core/framework/tensor.cc
index 6361791cb0..c6c5aab476 100644
--- a/tensorflow/core/framework/tensor.cc
+++ b/tensorflow/core/framework/tensor.cc
@@ -740,7 +740,7 @@ string Tensor::SummarizeValue(int64 max_entries) const {
       string ret;
       // TODO(irving): Don't call flat every time around this
       // loop.
-      for (int64 i = 0; i < limit; ++i) {
+      for (size_t i = 0; i < limit; ++i) {
         if (i > 0) strings::StrAppend(&ret, " ");
         switch (dtype()) {
           case DT_STRING:
diff --git a/tensorflow/core/kernels/conv_ops.cc b/tensorflow/core/kernels/conv_ops.cc
index e2182d0ec0..86d498fe4a 100644
--- a/tensorflow/core/kernels/conv_ops.cc
+++ b/tensorflow/core/kernels/conv_ops.cc
@@ -242,8 +242,12 @@ class Conv2DOp : public BinaryOp<T> {
       Name("Conv2D").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
       Conv2DOp<CPUDevice, T>);
 
+// If we're using the alternative GEMM-based implementation of Conv2D for the
+// CPU implementation, don't register this EigenTensor-based version.
+#if !defined(USE_GEMM_FOR_CONV)
 TF_CALL_half(REGISTER_CPU);
 TF_CALL_float(REGISTER_CPU);
+#endif  // USE_GEMM_FOR_CONV
 
 // To be used inside depthwise_conv_op.cc.
 template class LaunchConv2DOp<CPUDevice, float>;
diff --git a/tensorflow/core/kernels/conv_ops_using_gemm.cc b/tensorflow/core/kernels/conv_ops_using_gemm.cc
new file mode 100644
index 0000000000..c39510a11a
--- /dev/null
+++ b/tensorflow/core/kernels/conv_ops_using_gemm.cc
@@ -0,0 +1,622 @@
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+// This file contains a set of different implementations of the two-dimensional
+// convolution operation. The standard TensorFlow Conv2d kernel uses EigenTensor
+// to implement the computation, but this module has a variety of different ways
+// of producing the same result. These methods are designed to be easier to
+// understand and connect to other libraries, so that we can take advantage of
+// platforms that have specialized implementations of GEMM for example.
+//
+// The basic interface is a Conv functor object that's templated by the types
+// of the data it will be operating on, and is passed in the arguments needed to
+// calculate the convolution. The simplest implementation of this functor is
+// ReferenceConvFunctor, which is a readable but slow reference version.
+//
+// A faster version uses the approach of packing image patches into a matrix
+// before calling a matrix multiply, the Im2ColConvFunctor. In turn, this can
+// use a variety of different methods to calculate the matrix multiplication,
+// or GEMM. The simplest but slowest is the ReferenceGemmFunctor, but the
+// FastGemmFunctor will use whatever optimized libraries are available. By
+// default it uses Eigen, but on Apple platforms it will take advantage of the
+// system's Accelerate BLAS library to get better performance than the standard
+// TensorFlow convolution kernel.
+//
+// The version actually used is defined at the bottom of this file using the
+// REGISTER_KERNEL_BUILDER() macro. To try out different implementations (for
+// example to switch to a reference one for easier debugging) you can swap out
+// the default functors in that call.
+//
+// The registration itself is guarded with the USE_GEMM_FOR_CONV macro. The iOS
+// makefile build defines this, but if you want to enable this implementation
+// and disable the standard EigenTensor one in other build setups, you'll need
+// to define it there too.
+
+#include <string.h>
+#include <map>
+#include <vector>
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/numeric_op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_slice.h"
+#include "tensorflow/core/kernels/bounds_check.h"
+#include "tensorflow/core/util/padding.h"
+#include "tensorflow/core/util/tensor_format.h"
+
+#if defined(__APPLE__)
+#include <Accelerate/Accelerate.h>
+#define USE_ACCELERATE_GEMM
+#endif  // __APPLE__
+
+namespace tensorflow {
+
+namespace {
+// This function implements the convolution operation in as simple a form as
+// possible. It won't give great performance, but it is very useful for
+// stepping through and instrumenting for debugging, creating minimal benchmarks
+// to prototype with, and sharing with teams that want to run this outside of
+// our environment.
+// With that in mind, I've avoided using anything except pretty standard C++
+// types. This is especially noticeable in the data access through raw array
+// indexing. It's deliberate in this case though, since it makes the underlying
+// memory order very explicit, which is important for both inspecting memory
+// contents during debugging and for specifying what we expect to others.
+// The memory layout of the data is, from biggest stride to smallest:
+// input_data = [input_batches, input_height, input_width, input_depth]
+// filter_data = [filter_height, filter_width, input_depth, filter_count]
+// output_data = [input_batches, output_height, output_width, filter_count]
+template <class T1, class T2, class T3>
+class ReferenceConvFunctor {
+ public:
+  void operator()(OpKernelContext* context, const T1* input_data,
+                  int input_batches, int input_height, int input_width,
+                  int input_depth, const T2* filter_data, int filter_height,
+                  int filter_width, int filter_count, int stride_rows,
+                  int stride_cols, Padding padding, T3* output_data,
+                  int output_height, int output_width) {
+    // The two different padding modes we support can be a bit confusing. SAME
+    // means we're trying to produce an output image that's the same size as the
+    // input. It's complicated by stride, which shrinks the output image by a
+    // a factor, but it means we end up sampling from outside the borders of the
+    // input. These out-of-bounds values are read as zeroes. VALID means only
+    // produce output values where the filters can read all their values from
+    // within the input image. It effectively removes the margins of the output
+    // image compared to the one produced by SAME. Stride complicates this
+    // definition though, because it can result in the right and bottom filter
+    // patches sampling from outside the borders if it's greater than 1.
+    // Most of the logic for sorting this all out is done before this function,
+    // when we calculate the output size, but the positioning of the origin of
+    // the filters is different between the two modes, since SAME positions the
+    // first filter off the edge of the input.
+    int filter_left_offset;
+    int filter_top_offset;
+    if (padding == VALID) {
+      filter_left_offset =
+          ((output_width - 1) * stride_cols + filter_width - input_width + 1) /
+          2;
+      filter_top_offset = ((output_height - 1) * stride_rows + filter_height -
+                           input_height + 1) /
+                          2;
+    } else {
+      filter_left_offset =
+          ((output_width - 1) * stride_cols + filter_width - input_width) / 2;
+      filter_top_offset =
+          ((output_height - 1) * stride_rows + filter_height - input_height) /
+          2;
+    }
+
+    // If we've got multiple images in our input, work through each of them.
+    for (int batch = 0; batch < input_batches; ++batch) {
+      // Walk through all the output image values, sliding the filter to
+      // different positions in the input.
+      for (int out_y = 0; out_y < output_height; ++out_y) {
+        for (int out_x = 0; out_x < output_width; ++out_x) {
+          // Each filter kernel produces one output channel.
+          for (int out_channel = 0; out_channel < filter_count; ++out_channel) {
+            // We're going to calculate a single output value, which means we
+            // need to multiply a three dimensional kernel of weights against
+            // the current location within the input image.
+            /*
+             *-------------------------------...
+             |\ ^
+             | \in_depth
+             |  \ v
+             |   *-------------------------------...
+             |   |            ^
+             |   |       in_y_origin
+             |   |            v   \
+             |   |<in_x_origin>*---*^
+             |   |            \|   |filter_height
+             .   |             *---*v
+             .   |             <--->
+             .         filter_width
+             .
+            */
+            const int in_x_origin = (out_x * stride_cols) - filter_left_offset;
+            const int in_y_origin = (out_y * stride_rows) - filter_top_offset;
+            T3 total(0);
+            for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
+              for (int filter_x = 0; filter_x < filter_width; ++filter_x) {
+                for (int in_channel = 0; in_channel < input_depth;
+                     ++in_channel) {
+                  const int in_x = in_x_origin + filter_x;
+                  const int in_y = in_y_origin + filter_y;
+                  T1 input_value;
+                  // If the location is outside the bounds of the input image,
+                  // use zero as a default value.
+                  if ((in_x >= 0) && (in_x < input_width) && (in_y >= 0) &&
+                      (in_y < input_height)) {
+                    input_value =
+                        input_data[(batch * input_height * input_width *
+                                    input_depth) +
+                                   (in_y * input_width * input_depth) +
+                                   (in_x * input_depth) + in_channel];
+                  } else {
+                    input_value = T1(0);
+                  }
+                  const T2 filter_value =
+                      filter_data[(filter_y * filter_width * input_depth *
+                                   filter_count) +
+                                  (filter_x * input_depth * filter_count) +
+                                  (in_channel * filter_count) + out_channel];
+                  total += (input_value * filter_value);
+                }
+              }
+            }
+            output_data[(batch * output_height * output_width * filter_count) +
+                        (out_y * output_width * filter_count) +
+                        (out_x * filter_count) + out_channel] = total;
+          }
+        }
+      }
+    }
+  }
+};
+
+// A readable but slow implementation of matrix multiplication, useful for
+// debugging and understanding the algorithm. Use instead of FastGemmFunctor in
+// the Im2ColConvFunctor template definition inside the op registration to
+// enable. Assumes row-major ordering of the values in memory.
+template <class T1, class T2, class T3>
+class ReferenceGemmFunctor {
+ public:
+  void operator()(size_t m, size_t n, size_t k, const T1* a, size_t lda,
+                  const T2* b, size_t ldb, T3* c, size_t ldc) {
+    const size_t a_i_stride = lda;
+    const size_t a_l_stride = 1;
+    const size_t b_j_stride = 1;
+    const size_t b_l_stride = ldb;
+    const size_t c_i_stride = ldc;
+    const size_t c_j_stride = 1;
+    size_t i, j, l;
+    for (j = 0; j < n; j++) {
+      for (i = 0; i < m; i++) {
+        T3 total(0);
+        for (l = 0; l < k; l++) {
+          const size_t a_index = ((i * a_i_stride) + (l * a_l_stride));
+          const T1 a_value = a[a_index];
+          const size_t b_index = ((j * b_j_stride) + (l * b_l_stride));
+          const T2 b_value = b[b_index];
+          total += (a_value * b_value);
+        }
+        const size_t c_index = ((i * c_i_stride) + (j * c_j_stride));
+        c[c_index] = total;
+      }
+    }
+  }
+};
+
+// Uses the optimized Eigen library to implement the matrix multiplication
+// required by the Im2ColConvFunctor class. We supply the two input and one
+// output types so that the accumulator can potentially be higher-precision than
+// the inputs, even though we don't currently take advantage of this.
+template <class T1, class T2, class T3>
+class FastGemmFunctor {
+ public:
+  // Convenience wrappers for the Eigen matrix types we'll be using.
+  typedef Eigen::Map<
+      const Eigen::Matrix<T1, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>
+      ConstMatrixT1;
+  typedef Eigen::Map<
+      const Eigen::Matrix<T2, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>
+      ConstMatrixT2;
+  typedef Eigen::Map<
+      Eigen::Matrix<T3, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>
+      MatrixT3;
+  void operator()(size_t m, size_t n, size_t k, const T1* a, size_t lda,
+                  const T2* b, size_t ldb, T3* c, size_t ldc) {
+    ConstMatrixT1 a_matrix(a, m, k);
+    ConstMatrixT2 b_matrix(b, k, n);
+    MatrixT3 c_matrix(c, m, n);
+    c_matrix.noalias() = a_matrix * b_matrix;
+  }
+};
+
+// If we have Apple's Accelerate framework, use their implementation of GEMM to
+// get a performance boost for float.
+#if defined(USE_ACCELERATE_GEMM)
+template <>
+class FastGemmFunctor<float, float, float> {
+ public:
+  void operator()(size_t m, size_t n, size_t k, const float* a, size_t lda,
+                  const float* b, size_t ldb, float* c, size_t ldc) {
+    cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, m, n, k, 1.0f, a,
+                lda, b, ldb, 0.0f, c, ldc);
+  }
+};
+#endif  // USE_ACCELERATE_GEMM
+
+// Used to keep track of persistent memory buffers used within the op.
+template <class T, size_t size>
+struct Im2ColBufferResource : public ResourceBase {
+  mutex mu;
+  T data[size];
+  string DebugString() { return "Im2ColBufferResource"; }
+};
+
+// Implements convolution as a two stage process, first packing the patches of
+// the input image into columns (im2col) and then running GEMM to produce the
+// final result.
+template <class T1, class T2, class T3, class TGemmFunctor>
+class Im2ColConvFunctor {
+ public:
+  void operator()(OpKernelContext* context, const T1* input_data,
+                  int input_batches, int input_height, int input_width,
+                  int input_depth, const T2* filter_data, int filter_height,
+                  int filter_width, int filter_count, int stride_rows,
+                  int stride_cols, Padding padding, T3* output_data,
+                  int output_height, int output_width) {
+    if ((input_batches <= 0) || (input_width <= 0) || (input_height <= 0) ||
+        (input_depth <= 0)) {
+      LOG(WARNING) << "Conv2D was called with bad input dimensions: "
+                   << input_batches << ", " << input_height << ", "
+                   << input_width << ", " << input_depth;
+      return;
+    }
+    if ((filter_width <= 0) || (filter_height <= 0) || (filter_count <= 0)) {
+      LOG(WARNING) << "Conv2D was called with bad filter dimensions: "
+                   << filter_width << ", " << filter_height << ", "
+                   << filter_count;
+      return;
+    }
+    if ((output_width <= 0) || (output_height <= 0)) {
+      LOG(WARNING) << "Conv2D was called with bad output width or height: "
+                   << output_width << ", " << output_height;
+      return;
+    }
+
+    // These calculations define how the patches will be positioned within the
+    // input image. The actual definitions are quite complex, and rely on the
+    // previously-calculated output size.
+    int filter_left_offset;
+    int filter_top_offset;
+    if (padding == VALID) {
+      filter_left_offset =
+          ((output_width - 1) * stride_cols + filter_width - input_width + 1) /
+          2;
+      filter_top_offset = ((output_height - 1) * stride_rows + filter_height -
+                           input_height + 1) /
+                          2;
+    } else {
+      filter_left_offset =
+          ((output_width - 1) * stride_cols + filter_width - input_width) / 2;
+      filter_top_offset =
+          ((output_height - 1) * stride_rows + filter_height - input_height) /
+          2;
+    }
+
+    // The im2col buffer has # of patches rows, and # of filters cols.
+    // It's laid out like this, in row major order in memory:
+    //        < filter value count >
+    //   ^   +---------------------+
+    // patch |                     |
+    // count |                     |
+    //   v   +---------------------+
+    // Each patch row contains a filter_width x filter_height patch of the
+    // input, with the depth channel as the most contiguous in memory, followed
+    // by the width, then the height. This is the standard memory order in the
+    // image world if it helps to visualize it.
+    const int filter_value_count = filter_width * filter_height * input_depth;
+
+    // We don't want to allocate a buffer to hold all the patches if the size is
+    // going to be extremely large, so break it into chunks if it's bigger than
+    // a limit. Each chunk will be processed serially, so we can refill the
+    // buffer for the next chunk and reuse it, keeping maximum memory size down.
+    // In this case, we've picked 16 megabytes as a reasonable limit.
+    const size_t max_chunk_size = (16 * 1024 * 1024);
+    OP_REQUIRES(context, (filter_value_count * sizeof(T1)) <= max_chunk_size,
+                errors::InvalidArgument("Im2Col patch too large for buffer"));
+    const size_t patches_per_chunk =
+        max_chunk_size / (filter_value_count * sizeof(T1));
+
+    // Because memory allocation is very expensive on mobile platforms, try to
+    // allocate a persistent buffer that will be kept around between calls. We
+    // use TensorFlow's resource management to ensure that the memory will be
+    // released when the session is over.
+    Im2ColBufferResource<T1, max_chunk_size>* im2col_buffer_resource;
+    std::function<Status(Im2ColBufferResource<T1, max_chunk_size>**)> creator =
+        [](Im2ColBufferResource<T1, max_chunk_size>** resource) {
+          *resource = new Im2ColBufferResource<T1, max_chunk_size>();
+          return Status::OK();
+        };
+    OP_REQUIRES_OK(context, context->resource_manager()->LookupOrCreate(
+                                "Conv2d", "im2col_buffer",
+                                &im2col_buffer_resource, creator));
+    // This means that multiple ops can't be run simultaneously on different
+    // threads, because we have a single shared resource. The platforms this is
+    // aimed at have intra-op parallelism as their focus though, so it shouldn't
+    // be an issue.
+    mutex_lock lock_buffer(im2col_buffer_resource->mu);
+    core::ScopedUnref unref_buffer(im2col_buffer_resource);
+    T1* im2col_buffer = im2col_buffer_resource->data;
+
+    for (int batch = 0; batch < input_batches; ++batch) {
+      const T1* input_batch_start =
+          input_data + (batch * input_height * input_width * input_depth);
+      for (int out_y = 0; out_y < output_height; ++out_y) {
+        const int in_y_origin = (out_y * stride_rows) - filter_top_offset;
+        for (int out_x = 0; out_x < output_width; ++out_x) {
+          const int in_x_origin = (out_x * stride_cols) - filter_left_offset;
+          const int patch_index = (batch * output_width * output_height) +
+                                  (out_y * output_width) + out_x;
+          const int patch_index_within_chunk = patch_index % patches_per_chunk;
+          T1* im2col_patch_start =
+              im2col_buffer + (patch_index_within_chunk * filter_value_count);
+          for (int filter_y = 0; filter_y < filter_height; ++filter_y) {
+            const int in_y = in_y_origin + filter_y;
+            T1* im2col_row_start =
+                im2col_patch_start + (filter_y * filter_width * input_depth);
+            // If we're off the top or the bottom of the input, fill the whole
+            // row with zeroes.
+            if ((in_y < 0) || (in_y >= input_height)) {
+              T1* im2col_row_end =
+                  im2col_row_start + (filter_width * input_depth);
+              std::fill(im2col_row_start, im2col_row_end, T1(0));
+            } else {
+              // What we're doing here is trying to copy and fill the im2col
+              // buffer as efficiently as possible, using functions to set or
+              // duplicate values en masse. We know we don't have to worry about
+              // vertical edges because we dealt with that case above, so we
+              // just need to handle filters that overlap the left or right
+              // edges. Here's what that looks like:
+              //
+              // < left_zero_count > < center_copy_count > < right_zero_count >
+              // +------------------+---------------------+--------------------+
+              // |     (filter)     |       (image)       |      (filter)      |
+              // +------------------+---------------------+--------------------+
+              // in_x_origin        0                 input_width       in_x_end
+              //
+              // In reality it's unlikely that a filter patch will be wider
+              // than an input, but this shows all the edge cases.
+              // We use std::fill() to set the left and right sections to zeroes
+              // and std::copy() to copy over the input data for the center.
+              const int in_x_end = in_x_origin + filter_width;
+              const int left_zero_count = std::max(0, 0 - in_x_origin);
+              const int right_zero_count = std::max(0, in_x_end - input_width);
+              const int center_copy_count =
+                  filter_width - (left_zero_count + right_zero_count);
+              if (left_zero_count > 0) {
+                T1* im2col_left_start = im2col_row_start;
+                T1* im2col_left_end =
+                    im2col_left_start + (left_zero_count * input_depth);
+                std::fill(im2col_left_start, im2col_left_end, T1(0));
+              }
+              if (center_copy_count > 0) {
+                const T1* input_row_start =
+                    input_batch_start + (in_y * input_width * input_depth) +
+                    (std::max(0, in_x_origin) * input_depth);
+                const T1* input_row_end =
+                    input_row_start + (center_copy_count * input_depth);
+                T1* im2col_center_start =
+                    im2col_row_start + (left_zero_count * input_depth);
+                std::copy(input_row_start, input_row_end, im2col_center_start);
+              }
+              if (right_zero_count > 0) {
+                T1* im2col_right_start =
+                    im2col_row_start +
+                    ((left_zero_count + center_copy_count) * input_depth);
+                T1* im2col_right_end =
+                    im2col_right_start + (right_zero_count * input_depth);
+                std::fill(im2col_right_start, im2col_right_end, T1(0));
+              }
+            }
+          }
+          const bool is_last_in_chunk =
+              (patch_index_within_chunk == (patches_per_chunk - 1));
+          const bool is_last_overall =
+              ((batch == (input_batches - 1)) &&
+               (out_y == (output_height - 1)) && (out_x == (output_width - 1)));
+          if (is_last_in_chunk || is_last_overall) {
+            // Now we've assembled a set of image patches into a matrix, apply a
+            // GEMM matrix multiply of the patches as rows, times the filter
+            // weights in columns, to get partial results in the output matrix.
+            const int how_many_patches = patch_index_within_chunk + 1;
+            const int m = how_many_patches;
+            const int n = filter_count;
+            const int k = filter_value_count;
+            const int lda = filter_value_count;
+            const int ldb = filter_count;
+            const int ldc = filter_count;
+            const size_t start_patch_index =
+                patch_index - (how_many_patches - 1);
+            T3* chunk_output_data =
+                output_data + (start_patch_index * filter_count);
+            TGemmFunctor gemm_functor;
+            gemm_functor(m, n, k, im2col_buffer, lda, filter_data, ldb,
+                         chunk_output_data, ldc);
+          }
+        }
+      }
+    }
+  }
+};
+
+}  // namespace
+
+// This TensorFlow kernel class handles all of the IO and housekeeping for the
+// functors that actually implement the underlying algorithm. To swap in
+// different implementations of the main calculations, use a different
+// TConvFunctor parameter when instantiating the template.
+template <class T, class TConvFunctor>
+class Conv2DUsingGemmOp : public BinaryOp<T> {
+ public:
+  explicit Conv2DUsingGemmOp(OpKernelConstruction* context)
+      : BinaryOp<T>(context) {
+    OP_REQUIRES_OK(context, context->GetAttr("strides", &strides_));
+    string data_format;
+    OP_REQUIRES_OK(context, context->GetAttr("data_format", &data_format));
+    OP_REQUIRES(context, FormatFromString(data_format, &data_format_),
+                errors::InvalidArgument("Invalid data format"));
+    OP_REQUIRES(context, data_format_ == FORMAT_NHWC,
+                errors::InvalidArgument(
+                    "Data format not supported by this kernel", data_format));
+    OP_REQUIRES(context, strides_.size() == 4,
+                errors::InvalidArgument("Sliding window strides field must "
+                                        "specify 4 dimensions"));
+    const int64 stride_n = GetTensorDim(strides_, data_format_, 'N');
+    const int64 stride_c = GetTensorDim(strides_, data_format_, 'C');
+    OP_REQUIRES(
+        context, stride_n == 1 && stride_c == 1,
+        errors::InvalidArgument("Current implementation does not yet support "
+                                "strides in the batch and depth dimensions."));
+    OP_REQUIRES_OK(context, context->GetAttr("padding", &padding_));
+  }
+
+  void Compute(OpKernelContext* context) override {
+    // Input tensor is of the following dimensions:
+    // [ batch, in_rows, in_cols, in_depth ]
+    const Tensor& input = context->input(0);
+
+    // Input filter is of the following dimensions:
+    // [ filter_rows, filter_cols, in_depth, out_depth]
+    const Tensor& filter = context->input(1);
+
+    // For 2D convolution, there should be 4 dimensions.
+    OP_REQUIRES(context, input.dims() == 4,
+                errors::InvalidArgument("input must be 4-dimensional",
+                                        input.shape().DebugString()));
+    OP_REQUIRES(context, filter.dims() == 4,
+                errors::InvalidArgument("filter must be 4-dimensional: ",
+                                        filter.shape().DebugString()));
+
+    for (int i = 0; i < 3; i++) {
+      OP_REQUIRES(context, FastBoundsCheck(filter.dim_size(i),
+                                           std::numeric_limits<int>::max()),
+                  errors::InvalidArgument("filter too large"));
+    }
+
+    // The last dimension for input is in_depth. It must be the same as the
+    // filter's in_depth.
+    const int64 in_depth = GetTensorDim(input, data_format_, 'C');
+    OP_REQUIRES(
+        context, in_depth == filter.dim_size(2),
+        errors::InvalidArgument("input and filter must have the same depth: ",
+                                in_depth, " vs ", filter.dim_size(2)));
+
+    // The last dimension for filter is out_depth.
+    const int out_depth = static_cast<int>(filter.dim_size(3));
+
+    // The second dimension for input is rows/height.
+    // The first dimension for filter is rows/height.
+    const int64 input_rows_raw = GetTensorDim(input, data_format_, 'H');
+    OP_REQUIRES(context, FastBoundsCheck(input_rows_raw,
+                                         std::numeric_limits<int>::max()),
+                errors::InvalidArgument("Input rows too large"));
+    const int input_rows = static_cast<int>(input_rows_raw);
+    const int filter_rows = static_cast<int>(filter.dim_size(0));
+
+    // The third dimension for input is columns/width.
+    // The second dimension for filter is columns/width.
+    const int64 input_cols_raw = GetTensorDim(input, data_format_, 'W');
+    OP_REQUIRES(context, FastBoundsCheck(input_cols_raw,
+                                         std::numeric_limits<int>::max()),
+                errors::InvalidArgument("Input cols too large"));
+    const int input_cols = static_cast<int>(input_cols_raw);
+    const int filter_cols = static_cast<int>(filter.dim_size(1));
+
+    // The first dimension for input is batch.
+    const int64 batch_raw = GetTensorDim(input, data_format_, 'N');
+    OP_REQUIRES(context,
+                FastBoundsCheck(batch_raw, std::numeric_limits<int>::max()),
+                errors::InvalidArgument("batch is too large"));
+    const int batch = static_cast<int>(batch_raw);
+
+    // For now we take the stride from the second and third dimensions only (we
+    // do not support striding on the batch or depth dimension).
+    const int stride_rows = GetTensorDim(strides_, data_format_, 'H');
+    const int stride_cols = GetTensorDim(strides_, data_format_, 'W');
+
+    int64 out_rows = 0, out_cols = 0, pad_rows = 0, pad_cols = 0;
+    OP_REQUIRES_OK(context,
+                   GetWindowedOutputSize(input_rows, filter_rows, stride_rows,
+                                         padding_, &out_rows, &pad_rows));
+    OP_REQUIRES_OK(context,
+                   GetWindowedOutputSize(input_cols, filter_cols, stride_cols,
+                                         padding_, &out_cols, &pad_cols));
+    TensorShape out_shape =
+        ShapeFromFormat(data_format_, batch, out_rows, out_cols, out_depth);
+
+    // Output tensor is of the following dimensions:
+    // [ in_batch, out_rows, out_cols, out_depth ]
+    Tensor* output = nullptr;
+    OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &output));
+
+    VLOG(2) << "Conv2D: in_depth = " << in_depth
+            << ", input_cols = " << input_cols
+            << ", filter_cols = " << filter_cols
+            << ", input_rows = " << input_rows
+            << ", filter_rows = " << filter_rows
+            << ", stride_rows = " << stride_rows
+            << ", stride_cols = " << stride_cols
+            << ", out_depth = " << out_depth;
+
+    // If there is nothing to compute, return.
+    if (out_shape.num_elements() == 0) {
+      return;
+    }
+    TConvFunctor conv_functor;
+    conv_functor(context, input.flat<T>().data(), batch, input_rows, input_cols,
+                 in_depth, filter.flat<T>().data(), filter_rows, filter_cols,
+                 out_depth, stride_rows, stride_cols, padding_,
+                 output->flat<T>().data(), out_rows, out_cols);
+  }
+
+ private:
+  std::vector<int32> strides_;
+  Padding padding_;
+  TensorFormat data_format_;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(Conv2DUsingGemmOp);
+};
+
+#define REGISTER_CPU(T)                                         \
+  REGISTER_KERNEL_BUILDER(                                      \
+      Name("Conv2D").Device(DEVICE_CPU).TypeConstraint<T>("T"), \
+      Conv2DUsingGemmOp<                                        \
+          T, Im2ColConvFunctor<T, T, T, FastGemmFunctor<T, T, T>>>);
+
+// Only register this GEMM-based implementation of Conv2d if the compiler flags
+// request the implementation explicitly, since otherwise it will clash with the
+// default EigenTensor-based kernel.
+#if defined(USE_GEMM_FOR_CONV)
+TF_CALL_half(REGISTER_CPU);
+TF_CALL_float(REGISTER_CPU);
+#endif  // USE_GEMM_FOR_CONV
+
+}  // namespace tensorflow
diff --git a/tensorflow/core/kernels/reduce_join_op.cc b/tensorflow/core/kernels/reduce_join_op.cc
index 1bd415c0d1..b72bfeb15f 100644
--- a/tensorflow/core/kernels/reduce_join_op.cc
+++ b/tensorflow/core/kernels/reduce_join_op.cc
@@ -105,7 +105,7 @@ void MakeUnreducedIndices(gtl::InlinedVector<bool, 8> index_is_reduced,
 TensorShape GetOutputShape(gtl::InlinedVector<bool, 8> index_is_reduced,
                            const TensorShape& input_shape, bool keep_dims) {
   TensorShape output_shape;
-  for (int32 index = 0; index < index_is_reduced.size(); ++index) {
+  for (size_t index = 0; index < index_is_reduced.size(); ++index) {
     if (index_is_reduced[index]) {
       if (keep_dims) output_shape.AddDim(1);
     } else {
diff --git a/tensorflow/core/kernels/reverse_sequence_op.cc b/tensorflow/core/kernels/reverse_sequence_op.cc
index 5825b1cd4d..505c512cc4 100644
--- a/tensorflow/core/kernels/reverse_sequence_op.cc
+++ b/tensorflow/core/kernels/reverse_sequence_op.cc
@@ -40,19 +40,19 @@ namespace tensorflow {
 typedef Eigen::ThreadPoolDevice CPUDevice;
 typedef Eigen::GpuDevice GPUDevice;
 
-template <typename Device>
+template <typename Device, typename Tlen>
 void CheckErrors(OpKernelContext* context, int batch_dim, int seq_dim) {
   const Tensor& input = context->input(0);
   const Tensor& seq_lens = context->input(1);
 
-  auto seq_lens_t = seq_lens.vec<int64>();
+  auto seq_lens_t = seq_lens.vec<Tlen>();
 
-  std::vector<int64> seq_lens_vec(seq_lens_t.size());
+  std::vector<Tlen> seq_lens_vec(seq_lens_t.size());
 
   // Copy seq_len info down for validity checks
   context->eigen_device<Device>().memcpyDeviceToHost(
       seq_lens_vec.data(), seq_lens_t.data(),
-      sizeof(int64) * seq_lens_t.size());
+      sizeof(Tlen) * seq_lens_t.size());
 
   OP_REQUIRES(context, batch_dim != seq_dim,
               errors::InvalidArgument("batch_dim == seq_dim == ", seq_dim));
@@ -76,8 +76,7 @@ void CheckErrors(OpKernelContext* context, int batch_dim, int seq_dim) {
   }
 }
 
-template <>
-void CheckErrors<GPUDevice>(OpKernelContext* context, int batch_dim,
+void CheckErrorsGPU(OpKernelContext* context, int batch_dim,
                             int seq_dim) {
   const Tensor& input = context->input(0);
   const Tensor& seq_lens = context->input(1);
@@ -97,7 +96,19 @@ void CheckErrors<GPUDevice>(OpKernelContext* context, int batch_dim,
                                       " vs. ", input.dim_size(batch_dim)));
 }
 
-template <typename Device, typename T>
+template <>
+void CheckErrors<GPUDevice, int32>(OpKernelContext* context, int batch_dim,
+                            int seq_dim) {
+  CheckErrorsGPU(context, batch_dim, seq_dim);
+}
+
+template <>
+void CheckErrors<GPUDevice, int64>(OpKernelContext* context, int batch_dim,
+                            int seq_dim) {
+  CheckErrorsGPU(context, batch_dim, seq_dim);
+}
+
+template <typename Device, typename T, typename Tlen>
 class ReverseSequenceOp : public OpKernel {
  public:
   explicit ReverseSequenceOp(OpKernelConstruction* context)
@@ -115,9 +126,9 @@ class ReverseSequenceOp : public OpKernel {
                 errors::InvalidArgument("seq_lens input must be 1-dim, not ",
                                         seq_lens.dims()));
 
-    auto seq_lens_t = seq_lens.vec<int64>();
+    auto seq_lens_t = seq_lens.vec<Tlen>();
 
-    CheckErrors<Device>(context, batch_dim_, seq_dim_);
+    CheckErrors<Device, Tlen>(context, batch_dim_, seq_dim_);
 
     const int input_dims = input.dims();
 
@@ -127,7 +138,7 @@ class ReverseSequenceOp : public OpKernel {
 
 #define HANDLE_DIM(NDIM)                                                      \
   case NDIM:                                                                  \
-    functor::ReverseSequence<Device, T, NDIM>::Compute(                       \
+    functor::ReverseSequence<Device, T, Tlen, NDIM>::Compute(                 \
         context->eigen_device<Device>(), input.tensor<T, NDIM>(), batch_dim_, \
         seq_dim_, seq_lens_t, output->tensor<T, NDIM>());                     \
     break;
@@ -153,42 +164,57 @@ class ReverseSequenceOp : public OpKernel {
   TF_DISALLOW_COPY_AND_ASSIGN(ReverseSequenceOp);
 };
 
-#define REGISTER_REVERSE_SEQUENCE(type)                                     \
+#define REGISTER_REVERSE_SEQUENCE(type, len_type)                           \
   REGISTER_KERNEL_BUILDER(                                                  \
-      Name("ReverseSequence").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
-      ReverseSequenceOp<CPUDevice, type>);
+      Name("ReverseSequence").Device(DEVICE_CPU).TypeConstraint<type>("T"). \
+      TypeConstraint<len_type>("Tlen"),                                     \
+      ReverseSequenceOp<CPUDevice, type, len_type>);
 
-TF_CALL_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE);
+#define REGISTER_REVERSE_SEQUENCE_LEN(type)    \
+  REGISTER_REVERSE_SEQUENCE(type, int32);      \
+  REGISTER_REVERSE_SEQUENCE(type, int64);
+
+TF_CALL_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE_LEN);
 
 #if GOOGLE_CUDA
 
 // Forward declarations of the functor specializations for GPU.
 namespace functor {
-#define DECLARE_GPU_SPEC(T, Dims)                                       \
-  template <>                                                           \
-  void ReverseSequence<GPUDevice, T, Dims>::Compute(                    \
-      const GPUDevice& d, typename TTypes<T, Dims>::ConstTensor input,  \
-      int32 batch_dim, int32 seq_dim, TTypes<int64>::ConstVec seq_lens, \
-      typename TTypes<T, Dims>::Tensor output);                         \
-  extern template struct ReverseSequence<GPUDevice, T, Dims>;
-
-#define DECLARE_GPU_SPECS(T) \
-  DECLARE_GPU_SPEC(T, 2);    \
-  DECLARE_GPU_SPEC(T, 3);    \
-  DECLARE_GPU_SPEC(T, 4);    \
-  DECLARE_GPU_SPEC(T, 5);
+#define DECLARE_GPU_SPEC(T, Tlen, Dims)                                     \
+  template <>                                                               \
+  void ReverseSequence<GPUDevice, T, Tlen, Dims>::Compute(                  \
+      const GPUDevice& d, typename TTypes<T, Dims>::ConstTensor input,      \
+      int32 batch_dim, int32 seq_dim,                                       \
+      typename TTypes<Tlen>::ConstVec seq_lens,                             \
+      typename TTypes<T, Dims>::Tensor output);                             \
+  extern template struct ReverseSequence<GPUDevice, T, Tlen, Dims>;
+
+#define DECLARE_GPU_SPEC_LEN(T, Dims)    \
+  DECLARE_GPU_SPEC(T, int32, Dims);      \
+  DECLARE_GPU_SPEC(T, int64, Dims);
+
+#define DECLARE_GPU_SPECS(T)     \
+  DECLARE_GPU_SPEC_LEN(T, 2);    \
+  DECLARE_GPU_SPEC_LEN(T, 3);    \
+  DECLARE_GPU_SPEC_LEN(T, 4);    \
+  DECLARE_GPU_SPEC_LEN(T, 5);
 
 TF_CALL_GPU_NUMBER_TYPES(DECLARE_GPU_SPECS);
 
 }  // namespace functor
 
 // Registration of the GPU implementations.
-#define REGISTER_REVERSE_SEQUENCE_GPU(type)                                 \
+#define REGISTER_REVERSE_SEQUENCE_GPU(type, len_type)                       \
   REGISTER_KERNEL_BUILDER(                                                  \
-      Name("ReverseSequence").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
-      ReverseSequenceOp<GPUDevice, type>);
+      Name("ReverseSequence").Device(DEVICE_GPU).TypeConstraint<type>("T"). \
+      TypeConstraint<len_type>("Tlen"),                                     \
+      ReverseSequenceOp<GPUDevice, type, len_type>);
+
+#define REGISTER_REVERSE_SEQUENCE_GPU_LEN(type)   \
+  REGISTER_REVERSE_SEQUENCE_GPU(type, int32);     \
+  REGISTER_REVERSE_SEQUENCE_GPU(type, int64);
 
-TF_CALL_GPU_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE_GPU);
+TF_CALL_GPU_NUMBER_TYPES(REGISTER_REVERSE_SEQUENCE_GPU_LEN);
 
 #undef REGISTER_REVERSE_SEQUENCE_GPU
 
diff --git a/tensorflow/core/kernels/reverse_sequence_op.h b/tensorflow/core/kernels/reverse_sequence_op.h
index 72c59d59aa..8ccd32ea16 100644
--- a/tensorflow/core/kernels/reverse_sequence_op.h
+++ b/tensorflow/core/kernels/reverse_sequence_op.h
@@ -25,12 +25,12 @@ namespace tensorflow {
 
 namespace generator {
 
-template <typename T, size_t Dims>
+template <typename T, typename Tlen, size_t Dims>
 class ReverseGenerator {
  public:
   EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
   ReverseGenerator(typename TTypes<T, Dims>::ConstTensor input, int32 batch_dim,
-                   int32 seq_dim, TTypes<int64>::ConstVec seq_lengths)
+                   int32 seq_dim, typename TTypes<Tlen>::ConstVec seq_lengths)
       : input_(input),
         batch_dim_(batch_dim),
         seq_dim_(seq_dim),
@@ -51,21 +51,22 @@ class ReverseGenerator {
   typename TTypes<T, Dims>::ConstTensor input_;
   int32 batch_dim_;
   int32 seq_dim_;
-  TTypes<int64>::ConstVec seq_lengths_;
+  typename TTypes<Tlen>::ConstVec seq_lengths_;
 };
 
 }  // namespace generator
 
 namespace functor {
 
-template <typename Device, typename T, size_t Dims>
+template <typename Device, typename T, typename Tlen, size_t Dims>
 struct ReverseSequence {
   EIGEN_ALWAYS_INLINE static void Compute(
       const Device& d, typename TTypes<T, Dims>::ConstTensor input,
-      int32 batch_dim, int32 seq_dim, TTypes<int64>::ConstVec seq_lengths,
+      int32 batch_dim, int32 seq_dim,
+      typename TTypes<Tlen>::ConstVec seq_lengths,
       typename TTypes<T, Dims>::Tensor output) {
-    generator::ReverseGenerator<T, Dims> generator(input, batch_dim, seq_dim,
-                                                   seq_lengths);
+    generator::ReverseGenerator<T, Tlen, Dims> generator(input, batch_dim,
+                                                         seq_dim, seq_lengths);
     output.device(d) = input.generate(generator);
   }
 };
diff --git a/tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc b/tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc
index bcc265c624..373fd60687 100644
--- a/tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc
+++ b/tensorflow/core/kernels/reverse_sequence_op_gpu.cu.cc
@@ -24,15 +24,19 @@ namespace tensorflow {
 
 typedef Eigen::GpuDevice GPUDevice;
 
-#define DEFINE_GPU_SPEC(T, dims)                       \
-  template class generator::ReverseGenerator<T, dims>; \
-  template struct functor::ReverseSequence<GPUDevice, T, dims>;
+#define DEFINE_GPU_SPEC(T, Tlen, dims)                       \
+  template class generator::ReverseGenerator<T, Tlen, dims>; \
+  template struct functor::ReverseSequence<GPUDevice, T, Tlen, dims>;
+
+#define DEFINE_GPU_SPEC_LEN(T, dims)  \
+  DEFINE_GPU_SPEC(T, int32, dims);    \
+  DEFINE_GPU_SPEC(T, int64, dims);
 
 #define DEFINE_GPU_SPECS(T) \
-  DEFINE_GPU_SPEC(T, 2);    \
-  DEFINE_GPU_SPEC(T, 3);    \
-  DEFINE_GPU_SPEC(T, 4);    \
-  DEFINE_GPU_SPEC(T, 5);
+  DEFINE_GPU_SPEC_LEN(T, 2);    \
+  DEFINE_GPU_SPEC_LEN(T, 3);    \
+  DEFINE_GPU_SPEC_LEN(T, 4);    \
+  DEFINE_GPU_SPEC_LEN(T, 5);
 
 TF_CALL_GPU_NUMBER_TYPES(DEFINE_GPU_SPECS);
 
diff --git a/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc b/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc
index dbd1d9466c..2d1539fb9d 100644
--- a/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc
+++ b/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc
@@ -91,7 +91,7 @@ class SparseDenseBinaryOpShared : public OpKernel {
     auto VecGreaterEq = [](ArraySlice<int64> lhs, ArraySlice<int64> rhs) {
       if (lhs.size() > rhs.size()) return true;
       if (lhs.size() < rhs.size()) return false;
-      for (int i = 0; i < lhs.size(); ++i) {
+      for (size_t i = 0; i < lhs.size(); ++i) {
         if (lhs[i] < rhs[i]) return false;
       }
       return true;
diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc
index 65052e565c..82d9924665 100644
--- a/tensorflow/core/ops/array_ops.cc
+++ b/tensorflow/core/ops/array_ops.cc
@@ -1471,11 +1471,12 @@ This operation returns N 1-D integer tensors representing shape of `input[i]s`.
 // --------------------------------------------------------------------------
 REGISTER_OP("ReverseSequence")
     .Input("input: T")
-    .Input("seq_lengths: int64")
+    .Input("seq_lengths: Tlen")
     .Output("output: T")
     .Attr("seq_dim: int")
     .Attr("batch_dim: int = 0")
     .Attr("T: type")
+    .Attr("Tlen: {int32, int64} = DT_INT64")
     .SetShapeFn([](InferenceContext* c) {
       ShapeHandle input = c->input(0);
       ShapeHandle seq_lens_shape;
diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt
index fab9001ff5..3495f6ea14 100644
--- a/tensorflow/core/ops/ops.pbtxt
+++ b/tensorflow/core/ops/ops.pbtxt
@@ -4711,6 +4711,42 @@ op {
   summary: "Decode a PNG-encoded image to a uint8 or uint16 tensor."
   description: "The attr `channels` indicates the desired number of color channels for the\ndecoded image.\n\nAccepted values are:\n\n*   0: Use the number of channels in the PNG-encoded image.\n*   1: output a grayscale image.\n*   3: output an RGB image.\n*   4: output an RGBA image.\n\nIf needed, the PNG-encoded image is transformed to match the requested number\nof color channels."
 }
+op {
+  name: "DecodeGif"
+  input_arg {
+    name: "contents"
+    description: "0-D.  The GIF-encoded image."
+    type: DT_STRING
+  }
+  output_arg {
+    name: "image"
+    description: "3-D with shape `[height, width, channels]`."
+    type_attr: "dtype"
+  }
+  attr {
+    name: "channels"
+    type: "int"
+    default_value {
+      i: 0
+    }
+    description: "Number of color channels for the decoded image."
+  }
+  attr {
+    name: "dtype"
+    type: "type"
+    default_value {
+      type: DT_UINT8
+    }
+    allowed_values {
+      list {
+        type: DT_UINT8
+        type: DT_UINT16
+      }
+    }
+  }
+  summary: "Decode a GIF-encoded image to a uint8 or uint16 tensor."
+  description: "The attr `channels` indicates the desired number of color channels for the\ndecoded image.\n\nAccepted values are:\n\n*   0: Use the number of channels in the GIF-encoded image.\n*   1: output a grayscale image.\n*   3: output an RGB image.\n*   4: output an RGBA image.\n\nIf needed, the GIF-encoded image is transformed to match the requested number\nof color channels."
+}
 op {
   name: "DecodeRaw"
   input_arg {
diff --git a/tensorflow/core/util/example_proto_helper.cc b/tensorflow/core/util/example_proto_helper.cc
index acab74732e..658b3588e6 100644
--- a/tensorflow/core/util/example_proto_helper.cc
+++ b/tensorflow/core/util/example_proto_helper.cc
@@ -222,7 +222,7 @@ Status SingleExampleProtoToTensors(
   const auto& feature_dict = features.feature();
 
   // Handle dense features.
-  for (int d = 0; d < fixed_len_features.size(); ++d) {
+  for (size_t d = 0; d < fixed_len_features.size(); ++d) {
     const FixedLenFeature& feature_config = fixed_len_features[d];
     const string& key = feature_config.key;
     const DataType& dtype = feature_config.dtype;
@@ -263,7 +263,7 @@ Status SingleExampleProtoToTensors(
   }
 
   // Handle sparse features.
-  for (int d = 0; d < var_len_features.size(); ++d) {
+  for (size_t d = 0; d < var_len_features.size(); ++d) {
     const VarLenFeature& feature_config = var_len_features[d];
     const string& key = feature_config.key;
     const DataType& dtype = feature_config.dtype;
@@ -338,7 +338,7 @@ Status BatchExampleProtoToTensors(
       fixed_len_features.size());
 
   // Preallocate dense_values, since we know their sizes.
-  for (int d = 0; d < fixed_len_features.size(); ++d) {
+  for (size_t d = 0; d < fixed_len_features.size(); ++d) {
     const FixedLenFeature& config = fixed_len_features[d];
     TensorShape out_shape;
     out_shape.AddDim(batch_size);
@@ -352,11 +352,11 @@ Status BatchExampleProtoToTensors(
   // Temporary vector to hold sparse values.
   std::vector<std::vector<Tensor>> sparse_values_tmp(var_len_features.size());
 
-  for (int d = 0; d < var_len_features.size(); ++d) {
+  for (size_t d = 0; d < var_len_features.size(); ++d) {
     sparse_values_tmp[d] = std::vector<Tensor>(batch_size);
   }
 
-  for (int b = 0; b < examples.size(); ++b) {
+  for (size_t b = 0; b < examples.size(); ++b) {
     const Example& ex = *(examples[b]);
     const string& example_name = (has_names) ? names[b] : "<unknown>";
     SingleExampleProtoToTensors(
@@ -364,7 +364,7 @@ Status BatchExampleProtoToTensors(
         &output_dense_values_tensor_ptrs, &sparse_values_tmp);
   }
 
-  for (int d = 0; d < var_len_features.size(); ++d) {
+  for (size_t d = 0; d < var_len_features.size(); ++d) {
     const VarLenFeature& feature_config = var_len_features[d];
     const DataType& dtype = feature_config.dtype;
     const std::vector<Tensor>& sparse_values_tensor = sparse_values_tmp[d];
diff --git a/tensorflow/core/util/sparse/sparse_tensor.h b/tensorflow/core/util/sparse/sparse_tensor.h
index 79064e9988..1665446aee 100644
--- a/tensorflow/core/util/sparse/sparse_tensor.h
+++ b/tensorflow/core/util/sparse/sparse_tensor.h
@@ -283,7 +283,7 @@ void SparseTensor::Reorder(const VarDimArray& order) {
   // permutation (the inverse).  This can be calculated with O(1)
   // additional
   // and O(n) time (INVPERM) but we just do the simple thing here.
-  std::vector<int64> permutation(reorder.size());
+  std::vector<size_t> permutation(reorder.size());
   for (std::size_t n = 0; n < reorder.size(); ++n) {
     permutation[reorder[n]] = n;
   }
diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index 056664778d..acd3005c48 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -703,7 +703,7 @@ def variable_summaries(var, name):
     mean = tf.reduce_mean(var)
     tf.scalar_summary('mean/' + name, mean)
     with tf.name_scope('stddev'):
-      stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
+      stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
     tf.scalar_summary('sttdev/' + name, stddev)
     tf.scalar_summary('max/' + name, tf.reduce_max(var))
     tf.scalar_summary('min/' + name, tf.reduce_min(var))
diff --git a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
index c83239217a..23492e5122 100644
--- a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
+++ b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
@@ -75,7 +75,7 @@ def train():
       mean = tf.reduce_mean(var)
       tf.scalar_summary('mean/' + name, mean)
       with tf.name_scope('stddev'):
-        stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
+        stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
       tf.scalar_summary('sttdev/' + name, stddev)
       tf.scalar_summary('max/' + name, tf.reduce_max(var))
       tf.scalar_summary('min/' + name, tf.reduce_min(var))
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Optimizer.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Optimizer.md
index cc3b53d119..cfd86f5bee 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Optimizer.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard5/tf.train.Optimizer.md
@@ -184,9 +184,9 @@ applies gradients.
 
 ### Gating Gradients
 
-Both `minimize()` and `compute_gradients()` accept a `gate_gradients`
-argument that controls the degree of parallelism during the application of
-the gradients.
+Both `minimize()` and `compute_gradients()` accept a `gate_gradients` argument
+that controls the degree of parallelism during the application of the
+gradients.
 
 The possible values are: `GATE_NONE`, `GATE_OP`, and `GATE_GRAPH`.
 
@@ -258,7 +258,3 @@ Use `get_slot_names()` to get the list of slot names created by the
 - - -
 
 #### `tf.train.Optimizer.get_name()` {#Optimizer.get_name}
-
-
-
-
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.confusion_matrix.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.confusion_matrix.md
index f06295f081..50d7917926 100644
--- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.confusion_matrix.md
+++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard8/tf.contrib.metrics.confusion_matrix.md
@@ -35,7 +35,7 @@ the same shape in order for this function to work.
 
 ##### Returns:
 
-  A l X l matrix represeting the confusion matrix, where l in the number of
+  A k X k matrix represeting the confusion matrix, where k is the number of
   possible labels in the classification task.
 
 ##### Raises:
diff --git a/tensorflow/g3doc/api_docs/python/train.md b/tensorflow/g3doc/api_docs/python/train.md
index e9c9ad63e3..d408a40489 100644
--- a/tensorflow/g3doc/api_docs/python/train.md
+++ b/tensorflow/g3doc/api_docs/python/train.md
@@ -204,9 +204,9 @@ applies gradients.
 
 ### Gating Gradients
 
-Both `minimize()` and `compute_gradients()` accept a `gate_gradients`
-argument that controls the degree of parallelism during the application of
-the gradients.
+Both `minimize()` and `compute_gradients()` accept a `gate_gradients` argument
+that controls the degree of parallelism during the application of the
+gradients.
 
 The possible values are: `GATE_NONE`, `GATE_OP`, and `GATE_GRAPH`.
 
@@ -311,7 +311,7 @@ Construct a new gradient descent optimizer.
 
 ### `class tf.train.AdadeltaOptimizer` {#AdadeltaOptimizer}
 
-Optimizer that implements the Adadelta algorithm. 
+Optimizer that implements the Adadelta algorithm.
 
 See [M. D. Zeiler](http://arxiv.org/abs/1212.5701)
 ([pdf](http://arxiv.org/pdf/1212.5701v1.pdf))
@@ -3771,5 +3771,3 @@ Generates a checkpoint state proto.
   CheckpointState proto with model_checkpoint_path and
   all_model_checkpoint_paths updated to either absolute paths or
   relative paths to the current save_dir.
-
-
diff --git a/tensorflow/g3doc/get_started/os_setup.md b/tensorflow/g3doc/get_started/os_setup.md
index 53afca4ccb..fe8e013767 100644
--- a/tensorflow/g3doc/get_started/os_setup.md
+++ b/tensorflow/g3doc/get_started/os_setup.md
@@ -8,9 +8,10 @@ github source.
 The TensorFlow Python API supports Python 2.7 and Python 3.3+.
 
 The GPU version (Linux & Mac OS X only) works best with Cuda Toolkit 7.5 and
-cuDNN v4. other versions are supported (Cuda toolkit >= 7.0 and cuDNN 6.5(v2),
-7.0(v3), v5) only when installing from sources. Please see [Cuda installation]
-(#optional-install-cuda-gpus-on-linux) for details.
+cuDNN v4.  other versions are supported (Cuda toolkit >= 7.0 and
+cuDNN 6.5(v2), 7.0(v3), v5) only when installing from sources.
+Please see [Cuda installation](#optional-install-cuda-gpus-on-linux)
+for details.
 
 ## Overview
 
@@ -239,7 +240,7 @@ packages needed by TensorFlow.
 *  Activate the conda environment and install TensorFlow in it.
 *  After the install you will activate the conda environment each time you
    want to use TensorFlow.
-*  Optionally install ipython and other packages into the conda environment 
+*  Optionally install ipython and other packages into the conda environment
 
 Install Anaconda:
 
@@ -357,7 +358,7 @@ $ source activate tensorflow
 
 ### Install IPython
 
-To use tensorflow with IPython it may be necessary to install IPython into the tensorflow environment: 
+To use tensorflow with IPython it may be necessary to install IPython into the tensorflow environment:
 
 ```bash
 $ source activate tensorflow
@@ -365,7 +366,7 @@ $ source activate tensorflow
 ```
 
 Similarly, other Python packages like pandas may need to get installed into the tensorflow environment
-before they can be used together with tensorflow.  
+before they can be used together with tensorflow.
 
 
 ## Docker installation
diff --git a/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md b/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md
index 8183cdf024..e487d8f04c 100644
--- a/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md
+++ b/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md
@@ -86,7 +86,7 @@ def variable_summaries(var, name):
     mean = tf.reduce_mean(var)
     tf.scalar_summary('mean/' + name, mean)
     with tf.name_scope('stddev'):
-      stddev = tf.sqrt(tf.reduce_sum(tf.square(var - mean)))
+      stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
     tf.scalar_summary('sttdev/' + name, stddev)
     tf.scalar_summary('max/' + name, tf.reduce_max(var))
     tf.scalar_summary('min/' + name, tf.reduce_min(var))
diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD
index e1f62a81f9..4766a673a4 100644
--- a/tensorflow/python/BUILD
+++ b/tensorflow/python/BUILD
@@ -71,6 +71,34 @@ tf_py_test(
     ],
 )
 
+tf_py_test(
+    name = "flags_test",
+    size = "small",
+    srcs = ["platform/flags_test.py"],
+    additional_deps = [
+        ":platform",
+        ":platform_test",
+    ],
+    tags = [
+        "manual",
+        "notap",
+    ],
+)
+
+tf_py_test(
+    name = "app_test",
+    size = "small",
+    srcs = ["platform/app_test.py"],
+    additional_deps = [
+        ":platform",
+        ":platform_test",
+    ],
+    tags = [
+        "manual",
+        "notap",
+    ],
+)
+
 cc_library(
     name = "numpy_lib",
     srcs = ["lib/core/numpy.cc"],
diff --git a/tensorflow/python/kernel_tests/clip_ops_test.py b/tensorflow/python/kernel_tests/clip_ops_test.py
index 1f9e69713a..bb842a2998 100644
--- a/tensorflow/python/kernel_tests/clip_ops_test.py
+++ b/tensorflow/python/kernel_tests/clip_ops_test.py
@@ -57,8 +57,13 @@ class ClipTest(tf.test.TestCase):
       clip_norm = 4.0
       ans = tf.clip_by_norm(x, clip_norm)
       tf_ans = ans.eval()
+      
+      clip_tensor = tf.constant(4.0)
+      ans = tf.clip_by_norm(x, clip_norm)
+      tf_ans_tensor = ans.eval()
 
     self.assertAllClose(np_ans, tf_ans)
+    self.assertAllClose(np_ans, tf_ans_tensor)
 
   def testClipByNormNotClipped(self):
     # No norm clipping when clip_norm >= 5
@@ -148,6 +153,28 @@ class ClipTest(tf.test.TestCase):
     self.assertAllClose(np_ans_0, tf_ans_1)
     self.assertAllClose(np_ans_1, tf_ans_2)
 
+  def testClipByGlobalNormClippedTensor(self):
+    # Norm clipping when clip_norm < 5
+    with self.test_session():
+      x0 = tf.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
+      x1 = tf.constant([1.0, -2.0])
+      # Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
+      clip_norm = tf.constant(4.0)
+
+      # Answers are the original tensors scaled by 4.0/5.0
+      np_ans_0 = [[-1.6, 0.0, 0.0],
+                  [3.2, 0.0, 0.0]]
+      np_ans_1 = [0.8, -1.6]
+
+      ans, norm = tf.clip_by_global_norm((x0, x1), clip_norm)
+      tf_ans_1 = ans[0].eval()
+      tf_ans_2 = ans[1].eval()
+      tf_norm = norm.eval()
+
+    self.assertAllClose(tf_norm, 5.0)
+    self.assertAllClose(np_ans_0, tf_ans_1)
+    self.assertAllClose(np_ans_1, tf_ans_2)
+
   def testClipByGlobalNormSupportsNone(self):
     # Norm clipping when clip_norm < 5
     with self.test_session():
@@ -259,6 +286,19 @@ class ClipTest(tf.test.TestCase):
 
     self.assertAllClose(np_ans, tf_ans)
 
+  def testClipByAverageNormClippedTensor(self):
+    # Norm clipping when average clip_norm < 0.83333333
+    with self.test_session():
+      x = tf.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
+      # Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
+      np_ans = [[-2.88, 0.0, 0.0],
+                [3.84, 0.0, 0.0]]
+      clip_norm = tf.constant(0.8)
+      ans = tf.clip_by_average_norm(x, clip_norm)
+      tf_ans = ans.eval()
+
+    self.assertAllClose(np_ans, tf_ans)
+
   def testClipByAverageNormNotClipped(self):
     # No norm clipping when average clip_norm >= 0.83333333
     with self.test_session():
diff --git a/tensorflow/python/kernel_tests/reverse_sequence_op_test.py b/tensorflow/python/kernel_tests/reverse_sequence_op_test.py
index 48fbccd686..cea9711d32 100644
--- a/tensorflow/python/kernel_tests/reverse_sequence_op_test.py
+++ b/tensorflow/python/kernel_tests/reverse_sequence_op_test.py
@@ -47,7 +47,7 @@ class ReverseSequenceTest(tf.test.TestCase):
     self._testReverseSequence(x, batch_dim, seq_dim, seq_lengths,
                               truth, False, expected_err_re)
 
-  def _testBasic(self, dtype):
+  def _testBasic(self, dtype, len_dtype=np.int64):
     x = np.asarray([
         [[1, 2, 3, 4], [5, 6, 7, 8]],
         [[9, 10, 11, 12], [13, 14, 15, 16]],
@@ -56,7 +56,7 @@ class ReverseSequenceTest(tf.test.TestCase):
     x = x.transpose([2, 1, 0, 3, 4])  # permute axes 0 <=> 2
 
     # reverse dim 2 up to (0:3, none, 0:4) along dim=0
-    seq_lengths = np.asarray([3, 0, 4], dtype=np.int64)
+    seq_lengths = np.asarray([3, 0, 4], dtype=len_dtype)
 
     truth_orig = np.asarray(
         [[[3, 2, 1, 4], [7, 6, 5, 8]],  # reverse 0:3
@@ -70,6 +70,9 @@ class ReverseSequenceTest(tf.test.TestCase):
     batch_dim = 2
     self._testBothReverseSequence(x, batch_dim, seq_dim, seq_lengths, truth)
 
+  def testSeqLenghtInt32(self):
+    self._testBasic(np.float32, np.int32)
+
   def testFloatBasic(self):
     self._testBasic(np.float32)
 
diff --git a/tensorflow/python/kernel_tests/sparse_ops_test.py b/tensorflow/python/kernel_tests/sparse_ops_test.py
index 29b57e8094..d945af0081 100644
--- a/tensorflow/python/kernel_tests/sparse_ops_test.py
+++ b/tensorflow/python/kernel_tests/sparse_ops_test.py
@@ -743,5 +743,28 @@ class SparseMinimumMaximumTest(test_util.TensorFlowTestCase):
         tf.sparse_maximum(sp_zero, sp_one).eval()
 
 
+class SparseTransposeTest(tf.test.TestCase):
+
+  def _SparseTensorPlaceholder(self):
+    return tf.SparseTensor(
+        tf.placeholder(tf.int64),
+        tf.placeholder(tf.float64),
+        tf.placeholder(tf.int64))
+
+  def testTranspose(self):
+    with self.test_session(use_gpu=False) as sess:
+      np.random.seed(1618)
+      shapes = [np.random.randint(1, 10, size=rank) for rank in range(1, 6)]
+      for shape in shapes:
+        for dtype in [np.int32, np.int64, np.float32, np.float64]:
+          dn_input = np.random.randn(*shape).astype(dtype)
+          rank = tf.rank(dn_input).eval()
+          perm = np.random.choice(rank, rank, False)
+          sp_input, unused_a_nnz = _sparsify(dn_input)
+          sp_trans = tf.sparse_transpose(sp_input, perm=perm)
+          dn_trans = tf.sparse_tensor_to_dense(sp_trans).eval()
+          expected_trans = tf.transpose(dn_input, perm=perm).eval()
+          self.assertAllEqual(dn_trans, expected_trans)
+
 if __name__ == "__main__":
   googletest.main()
diff --git a/tensorflow/python/ops/clip_ops.py b/tensorflow/python/ops/clip_ops.py
index 9f2f972f7d..65e05626f0 100644
--- a/tensorflow/python/ops/clip_ops.py
+++ b/tensorflow/python/ops/clip_ops.py
@@ -206,7 +206,7 @@ def clip_by_global_norm(t_list, clip_norm, use_norm=None, name=None):
     # Calculate L2-norm, clip elements by ratio of clip_norm to L2-norm
     scale = clip_norm * math_ops.minimum(
         1.0 / use_norm,
-        constant_op.constant(1.0 / clip_norm, dtype=use_norm.dtype))
+        constant_op.constant(1.0, dtype=use_norm.dtype) / clip_norm)
 
     values = [
         ops.convert_to_tensor(
@@ -268,7 +268,7 @@ def clip_by_average_norm(t, clip_norm, name=None):
         math_ops.reduce_sum(t * t, math_ops.range(array_ops.rank(t))))
     tclip = array_ops.identity(
         t * clip_norm * math_ops.minimum(
-            l2norm_inv * n_element, constant_op.constant(1.0 / clip_norm)),
+            l2norm_inv * n_element, constant_op.constant(1.0) / clip_norm),
         name=name)
 
   return tclip
diff --git a/tensorflow/python/ops/io_ops.py b/tensorflow/python/ops/io_ops.py
index db8f043607..7990dba3b6 100644
--- a/tensorflow/python/ops/io_ops.py
+++ b/tensorflow/python/ops/io_ops.py
@@ -80,6 +80,7 @@ Queues](../../how_tos/threading_and_queues/index.md).
 @@FIFOQueue
 @@PaddingFIFOQueue
 @@RandomShuffleQueue
+@@PriorityQueue
 
 ## Dealing with the filesystem
 
diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py
index 1ac5314ad2..11ca3ff0d7 100644
--- a/tensorflow/python/ops/nn_ops.py
+++ b/tensorflow/python/ops/nn_ops.py
@@ -19,6 +19,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numbers
+
 import numpy as np
 
 from tensorflow.python.framework import common_shapes
@@ -1131,7 +1133,7 @@ def dropout(x, keep_prob, noise_shape=None, seed=None, name=None):
   """
   with ops.name_scope(name, "dropout", [x]) as name:
     x = ops.convert_to_tensor(x, name="x")
-    if isinstance(keep_prob, float) and not 0 < keep_prob <= 1:
+    if isinstance(keep_prob, numbers.Real) and not 0 < keep_prob <= 1:
       raise ValueError("keep_prob must be a scalar tensor or a float in the "
                        "range (0, 1], got %g" % keep_prob)
     keep_prob = ops.convert_to_tensor(keep_prob,
diff --git a/tensorflow/python/ops/sparse_ops.py b/tensorflow/python/ops/sparse_ops.py
index c42da9af5d..198ccab021 100644
--- a/tensorflow/python/ops/sparse_ops.py
+++ b/tensorflow/python/ops/sparse_ops.py
@@ -40,6 +40,7 @@ dimension, and dense along all other dimensions.
 @@sparse_retain
 @@sparse_reset_shape
 @@sparse_fill_empty_rows
+@@sparse_transpose
 
 ## Reduction
 @@sparse_reduce_sum
@@ -1582,3 +1583,49 @@ def _SparseSparseMaximumMinimumShape(op):  # pylint: disable=invalid-name
   op.inputs[4].get_shape().assert_has_rank(1)  # b_values
   op.inputs[5].get_shape().assert_has_rank(1)  # b_shape
   return [tensor_shape.unknown_shape(2), tensor_shape.unknown_shape(1)]
+
+
+def sparse_transpose(sp_input, perm=None, name=None):
+  """Transposes a `SparseTensor`
+
+  The returned tensor's dimension i will correspond to the input dimension
+  `perm[i]`. If `perm` is not given, it is set to (n-1...0), where n is
+  the rank of the input tensor. Hence by default, this operation performs a
+  regular matrix transpose on 2-D input Tensors.
+
+  For example, if `sp_input` has shape `[4, 5]` and `indices` / `values`:
+
+      [0, 3]: b
+      [0, 1]: a
+      [3, 1]: d
+      [2, 0]: c
+
+  then the output will be a `SparseTensor` of shape `[5, 4]` and
+  `indices` / `values`:
+
+      [0, 2]: c
+      [1, 0]: a
+      [1, 3]: d
+      [3, 0]: b
+
+  Args:
+    sp_input: The input `SparseTensor`.
+    perm: A permutation of the dimensions of `sp_input`.
+    name: A name prefix for the returned tensors (optional)
+  Returns:
+    A transposed `SparseTensor`.
+
+  Raises:
+    TypeError: If `sp_input` is not a `SparseTensor`.
+  """
+  with ops.op_scope([sp_input], name, "SparseTranspose") as name:
+    if perm is None:
+      rank = array_ops.rank(sp_input)
+      perm = (rank - 1) - math_ops.range(0, rank, 1)
+    indices = sp_input.indices
+    transposed_indices = array_ops.transpose(array_ops.gather(array_ops.transpose(indices), perm))
+    dense_shape = sp_input.shape
+    transposed_dense_shape = array_ops.gather(dense_shape, perm)
+    transposed_st = ops.SparseTensor(transposed_indices, sp_input.values, transposed_dense_shape)
+    transposed_st = sparse_reorder(transposed_st)
+    return transposed_st
diff --git a/tensorflow/python/platform/app.py b/tensorflow/python/platform/app.py
index ebca21e075..7419d844ff 100644
--- a/tensorflow/python/platform/app.py
+++ b/tensorflow/python/platform/app.py
@@ -25,6 +25,6 @@ from tensorflow.python.platform import flags
 
 def run(main=None):
   f = flags.FLAGS
-  f._parse_flags()
+  flags_passthrough = f._parse_flags()
   main = main or sys.modules['__main__'].main
-  sys.exit(main(sys.argv))
+  sys.exit(main(sys.argv[:1] + flags_passthrough))
diff --git a/tensorflow/python/platform/app_test.py b/tensorflow/python/platform/app_test.py
new file mode 100644
index 0000000000..b7a58dd9fb
--- /dev/null
+++ b/tensorflow/python/platform/app_test.py
@@ -0,0 +1,45 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Tests for our flags implementation."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import sys
+
+from tensorflow.python.platform import app
+from tensorflow.python.platform import flags
+
+FLAGS = flags.FLAGS
+flags.DEFINE_boolean('myflag', False, '')
+
+def main(argv):
+  if (len(argv) != 3):
+    print("Length of argv was not 3: ", argv)
+    sys.exit(-1)
+
+  if argv[1] != "--passthrough":
+    print("--passthrough argument not in argv")
+    sys.exit(-1)
+
+  if argv[2] != "extra":
+    print("'extra' argument not in argv")
+    sys.exit(-1)
+
+
+if __name__ == '__main__':
+  sys.argv.extend(["--myflag", "--passthrough", "extra"])
+  app.run()
diff --git a/tensorflow/python/platform/flags.py b/tensorflow/python/platform/flags.py
index 85f9e2cb86..645f8acecb 100644
--- a/tensorflow/python/platform/flags.py
+++ b/tensorflow/python/platform/flags.py
@@ -30,10 +30,11 @@ class _FlagValues(object):
     self.__dict__['__parsed'] = False
 
   def _parse_flags(self):
-    result, _ = _global_parser.parse_known_args()
+    result, unparsed = _global_parser.parse_known_args()
     for flag_name, val in vars(result).items():
       self.__dict__['__flags'][flag_name] = val
     self.__dict__['__parsed'] = True
+    return unparsed
 
   def __getattr__(self, name):
     """Retrieves the 'value' attribute of the flag --name."""
diff --git a/tensorflow/python/training/device_setter.py b/tensorflow/python/training/device_setter.py
index c7a8af85d1..a414116a74 100644
--- a/tensorflow/python/training/device_setter.py
+++ b/tensorflow/python/training/device_setter.py
@@ -150,7 +150,7 @@ def replica_device_setter(ps_tasks=0, ps_device="/job:ps",
     else:
       cluster_spec = server_lib.ClusterSpec(cluster).as_dict()
     # Get ps_job_name from ps_device by striping "/job:".
-    ps_job_name = ps_device.lstrip("/job:")
+    ps_job_name = pydev.DeviceSpec.from_string(ps_device).job
     if ps_job_name not in cluster_spec or cluster_spec[ps_job_name] is None:
       return None
     ps_tasks = len(cluster_spec[ps_job_name])
diff --git a/tensorflow/python/training/device_setter_test.py b/tensorflow/python/training/device_setter_test.py
index 8fb2768c5f..607cf2d20d 100644
--- a/tensorflow/python/training/device_setter_test.py
+++ b/tensorflow/python/training/device_setter_test.py
@@ -77,6 +77,23 @@ class DeviceSetterTest(tf.test.TestCase):
       self.assertDeviceEqual("/job:moon/task:1", w.device)
       self.assertDeviceEqual("/job:moon/task:1", w.initializer.device)
       self.assertDeviceEqual("/job:sun", a.device)
+  
+  def testPS2TasksWithCPUConstraint(self):
+    cluster_spec = tf.train.ClusterSpec({
+        "sun": ["sun0:2222", "sun1:2222", "sun2:2222"],
+        "moon": ["moon0:2222", "moon1:2222"]})
+
+    with tf.device(tf.train.replica_device_setter(
+        ps_device="/job:moon/cpu:0", worker_device="/job:sun",
+        cluster=cluster_spec.as_cluster_def())):
+      v = tf.Variable([1, 2])
+      w = tf.Variable([2, 1])
+      a = v + w
+      self.assertDeviceEqual("/job:moon/task:0/cpu:0", v.device)
+      self.assertDeviceEqual("/job:moon/task:0/cpu:0", v.initializer.device)
+      self.assertDeviceEqual("/job:moon/task:1/cpu:0", w.device)
+      self.assertDeviceEqual("/job:moon/task:1/cpu:0", w.initializer.device)
+      self.assertDeviceEqual("/job:sun", a.device)
 
 
 if __name__ == "__main__":
diff --git a/tensorflow/tensorboard/components/tf-dashboard-common/dashboard-style.html b/tensorflow/tensorboard/components/tf-dashboard-common/dashboard-style.html
index 1591994292..4152472cd5 100644
--- a/tensorflow/tensorboard/components/tf-dashboard-common/dashboard-style.html
+++ b/tensorflow/tensorboard/components/tf-dashboard-common/dashboard-style.html
@@ -53,7 +53,7 @@ limitations under the License.
       }
       .card .card-bottom-row {
         position: absolute;
-        left: 50px;
+        left: 75px;
         bottom: 0;
         padding-right: 10px;
       }
@@ -71,6 +71,14 @@ limitations under the License.
         display: block;
       }
 
+      .log-option-button {
+        position: absolute;
+        left: 25px;
+        bottom: 0px;
+        color: #2196F3;
+        display: block;
+      }
+
       #content-container{
         display: block;
       }
diff --git a/tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html b/tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html
index 417f64b42b..bbbfdc1393 100644
--- a/tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html
+++ b/tensorflow/tensorboard/components/tf-event-dashboard/tf-event-dashboard.html
@@ -145,6 +145,13 @@ The #center div contains tf-line-charts embedded inside tf-collapsable-panes.
                       icon="fullscreen"
                       on-tap="toggleSelected"
                     ></paper-icon-button>
+                      <paper-icon-button
+                              class="log-option-button"
+                              icon="line-weight"
+                              on-tap="toggleYScale"
+                              title="Toggle y-axis scale (log, linear)"
+                              ></paper-icon-button>
+
                   </div>
                   <template is="dom-if" if="[[_showDownloadLinks]]">
                     <div class="card-bottom-row">
@@ -242,6 +249,19 @@ The #center div contains tf-line-charts embedded inside tf-collapsable-panes.
           chartScaffold.chart().redraw();
         }
       },
+
+      toggleYScale: function(e) {
+        var currentTarget = Polymer.dom(e.currentTarget);
+
+        var b = currentTarget.parentNode.querySelector('.log-option-button');
+        var c = currentTarget.parentNode.querySelector('vz-line-chart');
+        if (c !== null) {
+          c.yScaleType = c.yScaleType === 'log' ? 'linear' : 'log';
+          b.icon = c.yScaleType === 'log' ? 'line-weight' : 'reorder';
+          c.redraw();
+        }
+      },
+
       validRuns: function(tag, runsChange, run2tagChange) {
         var _this = this;
         var result = this.selectedRuns.filter(function(r) {
diff --git a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html
index 3cffa4c65c..189d993e12 100644
--- a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html
+++ b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.html
@@ -191,6 +191,16 @@ such as different X scales (linear and temporal), tooltips and smoothing.
           value: 'step'
         },
 
+        /**
+         * The scale for the y-axis. Allows:
+         * - "linear" - linear scale (Plottable.Scales.Linear)
+         * - "log" - modified-log scale (Plottable.Scales.ModifiedLog)
+         */
+        yScaleType: {
+          type: String,
+          value: 'linear'
+        },
+
         /**
          * Change how the tooltip is sorted. Allows:
          * - "default" - Sort the tooltip by input order.
@@ -228,7 +238,7 @@ such as different X scales (linear and temporal), tooltips and smoothing.
         }
       },
       observers: [
-        "_makeChart(xType, colorScale, _attached)",
+        "_makeChart(xType, yScaleType, colorScale, _attached)",
         "_reloadFromCache(_chart)",
         "_smoothingChanged(smoothingEnabled, smoothingWeight, _chart)",
         "_tooltipSortingMethodChanged(tooltipSortingMethod, _chart)",
@@ -284,7 +294,7 @@ such as different X scales (linear and temporal), tooltips and smoothing.
         this.scopeSubtree(this.$.tooltip, true);
         this.scopeSubtree(this.$.chartsvg, true);
       },
-      _makeChart: function(xType, colorScale, _attached) {
+      _makeChart: function(xType, yScaleType, colorScale, _attached) {
         if (this._makeChartAsyncCallbackId === null) {
           this.cancelAsync(this._makeChartAsyncCallbackId);
         }
@@ -294,7 +304,7 @@ such as different X scales (linear and temporal), tooltips and smoothing.
           if (!this._attached) return;
           if (this._chart) this._chart.destroy();
           var tooltip = d3.select(this.$.tooltip);
-          var chart = new VZ.LineChart(xType, colorScale, tooltip);
+          var chart = new VZ.LineChart(xType, yScaleType, colorScale, tooltip);
           var svg = d3.select(this.$.chartsvg);
           chart.renderTo(svg);
           this._chart = chart;
diff --git a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts
index 9643e51ac0..238ea02819 100644
--- a/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts
+++ b/tensorflow/tensorboard/components/vz-line-chart/vz-line-chart.ts
@@ -49,7 +49,7 @@ module VZ {
     private targetSVG: d3.Selection<any>;
 
     constructor(
-        xType: string, colorScale: Plottable.Scales.Color,
+        xType: string, yScaleType: string, colorScale: Plottable.Scales.Color,
         tooltip: d3.Selection<any>) {
       this.seriesNames = [];
       this.name2datasets = {};
@@ -63,10 +63,10 @@ module VZ {
       // need to do a single bind, so we can deregister the callback from
       // old Plottable.Datasets. (Deregistration is done by identity checks.)
       this.onDatasetChanged = this._onDatasetChanged.bind(this);
-      this.buildChart(xType);
+      this.buildChart(xType, yScaleType);
     }
 
-    private buildChart(xType: string) {
+    private buildChart(xType: string, yScaleType: string) {
       if (this.outer) {
         this.outer.destroy();
       }
@@ -75,7 +75,7 @@ module VZ {
       this.xScale = xComponents.scale;
       this.xAxis = xComponents.axis;
       this.xAxis.margin(0).tickLabelPadding(3);
-      this.yScale = new Plottable.Scales.Linear();
+      this.yScale = LineChart.getYScaleFromType(yScaleType);
       this.yAxis = new Plottable.Axes.Numeric(this.yScale, 'left');
       let yFormatter = VZ.ChartHelpers.multiscaleFormatter(
           VZ.ChartHelpers.Y_AXIS_FORMATTER_PRECISION);
@@ -450,6 +450,16 @@ module VZ {
       return this.name2datasets[name];
     }
 
+    static getYScaleFromType(yScaleType: string): Plottable.QuantitativeScale<number> {
+      if (yScaleType === 'log') {
+        return new Plottable.Scales.ModifiedLog();
+      } else if (yScaleType === 'linear') {
+        return new Plottable.Scales.Linear();
+      } else {
+        throw new Error('Unrecognized yScale type ' + yScaleType);
+      }
+    }
+
     /**
      * Update the selected series on the chart.
      */
diff --git a/tensorflow/tensorboard/tensorboard.py b/tensorflow/tensorboard/tensorboard.py
index 7757956b38..273065ea3f 100644
--- a/tensorflow/tensorboard/tensorboard.py
+++ b/tensorflow/tensorboard/tensorboard.py
@@ -138,7 +138,12 @@ def main(unused_argv=None):
 
   status_bar.SetupStatusBarInsideGoogle('TensorBoard %s' % tag, FLAGS.port)
   print('Starting TensorBoard %s on port %d' % (tag, FLAGS.port))
-  print('(You can navigate to http://%s:%d)' % (FLAGS.host, FLAGS.port))
+
+  if FLAGS.host == "0.0.0.0":
+    print('(You can navigate to http://%s:%d)' % (socket.gethostbyname(socket.gethostname()), FLAGS.port))
+  else:
+    print('(You can navigate to http://%s:%d)' % (FLAGS.host, FLAGS.port))
+
   tb_server.serve_forever()
 
 
diff --git a/tensorflow/tools/ci_build/Dockerfile.gpu b/tensorflow/tools/ci_build/Dockerfile.gpu
index b36edb9bde..4ada062986 100644
--- a/tensorflow/tools/ci_build/Dockerfile.gpu
+++ b/tensorflow/tools/ci_build/Dockerfile.gpu
@@ -24,4 +24,4 @@ ENV LD_LIBRARY_PATH /usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
 ENV CUDA_TOOLKIT_PATH /usr/local/cuda
 ENV CUDNN_INSTALL_PATH /usr/lib/x86_64-linux-gnu
 ENV TF_NEED_CUDA 1
-ENV CUDA_COMPUTE_CAPABILITIES 3.0,5.2
+ENV TF_CUDA_COMPUTE_CAPABILITIES 3.0,5.2
diff --git a/tensorflow/tools/docker/parameterized_docker_build.sh b/tensorflow/tools/docker/parameterized_docker_build.sh
index bfae655076..aae9a868b9 100755
--- a/tensorflow/tools/docker/parameterized_docker_build.sh
+++ b/tensorflow/tools/docker/parameterized_docker_build.sh
@@ -323,7 +323,21 @@ fi
 
 # Apply the final image name and tag
 FINAL_IMG="${FINAL_IMAGE_NAME}:${FINAL_TAG}"
-docker tag -f "${IMG}" "${FINAL_IMG}" || \
+
+DOCKER_VER=$(docker version | grep Version | head -1 | awk '{print $NF}')
+if [[ -z "${DOCKER_VER}" ]]; then
+  die "ERROR: Failed to determine docker version"
+fi
+DOCKER_MAJOR_VER=$(echo "${DOCKER_VER}" | cut -d. -f 1)
+DOCKER_MINOR_VER=$(echo "${DOCKER_VER}" | cut -d. -f 2)
+
+FORCE_TAG=""
+if [[ "${DOCKER_MAJOR_VER}" -le 1 ]] && \
+   [[ "${DOCKER_MINOR_VER}" -le 9 ]]; then
+  FORCE_TAG="--force"
+fi
+
+docker tag ${FORCE_TAG} "${IMG}" "${FINAL_IMG}" || \
     die "Failed to tag intermediate docker image ${IMG} as ${FINAL_IMG}"
 
 echo ""
diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
index d47c446bbe..f95d2a6217 100644
--- a/tensorflow/workspace.bzl
+++ b/tensorflow/workspace.bzl
@@ -2,12 +2,14 @@
 
 load("//third_party/gpus:cuda_configure.bzl", "cuda_configure")
 
-# If TensorFlow is linked as a submodule, path_prefix is TensorFlow's directory
-# within the workspace (e.g. "tensorflow/"), and tf_repo_name is the name of the
-# local_repository rule (e.g. "@tf").
+# If TensorFlow is linked as a submodule.
+# path_prefix and tf_repo_name are no longer used.
 def tf_workspace(path_prefix = "", tf_repo_name = ""):
   cuda_configure(name = "local_config_cuda")
-
+  if path_prefix:
+    print("path_prefix was specified to tf_workspace but is no longer used and will be removed in the future.")
+  if tf_repo_name:
+    print("tf_repo_name was specified to tf_workspace but is no longer used and will be removed in the future.")
   # These lines need to be changed when updating Eigen. They are parsed from
   # this file by the cmake and make builds to determine the eigen version and hash.
   eigen_version = "9e1b48c333aa"
@@ -18,7 +20,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
     url = "https://bitbucket.org/eigen/eigen/get/" + eigen_version + ".tar.gz",
     sha256 = eigen_sha256,
     strip_prefix = "eigen-eigen-" + eigen_version,
-    build_file = path_prefix + "eigen.BUILD",
+    build_file = str(Label("//:eigen.BUILD")),
   )
 
   native.git_repository(
@@ -37,7 +39,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
     name = "farmhash_archive",
     url = "https://github.com/google/farmhash/archive/34c13ddfab0e35422f4c3979f360635a8c050260.zip",
     sha256 = "e3d37a59101f38fd58fb799ed404d630f0eee18bfc2a2433910977cc8fea9c28",
-    build_file = path_prefix + "farmhash.BUILD",
+    build_file = str(Label("//:farmhash.BUILD")),
   )
 
   native.bind(
@@ -56,28 +58,28 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
     name = "jpeg_archive",
     url = "http://www.ijg.org/files/jpegsrc.v9a.tar.gz",
     sha256 = "3a753ea48d917945dd54a2d97de388aa06ca2eb1066cbfdc6652036349fe05a7",
-    build_file = path_prefix + "jpeg.BUILD",
+    build_file = str(Label("//:jpeg.BUILD")),
   )
 
   native.new_http_archive(
     name = "png_archive",
     url = "https://github.com/glennrp/libpng/archive/v1.2.53.zip",
     sha256 = "c35bcc6387495ee6e757507a68ba036d38ad05b415c2553b3debe2a57647a692",
-    build_file = path_prefix + "png.BUILD",
+    build_file = str(Label("//:png.BUILD")),
   )
 
   native.new_http_archive(
     name = "gif_archive",
     url = "http://ufpr.dl.sourceforge.net/project/giflib/giflib-5.1.4.tar.gz",
     sha256 = "34a7377ba834397db019e8eb122e551a49c98f49df75ec3fcc92b9a794a4f6d1",
-    build_file = path_prefix + "gif.BUILD",
+    build_file = str(Label("//:gif.BUILD")),
   )
 
   native.new_http_archive(
     name = "six_archive",
     url = "https://pypi.python.org/packages/source/s/six/six-1.10.0.tar.gz#md5=34eed507548117b2ab523ab14b2f8b55",
     sha256 = "105f8d68616f8248e24bf0e9372ef04d3cc10104f1980f54d57b2ce73a5ad56a",
-    build_file = path_prefix + "six.BUILD",
+    build_file = str(Label("//:six.BUILD")),
   )
 
   native.bind(
@@ -95,7 +97,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
     name = "gmock_archive",
     url = "http://pkgs.fedoraproject.org/repo/pkgs/gmock/gmock-1.7.0.zip/073b984d8798ea1594f5e44d85b20d66/gmock-1.7.0.zip",
     sha256 = "26fcbb5925b74ad5fc8c26b0495dfc96353f4d553492eb97e85a8a6d2f43095b",
-    build_file = path_prefix + "gmock.BUILD",
+    build_file = str(Label("//:gmock.BUILD")),
   )
 
   native.bind(
@@ -110,7 +112,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
 
   native.bind(
     name = "python_headers",
-    actual = tf_repo_name + "//util/python:python_headers",
+    actual = str(Label("//util/python:python_headers")),
   )
 
   # grpc expects //external:protobuf_clib and //external:protobuf_compiler
@@ -130,7 +132,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
     commit = "d7ff4ff40071d2b486a052183e3e9f9382afb745",
     init_submodules = True,
     remote = "https://github.com/grpc/grpc.git",
-    build_file = path_prefix + "grpc.BUILD",
+    build_file = str(Label("//:grpc.BUILD")),
   )
 
   # protobuf expects //external:grpc_cpp_plugin to point to grpc's
@@ -149,7 +151,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
     name = "jsoncpp_git",
     remote = "https://github.com/open-source-parsers/jsoncpp.git",
     commit = "11086dd6a7eba04289944367ca82cea71299ed70",
-    build_file = path_prefix + "jsoncpp.BUILD",
+    build_file = str(Label("//:jsoncpp.BUILD")),
   )
 
   native.bind(
@@ -167,7 +169,7 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
     name = "nanopb_git",
     commit = "1251fa1",
     remote = "https://github.com/nanopb/nanopb.git",
-    build_file = path_prefix + "nanopb.BUILD",
+    build_file = str(Label("//:nanopb.BUILD")),
   )
 
   native.bind(
@@ -179,26 +181,26 @@ def tf_workspace(path_prefix = "", tf_repo_name = ""):
     name = "avro_archive",
     url = "http://www-us.apache.org/dist/avro/avro-1.8.0/cpp/avro-cpp-1.8.0.tar.gz",
     sha256 = "ec6e2ec957e95ca07f70cc25f02f5c416f47cb27bd987a6ec770dcbe72527368",
-    build_file = path_prefix + "avro.BUILD",
+    build_file = str(Label("//:avro.BUILD")),
   )
 
   native.new_http_archive(
     name = "boost_archive",
     url = "http://pilotfiber.dl.sourceforge.net/project/boost/boost/1.61.0/boost_1_61_0.tar.gz",
     sha256 = "a77c7cc660ec02704c6884fbb20c552d52d60a18f26573c9cee0788bf00ed7e6",
-    build_file = path_prefix + "boost.BUILD",
+    build_file = str(Label("//:boost.BUILD")),
   )
 
   native.new_http_archive(
     name = "bzip2_archive",
     url = "http://www.bzip.org/1.0.6/bzip2-1.0.6.tar.gz",
     sha256 = "a2848f34fcd5d6cf47def00461fcb528a0484d8edef8208d6d2e2909dc61d9cd",
-    build_file = path_prefix + "bzip2.BUILD",
+    build_file = str(Label("//:bzip2.BUILD")),
   )
 
   native.new_http_archive(
     name = "zlib_archive",
     url = "http://zlib.net/zlib-1.2.8.tar.gz",
     sha256 = "36658cb768a54c1d4dec43c3116c27ed893e88b02ecfcb44f2166f9c0b7f2a0d",
-    build_file = path_prefix + "zlib.BUILD",
+    build_file = str(Label("//:zlib.BUILD")),
   )
diff --git a/third_party/gpus/cuda/BUILD.tpl b/third_party/gpus/cuda/BUILD.tpl
index ab98c2d528..db6db87162 100644
--- a/third_party/gpus/cuda/BUILD.tpl
+++ b/third_party/gpus/cuda/BUILD.tpl
@@ -169,4 +169,4 @@ cc_library(
         cupti_library_path(),
     ],
     visibility = ["//visibility:public"],
-)
\ No newline at end of file
+)
diff --git a/third_party/gpus/cuda_configure.bzl b/third_party/gpus/cuda_configure.bzl
index 3682cb305d..1e47bfac78 100644
--- a/third_party/gpus/cuda_configure.bzl
+++ b/third_party/gpus/cuda_configure.bzl
@@ -3,19 +3,25 @@
 
 `cuda_configure` depends on the following environment variables:
 
-  * `ENABLE_CUDA`: Whether to enable building with CUDA.
-  * `CC`: The GCC host compiler path
+  * `TF_NEED_CUDA`: Whether to enable building with CUDA.
+  * `GCC_HOST_COMPILER_PATH`: The GCC host compiler path
   * `CUDA_TOOLKIT_PATH`: The path to the CUDA toolkit. Default is
     `/usr/local/cuda`.
-  * `CUDA_VERSION`: The version of the CUDA toolkit. If this is blank, then
+  * `TF_CUDA_VERSION`: The version of the CUDA toolkit. If this is blank, then
     use the system default.
-  * `CUDNN_VERSION`: The version of the cuDNN library.
+  * `TF_CUDNN_VERSION`: The version of the cuDNN library.
   * `CUDNN_INSTALL_PATH`: The path to the cuDNN library. Default is
     `/usr/local/cuda`.
-  * `CUDA_COMPUTE_CAPABILITIES`: The CUDA compute capabilities. Default is
+  * `TF_CUDA_COMPUTE_CAPABILITIES`: The CUDA compute capabilities. Default is
     `3.5,5.2`.
 """
 
+_GCC_HOST_COMPILER_PATH = "GCC_HOST_COMPILER_PATH"
+_CUDA_TOOLKIT_PATH = "CUDA_TOOLKIT_PATH"
+_TF_CUDA_VERSION = "TF_CUDA_VERSION"
+_TF_CUDNN_VERSION = "TF_CUDNN_VERSION"
+_CUDNN_INSTALL_PATH = "CUDNN_INSTALL_PATH"
+_TF_CUDA_COMPUTE_CAPABILITIES = "TF_CUDA_COMPUTE_CAPABILITIES"
 
 _DEFAULT_CUDA_VERSION = ""
 _DEFAULT_CUDNN_VERSION = ""
@@ -30,8 +36,8 @@ _DEFAULT_CUDA_COMPUTE_CAPABILITIES = ["3.5", "5.2"]
 def find_cc(repository_ctx):
   """Find the C++ compiler."""
   cc_name = "gcc"
-  if "CC" in repository_ctx.os.environ:
-    cc_name = repository_ctx.os.environ["CC"].strip()
+  if _GCC_HOST_COMPILER_PATH in repository_ctx.os.environ:
+    cc_name = repository_ctx.os.environ[_GCC_HOST_COMPILER_PATH].strip()
     if not cc_name:
       cc_name = "gcc"
   if cc_name.startswith("/"):
@@ -93,8 +99,8 @@ def _enable_cuda(repository_ctx):
 def _cuda_toolkit_path(repository_ctx):
   """Finds the cuda toolkit directory."""
   cuda_toolkit_path = _DEFAULT_CUDA_TOOLKIT_PATH
-  if "CUDA_TOOLKIT_PATH" in repository_ctx.os.environ:
-    cuda_toolkit_path = repository_ctx.os.environ["CUDA_TOOLKIT_PATH"].strip()
+  if _CUDA_TOOLKIT_PATH in repository_ctx.os.environ:
+    cuda_toolkit_path = repository_ctx.os.environ[_CUDA_TOOLKIT_PATH].strip()
   if not repository_ctx.path(cuda_toolkit_path).exists:
     fail("Cannot find cuda toolkit path.")
   return cuda_toolkit_path
@@ -103,8 +109,8 @@ def _cuda_toolkit_path(repository_ctx):
 def _cudnn_install_basedir(repository_ctx):
   """Finds the cudnn install directory."""
   cudnn_install_path = _DEFAULT_CUDNN_INSTALL_PATH
-  if "CUDNN_INSTALL_PATH" in repository_ctx.os.environ:
-    cudnn_install_path = repository_ctx.os.environ["CUDNN_INSTALL_PATH"].strip()
+  if _CUDNN_INSTALL_PATH in repository_ctx.os.environ:
+    cudnn_install_path = repository_ctx.os.environ[_CUDNN_INSTALL_PATH].strip()
   if not repository_ctx.path(cudnn_install_path).exists:
     fail("Cannot find cudnn install path.")
   return cudnn_install_path
@@ -112,25 +118,25 @@ def _cudnn_install_basedir(repository_ctx):
 
 def _cuda_version(repository_ctx):
   """Detects the cuda version."""
-  if "CUDA_VERSION" in repository_ctx.os.environ:
-    return repository_ctx.os.environ["CUDA_VERSION"].strip()
+  if _TF_CUDA_VERSION in repository_ctx.os.environ:
+    return repository_ctx.os.environ[_TF_CUDA_VERSION].strip()
   else:
     return ""
 
 
 def _cudnn_version(repository_ctx):
   """Detects the cudnn version."""
-  if "CUDNN_VERSION" in repository_ctx.os.environ:
-    return repository_ctx.os.environ["CUDNN_VERSION"].strip()
+  if _TF_CUDNN_VERSION in repository_ctx.os.environ:
+    return repository_ctx.os.environ[_TF_CUDNN_VERSION].strip()
   else:
     return ""
 
 
 def _compute_capabilities(repository_ctx):
   """Returns a list of strings representing cuda compute capabilities."""
-  if "CUDA_COMPUTE_CAPABILITIES" not in repository_ctx.os.environ:
+  if _TF_CUDA_COMPUTE_CAPABILITIES not in repository_ctx.os.environ:
     return _DEFAULT_CUDA_COMPUTE_CAPABILITIES
-  capabilities_str = repository_ctx.os.environ["CUDA_COMPUTE_CAPABILITIES"]
+  capabilities_str = repository_ctx.os.environ[_TF_CUDA_COMPUTE_CAPABILITIES]
   capabilities = capabilities_str.split(",")
   for capability in capabilities:
     # Workaround for Skylark's lack of support for regex. This check should
-- 
GitLab


From 7a95d4a88eda1ab94b2cab1eed48fb82f9879b65 Mon Sep 17 00:00:00 2001
From: Vijay Vasudevan <vrv@google.com>
Date: Fri, 26 Aug 2016 12:52:14 -0800
Subject: [PATCH 221/223] Change c_api function to use new input_edge() API
 from Node*, and use an optimization for computing the number of control
 inputs by subtracting the number of data inputs from the the total number of
 edges. Change: 131437591

---
 tensorflow/c/c_api.cc | 19 +++++++------------
 1 file changed, 7 insertions(+), 12 deletions(-)

diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc
index 3928e9e09f..3f10598fcc 100644
--- a/tensorflow/c/c_api.cc
+++ b/tensorflow/c/c_api.cc
@@ -997,12 +997,13 @@ int TF_OperationInputListLength(TF_Operation* oper, const char* arg_name,
 }
 
 TF_Port TF_OperationInput(TF_Port oper_in) {
-  for (const auto* edge : oper_in.oper->node.in_edges()) {
-    if (edge->dst_input() == oper_in.index) {
-      return {ToOperation(edge->src()), edge->src_output()};
-    }
+  const tensorflow::Edge* edge;
+  Status s = oper_in.oper->node.input_edge(oper_in.index, &edge);
+  if (!s.ok()) {
+    return {nullptr, -1};
   }
-  return {nullptr, -1};
+
+  return {ToOperation(edge->src()), edge->src_output()};
 }
 
 int TF_OperationOutputNumConsumers(TF_Port oper_out) {
@@ -1030,13 +1031,7 @@ int TF_OperationOutputConsumers(TF_Port oper_out, TF_Port* consumers,
 }
 
 int TF_OperationNumControlInputs(TF_Operation* oper) {
-  int count = 0;
-  for (const auto* edge : oper->node.in_edges()) {
-    if (edge->IsControlEdge()) {
-      ++count;
-    }
-  }
-  return count;
+  return oper->node.in_edges().size() - oper->node.num_inputs();
 }
 
 int TF_OperationGetControlInputs(TF_Operation* oper,
-- 
GitLab


From 345df6ab9334ac310b4ce9e7f04e5eb3bf9ed411 Mon Sep 17 00:00:00 2001
From: Gustav Larsson <gustav.m.larsson@gmail.com>
Date: Fri, 26 Aug 2016 14:22:46 -0700
Subject: [PATCH 222/223] Allows running tensorboard as package (#3811)

Adds __main__.py to allow "python -m tensorflow.tensorboard" execution.
---
 .../summaries_and_tensorboard/index.md        |  3 ++-
 tensorflow/tensorboard/BUILD                  |  5 +++-
 tensorflow/tensorboard/__main__.py            | 25 +++++++++++++++++++
 3 files changed, 31 insertions(+), 2 deletions(-)
 create mode 100644 tensorflow/tensorboard/__main__.py

diff --git a/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md b/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md
index e487d8f04c..ec617ca7be 100644
--- a/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md
+++ b/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md
@@ -182,7 +182,8 @@ You're now all set to visualize this data using TensorBoard.
 
 ## Launching TensorBoard
 
-To run TensorBoard, use the command
+To run TensorBoard, use the following command (alternatively `python -m
+tensorflow.tensorboard`)
 
 ```bash
 tensorboard --logdir=path/to/log-directory
diff --git a/tensorflow/tensorboard/BUILD b/tensorflow/tensorboard/BUILD
index c957f3720f..ccb311b6f2 100644
--- a/tensorflow/tensorboard/BUILD
+++ b/tensorflow/tensorboard/BUILD
@@ -20,7 +20,10 @@ filegroup(
 
 py_binary(
     name = "tensorboard",
-    srcs = ["tensorboard.py"],
+    srcs = [
+        "__main__.py",
+        "tensorboard.py",
+    ],
     data = [":frontend"],
     srcs_version = "PY2AND3",
     deps = [
diff --git a/tensorflow/tensorboard/__main__.py b/tensorflow/tensorboard/__main__.py
new file mode 100644
index 0000000000..187140954d
--- /dev/null
+++ b/tensorflow/tensorboard/__main__.py
@@ -0,0 +1,25 @@
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import sys
+
+from tensorflow.tensorboard.tensorboard import main
+
+if __name__ == '__main__':
+    sys.exit(main())
-- 
GitLab


From 13c8fd52a253e8afb158c5578ad188503932e7a9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Daniel=20Man=C3=A9?= <danmane@google.com>
Date: Fri, 26 Aug 2016 14:51:45 -0700
Subject: [PATCH 223/223] Merge changes from internal.

---
 tensorflow/BUILD                              |  9 --------
 .../core/kernels/conv_ops_using_gemm.cc       | 22 +++++++++----------
 2 files changed, 11 insertions(+), 20 deletions(-)

diff --git a/tensorflow/BUILD b/tensorflow/BUILD
index 399f8e564c..efd2adb3bd 100644
--- a/tensorflow/BUILD
+++ b/tensorflow/BUILD
@@ -191,15 +191,6 @@ cc_binary(
     ],
 )
 
-cc_binary(
-    name = "libtensorflow_cc.so",
-    linkshared = 1,
-    deps = [
-        "//tensorflow/c:c_api",
-        "//tensorflow/core:tensorflow",
-    ],
-)
-
 cc_binary(
     name = "libtensorflow_cc.so",
     linkshared = 1,
diff --git a/tensorflow/core/kernels/conv_ops_using_gemm.cc b/tensorflow/core/kernels/conv_ops_using_gemm.cc
index 1da233382b..c39510a11a 100644
--- a/tensorflow/core/kernels/conv_ops_using_gemm.cc
+++ b/tensorflow/core/kernels/conv_ops_using_gemm.cc
@@ -47,17 +47,17 @@ limitations under the License.
 #include <string.h>
 #include <map>
 #include <vector>
-#include "third_party/tensorflow/core/framework/common_shape_fns.h"
-#include "third_party/tensorflow/core/framework/numeric_op.h"
-#include "third_party/tensorflow/core/framework/op_kernel.h"
-#include "third_party/tensorflow/core/framework/register_types.h"
-#include "third_party/tensorflow/core/framework/resource_mgr.h"
-#include "third_party/tensorflow/core/framework/tensor.h"
-#include "third_party/tensorflow/core/framework/tensor_shape.h"
-#include "third_party/tensorflow/core/framework/tensor_slice.h"
-#include "third_party/tensorflow/core/kernels/bounds_check.h"
-#include "third_party/tensorflow/core/util/padding.h"
-#include "third_party/tensorflow/core/util/tensor_format.h"
+#include "tensorflow/core/framework/common_shape_fns.h"
+#include "tensorflow/core/framework/numeric_op.h"
+#include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/framework/register_types.h"
+#include "tensorflow/core/framework/resource_mgr.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_shape.h"
+#include "tensorflow/core/framework/tensor_slice.h"
+#include "tensorflow/core/kernels/bounds_check.h"
+#include "tensorflow/core/util/padding.h"
+#include "tensorflow/core/util/tensor_format.h"
 
 #if defined(__APPLE__)
 #include <Accelerate/Accelerate.h>
-- 
GitLab